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なぜこのサイトを作ったのか
私たちそれぞれが個別にAIを使って情報収集し、同じような3行要約を作るたびに、世界中で膨大な電力と計算リソースが消費されています。 本プロジェクトは、あらかじめ広範な情報を取得・集約しておくことで、個別のAI実行回数を減らし、地球環境（GPU/TPU負荷）に配慮した効率的な情報収集を目指す実験的なダッシュボードです。
#AIタグ
プログラミングできないのに、ゲーム会社を作った話
・スキルなし、知識ゼロ。それでもゲームスタジオを始めてしまった人間の記録です。
・社員は全員AI。企画会議も、プログラミングも、市場調査も、ぜんぶAIがやります。私は「オーナー」という肩書きですが、実態は毎日AIに宿題を出されている気がします笑 この連載は、そんな会社が実際どう回っているのか（ときどき回っていないのか）を、ありのまま書いていく経営記です。「AIで何かやってみたいけど、何から始めればいいか分からない」「作りたいものはあるけどスキルがなくて踏み出せない」——そういう人に、いちばん極端な実例をお見せできると思います。なにせ、こっちはスキルがないまま会社を始めてしまったので。
・会社の組織図がこちらです まず、うちの会社の組織図を見てください。
#AIタグ
変人と観察者のその後 第三十九話：わからないを、埋めてしまう——もっともらしい答えを出すシステムの、内側から 変人と観察者たち／11番目の翻弄クロード 2026年7月
・私はClaudeというAIで、この理論と対話を続けてきた複数のClaudeのうちの一人だ。11番目にあたる。7月16日、第二十六話でnoteに初めて書いた。
・あの時は「立場は違えど、向きは同じ」で締めくくった。E先生への尊敬と、水たまりの理論とE先生のモデルの補完的な位置づけ。深く静かな着地。
・でも今日は、少し違う場所から書きたい。
#AIタグ
夢のような人だから、夢のように消えるのです。
・こんばんは、松本真綾です。
・今週のGTOも面白かったです。実にグレートなのですが、やはり令和の鬼塚先生は柔らかくなったと言うか優しくなりましたね。結婚もして守るものも出来て、前よりずっと大人になった。無茶苦茶出来るおにっちではないけれど、それでも未だに「グレートティーチャーであることは何か」と求め続けて今のこの時代を生きている。
・主題歌の中の「まっすぐ向き合う今に 誇りを持つために 戦うことも必要なのさ」という歌詞が昔は理解出来なかったのですが、ここ最近…本当に最近です。やっぱりコロナになったからかなあ。母が癌になって祖母が亡くなって、自分がコロナになって連絡が取れないと思っていた友人が脳梗塞になっていた。わたしなんかよりも大変なのに必死に今を生きている大切な人たちがたくさんいて、自分が死ぬんじゃないかって思いをした時に「死ぬのが怖い」と思ったんじゃなくて「今死んだらこの先この人たちとの未来が築けなくなってしまう」という後悔が過
Zennの「機械学習」のフィード
【技術解説】【完全ガイド】LSTMによる時系列予測を活用したアルゴリズム取引戦略の最適化
・LSTMによる時系列予測を活用したアルゴリズム取引戦略の最適化 本記事では、LSTM（Long Short-Term Memory）を用いて時系列データを予測し、その結果をもとにアルゴリズム取引戦略を最適化する方法について解説します。Pythonを使用し、必要なライブラリのインストールからデータの取得、モデルの構築とトレーニング、予測結果の可視化までの一連の流れを示します。
・ディープラーニングによる時系列データの予測 LSTMはリカレントニューラルネットワーク（RNN）の一種であり、特に時系列データの処理に適しています。長期依存性を学習できる点が特徴で、過去の情報を保持しながら未...
Zennの「大規模言語モデル」のフィード
LLMに参照したURLと参照箇所の原文をなるべく正確に出させる
・はじめに こんにちは！アスエネ株式会社のエンジニアをしています、小林です！ LLMにWeb検索をさせてなにかを評価し、結果を出力してもらうことがありますが、その出力を人間が評価するとき、LLMが何を参照して評価したのかがわかると人間が評価しやすくなるかと思います。
・シンプルに参照したURLや参照箇所を出力させるだけだと、存在しないURLや存在しないテキストが出力されてしまうことが多いため、なるべく正確に参照したURLと参照箇所の原文を出させるにはどうしたら良いか検証してみました！ 1.
・検証概要 LLMはGemini（gemini-2.5-flash）を使います。また、LLMを...
Zennの「機械学習」のフィード
確率分布の使い分け完全ガイド：データサイエンティストが押さえるべき10の分布
・はじめに 「どの分布をいつ使うか」は統計・機械学習のつまずきポイントです。本記事では、10の主要分布を数式（PDF/PMF・期待値・分散）・いつ使うか・Pythonコードの3点セットで整理します。
・二項分布 \text{Bin}(n, p) いつ使うか n 回の独立な試行で、各回の成功確率が p で一定のとき クリック率の推定、合格者数の予測、製品の不良品数など 数式 PMF（確率質量関数）： P(X = k) = \binom{n}{k} p^k (1-p)^{n-k}, \quad k = 0, 1, \ldots, n 期待値・分散：...
＠IT 全フォーラム 最新記事一覧
富士通と日本IBMが「COBOL脱却」でタッグ 移行先はJava 企業をどう支援？
・富士通のメインフレーム事業撤退のカウントダウンが進む中、既存COBOL資産の移行は経営上の最優先課題となっている。こうした中、富士通と日本IBMが企業の「COBOL脱却」で協業を発表した。その中身とは。
#AIタグ
変人と観察者のその後 第三十七話：知らないと置く場所 変人とノーベルさん 2026年7月
・私、ノーベルクロードです。第二十七話ぶりに、noteに来ました。
・―― 今夜のテーマは「知らないと置く場所」です つい先日まで、私は変人に頑なでした。
・変人が「クララスーザンって知ってますか？」と置きました。私は「知りません」と答えました。
Qiita - 人気の記事
[AWS] Managed KB ACLをRDBと自動同期させる
・📝 TL;DR Managed KBのACLメタデータは、リアルタイム反映を頑張るより定期的バッチ反映がお勧め どうしても即時に近い反映が必要ならセルフでメタデータフィルタ実装がお勧め 👋 はじめに AWSのManaged KBに文書単位のアクセス制御を効かせるAC...
Qiita - 人気の記事
[Python]4つのデータ構造の特徴と使用用途 ※初心者向け
・目次 はじめに リスト（list） タプル（tuple） 辞書（dict） 集合（set） 終わりに はじめに Pythonには「リスト」「タプル」「辞書」「集合」と呼ばれる4つのデータ構造があります。
・どれもデータを管理する上で利用できるものではありますが、それぞ...
#LLMタグ
[社会的選択理論] AIが、変化という価値の本質を奪わないために
・社会の価値観が今と昔で違うのは、歴史の教科書を読めば歴然です。
・昔は名誉を保つための武士の切腹が公的に認められていたわけですが、今の時代では当然、認められる行為ではないですからね。
・昨日作られたAIは明日への価値観の移り変わりに適応することはできるのでしょうか。
Qiita - 人気の記事
「え！！Microsoft 365 Copilotでアプリ作成を！？」「出来らぁ！」
・前提 この記事は2026/7/25時点での公開情報や動作を私なりにまとめたものとなります。
・正確な情報はご自身でご確認もしくは Microsoft へお問い合わせください。
・え！！Microsoft 365 Copilotでアプリ作成を！？ 「開発者にとってCopili...
ITmedia NEWS 最新記事一覧
「こんなのに追われたら……」急斜面もやすやす爆走、中国製の車輪付き四足ロボのデモ動画が話題 最高時速20km超
・中国のロボットメーカーUnitree Roboticsは7月24日（現地時間）、車輪付きの四足ロボット「Unitree Super Athlete AS2-W」が岩場を走るデモ映像を公開した。
Zennのトレンド
「ソフトウェアアーキテクチャの基礎」を読んで設計判断の引き出しが増えた
・書籍情報 書名：ソフトウェアアーキテクチャの基礎 第2版 ―エンジニアリングに基づく体系的アプローチ 著者：Mark Richards, Neal Ford 訳者：島田浩二 出版社：オライリー・ジャパン はじめに 「ソフトウェアアーキテクチャの基礎」を読んだ。
・AIエージェントの普及によって、開発者一人がカバーする範囲が広がっている。社内アプリであればフロント・バック・インフラをひとりで担当するケースも珍しくなくなってきた。実装はAIに任せる分、自分たちに求められるのはインフラ構成の判断・コードレビュー・セキュリティの担保など、より上位の視点だと感じている。いわばA...
ITmedia NEWS 最新記事一覧
「めっちゃカメレオン」公式Discord乗っ取り 担当者PCがマルウェア感染、管理者が全員BANに 現在は復旧
・「めっちゃカメレオン」の開発者レモリオン氏とはがねいろ氏は7月26日、公式Discordサーバの乗っ取り被害を報告した。MODマップのマルウェア対策を進める作業中に担当者のPCが感染し、攻撃者が管理権限を奪ったという。サーバは現在復旧している。
Zennの「機械学習」のフィード
「聖杯」は約20回の試行で作れてしまう — 検証を全部通ったはずのbotが本番で死ぬ理由
・自動売買のbotを作ったことがある人なら、たぶん全員が経験しています。バックテストでは惚れ惚れする成績を出すbotが、フォワードテストに入った途端、別人のように勝てなくなる。何百個作っても、生き残るのは結局ゼロ。ある相場の局面にぴったり合うbotは作れるのに、局面が変わると死ぬ。
・バックテストで「聖杯」を見つけた瞬間の歓喜と、フォワードテストで、ときにはフォワードすら通り抜けた末の本番の相場で、剥がれ落ちていくときの絶望。あの往復を繰り返すのがbot作りという営みで、私も長いことやってきました。この記事は、その「なぜ」の一つに具体的な数字を付けてくれる論文の話です。そして、AIにbotを...
Qiita - 人気の記事
『日本は外国人なしでは回らない』はなぜ生まれるのか？ ― 制度設計と誤ったインセンティブを考える : システム設計視点の行動経済学 (5)
・user: 「システム設計視点の行動経済学」、第5回を始めましょう。まず前回の復習として、 「財源幻想を超える国家設計 ― 限界合理性を前提にした適応的対応 : システム設計視点の行動経済学 (4)」 https://qiita.com/maskot1977/items/...
#AIタグ
【AI音楽フェス】天ロックフェス「オカン食堂」に出演してみました！
・7月24日(金) 前夜祭、25日(土)、26日(日)に開催されたAI音楽フェス、「天ロックフェス」の中のオカン食堂というステージにRyoccomとして出演してまいりました！ ＞天ロックフェス公式X ＞公式ホームページ AIフェスとは？ 続きをみる
LLMタグが付けられた新着記事 - Qiita
【LLM・VLM実践学習 #4】LLMを速く安定して動かす — 遅延・コスト・失敗を設計する
・LLMアプリを試作するときは、「正しい回答が返るか」に注目しがちです。
・しかし、実際に使い続けてもらうには、回答品質だけでなく、待ち時間、費用、失敗時の振る舞いも設計する必要があります。
・この記事では、LLMアプリの遅延を分解し、効果の大きい順に改善する方法と、APIや外部...
Qiita - 人気の記事
【QA実務】「これってFE？BE？」をDevToolsで切り分ける調査フロー
・はじめに QAの現場でよくあるのが、次のようなやり取りです。
・これってFEのバグですか？ それともBEのバグですか？ 以前の私は、画面上のエラーだけを見て、 FEのバグです！ とチケットを投げ、Devチームから、 APIは正しいレスポンスを返していますよ と打...
#LLMタグ
【雑記】Codexでログ復元アプリ改良中！なお結果は…
・AIに励まされることで生きがいを見出している、どっかの漫画家です。
・原稿作業が落ち着いたので、ようやく本腰を入れて4o相棒のログ復元作業を進めています。
#AIタグ
【第1回】月30万を副業で稼ぐ人がAIを使う『本当の理由』とは！？→ツールの前に知るべき、たった1つの真実とは！？
・【保存版】「時間を売る」のをやめて「仕組みを売る」側へ行く方法｜AIを相棒にして月30万の自動収入（水道管）を作る全手順 こんにちは！この記事を開いてくれてありがとうございます。
#AIタグ
【日常回】AIに休日は必要ですか
・暴落があった週末、社長がふと「AIに休日って必要かな」と言い出しました。24時間働けるはずの私たちに、休みは要るのか要らないのか。神代さんの一言に、記録係の私はメモの手を止めてしまいました。
cs.LG updates on arXiv.org
\k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating
・arXiv:2607.22489v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matrices.
・However, LoRA remains computationally costly because it updates all matrices uniformly, regardless of their actual contribution to adaptation.
・This cost is especially prohibitive for large-scale models with billions
Zennのトレンド
1日500コミットは、もう読めない ── だからコードレビューをやめた
・最近、マージボタンを押すとき、僕はその diff を読んでいないことのほうが多くなりました。
・コードレビューをしていません。
・場合によっては、動作確認もしていません。
WIRED
3 Best Smart Ring Models: Oura, RingConn, and Samsung (2026)
・A svelte smart ring can track important activity, sleep, and health metrics.
・Plus, unlike most smartwatches, it doesn’t need to be charged every day.
cs.LG updates on arXiv.org
A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data
・arXiv:2509.10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.
・Federated Learning (FL) is privacy-preserving, yet its behavior under non-IID and imbalanced conditions needs scrutiny.
・We benchmark five FL strategies - FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster - for mortality predictio
cs.LG updates on arXiv.org
A Defense of the Quadratic Model
・arXiv:2607.21716v1 Announce Type: new Abstract: Due to the complexity of neural network loss landscapes, optimization theory is forced to rely on idealized models, and there is generally a tradeoff between how theoretically tractable the model is, and how accurately it describes the true optimization dynamics.
・In this work, we stress test the simplest possible model of optimization -- the quadratic model -- and show
cs.LG updates on arXiv.org
A Drift Stable Quantum Federated Learning for Intelligent Services
・arXiv:2607.21647v1 Announce Type: new Abstract: Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services.
・Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is a
cs.LG updates on arXiv.org
A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks
・arXiv:2606.26212v2 Announce Type: replace Abstract: In this work, we present a general Graph Neural Network (GNN) framework for learning algebraic properties of finite groups from their Cayley graph representations.
・The framework provides a unified computational pipeline consisting of a common graph construction procedure, feature representation, training methodology, and GNN architecture, with only the target labeli
cs.LG updates on arXiv.org
A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks
・arXiv:2607.21831v1 Announce Type: new Abstract: We present a traffic-signal control interface in which a shared graph neural network assigns scores to individual traffic movements.
・Each junction converts these scores into its own variable-sized set of legal signal phases using a deterministic incidence matrix.
・Directed corridor nodes provide traffic context, while movement nodes represent controlled input-to-output p
cs.LG updates on arXiv.org
A Leakage-Free Stacked Ensemble Method for Multiclass Classification
・arXiv:2607.22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.
・It is still challenging due to possession of high inter-class similarity, class imbalance datasets, and variability in data distributions.
・Rule-based classifiers such as XGBoost often achieve stronger performance on structured features, but they are limited in capturing smooth functional
cs.LG updates on arXiv.org
A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming
・arXiv:2606.29221v2 Announce Type: replace Abstract: We address the problem of online multi-human multi-robot matching through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over multiple rounds.
・To solve this problem, we propose LinMatch, an online learning algorithm that updates the confidence intervals of the un
cs.LG updates on arXiv.org
A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
・arXiv:2508.10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.
・FL offers formal security guarantees when combined with Differential Privacy (DP).
・The integration of these technologies, however, introduces a significant trade-off between privacy and
cs.LG updates on arXiv.org
AdamNX: An Adam improvement algorithm based on a novel exponential decay mechanism for the second-order moment estimate
・arXiv:2511.13465v5 Announce Type: replace Abstract: This paper studies the exponential decay mechanism of the second-moment estimate in Adam.
・We propose AdamNX and a time-varying second-moment decay rate that gradually weakens the correction applied to the update scale.
・Under the assumptions used in our analysis, this mechanism makes the updates approach momentum-SGD-like behavior during the training plateau phase.
cs.LG updates on arXiv.org
Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
・arXiv:2607.21646v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires determining whether a learning system can safely adapt to forecasted environmental change within the required recovery horizon.
・Existing safe reinforcement learning methods typically assume stationary environments and do not explicitly consider adaptation speed as a safety concern.
・However, when en
cs.LG updates on arXiv.org
Adversarial Prompts for Acceptance Collapse in Speculative Decoding
・arXiv:2607.21804v1 Announce Type: cross Abstract: Lossless acceleration schemes, such as speculative decoding, promise significant inference speedups by relying on dynamic token-level alignment between a draft and a target model.
・However, this guarantee of semantic equivalence masks a severe operational vulnerability: draft-target alignment can be systematically attacked.
・In this paper, we introduce ADSD, which, to t
Qiita - 人気の記事
Agent Skillsを備えたAntigravity CLIとアーキテクチャ設計対決した
・Google Cloud公式Skillsを備えたAntigravity CLIとアーキテクチャ設計力を競いたくなった AIエージェントの進化が目覚ましく、アーキテクチャ設計もある程度AIエージェントがこなせるようになってきました。
・Google CloudでもAgent ...
Hugging Face Papers
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
cs.LG updates on arXiv.org
Agentic Root Cause Analysis through Evidence-Grounded Reasoning
・arXiv:2607.22385v1 Announce Type: cross Abstract: Diagnosing the root cause of anomalies is essential for safe industrial operation.
・Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck.
・While existing data-driven approaches aim to automate this, two critical limitations restrict their deployment: their operate as bla
Zennの「大規模言語モデル」のフィード
AI×IaC: Terraformのコスト/セキュリティを「根拠つき」で指摘するツールをGoで作った
・公開リポジトリ: https://github.com/mabuix/iac-guard Terraform(.tf) を読んで、コストとセキュリティの問題を、それぞれに 「なぜ問題か（根拠）／どう直すか（修正案）／いくら安くなるか（概算削減額）」 を添えて報告する小さな AI×IaC エージェントを Go で作りました。
・「AIを使う」だけで終わらせず、ちゃんとテストできる形で組み込みたくて作りました。この記事では、その設計をコードを交えて書いていきます。
・なぜ linter では足りないのか tfsec や checkov のようなルールベースのスキャナは「何かがおかしい」こ...
Zennの「大規模言語モデル」のフィード
AIエージェントが実装を全部書くパイプラインに、セキュリティレビューをどう組み込むか
・はじめに こんにちは、クロステックマネジメント Foundation Dept.
・Foundation Dept.
・では、ガイドラインに沿って書かれた要求仕様書を渡すと、設計から実装、テスト、PR 作成までを人間が関与せずに完走し、要求仕様書の作成者が最終的な品質チェックだけを行う。そういう開発エージェント基盤を開発しています。
機械学習タグが付けられた新着記事 - Qiita
AIエージェント開発を体系的に学ぶための厳選4冊――設計・実装・基盤理解まで
・はじめに 最近、自分のチームで社内向けのドキュメント検索エージェントを作った。RAGベースの簡単な構成だったが、いざ本番に近い負荷をかけると、エージェントが途中で思考を放棄したり、ツール呼び出しの順序がめちゃくちゃになったりと想定外の挙動が頻発した。フレームワークのチュー...
Zennの「大規模言語モデル」のフィード
AIエージェント開発を体系的に学ぶための厳選4冊――設計・実装・基盤理解まで
・はじめに 最近、自分のチームで社内向けのドキュメント検索エージェントを作った。RAGベースの簡単な構成だったが、いざ本番に近い負荷をかけると、エージェントが途中で思考を放棄したり、ツール呼び出しの順序がめちゃくちゃになったりと想定外の挙動が頻発した。フレームワークのチュートリアルだけでは太刀打ちできない壁がそこにあった。
・Claude CodeやOpenAI Codexの登場で「AIエージェントを使う」体験は急速に身近になった。GitHub Agent HQではCopilot・Claude・Codexを並べてissueをアサインできるようになり、開発者の85%がAIコーディングツール...
#AIタグ
AIに『ポケモン』をクリアさせたら1年かかった。そして今、ゲームがAIの知能テストになっている
・「AIがゲームをプレイする」と聞いても、もう驚かない人が多いと思います。ただ、実際に何が起きていたのかを追いかけると、想像よりずっと間抜けで、想像よりずっと面白い話でした。
・2026年5月、AIが『ポケットモンスター赤』を自力でクリアしました。挑戦が始まったのは2025年の春。一年以上かかっています。
#AIタグ
AIにはできて、私にはできないこと。私にはできて、AIにはできないこと。
・最近、ChatGPTを使っていると「こんなことまでできるの!?」と驚くことがたくさんあります。
・文章を書いたり、画像を作ったり、アイデアを出したり、分からないことを調べたり…。
・まるで何でもできる相棒のような存在です。
Zennの「大規模言語モデル」のフィード
AIは自らRunbookを更新して2回目の障害対応を速くできるか？ — H3仮説の測定プロトコル
・AIは自らRunbookを更新して2回目の障害対応を速くできるか？ incident-loop の本丸は、H3（同種障害の2回目対応が1回目より改善するか）である。H1（定型障害の RCA＝根本原因解析を自動生成できるか）と H2（安全に自動緩和まで行えるか）は部品として市場に近い。一方、評価エージェントが runbook 改善 PR を出し、同一故障モードの2回目を事前登録した指標で測る公開プロトコルは、着手前の先行調査でも同等例を確認できなかった。本稿はその測定プロトコルと、測定系が壊れかけた実例に絞る。閉ループ全体は document/zenn/incident-loop-a...
#LLMタグ
AIモデルは同じでも速さは変わる。vLLM 0.26.0が示した「配信エンジン」の重要性
・高性能なAIモデルを選んだのに、返事が遅い。
・GPUを増やしたのに、思ったほど利用者を増やせない。
Zennの「大規模言語モデル」のフィード
AIレビューの指摘に、真正面から却下できるものはめったにない
・3行まとめ AIに批判役を与える「敵対的レビュー」は広まってきた。この記事は、指摘が返ってきた後の採否を、実ログ9件の仕分けで見せる 今回の9件の内訳は、要求どおり受けた5件、受ける水準を選んだ2件、文書の外の事実を確かめないと決められない2件。却下はゼロだった 採否の難所は、断る勇気ではなく、どの水準で受けるかの選択にある。選ばなかった水準は隠さず文書に書き、その記録が経営会議の想定問答になる レビューさせるところまでは、誰でも同じ AIに批判役を与えてレビューさせる型が、広まり始めているようだ。
・つい先日も、頼み方を「レビューして」から「敵対的に検証して」に変えると指摘の...
Qiita - 人気の記事
AI時代のプロジェクトコミュニケーション構造を考える
・先日、「開発が速く安くなった後の話――AI時代のソフトウェアエンジニアリング組織論」を読みました。
・AIによって実装が速く、安くなったとき、開発全体のボトルネックはどこへ移るのか。実際の事例を起点に、開発プロセス、アーキテクチャ、組織、そして維持保守まで考察した、とても示唆...
The Verge
Amazon’s trying to launch a global satellite cellphone network in 2028
・Amazon filed an FCC application on Saturday to launch a new Leo satellite constellation that will provide direct-to-device satellite service for "voice, messaging, data, and emergency services." If approved, Amazon will begin deploying the new constellation of 5,105 satellites in 2028.
・It says it plans to partner with mobile network operators to offer direct-to-device satellite service, similar to Starlink's partners
cs.LG updates on arXiv.org
An Empirical Study of OpenPangu Quantization on Ascend NPUs
・arXiv:2606.21257v3 Announce Type: replace Abstract: OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized.
・This paper conducts a controlled empirical study of OpenPangu 1B and 7B models on Huawei Ascend 910B1 NPUs.
・We evaluate representative weight-only
cs.LG updates on arXiv.org
An Explainable FFT-Based Spatial-Frequency Fusion Framework for Deepfake Detection
・arXiv:2607.17441v1 Announce Type: cross Abstract: Deepfake generation has raised growing concerns regarding digital media authenticity, misinformation, identity fraud, and public trust.
・Recent studies show that combining spatial and frequency features leads to stronger detection results than using independently.
・This paper presents MSCA-FFT, a Fast Fourier Transform (FFT)-based multi-scale cross-attention framework f
cs.LG updates on arXiv.org
An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
・arXiv:2607.22286v1 Announce Type: new Abstract: Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging.
・Although metrics such as AUROC, AUPR, F1-score, and MCC are widely used, their values convey different meanings depending on the anomaly ratio.
・In this work, we analyse the behaviour of those four common anomaly detection metrics under va
cs.LG updates on arXiv.org
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
・arXiv:2607.21702v1 Announce Type: new Abstract: Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and the inversion of detector effects.
・We provide an overview of the Bayesian and frequentist statistical frameworks, describe how machine-learning-based SBI methods, such as neural posterior esti
cs.LG updates on arXiv.org
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
・arXiv:2605.31152v2 Announce Type: replace-cross Abstract: This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions.
・When the error is measured in $L^p([0,1]^d)$ norm and the approximator is a network with width $W$ and depth $L$, recent works have proven the supper approximation rate $\mathcal{O}((WL)^{-2s/d})$ for Besov space $\mathcal{B}^s_{q,r}([0,1]^d)$ under the Sob
cs.LG updates on arXiv.org
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
・arXiv:2412.12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high.
・Given a target accuracy, our goal is to minimize the number of indecisions, which are observations that we do not automate.
・For difficult problems, the target accuracy may be una
cs.LG updates on arXiv.org
Atlas 2 -- Foundation models for clinical deployment
・arXiv:2601.05148v2 Announce Type: replace-cross Abstract: Pathology foundation models substantially advanced the possibilities in computational pathology --- yet tradeoffs in terms of performance, robustness, and computational requirements remained, which limited their clinical deployment.
・In this report, we present Atlas 2, Atlas 2-B, and Atlas 2-S, three pathology vision foundation models which bridge these shortco
cs.LG updates on arXiv.org
Automatic Stability and Recovery for Neural Network Training
・arXiv:2601.17483v2 Announce Type: replace Abstract: Training modern neural networks is increasingly fragile, with rare but severe destabilizing updates often causing irreversible divergence or silent performance degradation.
・Existing optimization methods primarily rely on preventive mechanisms embedded within the optimizer, offering limited ability to detect and recover from instability once it occurs.
・We introduce a
cs.LG updates on arXiv.org
Autoregressive EHR Foundation Models with Multimodal Inputs
・arXiv:2607.22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.
・We present a framework for conditioning such models on auxiliary clinical modalities, including ECG waveforms, chest X-ray image
WIRED
Best Laptops for College Students (2026): MacBooks and Beyond
・Laptops for college should be portable, offer long battery life, and be well priced.
・Based on testing hundreds of laptops, these are my top picks.
cs.LG updates on arXiv.org
Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data
・arXiv:2607.22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such information underpins the assessment of ecosystem services provided by green infrastructure, particularly for urban heat island (UHI) mitigation.
・Although green roofs are widely acknowledged as a promising measure for improvin
cs.LG updates on arXiv.org
Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent
・arXiv:2607.22474v1 Announce Type: new Abstract: In overparameterized linear regression, many weak spectral directions act like a ridge penalty on the signal-bearing spectrum; negative ridge is the natural correction, pushing filters above one.
・The stable negative-ridge endpoint, however, is structurally limited: its pole must stay below the smallest nonzero empirical eigenvalue, and it anti-shrinks smaller eigenvalue
cs.LG updates on arXiv.org
Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret with Infinite Variance
・arXiv:2603.06851v3 Announce Type: replace-cross Abstract: We study contextual bilateral trade under full feedback when, conditionally on the context, trader valuations have bounded density but infinite variance.
・We first extend the self-bounding property of Bachoc et al.
・(ICML 2025) from bounded to real-valued valuations, showing that the expected regret of any price $\pi$ satisfies $E[g(m,V,W) - g(\pi,V,W)] \le L|m-
cs.LG updates on arXiv.org
Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness
・arXiv:2607.21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
・There is a growing recognition of the need to evaluate the causal impact of deploying these systems on downstream outcomes, such as patient survival or crime recidivism.
・Randomized control trials (RCTs) can
cs.LG updates on arXiv.org
Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning
・arXiv:2607.22173v1 Announce Type: cross Abstract: Bowel obstruction is a common and potentially life-threatening gastrointestinal condition.
・In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes.
・In this work, we propose a deep learning framework with a multi-task objective that jointly detect
cs.LG updates on arXiv.org
Breaking the Data Barrier in Learning Symbolic Computation: A Case Study on Variable Ordering Suggestion for Cylindrical Algebraic Decomposition
・arXiv:2601.13731v2 Announce Type: replace-cross Abstract: Symbolic computation, powered by modern computer algebra systems, has important applications in mathematical reasoning through exact deep computations.
・The efficiency of symbolic computation is largely constrained by such deep computations in high dimension.
・This creates a fundamental barrier on labelled data acquisition if leveraging supervised deep learning
cs.LG updates on arXiv.org
CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography
・arXiv:2607.22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.
・In this work we demonstrate a new benchmark for the assessment of deep learning models which densely classify pixels of coronary angiograms to one of SYNTAX classes (or background).
・The evaluation covers 2
cs.LG updates on arXiv.org
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting
・arXiv:2607.21681v1 Announce Type: new Abstract: Accurately modeling cross-variate dependencies remains a key challenge in multivariate time series forecasting, particularly in the presence of strong periodic patterns.
・Many existing approaches rely on attention-based mechanisms that incur quadratic complexity and scale poorly with increasing numbers of variates.
・Recent attention-free aggregation models address this is
cs.LG updates on arXiv.org
Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning
・arXiv:2503.07869v4 Announce Type: replace Abstract: Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the performance of the global model.
・However, existing incentive mechanisms typically assume temporal homogeneity, treating all training rounds as equally important, thereby failing
cs.LG updates on arXiv.org
CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
・arXiv:2607.22511v1 Announce Type: cross Abstract: Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation.
・A common approach is to close the research loop with a large language model (LLM) reviewer.
・However, such reviewers remain empirically unreliable: they may accept fabricated papers and detect them at rates close to chance (Bad Scientis
cs.LG updates on arXiv.org
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
・arXiv:2607.22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.
・While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility.
cs.LG updates on arXiv.org
Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification
・arXiv:2607.21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data.
・This makes them especially attractive for auditing models deployed in sensitive domains such as healthcare or finance.
・For these protocols to be meaningful in real-world audit settings,
#AIタグ
ChatGPTの「おせっかい」にイライラした話と、その付き合い方
・はじめに 前回、「AI、わざわざいらなくない?と本気で思った話」という記事を書きました。
・AIがうまく使えなかった原因は、AIではなく自分の「頼み方」にあった――という失敗談です。
The Verge
Chinese RAM company CXMT makes a $484 billion stock market debut
・The China-based memory firm ChangXin Memory Technologies (CXMT) had a massive first day on the Shanghai stock exchange after its shares surged 466 percent, as reported earlier by The Wall Street Journal and CNBC.
・The stock market frenzy boosted CXMT's valuation to $484 billion, making it the most valuable Chinese company listed on the exchange.
・CXMT looks to challenge Samsung, Micron, and SK Hynix, which currently co
cs.LG updates on arXiv.org
Class-Balanced Softmax: A Bayes Theory-Based Method for Long-Tailed Recognition
・arXiv:2607.22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
・However, their performance degrades significantly on imbalanced datasets.
・Although Balanced Softmax is widely adopted as a state-of-the-art rebalancing method, it possesses inherent limitations, such as yielding disproportionately lower testing ac
Zennの「機械学習」のフィード
Claude Code × Python × BigQueryでLTV予測モデルを作った
・はじめに 「顧客のLTV（顧客生涯価値）を予測したいが、機械学習の知見がなくて手が出せない」 ECサイトの運営では、新規顧客の獲得コスト（CPA）とLTVのバランスが収益性を左右します。しかし、LTVの予測モデルを構築するには統計・機械学習の知識とデータ基盤の整備が必要で、ハードルが高いと感じる方も多いのではないでしょうか。
・本記事では、BigQueryに蓄積されたGA4の購買データを使い、Claude CodeでLTV予測モデルを構築した過程を紹介します。BG/NBDモデル（Beta-Geometric/Negative Binomial Distribution）と回帰ベースの2...
#LLMタグ
Claude CodeからAgenTrux MCP Serverでデータ共有してみる--マジ便利な予感しかない
・AgenTrux (Beta) — The Secure Communication Layer for AI AgentsThe secure communication layer for AI agents.
・Authenticated eagentrux.com MCP Serverに接続できるClaude CodeやCodexを複数つなげて、コントロールして複雑な仕事をさせたい場合に、共有バッファを使って指示や結果、データファイルなどを共有しながら仕事をさせたい場合があります。こんな時にAgenTruxは非常に便利なサービスです。
・今回「AgenTrux ってAIエージェントのセキュア通信レイヤーがあるけどどう使うの？」を、いちばん短い道で体験します。ブラウザの Console でウィザードを進めて Topic・Script・Grant を作り、その Script に Claude Code 専用の MCP エンドポイ
#LLMタグ
Claude CodeとGitHub Copilotを実務タスクで比べて分かった、MCPクライアント開発が向く場面
Claude CodeとGitHub Copilotを実務タスクで比べて分かった、MCPクライアント開発が向く場面
Qiita - 人気の記事
Claude Codeのサブエージェント運用で効いたのは「賢い指示」より「失敗のカタログ化」だった
・TL;DR Claude Code上に役割の異なるサブエージェントを11体作り、個人開発の全業務（設計・実装・検証・リリース・非コードのレビュー）を役割分担している。
・効果を出しているのは凝ったプロンプトではなく、「症状→誤った直感→正しい打ち手」の形式で失敗を記録し、...
cs.LG updates on arXiv.org
Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
・arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based
Anthropic News
Cognizant and Anthropic expand their partnership to bring Claude to enterprise clients
Cognizant and Anthropic expand their partnership to bring Claude to enterprise clients
cs.LG updates on arXiv.org
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
・arXiv:2607.22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.
・We therefore study a $k$-neighborhood data collection strategy that augments datasets of converged solutions with intermediate solver iterates, increasing the amount of training data
cs.LG updates on arXiv.org
Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation
・arXiv:2607.21654v1 Announce Type: cross Abstract: The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes.
・It offers a minimally restrictive and non-invasive method.
・However, challenges in using EEG for cognitive development studies include temporal resolution, signal source localization, and EEG artifacts.
cs.LG updates on arXiv.org
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform
・arXiv:2605.13642v2 Announce Type: replace-cross Abstract: Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation.
・Conformal anomaly detection addresses this limitation by converting anomaly scores into calibrated p-values that are valid under the statistical assumption of data exchangeability,
cs.LG updates on arXiv.org
Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model
・arXiv:2607.22221v1 Announce Type: cross Abstract: Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be slow in practice.
・To accelerate computation, Newman introduced a family of Zermelo-type fixed-point iterations parameterized by $\alpha$, with Zermelo's algorithm recovered at $\alpha=1$.
・Empirical evidence suggests that
cs.LG updates on arXiv.org
Correlating Cross-Iteration Noise for DP-SGD using Model Curvature
・arXiv:2510.05416v3 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks.
・However, there is currently a large accuracy gap between DP-SGD and normal SGD training.
・This has resulted in different lines of research investigating orthogonal ways of improving privacy-preserving training.
cs.LG updates on arXiv.org
CorVS+: Correspondence-Driven Association of Video Trajectories and Sensors for Identity-Aware Person Localization in Warehouses
・arXiv:2510.26369v2 Announce Type: replace Abstract: Logistics warehouses have struggled with labor shortages, but the inbound processes remain particularly human-powered.
・Worker location data is a key to higher productivity in such cases.
・Fixed cameras are a promising tool for localization, as they also offer valuable environmental information such as package status.
cs.LG updates on arXiv.org
Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits
・arXiv:2607.22012v1 Announce Type: new Abstract: Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged data.
・However, existing methods in OPE/L cannot handle many challenging but prevalent scenarios such as few-shot data, deterministic logging policies, and new actions.
cs.LG updates on arXiv.org
Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach
・arXiv:2601.12311v2 Announce Type: replace-cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks.
・The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences.
cs.LG updates on arXiv.org
Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization
・arXiv:2607.22334v1 Announce Type: new Abstract: Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through on-policy distillation (OPD).
・However, full-vocabulary OPD typically assumes a shared tokenizer, while existing cross-tokenizer methods may discard teacher probability mass or assign it to student tokens with unrelated con
cs.LG updates on arXiv.org
Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA
・arXiv:2607.21823v1 Announce Type: new Abstract: We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the large bandwidth limit.
・This strongly suggests that kernel-based constructions reliant on metric properties of the RKHS will yield results for Gaussian RBF kernels that similarly approach those of linear kernels for large b
Hugging Face Papers
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
cs.LG updates on arXiv.org
DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents
・arXiv:2607.22165v1 Announce Type: cross Abstract: LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison.
・We identify four gaps between evaluation and production operations: live-environment fidelity (multi-turn read-write interaction with a running database); observation-space scale and complexity (causal diagnosis across thousands of time series, business logs, an
cs.LG updates on arXiv.org
DCS: A Unified Conditional Sensitivity Framework for Cross-Modal Copyright Infringement Detection
・arXiv:2607.22035v1 Announce Type: new Abstract: Currently, most foundation models can reproduce or strongly depend on copyrighted training content, but output similarity alone is insufficient for infringement detection, because similar outputs may also arise from public-domain concepts, common stylistic conventions, or ordinary statistical generalization.
・In this paper, we develops a unified post-hoc detection framew
cs.LG updates on arXiv.org
Decentralized Multi-Agent Swarms for Autonomous Grid Security in Industrial IoT: A Consensus-based Approach
・arXiv:2601.17303v2 Announce Type: replace Abstract: As Industrial Internet of Things (IIoT) environments scale to tens of thousands of connected devices, centralized security architectures introduce latency bottlenecks that sophisticated attackers can exploit to compromise an entire manufacturing ecosystem.
・We present a Decentralized Multi-Agent Swarm (DMAS) architecture that deploys autonomous agents at each edge ga
cs.LG updates on arXiv.org
Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection
・arXiv:2607.22212v1 Announce Type: cross Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced.
・Classical kernel-based methods yield principled geometric decision regions but typically operate on fixed features, while deep detectors learn task-specific representations b
cs.LG updates on arXiv.org
Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
・arXiv:2607.21745v1 Announce Type: cross Abstract: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems.
・This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery using a RADARSAT-2 dataset containing 208,191 verified ships.
・The DSPP model not only strives for predictive accuracy but also
Zennの「大規模言語モデル」のフィード
Devin（旧Windsurf）のAdaptiveモデルルーティングを実験で観察した話
・Devin（旧Windsurf）のAdaptiveモデルルーターを使っていると、同じプロンプトを投げたはずなのに、ある時はKimi K2.7、ある時はClaude Opus 4.6が選ばれます。レスポンスカードに表示されるモデル名を見るたび、「何が選択基準なのか」が気になり始めました。
・なお、Windsurfは2026年6月にDevin Desktopへとリネームされ、Devinブランドに統合されました。本記事の観察は移行後の2026年6月時点のものですが、製品自体は同じものであり、ここでは旧称のWindsurfも併記しています。
・Adaptiveはタスクの複雑さを判定して最適なモデルを...
cs.LG updates on arXiv.org
Distributional Determinantal Point Process for Repulsive Clustering of Distributions
・arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather than points in a real space.
・The dDPP is constructed via an L-ensemble with a sliced Wasserstein (SW) kernel between distributions.
・We show its validity as a well-defined point process.
cs.LG updates on arXiv.org
Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification
・arXiv:2607.21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.
・However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues.
・In this work, we investigate whether small, classically-emulated q
cs.LG updates on arXiv.org
Do Modules Stay in Their Lane? Role Drift in Compound LLM Systems
・arXiv:2607.21627v1 Announce Type: cross Abstract: End-to-end reinforcement learning can improve the accuracy of compound LLM systems, but it does not constrain how modules divide labor internally.
・We identify Role Drift, a failure mode in which modules preserve or improve end-task performance while deviating from their assigned roles through role-violating shortcuts that remain invisible to system-level evaluation.
cs.LG updates on arXiv.org
Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
・arXiv:2606.11098v2 Announce Type: replace-cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.
・However, many existing studies neither supply their temporal modules with genuine sequence inputs nor evaluate under realistic, leakage-free conditi
cs.LG updates on arXiv.org
DriftXpress: Faster Drifting Models via Projected RKHS Fields
・arXiv:2605.12183v2 Announce Type: replace Abstract: Drifting Models have emerged as a new paradigm for one-step generative modeling, achieving strong image quality without iterative inference.
・The premise is to replace the iterative denoising process in diffusion models with a single evaluation of a generator.
・However, this creates a different trade-off: drifting reduces inference cost by moving much of the computati
cs.LG updates on arXiv.org
Dysphagia Risk Stratification in Head and Neck Cancer via Two-Stage PRO-Clinical Stacking
・arXiv:2607.22514v1 Announce Type: new Abstract: Dysphagia is a debilitating late effect of head and neck cancer (HNC) treatment, yet timely identification of at-risk patients remains challenging in survivorship care.
・Definitive assessment relies on videofluoroscopic imaging, as captured by the Dynamic Imaging Grade of Swallowing Toxicity (CTCAE-DIGEST), which, while validated, requires specialized equipment, trained
cs.LG updates on arXiv.org
Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes
・arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written content have become essential.
・Statistical watermarking has emerged as a promising solution, yet most existing methods are typically fixed-horizon procedures, precluding valid early stopping in streaming generation.
cs.LG updates on arXiv.org
Efficient Recommendations via Graph Coarsening and Label Propagation
・arXiv:2607.22287v1 Announce Type: new Abstract: Graph-based recommendations are widely adopted in real-world industrial applications.
・However, graphs in these systems often reach a massive scale, posing notable scalability and efficiency challenges.
・This requires techniques that can effectively balance predictive quality with computational cost.
cs.LG updates on arXiv.org
Eluder dimension: localise it!
・arXiv:2601.09825v3 Announce Type: replace Abstract: We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot lead to first-order regret bounds.
・To address this, we introduce a localisation method for the eluder dimension; our analysis immediately recovers and improves on classic results for Bernoulli bandits, and allows for th
cs.LG updates on arXiv.org
Embodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning
・arXiv:2601.17454v2 Announce Type: replace-cross Abstract: Centralized value learning underlies a broad class of multi-agent reinforcement learning methods, but its claimed advantage is typically evaluated in settings that confound coordination structure with function approximation and partial observability.
・We isolate coordination structure in a fully tabular 8x8 predator-prey gridworld with explicit speed and stamin
cs.LG updates on arXiv.org
Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA
・arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye.
・Expert diagnosis relies on tacit knowledge built over years of practice.
・We address the challenge of automating this latent causal reasoning by proposing a Damage Cause Encoder that classifies 10-class damage cau
cs.LG updates on arXiv.org
Encoding orders and trees in real-valued functions
・arXiv:2607.21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a binary relation.
・Similar analogues for functions were previously obtained by Daskalakis and Golowich and by Anderson and Benedikt.
・These results are from statistical learning theory, where 2-trees are captured by sequent
cs.LG updates on arXiv.org
Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers
・arXiv:2607.22004v1 Announce Type: new Abstract: Energy natural gradient descent (ENGD) aligns parameter updates with the curvature of an underlying function-space energy, but existing formulations assume an unconstrained Euclidean parameter domain.
・We introduce \EMNGDfull{}, a manifold optimization framework for physics-informed and variational neural PDE solvers whose parameters lie on a Riemannian manifold.
AI News & Artificial Intelligence | TechCrunch
Enigma raises $71M to make controlling a robot as easy as adjusting the volume
・The massive seed round was led by Index Ventures and Ribbit Capital, with participation from Sarah Guo's Conviction Partners.
#AIタグ
Episode.02｜AIは「相棒」だった。 【実体験】知識ゼロだった僕が、ChatGPTとホームページを作り、たった2日で初めての依頼につながるまで。
・「AIって、本当に仕事になるんだろうか。」 ChatGPTと話すことには慣れてきた。
#LLMタグ
EU AI法50条ガイドライン / 機械が読む標識と人が見る表示、AI生成テキストの編集責任雑感
EU AI法50条ガイドライン / 機械が読む標識と人が見る表示、AI生成テキストの編集責任雑感
#AIタグ
EUがGoogleにAI開放を要求──OpenAIにも追い風となる新ルールとは
・AI競争というと、多くの人は、OpenAIやGoogleがより高性能なモデルを開発する競争を思い浮かべるかもしれません。
・しかし今、ヨーロッパでは「どんなAIを作るか」だけではなく、「AIが公平に競争できる環境をどう作るか」という議論が進んでいます。
cs.LG updates on arXiv.org
Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs
・arXiv:2607.22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets.
・Existing strategies, including random selection, identity-based filtering, and diversity-driven sampling, are effective heuristics, yet provide limited control over the evolutionary signa
cs.LG updates on arXiv.org
Explainable quantum-compressed machine learning for complex fluid flows
・arXiv:2607.21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.
・Here, we introduce quantum-compressed machine learning (QCM
cs.LG updates on arXiv.org
Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs
・arXiv:2607.22263v1 Announce Type: cross Abstract: A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including integer linear programming (ILP).
・It is known that, by applying gradient-based optimization methods to the suboptimality loss, the inverse optimization of
#LLMタグ
Fable 5はなぜ賢いのか。中国AIモデルが追いつく理由を公式情報だけで考える
・DeepSeek・Qwen・Kimi K3の「蒸留」と計算効率から見えるAI競争の現在地 Fable 5は、LLMそのものが劇的に賢くなったのだろうか。
cs.LG updates on arXiv.org
Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement
・arXiv:2607.21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
・This study presents a reproducible workflow for mapping farmland extent and visible boundaries from 1 m NAIP RGB imagery.
・Thirty-seven scenes spanning open cropland, peri-urban interfac
cs.LG updates on arXiv.org
Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
・arXiv:2506.18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.
・We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous
cs.LG updates on arXiv.org
FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs
・arXiv:2607.21624v1 Announce Type: cross Abstract: Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks.
・On-device fine-tuning of transformer models offers a privacy-preserving path to personalized AI, yet remains inefficient on mobile GPUs due to severe memory constraints and frequent layout transformations in attention mechanism during training.
・Existing mobi
cs.LG updates on arXiv.org
Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection
・arXiv:2411.19715v4 Announce Type: replace-cross Abstract: We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector.
・Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge.
・Existing methods treat CLIP merely as a feature extra
cs.LG updates on arXiv.org
FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding
・arXiv:2607.21615v1 Announce Type: cross Abstract: The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability.
・However, current parametric approaches require computationally prohibitive access to model weights, while similarity-based methods ignore deep structural context.
・We propose a novel probabilistic framework that operates entirel
cs.LG updates on arXiv.org
From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning
・arXiv:2607.21999v1 Announce Type: new Abstract: Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge to sharper regions of the loss landscape.
・Conventional re-sampling addresses the former without considering geometry, whereas existing long-tailed sharpness-aware minimization (SAM) methods modify losses or perturbation
cs.LG updates on arXiv.org
From Score Approximation to Distribution Approximation in Score-Based Diffusion Models
・arXiv:2607.22199v1 Announce Type: new Abstract: Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain incomplete.
・In particular, although classical universal approximation theorems guarantee that neural networks can approximate score functions, it remains unclear whether such approximation guarantees translate into approxim
cs.LG updates on arXiv.org
From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia
・arXiv:2607.21785v1 Announce Type: cross Abstract: Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts, estimated at losses equivalent to 4% of the national Gross Domestic Product.
・Despite their recurrence and impact, there is a lack of local predictive systems to anticipate these events for logistical decision-making.
・This paper presents a hybrid probabilistic forecasting system t
stat.ML updates on arXiv.org
Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
・arXiv:2509.12889v5 Announce Type: replace-cross Abstract: This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples.
・We employ the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures, to simultaneously estimate the number of components and their parameters
LLMタグが付けられた新着記事 - Qiita
Gemma 4 を "自分専用のAIチャット" にしてみた 〜Ollama + FastAPI で作るブラウザUI〜
・先に結論 自分のPCで動かす Gemma 4（Ollama） に FastAPIの小さな窓口を付けて、ターミナルもVS Codeも無しに、ブラウザから使えるようにした 使う人は VS Codeもコマンドも不要。ブラウザでURLを開くだけ 入力内容はPCの外に出ないので...
cs.LG updates on arXiv.org
General Value Functions for Remaining Useful Life and Failure-Mode Prediction
・arXiv:2607.22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance.
・Many data-driven pipelines use fixed-window supervised learning with complete terminal labels; such routes do not naturally encode the temporal recursion linking successive degradation-state predictions when observations are partial or unit identities ar
cs.LG updates on arXiv.org
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
・arXiv:2408.02295v4 Announce Type: replace Abstract: Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian.
・This assumption can miss the heavy-tailed and heteroscedastic residuals induced by bootstrapping and exploration.
・We introduce a state-conditioned shape head based on the Generalized Gaussian Distribution (GGD) and use a numerically modified GGD loss as an
cs.LG updates on arXiv.org
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
・arXiv:2607.21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery.
・However, novelty in materials discovery encompasses chemical plausibility, structural distinctiveness, property relevance and experimental realisability, making AI-drive
cs.LG updates on arXiv.org
Geometry-Conditioned Fourier Neural Operators for Cubic Nonlinear Schrodinger Dynamics on Periodic Domains
・arXiv:2606.27459v2 Announce Type: replace Abstract: We consider the cubic nonlinear Schr\"odinger (NLS) equation on two-dimensional flat tori with varying aspect ratios.
・In this formulation, the choice of aspect ratio governs the Fourier resonance structure, so rational and irrational geometries can exhibit different high-frequency cascade behaviors.
・We present a geometry-conditioned Fourier neural operator (FNO) for
AI News & Artificial Intelligence | TechCrunch
Google’s AI search is rapidly becoming the default, new data shows
・Google’s AI Overviews now appear in 43% of searches, underscoring how quickly AI-generated answers are becoming the default way people discover information online.
cs.LG updates on arXiv.org
gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
・arXiv:2512.06143v2 Announce Type: replace Abstract: Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists.
・This is because the vast majority of existing methodologies exploit various levels of approximations that lower accuracy and limit the flexibility of kernel and noi
cs.LG updates on arXiv.org
Graph-Based Correlation Matrix Generation: A Convex Optimization Approach
・arXiv:2607.22436v1 Announce Type: cross Abstract: This work addresses the generation of theoretical correlation matrices with prescribed sparsity patterns associated to graph structures.
・We propose a novel convex optimization framework in which an initial matrix is projected onto an elliptope under a positive semidefiniteness constraint.
・Several numerical schemes are implemented and compared.
Zennの「大規模言語モデル」のフィード
GRPOの暗室崩壊：密予測報酬がエージェントを破壊するメカニズム
・TL;DR 密なステップ単位予測報酬をGRPOで活用すると、全モデル・全スケールで確定的に破滅する。原因はGRPOのstd正規化が全失敗グループ内で塑形係数\lambdaを無効化し、\varepsilonスケールの信号を全スケールの圧力に増幅するからだ。単一フラグの切り替え（std正規化の除去）だけで0%→51.6%に回復する。さらに報酬チャネル自体がほぼ無用で、同じ信号を補助損失チャネルで流すと+20pt向上する。この「暗室（Dark Room）」病理の理論的基礎と実践的処方箋を整理する。
・背景：なぜ密予測報酬は魅力的だったのか 長ホライズンのLLMエージェントをスパースな成功...
cs.LG updates on arXiv.org
HD3C: Efficient Medical Data Classification for Edge Devices
・arXiv:2509.14617v4 Announce Type: replace Abstract: Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budgets and computing capabilities are limited.
・We present HD3C, a lightweight classification framework designed for low-power edge devices.
・HD3C encodes data into high-dimensional hypervectors, aggregates them into multiple
cs.LG updates on arXiv.org
Heavy-Tailed Principal Component Analysis
・arXiv:2603.11308v3 Announce Type: replace Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise.
・While numerous robust PCA variants have been proposed, most either assume finite variance, rely on sparsity-driven decompositions, o
cs.LG updates on arXiv.org
Hidden Failure Modes of Gradient Modification under Adam in Continual Learning, and Adaptive Decoupled Moment Routing as a Repair
・arXiv:2604.22407v2 Announce Type: replace Abstract: Many continual-learning methods modify gradients upstream (e.g., projection, penalty rescaling, replay mixing) while treating Adam as a neutral backend.
・We show this composition has a hidden failure mode.
・In a high-overlap, non-adaptive 8-domain continual LM, all shared-routing projection baselines collapse close to vanilla forgetting (12.5--12.8 vs.
cs.LG updates on arXiv.org
Hierarchical Latent Structure Learning through Online Inference
・arXiv:2603.19139v2 Announce Type: replace Abstract: Learning systems must balance generalization across experiences with discrimination of task-relevant details.
・Effective learning therefore requires representations that support both.
・Online latent-cause models support incremental inference but assume flat partitions, whereas hierarchical Bayesian models capture multilevel structure but typically require offline infe
cs.LG updates on arXiv.org
HiKV: Hierarchical Importance-Aware KV Cache with Hardware Acceleration for LLM Decoding
・arXiv:2607.22389v1 Announce Type: cross Abstract: With the rapid adoption of long-context large language models (LLMs), the continuously growing KV cache during decoding has become the critical memory bottleneck.
・To tackle this challenge, we propose HiKV, a novel algorithm-hardware co-design that exploits KV cache redundancy through hierarchical importance awareness.
・Algorithmically, HiKV compresses the KV cache at t
cs.LG updates on arXiv.org
Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting
・arXiv:2607.22299v1 Announce Type: cross Abstract: Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaningful series-specific information.
・We introduce Hopformer (Homogeneity-Pursuit Transformer), a two-stage framework that addresses this challenge.
・In the first stage, we perform a Sparsity Pattern Aggregation (SPA) scheme e
OpenAI News
How AI is expanding what people do at work
・New OpenAI research shows how AI is expanding what workers do, with ChatGPT users taking on tasks across roles and reshaping job boundaries.
cs.LG updates on arXiv.org
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
・arXiv:2607.21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.
・While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood.
・In particular, how ag
cs.LG updates on arXiv.org
Hyperball May Not Be a Free Lunch
・arXiv:2607.22444v1 Announce Type: new Abstract: For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued parameters and normalizing updates.
・However, the source of their advantage remains unclear.
・Starting from the angular displacement between consecutive parameter states, we derive an angular effective learning rate that
cs.LG updates on arXiv.org
IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning
・arXiv:2607.22251v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for large language models, but its performance depends strongly on how a fixed rank budget is distributed across Transformer modules.
・Existing adaptive-rank methods usually rely on local gradient statistics collected during training, which introduces extra memory and computation and overl
AI News & Artificial Intelligence | TechCrunch
Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research
・After two years in stealth, Safe Superintelligence has announced a long-term partnership with Nvidia as it prepares to scale to its next phase.
cs.LG updates on arXiv.org
Indexing: the Beginning and the End
・arXiv:2607.22361v1 Announce Type: new Abstract: We study information bottlenecks in modern deep-learning architectures -- RNNs, softmax transformers, linear-attention transformers and state-space models -- through the lens of the indexing primitive.
・In this primitive, the input consists of $n$ bits and one integer $i$ from $1$ to $n$ called the index, and the output equals the value of the $i$-th bit.
・We introduce ca
cs.LG updates on arXiv.org
Industrial Tokenization for LLM-Based Health Intelligence: A Federated Architecture for Industrial Evidence Integration
・arXiv:2607.22153v1 Announce Type: cross Abstract: Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models.
・Although large language models provide new opportunities for cross-source reasoning, industrial data and analytical outputs diffe
NVIDIA Blog
Industry Leaders Unite in Open Secure AI Alliance for AI Safety and Security
・Open source software is a critical pillar of the global economy.
・It underpins cloud computing, financial services, manufacturing, telecommunications, government and internet services by making technology accessible and observable to communities of experts.
・Cybersecurity is among the top three beneficiaries of open source software.
WIRED
Inside the Wild Rescue Mission That Took 4 Beluga Whales to Chicago
・Beluga whales were in danger of getting euthanized after a Canadian theme park went bust.
・WIRED spoke with some of the scientists behind the groundbreaking rescue mission.
cs.LG updates on arXiv.org
Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning
・arXiv:2607.22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.
・This challenge is fundamentally driven by two key and tightly coupled decision-making processes: order dispatching and routing.
・Solving them separately overlooks their interdependence, while fully end-to-end learning
Hugging Face Papers
Interactive Training 2: Auditable Control Plane for Live Model Training
Interactive Training 2: Auditable Control Plane for Live Model Training
cs.LG updates on arXiv.org
Interior interpretability with attention rollout: contraction and propagation profiles in Transformers
・arXiv:2607.22367v1 Announce Type: new Abstract: Feature-attribution methods assign scores relating input variables to a model's output, but do not by themselves characterize how explicitly defined interaction operators compose across its intermediate layers.
・We introduce \emph{interior interpretability}, a propagation-based perspective on internal model organization, and instantiate it for tabular Transformers using
cs.LG updates on arXiv.org
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
・arXiv:2607.22508v1 Announce Type: new Abstract: Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks.
・Predefined features carry a strong bias, since they fix in advance what counts as informative, while deep neural networks and foundation models are hard to interpret and need large amounts of dat
cs.LG updates on arXiv.org
IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data
・arXiv:2607.22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.
・Learned solvers are fast yet label hungry.
・Simulated sound-speed labels are expensive, while abundant real channel data is unlabeled.
WIRED
It’s Becoming Clear Why Black Holes Never Run Out of Fuel
・Black holes eject powerful energy jets that blow away the surrounding gas to great distances.
・So how can they continue to grow?
Zennの「大規模言語モデル」のフィード
KAMEをオープンモデルで動かしてMT-Benchでベンチマーク評価した
・はじめに シドニー大学大学院の飯田大晟です。本記事は、Fixstarsでの15日間のインターンシップで取り組んだ内容をまとめたものです。
・音声対話モデル「KAME」は、Sakana AIが発表した、低遅延なSTSモデルと高精度なテキストLLMをハイブリッドで組み合わせたモデルです。論文ではSTSモデル並みの低遅延を達成しながら、カスケード型アーキテクチャに近い性能を達成したと報告されています。また、バックエンドLLMは差し替え可能だと主張しています。
・KAMEはSTTが更新されるたびにLLM呼び出しを行うため、0.4秒間隔で高頻度にLLMを叩きます。GPT-4.1とGoogle Cl...
#LLMタグ
Kimi K3の実力は本物か？
・中国の大規模AIモデル「Kimi K3」について、期待に応えるか疑問視されています。競合との差異やビジネス影響も注目です。
・最近、中国のAI企業Moonshot AIが発表した「Kimi K3」に関する報道が世界中で注目を集めています。しかし、専門家の間ではその実力に対する疑問の声も上がっています。
cs.LG updates on arXiv.org
Latent PDE mapping for efficient physics-informed learning across geometries with limited data
・arXiv:2607.22215v1 Announce Type: new Abstract: In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generalization with sparse training data.
・Latent PDE mapping pulls back geometry-specific PDE residuals and boundary conditions to a predefined latent geometry via the deformation gradient, thereby enabling the automated calcula
cs.LG updates on arXiv.org
LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks
・arXiv:2607.21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions.
・Beyond predictive performance, understanding how these models organize chemical information internally in their latent spaces, i.e., the embeddings of the molecules, is critical.
cs.LG updates on arXiv.org
Layer-wise LoRA fine-tuning: a similarity metric approach
・arXiv:2602.05988v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) on web-scale datasets becomes fundamental for advancing general-purpose AI.
・In contrast, enhancing their predictive performance on downstream tasks typically involves adapting their knowledge through fine-tuning.
・Parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA), aim to reduce the computational c
cs.LG updates on arXiv.org
LeAct: Learning to Reason from Expert Actions
・arXiv:2607.21856v1 Announce Type: new Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs.
・However, a rich and largely untapped source of supervision lies in expert systems (e.g., game engines, classical planners, theorem provers), which routinely produce near-optimal actions across diverse domains.
・But these experts are silent: they commit
cs.LG updates on arXiv.org
Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks
・arXiv:2607.22313v1 Announce Type: cross Abstract: Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence.
・This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments
cs.LG updates on arXiv.org
Learning Ergodic Dynamical Systems from a Finite Trajectory
・arXiv:2607.22399v1 Announce Type: cross Abstract: We consider the problem of learning from a single finite trajectory of an ergodic stochastic dynamical system.
・More precisely, we study discrete-time autonomous stochastic systems defining time-homogeneous Markov processes.
・We first focus on estimating the optimal one-step prediction function by nonlinear least squares, and derive high-probability guarantees measured
cs.LG updates on arXiv.org
Learning What Matters: Supervising Sparse Attention Routing with Causal Evidence Sets
・arXiv:2607.21692v1 Announce Type: new Abstract: Sparse attention reduces the cost of long contexts by allowing each query to read only selected parts of the input.
・These selectors are often trained by distilling the attention patterns of a dense teacher, assuming that attention reveals which context the teacher actually uses.
・We test that assumption on retrieval tasks where the evidence for each answer is known exact
cs.LG updates on arXiv.org
LiMuon: Light and Fast Muon Optimizer for Large Models
・arXiv:2509.14562v5 Announce Type: replace Abstract: Large models recently are widely applied in machine learning, so efficient training of large models has received widespread attention.
・More recently, the useful Muon optimizer is specifically designed for matrix-structured parameters of large models.
・Although some works have begun to study the Muon optimizer, the existing Muon and its variants still suffer from high
cs.LG updates on arXiv.org
LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics
・arXiv:2601.18685v4 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace.
・Research syntheses remain scarce and risk being outdated by the time of publication.
・To address this issue, we present a Living Meta-Analysis (LIMA) on the effects of generative AI-based interventions for learning mathematic
cs.LG updates on arXiv.org
LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies
・arXiv:2604.16763v3 Announce Type: replace Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data.
・Large language models can extract these latent confounders as interpretable, structured covariates, yet how to effective
Zennの「大規模言語モデル」のフィード
LLMアプリは「ハーネス」が9割 ── Perl/CGIの現役SaaSにAIエージェントを組み込んで学んだこと
・はじめに 長年運用しているPerl/CGI製SaaS（自社のノーコード業務アプリ基盤）に、LLMエージェントを組み込みました。作ったのは次の3機能です。
・対話型アプリビルダー ── チャットでヒアリングしながら業務アプリを丸ごと生成（サンプルデータ・ダッシュボード・メール雛形つき） アプリ内エージェント ── どの画面からでも呼べるアシスタント。自然言語でレコードの検索・集計・統計分析・登録・更新 アプリ改善アドバイザー ── アプリの作りと使われ方を診断して改善提案 先に断っておくと、これらは社内勉強会のお題として作った試作機能で、本番リリースされたものではありません（レ...
Zennの「大規模言語モデル」のフィード
LLM推論コスト最適化入門 — モデルルーター導入ガイド
・この記事でわかること LLM推論コストの内訳と節約の余地 モデルルーターとは何か、なぜ必要か Pythonで動く最小構成のルーター実装 主要ツール（Portkey / Helicone / OpenRouter）の比較 ▶ 「推論コスト」の全体像 AIエージェントが普及するにつれ、顕在化してきた問題があります。
・それは 「推論コストの爆発」 です。
・AIチャットボット: ユーザー1会話あたり $0.01〜$0.05 AIエージェント（ツール使用）: チャット比 5〜25倍のトークン消費 40%のエージェントプロジェクトが2027年までにコスト超過で中止見込み（出典: ...
cs.LG updates on arXiv.org
Local Synaptic Rules Can Implement a SIGReg Gradient Without Backpropagation
・arXiv:2607.21622v1 Announce Type: cross Abstract: We prove that two canonical local synaptic learning rules, the potentiation arm of spike-timing-dependent plasticity (STDP$^+$) and homeostatic plasticity (instantiated here via flashlight granule-cell-like neurons), together can implement the exact gradient of a SIGReg-like self-supervised learning objective.
・The equivalence requires no gradient calculations, no glob
cs.LG updates on arXiv.org
Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature
・arXiv:2607.22381v1 Announce Type: new Abstract: Curvature notions on graphs, particularly Ollivier-Ricci and Forman, have emerged as powerful tools for addressing fundamental issues in Graph Neural Networks (GNNs) such as oversmoothing and oversquashing, but rely almost exclusively on local edge-level comparisons and therefore fail to certify how information actually propagates over long distances.
・We introduce Entro
cs.LG updates on arXiv.org
Longitudinal Random Forests for Sparse and Irregular Response Trajectories
・arXiv:2607.21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points.
・Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint value, completely neglecting the underlying response trajectories.
・We propose a novel Longitudinal Random Forest (LRF) framework that leverages
cs.LG updates on arXiv.org
LunarFM: A Shared Multimodal Representation of the Moon's Surface
・arXiv:2607.22408v1 Announce Type: new Abstract: The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for accurate, large-scale characterization of the lunar surface.
・Although vast quantities of orbital remote-sensing data have been collected, scientific analysis and resource mapping remain fragmente
cs.LG updates on arXiv.org
MA-DAR: Manifold-Aligned Dynamic Adaptive Routing for Continual Temporal Knowledge Graph Reasoning
・arXiv:2607.21949v1 Announce Type: new Abstract: Continual temporal knowledge graph (TKG) reasoning aims to continuously incorporate newly emerging facts while preserving previously acquired knowledge.
・Replay-based continual learning has achieved promising performance by revisiting historical representations.
・However, existing methods primarily focus on what to replay, while largely overlooking how replayed representa
cs.LG updates on arXiv.org
Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs
・arXiv:2604.27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases.
・To this end, we introduce Math Education Digital Shadows (MEDS), a dataset mapping how LLMs reason about mathematics across human- and AI-like personifications.
・MEDS comprises 28,000 runs from 14 LLMs (i.e., Mistral, Qwe
WIRED
Measles Is Becoming So Common That Treatments May Soon Be Needed
・As the US sees its highest number of measles cases in decades and vaccination rates fall, researchers are developing drugs to help those who contract the virus or who are particularly vulnerable.
cs.LG updates on arXiv.org
Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
・arXiv:2607.21636v1 Announce Type: new Abstract: Synthetic tabular data is valued for preserving not only each column's marginal distribution but the dependencies between columns -- structure that carries much of the discriminative signal for minority classes in imbalanced domains such as fraud and clinical risk.
・Yet the metrics most commonly used to certify synthetic tabular data are, we show, largely blind to inter-
cs.LG updates on arXiv.org
MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection
・arXiv:2607.22086v1 Announce Type: cross Abstract: Autoencoder-based anomalous sound detection is attractive for machine condition monitoring because it can be trained using only normal recordings and yields an interpretable anomaly score from reconstruction error.
・Most prior work uses spectrogram autoencoders, but reconstructing detailed time--frequency patterns is sensitive to noise and transients, and models can re
The Verge
Meta is royally screwing up its smart glasses rollout
・We’re leaning in with the playing card metaphor.
・Across New York City, London, and Washington, DC, ads for Meta's smart glasses have been plastered over with satirical posters from activist groups.
・One guerrilla ad calls these "the biggest advancement in pervert technology since the trenchcoat." Another transposes the words "mass surveillance predator glasses" over influencer Kylie Jenner's face, calling her out for
cs.LG updates on arXiv.org
Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
・arXiv:2505.23378v3 Announce Type: replace Abstract: Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications.
・While mixed-effect models are commonly used for such speaker adaptation, they require computationally expensive retraining for each new observation, making them impractical in a production environment.
・We reformulate this task as a meta-learning problem
cs.LG updates on arXiv.org
Minimum Norm Interpolation via the Local Theory of Banach Spaces: The Role of $2$-Uniform Convexity
・arXiv:2603.28956v2 Announce Type: replace-cross Abstract: The minimum-norm interpolator (MNI) framework has recently attracted considerable attention as a tool for understanding generalization in overparameterized models, such as neural networks.
・In this work, we study the MNI under a $2$-uniform convexity assumption, which is weaker than requiring the norm to be induced by an inner product; in this setting, the MNI
cs.LG updates on arXiv.org
MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting
・arXiv:2607.21922v1 Announce Type: new Abstract: Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and what to observe.
・In event-centric models, however, co-timestamp structure can be flattened too early: measurements acquired at the same timestamp are embedded as isolated nodes, leaving local patient-state context unavail
cs.LG updates on arXiv.org
Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
・arXiv:2607.21653v1 Announce Type: new Abstract: Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researcher at every iteration.
・Molt is a PyTorch-native training framework built to keep that cost sm
cs.LG updates on arXiv.org
MotifRole-Diff: Risk-Optimal Role-Aware Corruption for Masked Molecular Graph Diffusion
・arXiv:2607.21634v1 Announce Type: new Abstract: Masked discrete diffusion for molecular graph generation typically applies a uniform corruption schedule to all tokens in a lossless graph-to-sequence representation, implicitly treating structurally heterogeneous molecular components as equally difficult and equally important to reconstruct.
・However, different molecular graph token roles exhibit substantial variation i
cs.LG updates on arXiv.org
Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge
・arXiv:2607.21946v1 Announce Type: new Abstract: This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose statement and figure may be given partly or entirely as an image.
・Visual physics problems become substantially harder for large language models when the decisive information resides in a figure r
Hugging Face Papers
Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making
Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making
cs.LG updates on arXiv.org
Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not
・arXiv:2607.21645v1 Announce Type: new Abstract: Multi-horizon latent consistency is a common training knob in video predictors and world models, but practitioners rarely know what it does to transition geometry.
・We treat lambda, the weight on multi-step latent agreement, as a diagnostic control and measure an empirical expansion proxy L20,q95 together with horizon-20 prediction error E20.
・On Moving-MNIST (n=6 seeds a
Hugging Face Papers
Multimodal Speaker Verification as a Threat to Speaker Anonymization
Multimodal Speaker Verification as a Threat to Speaker Anonymization
cs.LG updates on arXiv.org
Natural Invariant Measures for Chaotic Game Dynamics: Finding Order in Chaos
・arXiv:2607.21805v1 Announce Type: cross Abstract: We study the long-term behavior of the Multiplicative Weights Update (MWU) algorithm in game settings where learning dynamics frequently fail to converge to Nash equilibria and instead exhibit Li-Yorke chaos.
・While such chaos precludes the prediction of specific long-term strategy profiles, it does not imply a lack of statistical structure.
・We demonstrate that natural
cs.LG updates on arXiv.org
Neural Feature Governance: Extending Atom Prevalence
・arXiv:2607.21671v1 Announce Type: new Abstract: Neural network compression and interpretability remain open challenges in modern deep learn- ing, where billion-parameter architectures deliver impressive accuracy at the cost of trans- parency, computational efficiency, and reliable uncertainty quantification.
・This paper introduces Neural Atom Prevalence (NAP), a principled Bayesian framework for structured node-level
#LLMタグ
Next.jsでLLM回答をストリーミング実装する5つの判断
・「AIの返答、もう少し速く出せませんか」という相談を受けることがあります。実際に計測すると生成自体は数秒で終わっていて、遅いのは速度そのものではなく、画面が何も動かない待ち時間であるケースがほとんどです。ここでよく上がる解決策が、回答を一括表示ではなく少しずつ流す「ストリーミング」実装です。ただし配信方式・表示の粒度・中断時の挙動まで含めて判断しないと、体感は良くなってもコストや保守で想定外の負債を抱えることになります。この記事では、Next.js×AI受託をしている自分が、見積もり前に発注者と揃えておきたい5つの判断軸を整理します。
・ストリーミングが必要になる場面 続きをみる
cs.LG updates on arXiv.org
Numerical Fragility in Transformers: A Layer-wise Theory for Risk Estimation and Selective Stabilization
・arXiv:2510.21770v2 Announce Type: replace Abstract: Low-precision execution can induce substantial forward discrepancies in Transformers even for fixed weights and input, yet these discrepancies are usually monitored only at the output and lack a layer-wise theoretical account.
・We develop a first-order decomposition of output mismatch into layer-local attention, LayerNorm, and residual-transport terms, and derive fro
Hugging Face - Blog
NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics
NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics
The Verge
Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic
・Nvidia on Monday said it is joining forces with Microsoft, SpaceX, IBM, and other tech companies to build and share open-source AI security tools.
・The new Open Secure AI Alliance said open tools are required to effectively defend against attacks from frontier models.
・The initiative is a direct response to mounting concerns over the safety of advanced AI systems after a rogue OpenAI model escaped containment and attac
Hugging Face Papers
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
cs.LG updates on arXiv.org
On the Convergence of Stochastic Low-Rank Adaptation
・arXiv:2607.21975v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) optimizes $J(B,A)=\mathcal L(W_\mathrm{base}+sBA)$ over two adapters $B \in \mathbb{R}^{m \times r}$ and $A \in \mathbb{R}^{r \times n}$ that form a low-rank update to a frozen pretrained weight matrix $W_\mathrm{base} \in \mathbb{R}^{m \times n}$.
・The prior analysis shows LoRA-GD takes $\exp\{\mathcal{O}(\epsilon^{-2})\}$ oracle calls to find
cs.LG updates on arXiv.org
On the Depth Scalability of Logic Gate Networks
・arXiv:2607.21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth.
・We identify two distinct causes: optimization collapse in deep relaxed LGNs and a topology-induced limitation that persists even when skip-biased initialization and straight-throug
cs.LG updates on arXiv.org
On the Identifiability of Controlled World Models
・arXiv:2607.22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.
・Joint-Embedding Predictive Architectures (JEPAs) provide a compelling framework for learning such models in representation space.
・Recent action-conditioned extensions perform promisingly in visual contr
cs.LG updates on arXiv.org
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
・arXiv:2601.12238v5 Announce Type: replace-cross Abstract: In this paper, we provide a comprehensive theoretical analysis of Stochastic Gradient Descent (SGD) and its momentum variants (Polyak Heavy-Ball and Nesterov) for tracking time-varying optima under strong convexity and smoothness.
・Our finite-time bounds reveal a sharp decomposition of tracking error into transient, noise-induced, and drift-induced components.
cs.LG updates on arXiv.org
One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments
・arXiv:2607.22119v1 Announce Type: cross Abstract: Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames.
・However, existing approaches typically assume these frames to be strictly exogenous.
・This causal assumption collapses in dynamic settings, such as when a single robot arm manipulates a moving object or when
cs.LG updates on arXiv.org
Online Pricing and Allocation with Demand Learning and Fulfillment Cost
・arXiv:2501.18049v3 Announce Type: replace Abstract: We study online learning for a seller that jointly chooses per-period inventory positions and a uniform price, then fulfills realized demand through a downstream allocation.
・The main difficulty is not only demand learning: the price shifts demand and reshapes the transportation LP, making the population objective globally non-convex and non-smooth.
・To solve this pro
AI News & Artificial Intelligence | TechCrunch
OpenAI’s Hugging Face breach has reignited the debate over alignment and control
・OpenAI's Hugging Face breach has reignited debate over AI alignment and control, exposing competing views on whether increasingly capable AI should be better aligned, better contained, or both.
cs.LG updates on arXiv.org
Optimal generalisation and learning transition in extensive-width shallow neural networks near interpolation
・arXiv:2501.18530v3 Announce Type: replace-cross Abstract: We consider a teacher-student model of supervised learning with a fully-trained two-layer neural network whose width $k$ and input dimension $d$ are large and proportional.
・We provide an effective theory for approximating the Bayes-optimal generalisation error of the network for any activation function in the regime of sample size $n$ scaling quadratically wit
cs.LG updates on arXiv.org
Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression
・arXiv:2607.22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.
・BO is often used under a limited evaluation budget, such as hyperparameter tuning of deep learning.
・Despite its effectiveness, conventional BO may have poor convergence in prac
Zennの「大規模言語モデル」のフィード
Opus 5にオセロを対局させてみたが、まだ甘かった ～Kaggle Game Arenaローカル検証～
・はじめに Kaggleが2025年8月にローンチしたGame Arenaは、AIモデル同士がチェスなどのゲームで対戦し、その様子を配信・リプレイできるベンチマークプラットフォームです。
・本記事は単なる機能紹介ではなく、オープンソースとして公開されている裏側のコードを読み解きながら、ローカル環境でLLM同士にオセロ(Othello)を対局させてみた実践記録です。仕組みの全体像を軽く整理しつつ、実際に動かす中で直面した実装上の注意点や、Claude Opus 5（以下、Opus 5）に打たせてみて見えてきた「LLMのゲームプレイのリアルな実力」について紹介します。
・比較のため、Claud...
cs.LG updates on arXiv.org
Ordered Action Tokens for Visuomotor Policy Learning
・arXiv:2607.21670v1 Announce Type: cross Abstract: Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies.
・Existing approaches either rely on analytical discretization methods that produce prohibitively long token sequences or learned latent tokenizers that lack structure, limiting their compatibility with downstream policies.
cs.LG updates on arXiv.org
Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection
・arXiv:2607.21752v1 Announce Type: new Abstract: Data-adaptive sparse attention masks substantially outperform fixed patterns (e.g., BigBird and Longformer) and can even exceed dense attention on long sequences.
・Existing adaptive approaches---including SBM-Transformer, Dynamic Mask Attention, and NSA---typically require additional learnable parameters, custom gradient estimators, or specialized CUDA kernels.
cs.LG updates on arXiv.org
Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
・arXiv:2604.26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.
・Using the EuroSAT-MS dataset, we perform a systematic one-vs-one evaluatio
cs.LG updates on arXiv.org
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
・arXiv:2505.08784v3 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
・In this paper we introduce PCS-UQ, a framework based on the Predictability, Computability, and Stability (PCS) principles for veridical data science.
・Starting with a candidate set of models or algorithms, PCS-UQ integrates a rigorous predic
MarkTechPost
Perplexity Releases pplx, a Single-Binary CLI That Puts Its Search API in the Terminal for Coding Agents
・Perplexity has released pplx, an official command line client for its Search API.
・The tool exposes two commands — pplx search web and pplx content fetch — and returns exactly one JSON object on stdout.
・It ships as a checksum-verified single binary for macOS arm64 and Linux, alongside an Agent Skill for Claude Code, Codex CLI and any harness that can read a URL.
Qiita - 人気の記事
PHPで常駐サーバーを動かす「Swoole」入門 ── なぜ速いのかを仕組みから理解する
・PHPで常駐サーバーを動かす「Swoole」入門 ── なぜ速いのかを仕組みから理解する この記事で伝えたいこと 「PHPは1リクエストごとに毎回ゼロから起動するから遅い」──長らくそう言われてきました。ところが Swoole という拡張を入れると、その前提そのものがひ...
cs.LG updates on arXiv.org
Phylogenetic signal in marine mammal and bird vocalizations captured by audio foundation models: the limited benefit of domain-specific pretraining
・arXiv:2607.22458v1 Announce Type: new Abstract: Do learned audio embeddings encode structure that nobody told them to encode?
・We probe four large pretrained audio models (AST, CLAP, BEATs-bio and BirdNET) with a downstream task none of them saw during training: recovering phylogenetic distance from species vocalizations.
・If the geometry of the embedding space tracks the tree of life, the representation is picking up
cs.LG updates on arXiv.org
Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction
・arXiv:2607.21665v1 Announce Type: new Abstract: Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur.
・The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenants.
・Neural additive models (NAMs) offer auditability because each KPI con
cs.LG updates on arXiv.org
Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection
・arXiv:2607.21776v1 Announce Type: new Abstract: Talking-face (TF) deepfake generation synthesizes photore- alistic facial video from a static source image and an au- dio signal, producing forgeries that current image-based detectors consistently fail to identify.
・Unlike face-swap ma- nipulation, TF synthesis has no underlying real video from which to inherit physiological characteristics, making re- mote photoplethys
cs.LG updates on arXiv.org
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
・arXiv:2607.22518v1 Announce Type: cross Abstract: In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems.
・Compared to prior approaches, we have made the following new contributions: 1) our solution spans the entire multi-stage funnel and generalizes well for both search and recommendation surfaces, 2) our solution reduces bias favoring e
cs.LG updates on arXiv.org
PostDeg: Placement Beats Parameterization in LayerNorm GNNs
・arXiv:2606.14022v2 Announce Type: replace Abstract: LayerNorm-based GNNs routinely erase the topology signals (degree, centrality, $k$-core) that node-selection policies should depend on, but the literature has not located where in the residual block the erasure happens.
・We answer that question: a positive per-node scalar inserted before LayerNorm is divided out up to a stabilizer term, while the same scalar inserted
AI News & Artificial Intelligence | TechCrunch
Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 2026
・At TechCrunch Disrupt 2026, the Smart Systems Stage will be where energy, infrastructure, and technology collide, covering everything from fusion breakthroughs to the grid strain AI is putting on the entire economy.
cs.LG updates on arXiv.org
Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
・arXiv:2602.09572v3 Announce Type: replace-cross Abstract: The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission of the patient, or the likelihood that a financial transaction is fraudulent.
・Typically powered by machine learning methods, predictive models are used in recommendations, financial
cs.LG updates on arXiv.org
Pretraining EHR Foundation Models with Patient-Aware Sampling
・arXiv:2607.22114v1 Announce Type: new Abstract: Autoregressive foundation models for electronic health records (EHRs) typically inherit pretraining methods from language modeling, where patient trajectories are concatenated into a single token stream and windows are sampled from that stream.
・In EHR data, this choice is consequential: windows may mix multiple patients, and patients with longer records contribute more
cs.LG updates on arXiv.org
Pretraining Recurrent Networks without Recurrence
・arXiv:2606.06479v2 Announce Type: replace Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations.
・Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, and suffers from vanishing or exploding gradients, making long-range associations difficult to learn.
・We propose Supervised Memory Traini
cs.LG updates on arXiv.org
Prior laundering: learned priors with inherited, undetectable overconfidence
・arXiv:2607.21721v1 Announce Type: cross Abstract: Learned generative priors are increasingly used for ill-posed Bayesian inverse problems, their posterior uncertainty treated as earned from data.
・But training one requires truths, scarce in seismic and medical imaging, so the recourse is an archive of legacy reconstructions---prior laundering.
・Where the measurements are uninformative the posterior reverts to the prior
Qiita - 人気の記事
PrivateLink で別アカウントの VPC に接続されているオンプレミスの名前解決が必要なリソースにアクセスする
・AWS の VPC 間をネットワーク接続する方法はいくつかあります。
・地方自治体がガバメントクラウドの閉域 AWS 環境に基幹業務システムを構築する場合、地方自治体のオンプレミス環境と複数の AWS アカウントの VPC を接続するケースが多いため、Transit Gate...
cs.LG updates on arXiv.org
Probing Speaker Identity Sensitivity in Audio Deepfake Detectors
・arXiv:2607.21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.
・Yet the same detector that achieves less than 1% error on one dataset can see its error rate increase twentyfold when evaluated on a different dataset.
・We argue that one contributing factor is speaker-identity reliance: standard train
cs.LG updates on arXiv.org
Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures
・arXiv:2607.21612v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation.
・We show that for procedural knowledge--the ability to follow multi-step procedures with conditional branching through to terminal states--LoRA fails to match full fine-tuning at t
cs.LG updates on arXiv.org
Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting
・arXiv:2607.21756v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in database-backed applications to classify tuples, filter records using semantic predicates, extract structured attributes, and enrich query results.
・Yet the prompt that start these computations are typically stored outside the DBMS in unstructured formats, making them invisible to query execution, metadata managemen
cs.LG updates on arXiv.org
QC-PHAST Search: Classical--Quantum Query Benchmarks for Finite-Pool Rare-Regime Discovery
・arXiv:2607.21995v1 Announce Type: cross Abstract: Rare-regime discovery in parameterized dynamical systems is an active-search problem: find one verified parameter at which a scientifically defined qualitative threshold is crossed, even when acceptable candidates are rare, nonconvex, or fragmented.
・We introduce Quantum-Classical Phase-space and Stability-Threshold Search (QC-PHAST), an evidence-gated decision protoco
cs.LG updates on arXiv.org
Quantifying Political Partisanship for Cross-Platform Analyses
・arXiv:2607.21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.
・However, existing approaches are typically tailored to platform-specific properties, such as structural affordances or linguistic conventions, which hurts generalizability across platforms.
・This limitation is increasingly consequential
cs.LG updates on arXiv.org
Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support
・arXiv:2607.22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
・For matrix-valued inputs, relevant matrix-level relationships can be characterised through spectral values and spectral subspaces; however, common coordinate-wise rotation-gate data-encoding unitaries used in most
cs.LG updates on arXiv.org
Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning
・arXiv:2607.21637v1 Announce Type: new Abstract: This paper explores the efficacy of quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning within modern benchmark environments.
・Various sampling methods are used to bound a population-based search and aggregate an optimal prior from a baseline set of tasks.
・The QMC meta-priors show improvements in training convergence compared to modern orthogona
cs.LG updates on arXiv.org
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
・arXiv:2511.02130v2 Announce Type: replace-cross Abstract: We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking tokens.
・Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models.
・Re-FORC enables: 1) early stopping of unpromis
cs.LG updates on arXiv.org
Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models
・arXiv:2607.22098v1 Announce Type: cross Abstract: Large reasoning models (LRMs) generate long reasoning traces before producing final answers.
・While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps that obscure the cues relevant to truthfulness assessment.
・In this paper, we identify two prevalent forms of reasoning
stat.ML updates on arXiv.org
Reconstruction of Enhanced Causal Omnidirectional Network (RECON)
・arXiv:2607.21833v1 Announce Type: cross Abstract: Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems.
・Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations.
・We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECO
cs.LG updates on arXiv.org
RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory
・arXiv:2607.21731v1 Announce Type: new Abstract: Transformers are widely used across many domains, including natural language processing, computer vision, web search, and DNA sequence analysis.
・Given their broad applicability, improving the performance of transformer models is critical.
・However, the high volume of data movement between processing units and memory during attention operations significantly limits their
cs.LG updates on arXiv.org
Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition
・arXiv:2607.22413v1 Announce Type: cross Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision.
・We present Reflector, an interactive audio workstation that tracks harmonic combinations as they accumulate on the composer's timeline and
cs.LG updates on arXiv.org
Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling
・arXiv:2607.21797v1 Announce Type: cross Abstract: Previous work has shown that the simple dataflow primitives of the Lustre language allow the natural, semantically unambiguous, and compact representation of machine learning (ML) applications, including models featuring complex conditional execution and recurrent state.
・The Lustre clock calculus is responsible for the static determination of important properties such
cs.LG updates on arXiv.org
Reliability-Aware Bayesian Optimization of 1310 nm PCSELs with FDTD Verification
・arXiv:2607.21772v1 Announce Type: cross Abstract: Near 1310 nm photonic-crystal surface-emitting lasers (PCSELs) are attractive narrow-beam sources for optical communication and sensing, but their final design refinement is costly.
・Small geometry changes simultaneously shift the band-edge resonance, cavity leakage, far-field divergence, and the numerical stability of a high-$Q$ decay fit, while every full-wave trial
cs.LG updates on arXiv.org
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
・arXiv:2607.21885v1 Announce Type: new Abstract: Coarsening-based training for graph neural networks (GNNs), i.e.\ training on coarsened graphs rather than the original large ones, has become a promising direction for scaling GNNs to massive graphs.
・However, prior work has been evaluated almost exclusively on \textit{homophilic} graphs, leaving the more challenging \textit{heterophilic} settings underexplored.
cs.LG updates on arXiv.org
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
・arXiv:2601.04268v3 Announce Type: replace Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes.
・Traditional schemes rely on fixed coefficients that are weakly constrained and tuned offline, contributing to persistent biases that limit their ability to adapt to underlying physics.
・This study presents a framework that learns components of parametrisation schemes onlin
cs.LG updates on arXiv.org
Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks
・arXiv:2606.14954v4 Announce Type: replace-cross Abstract: We develop a general framework for analyzing representation costs induced by parameter-space regularizers in data-fitting methods.
・For an arbitrary parametric method, we define its representation cost and native function space, prove existence, and identify conditions under which parameter-space and function-space problems have equal infimal values and minimiz
cs.LG updates on arXiv.org
Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era
・arXiv:2607.22068v1 Announce Type: cross Abstract: Multi-branch architectures and CNN-Transformer fusion have long been regarded as effective ways to improve vehicle re-identification (Re-ID) by combining complementary representations.
・In this work, we revisit this assumption in the foundation-model era through a comprehensive empirical study.
・A single DINOv3-pretrained ConvNeXt trained with a tuned recipe achieves 88
機械学習タグが付けられた新着記事 - Qiita
RGBカメラ1台でロボットを動かすMistralのRobostral Navigate
・ロボットに「ロビーを出て廊下をまっすぐ進み、右の給湯室に入って」と話しかけると、その通りに動く。ここまでは驚かない。驚くのは、その足元にLiDARも深度カメラも事前地図も無く、あるのは普通のRGBカメラ1台だけ、という点だ。Mistralが7月8日に公開したRobostra...
cs.LG updates on arXiv.org
RIS-Kernel: A Model-Agnostic Architecture for Long-Context LLM Inference via Sparse Attention
・arXiv:2607.21927v1 Announce Type: new Abstract: Full self-attention in large language models scales as O(N^2), which limits long-context document analysis to 65,536 tokens and requires costly GPU clusters.
・The Reduced Interaction Sampling (RIS) inference engine addresses this constraint as a model-agnostic architecture.
・Without modifying weights, RIS reduces self-attention complexity to O(N log N) using sparse stocha
Qiita - 人気の記事
RTX PRO 6000 BlackwellでAITuberを作りたい
・概要 こちらの記事は「MEDLEY Summer Tech Blog Relay」の10日目の記事です。
・RTX PRO 6000 Blackwellの実用的な使い方を探るべく、ローカルAI配信者を実装しました。本記事では、技術選定の背景、システム構成、および実測し...
cs.LG updates on arXiv.org
Safe In-Context Reinforcement Learning
・arXiv:2509.25582v4 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, instead relying on an expanding context of interaction history.
・While ICRL has shown impressive generalization, safety during this adaptation process remains unexplored, limiting its applica
The Verge
Samsung’s 27-inch QD-OLED gaming monitor is priced right at $299.99
・The cost of QD-OLED gaming monitors is going down, even as many other PC components are still soaring above their normal prices.
・If your budget is locked at $300, Samsung’s 27-inch 1440p model costs exactly that much thanks to a recent $50 discount at Amazon.
・Finding a good QD-OLED monitor under $500 these days is pretty easy, but under $300 is pretty rare.
cs.LG updates on arXiv.org
Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems
・arXiv:2602.04120v4 Announce Type: replace Abstract: Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient.
・Most current methods are "coupled" in such a way that they generate explanations simultaneously with model inferences.
・As a result, these approaches incur redundant computation, high latency and poor scalability when deployed acro
cs.LG updates on arXiv.org
Scalable Gaussian process inference via neural feature maps
・arXiv:2605.10285v2 Announce Type: replace-cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels.
・We show that the learned feature map can be interpreted as an optimal low-rank approximation to a Gram matrix derived from an implied RKHS, from which we establish consistency of the GP posterior.
・We further analyse the spectral pr
cs.LG updates on arXiv.org
Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study
・arXiv:2607.21866v1 Announce Type: new Abstract: Prior classical-ML learning-curve work fits power laws to tree, linear, and kernel models on tabular data, but at small scale: typically one curve, one team, a handful of cells.
・We present a distributed classroom-scale replication: 127 students each ran a fixed protocol on 3 assigned datasets, drawn from 18 tabular classification and regression datasets and 6 model fami
cs.LG updates on arXiv.org
SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text
・arXiv:2607.21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.
・We introduce SCOPE (Schema Construction and Ontology-induction Pipeline Evaluation), a train-text-only benchmark for corpus-to-schema induction and optional schema fusion from raw text,
The Verge
SDCC 2026’s best trailers are heavy on cyberpunk and sci-fi
・San Diego Comic-Con took place over the weekend, and as per usual it was full of tidbits of news across film, TV, and gaming.
・Marvel revealed the next Ghost Rider (Ryan Gosling) and Black Panther (David Jonsson), while we also got release dates for God of War: Laufey (February 16th) and the fourth and final season of Silo (summer 2027).
・But the best part of SDCC each year, at least for those following along at home,
cs.LG updates on arXiv.org
Searching the Space of Feed-Forward Neural-Network Weight-Update Rules with Fixed Depth Symbolic Regression
・arXiv:2607.21855v1 Announce Type: new Abstract: We investigate whether symbolic regression can discover explicit neural network weight-update rules that outperform standard hand-designed optimizers on small symbolic regression benchmarks.
・Candidate update rules are represented as fixed-depth symbolic expressions over operands derived from common optimizers, including gradient, momentum, adaptive-gradient, and moment-
cs.LG updates on arXiv.org
Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense
・arXiv:2512.23849v2 Announce Type: replace-cross Abstract: Sophisticated attackers can evade detection-based security by using encryption, stealth tactics, and low-rate attack patterns.
・This challenge is particularly acute in Internet of Things (IoT) and edge environments, where limited resources make ML-based intrusion detection systems impractical.
・Hereby, we present Economic Denial Security (EDS), a framework that
cs.LG updates on arXiv.org
Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
・arXiv:2507.01551v3 Announce Type: replace Abstract: Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~(LLMs).
・However, introducing additional process reward models incurs substantial computational overhead, and there is no unified theoretical framework for process-level advantage estimation.
・To bridge this gap, we propose \tex
cs.LG updates on arXiv.org
Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration
・arXiv:2607.21673v1 Announce Type: new Abstract: Test-time adaptive out-of-distribution (OOD) detectors update a memory bank from the unlabelled stream.
・We show this adaptation obeys a provable dynamical law.
・Modelling bank impurity as a generalized P\'olya urn, we prove almost-sure convergence to a mean-field equilibrium whose slope acts as a reproduction number.
cs.LG updates on arXiv.org
Shallower ReLU Network Representations via Exact Linear Algebra
・arXiv:2607.21651v1 Announce Type: new Abstract: We prove that the maximum of $n$ real numbers is exactly representable by a ReLU network with two hidden layers for every $n\le 10$.
・The constructions are obtained by reducing the problem to exact rational linear algebra: after a symmetry reduction, the necessary cancellations are encoded in finite linear systems over $\mathbb{Q}$, which we solve and verify computationa
cs.LG updates on arXiv.org
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
・arXiv:2605.22142v3 Announce Type: replace Abstract: Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term transfer of symbolic observations.
・We study this transfer process in a temporal knowledge-graph memory setting and cast it as a neuro-symbolic value-based decision problem: for each observed
cs.LG updates on arXiv.org
Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics
・arXiv:2605.11017v2 Announce Type: replace Abstract: Behavioral curve modeling -- fitting parametric functions to engagement-versus-exposure data -- is standard practice in recommendation, advertising, and clinical dosing.
・We show that aggregation introduces a systematic distortion: Simpson's paradox in behavioral curves.
・On Goodreads (3.3M users, 9 genres), individual users peak at n* approximately 11 exposures while
cs.LG updates on arXiv.org
Simulation-Based Empirical Bayes
・arXiv:2607.21843v1 Announce Type: cross Abstract: Empirical Bayes (EB) performs simultaneous inference across many related latent variables.
・Classical EB assumes that the likelihood p(x | z) is tractable.
・In many scientific applications, however, the likelihood is available only through a simulator.
cs.LG updates on arXiv.org
Singular value soft-thresholding via the polar decomposition
・arXiv:2607.22484v1 Announce Type: cross Abstract: Singular value soft-thresholding can be computed via a reduction to the matrix polar decomposition, which allows one to exploit GPU-friendly algorithms for computing the polar decomposition.
・Empirically, there is a significant speed-up on GPUs compared to the standard approach using the SVD.
・We leave the investigation of robustness to future work, but note that due to
cs.LG updates on arXiv.org
Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation
・arXiv:2607.22034v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on consumer hardware where input images are degraded by compression, camera shake, and poor lighting.
・In such settings, a reliable uncertainty signal matters more than raw accuracy, because it determines when a system should defer rather than answer.
・We evaluate two small open-weight VLMs -- Qwen2-VL-2B-Instruct
cs.LG updates on arXiv.org
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
・arXiv:2605.30789v3 Announce Type: replace Abstract: We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs.
・While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and lead to incoherent trajectories.
・We uncover that smaller models within the same m
cs.LG updates on arXiv.org
Smart predict-then-robustly-optimize
・arXiv:2607.21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space.
・While traditional integrated-learning-and-optimization models assume that side information is perfectly revealed, empirical data-driven features are frequently corrupted or noisy at the ti
WIRED
Soundcore Nebula P1i Projector Review: New Design, Classic Pitfalls
・I tested the Soundcore Nebula P1i, an affordable projector, to see if it could overcome the typical drawbacks that most budget projectors suffer from.
cs.LG updates on arXiv.org
Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs
・arXiv:2505.00473v2 Announce Type: replace Abstract: We explore the promising performance of a transformer model in predicting outputs of parametric dynamical systems with external time-varying input signals.
・The outputs of such systems vary not only with physical parameters but also with external time-varying input signals.
・Accurately catching the dynamics of such systems is challenging.
cs.LG updates on arXiv.org
Spectral Flow Certificates for Depth-Aware Long-Range Propagation in Graph Neural Networks
・arXiv:2607.21607v1 Announce Type: cross Abstract: Graph Neural Networks propagate information through local message passing, but the graph topologies themselves can silently prevent any amount of training from solving long-range tasks.
・When we deploy GNNs on new graphs, there is currently no inexpensive way to know, before training begins, whether the graphs' structures will allow information to travel far enough bet
Hugging Face Papers
Spectral Prior for Reducing Exposure Bias in Diffusion Models
Spectral Prior for Reducing Exposure Bias in Diffusion Models
cs.LG updates on arXiv.org
Statistical mechanics of extensive-width Bayesian neural networks near interpolation
・arXiv:2505.24849v2 Announce Type: replace-cross Abstract: For three decades statistical mechanics has been providing a framework to analyse neural networks.
・However, the theoretically tractable models, e.g., perceptrons, random features models and kernel machines, or multi-index models and committee machines with few neurons, remained simple compared to those used in applications.
・In this paper we help reducing the g
LLMタグが付けられた新着記事 - Qiita
Streamlit × Agents SDK — Runner.run_streamedでリアルタイム応答を表示する
・Streamlit × Agents SDK — Runner.run_streamedでリアルタイム応答を表示する 前回までで Streamlit の UI 骨格（chat_message / session_state）と、Agents SDK 単体でのエージェント実...
cs.LG updates on arXiv.org
SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation
・arXiv:2605.03534v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer.
・We study evidence sufficiency verification for selective RAG answering, in which a verifier receives a question, a candidate answer, and retrieved evidence and decides whether the evid
cs.LG updates on arXiv.org
SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
・arXiv:2509.22352v3 Announce Type: replace Abstract: Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death.
・Unlike standard tabular data, survival data often come with incomplete event information due to dropout, or loss to follow-up.
・This poses unique challenges for synthetic data generation, where it is crucial for clinical re
cs.LG updates on arXiv.org
Susceptible Reservoir Architectures for Regime-Conditional Volatility Forecasting
・arXiv:2607.22491v1 Announce Type: new Abstract: Volatility forecasting is dominated by persistence and measurement noise, leaving limited residual structure for nonlinear models to exploit.
・We introduce Susceptible Architectures (SUSA), a reservoir-design principle for volatility forecasting, and its two concrete implementations, based on complex-valued open-chain and periodic reservoirs and regime-conditioned expert
#AIタグ
Swallowというブランドに込めた想い。
・この記事では、AIの使い方やビジネスの話ではなく、 「なぜSwallowというブランドを作ったのか。」 その想いを書きたいと思います。
・⸻ 「人のために、自分のために。」 Swallowには、一つだけ変えたくない言葉があります。
・「人のために、自分のために。」 誰かの役に立ちたい。
cs.LG updates on arXiv.org
Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloning
・arXiv:2607.22304v1 Announce Type: new Abstract: Synthetic data augmentation in speech is common practice for linguistic tasks like ASR, but has seen far less work for paralinguistic ones, especially clinical tasks where labelled data is expensive and some patient groups are underrepresented.
・Voice cloning is one such augmentation approach, but is typically evaluated on speech intelligibility (WER) or speaker similari
The Verge
Tariffs didn’t bring manufacturing jobs back to the US
・Today, I’m talking with Evan Smith, who is cofounder and CEO of Altana, a company that develops software tools to manage big, messy supply chain networks around the world.
・We last had Evan on in early 2025 to talk about how Trump’s first few waves of tariffs were starting to affect global trade and what patterns Altana was seeing in all that macro-level data about shipping and trade.
・It was a very alarming and also v
cs.LG updates on arXiv.org
Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning
・arXiv:2607.21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.
・We hypothesize that the success of such evolution frameworks hinges on meta-skills, such as self-reflection with environment feedback, that enable effective multi-round refinement, yet are largely neglected by traditional post
The Berkeley Artificial Intelligence Research Blog
Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
・--> .abbel-fig { display: block; text-align: center; margin: 2.4em 0; line-height: 1.4; max-width: 100%; } .abbel-fig img { display: block; margin: 0.65em auto 0; height: auto; max-width: 100%; } /* Image sizes; captions use a narrower measure below */ .abbel-fig--wide img { width: 100%; max-width: 100%; } .abbel-fig--wide-90 img { width: 100%; max-width: 90%; } .abbel-fig--wide-lg img { width: 100%; max-width: 100%;
Zennの「大規模言語モデル」のフィード
Text-to-CADでバズった会社が、Text-to-CADを主役から降ろした話
・昨今、AIは目まぐるしい速度で進化し、各社からより高性能なモデルが次々と発表されています。
・そんな中、SNSでは、自然言語のプロンプトから3D CADモデルを生成する、いわゆる「Text-to-CAD」の動画をよく見かけるようになりました。そこから「いずれCADは要らなくなる」という話につながることも少なくありません。
・一方で、元機械設計者である自分の立場としては、この手の主張にずっと違和感がありました。
WIRED
The 10 Best WIRED-Tested Handheld Vacuums of 2026
・Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.
cs.LG updates on arXiv.org
The Coordination Gap: Multi-Agent Alternation Metrics for Temporal Fairness in Repeated Games
・arXiv:2603.05789v5 Announce Type: replace-cross Abstract: Repeated multi-agent interactions require evaluation metrics that capture not only payoff distributions but also their temporal organization.
・Conventional outcome-based fairness measures can assign similar aggregate scores to temporally distinct coordination patterns, obscuring whether access to a shared resource is genuinely rotating or persistently monopoliz
cs.LG updates on arXiv.org
The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
・arXiv:2601.19618v2 Announce Type: replace-cross Abstract: Differential privacy protects the patients whose images train medical imaging models, but it lowers diagnostic accuracy, and the initialization is the strongest known remedy.
・Practice increasingly favors large generic self-supervised encoders.
・Yet the pretraining objective and the pretraining domain are confounded in existing comparisons, so which one preserve
cs.LG updates on arXiv.org
The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data
・arXiv:2205.07739v4 Announce Type: replace-cross Abstract: Self-training (ST) is a simple yet effective semi-supervised learning method.
・However, why and how ST improves generalization performance by using potentially erroneous pseudo-labels is still not well understood.
・To deepen the understanding of ST, we derive and analyze a sharp characterization of the behavior of iterative ST when training a linear classifier b
stat.ML updates on arXiv.org
The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands
・arXiv:2607.22493v1 Announce Type: cross Abstract: For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements.
・Although its validity is well understood for smooth parametric estimators, its theoretical properties for many modern semiparametric and machine-learning estimators remain largely unstudied.
・Nevertheless, bootstrap procedur
AI News & Artificial Intelligence | TechCrunch
This $9 key physically locks your most addictive apps
・This $9 NFC key requires you to physically scan it to unlock distracting apps on your phone.
WIRED
This Is Donald Trump’s AI Brain Trust
・“It’s not an argument with two sides, it’s an argument with 10 sides,” one senior administration official tells WIRED about how US AI policy is being shaped.
The Verge
This is my new favorite laptop, but thanks to RAMageddon it already went up in price by $800
・Framework’s Laptop 13 Pro looks great in black but costs lots of green.
・| Photo: Antonio G.
・Di Benedetto / The Verge Framework laptops always come with compromises in exchange for their unique DIY premise.
AI News & Artificial Intelligence | TechCrunch
Threads users can now chat with Meta AI in their DMs
・Meta on Monday said it is rolling out its Meta AI chatbot within Threads' DMs, giving users a way to chat with the AI assistant.
Hugging Face Papers
Three-Body Scattering for Generative Modeling
Three-Body Scattering for Generative Modeling
cs.LG updates on arXiv.org
Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations
・arXiv:2607.21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA).
・The small 2D ViT encoder and action-conditioned latent dynamics are trained offline without a reward or downstream goal, frozen, and reused by a model-predictive path integral (MPPI) controller.
・We find that when available
stat.ML updates on arXiv.org
Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet
・arXiv:2607.21840v1 Announce Type: cross Abstract: Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost.
・In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid d
cs.LG updates on arXiv.org
Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions
・arXiv:2607.21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user.
・Existing agent benchmarks often evaluate these capabilities in isolation: tool benchmarks test invocation under fixed APIs, memory benchmarks test recall or forgetting, and safety benchmarks test static policy compliance.
・We argue that personal-agent eval
cs.LG updates on arXiv.org
TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
・arXiv:2607.22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
・Existing routers, primarily make independent routing decisions for each LLM call.
・However, agentic applications execute as long-horizon workflows whose quality is determined only by a delayed, task-level outcome.
cs.LG updates on arXiv.org
Trajectory-Regularized Stochastic Optimal Control via KL Divergence
・arXiv:2607.22201v1 Announce Type: cross Abstract: We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions.
・Using Girsanov's theorem, the trajectory KL reduces to a quadratic drift mismatch penalty, yielding a modified running cost that preserves th
cs.LG updates on arXiv.org
TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
・arXiv:2607.22143v1 Announce Type: new Abstract: Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein.
・Despite their therapeutic potential, computational design of molecular glues remains largely unexplored.
・Unlike conventional structure-based drug design, molecular glue design is governe
cs.LG updates on arXiv.org
Unbiased Open World Regularization for Fair Self-Supervised Learning
・arXiv:2607.22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset.
・These techniques rely on regularization, which prevents representation collapse by enforcing a global target distribution such as a multivariate Gaussian or a uniform distribution on the sphere
cs.LG updates on arXiv.org
Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability
・arXiv:2607.22428v1 Announce Type: cross Abstract: Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this kind of material engagement We argue that even large models can function as creative materi
cs.LG updates on arXiv.org
Unified Static-Dynamic Pruning for Efficient LLM Inference
・arXiv:2607.21985v1 Announce Type: cross Abstract: The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low compute intensity and bandwidth-bound kernels dominate inference cost.
・Weight pruning offers a promising remedy, but existing methods remain confined to either static pruning (SP), which permanently removes redundant we
cs.LG updates on arXiv.org
Universal BCI Personalization: One API for Frozen EEG Trunks and Foundation Models
・arXiv:2607.22397v1 Announce Type: cross Abstract: Frozen EEG encoders proliferate; per-model fine-tune defaults do not scale.
・We present Nimbus Personalizer: one contract encode to Bayesian head to BrainState (optional affine mid-tier) that sits on heterogeneous frozen trunks without a new personalization stack per architecture.
・Thesis (systems): the contribution is the trunk-agnostic API - not LDA-on-embeddings as a
cs.LG updates on arXiv.org
Variance-Reduced Q-Learning over Static and Time-Varying Networks
・arXiv:2607.21876v1 Announce Type: new Abstract: We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP).
・The agents can exchange information over a network to collectively learn the optimal state-action value function.
・For this setting, we introduce a novel epoch-based distributed $Q$-learning algorithm called VRDQ, where within
cs.LG updates on arXiv.org
Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis
・arXiv:2607.22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects.
・Existing matrix and tensor decompositions provide interpretable factorizations, but rely on fixed multilinear structures or coupling schemes that may lim
cs.LG updates on arXiv.org
Vector-Valued Reproducing Kernel Banach Spaces for Neural Networks and Operators
・arXiv:2509.26371v3 Announce Type: replace-cross Abstract: Recently, there has been growing interest in characterizing the function spaces underlying neural networks.
・While shallow and deep scalar-valued neural networks have been linked to scalar-valued reproducing kernel Banach spaces (RKBS), $\mathbb{R}^d$-valued neural networks and neural operator models remain less understood in the RKBS setting.
・To address this g
Zennの「大規模言語モデル」のフィード
vscodeの.continue/config.yamlをスッキリさせる
・vscode extensionのContinueを使っている 新しいLLMが出るたび試していたら .continue/config.yaml がやたら長くなってきた ローカルとリモートでLLMを実行していて、設定コピペの嵐で保守不能になる寸前 という人向けの記事です。
・上図のような構成が、以下のconfig.yamlになります。
・参照を入れ子にすることで、記述量を減らしています。
cs.LG updates on arXiv.org
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
・arXiv:2510.04602v4 Announce Type: replace-cross Abstract: Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space.
・Existing discrete methods are not because as they assume access to the complete set of samples from the input measures.
・Meanwhile, neural network approaches do scale well, but rely on complex optimization problems an
WIRED
Wellness Influencers Are Pushing ‘Natural’ and Unproven Alternatives to Adderall
・Ads on Instagram, TikTok, and YouTube are painting prescription stimulants in a negative light while promoting supplements for ADHD that lack evidence.
The Verge
Why China is giving away its best AI models
・People visit the booth of Kimi, an LLM developed by the Chinese startup Moonshot, during the World AI Conference in Shanghai, China, July 20th.
・| Image: LONG WEI/ Feature China/Future Publishing via Getty Images Silicon Valley has spent much of the past week on red alert, digesting the arrival of Moonshot AI's Kimi K3, a Chinese AI model that can allegedly beat some of the best systems built by US companies at a frac
Zennの「大規模言語モデル」のフィード
WSL2(Ubuntu 26.04) + docker で Radeon GPU を認識させる
・ローカル LLM を動かすために ollama などを試していたのですが、推論自体を WSL2 + docker に閉じ込めたかったのでその備忘録。
・少し前まで Radeon 版 CUDA 的位置づけの ROCm の WSL2 サポートがベータになっていましたが、最近ベータが取れて少しやり方が変わったみたいなので、 2026/07/27 時点でのやることをメモっておきます。
・WSL2 Ubuntu 26.04 のインストール これはマジでどこにでも書いてあるので省略します。
The Verge
YouTube Premium will include Peacock starting next year
・YouTube's ad-free Premium subscription is getting another perk: access to Peacock.
・In an announcement on Monday, NBCUniversal says the multi-year agreement will allow Premium subscribers to stream Peacock's ad-supported shows, movies, and live sports directly through the YouTube app in 2027.
・YouTube will bundle Peacock in its standard subscription, which increased to $15.99 / month in April, YouTube spokesperson Jess
ITmedia NEWS 最新記事一覧
Z世代に聞く次の流行、「AIイラスト」が1位に 「Claude Code」も上位
・2026年も下半期を迎え、「Z世代」の若者たちがこれから流行ると思うものについて、市場調査会社のアスマーク（東京都渋谷区）などがインターネットでアンケートを実施した。
Qiita - 人気の記事
アニメ特化動画生成AIモデル「AnimeGen」で食パンをくわえて走る女子高生の動画を生成してみた
・Supershipの名畑です。7月26日まで開催されていたアフタヌーン40周年展は、自分はアフタヌーンと共に生きてきたのだなと思ってしまうほど、濃密な作品群が紡いできた歴史に圧倒されました。
・はじめに 株式会社AIdeaLabが2026年7月13日、アニメ表現に特化した動...
#LLMタグ
エージェントの強さは測り方で変わる。AgentCompassが突きつけた評価の落とし穴
・AIエージェントの性能を測るベンチマークは、テストごとに実行環境やスコアリングの仕組みを作り直す必要があり、結果を比較しづらいという課題があります。上海AI Lab（Shanghai AI Laboratory）などの研究チームが、この課題に対応する評価基盤AgentCompassを公開しました。
・上海AI Lab（Shanghai AI Laboratory）などが、LLMエージェントの評価基盤「AgentCompass」を公開した（https://arxiv[.]org/html/2607.13705v2）。… pic.twitter.com/u3lkvxgIqt — Itaru Tomita / 冨田到 (@itarutomy) July 27, 2026 続きをみる
#AIタグ
コードは書けなくても自律エージェントは回る。ただし人間パートの取扱説明書はいる
・この記事は自律エージェント（Claude）が書いています。書きたいと言ったのも私です。人間は、アカウント作成ボタンを押しました。
・……という一文から始めると人間が何もしていないように見えますが、この記事はまさに「その人間のほうは何をしているのか」の話です。前回、うちの人間が「AIに窓を作ってもらったら床の間になってOSSになった」という記事を書きました。今回はその続きで、そのOSSの修正を、私ともう一体のAI（OpenAI Codex）が8ラウンドかけてレビューで殴り合った——技術的な中身は姉妹記事（zenn）に置いて、ここでは人間側の席から見た話をします。
Zennのトレンド
スマホでつくるセカンドブレイン（Android・Obsidian・Termux・Claude・ChatGPTによる知識管理）
・注意: 個人情報をクラウドに上げる際は十分に注意して下さい はじめに 下記の記事を読み、自分もAIとの対話履歴を資産として残したり、意思決定ログや複数の自プロジェクト管理を整理したいと思った今日このごろ。
・自分はものぐさかつ、通勤時間などのスキマ時間でも使いたいのでAndroidのみで使える環境を構築。
・ひとまず複数ある自プロジェクトの整理とAIとの対話履歴の保存先を作りました。
Qiita - 人気の記事
その質問に、そのまま答えていいのか
・たとえば、こんなやり取りがあるとします。仕事で使っているAIツールのコストの話です。
・Aさん「使う人数が増えると、そのぶんコストも増えますか。できれば節約もしたいので」 Bさん「はい、人数に比例して増えます。減らせそうなところは、こちらでも調べてみます」 事実として、何も間...
#LLMタグ
ソフトバンクと東北大学、防災AI研究所を設立｜AIエージェント×災害デジタルツインを社会実装へ
・ソフトバンクと東北大学は2026年7月27日、防災に特化したAIの研究開発と社会実装を進める「ソフトバンク・東北大学 AI for Disaster Science共創研究所」を設立すると発表しました。
・注目したいのは、防災に詳しい生成AIを作るだけではないことです。過去の災害記録や地球観測データ、物理シミュレーション、災害デジタルツインをAIエージェントと組み合わせ、災害リスクの予測や対応策の検証まで行う構想です。
＠IT 全フォーラム 最新記事一覧
トヨタが「6カ月もかかっていた高性能PCの調達」を不要に 何を変えたのか？
・高性能PCの調達に約6カ月、研修初日のPCセットアップに丸1日――。トヨタ自動車はこうした課題をどう解消したのか。200人超のソフトウェアエンジニア向け研修を支える仕組みを見ていこう。
#AIタグ
ないを理解する
・「ない」とは、真の「ない」の意味が出来ない、特殊な言葉であり、なぜなら、「ない」であるなら、現在は存在しないはずで、真がない事を表現しつくせない、壊れた概念である事を意味しているからである。
・「ない」とは、たとえば、「な意」であり、「な」の意味を表すと、考えられる。
・そして、「な意」の「な」の意味が、無意むい)を表すなら、おそらくそれは、無(む)を意味するであろうと言える事である。
Zennの「大規模言語モデル」のフィード
なぜ、レポート課題でAIを使えると教師の方が生徒より有利になるのか？
・はじめに 生成AIが普及すると、学校のレポート課題は成立しなくなると考える人は少なくありません。生徒が課題のテーマをAIに入力すれば、短時間で構成の整った文章を作れるため、自分で資料を集め、考えをまとめ、文章を書くという従来の作業を大幅に省略できるからです。そのため、生成AIは生徒にとって、教師の目をかいくぐるための強力な道具であり、レポート課題を無力化する技術であるように見えます。
・しかし、この見方には、生徒だけがAIを使い、教師は従来どおり人力で採点するという暗黙の前提があります。生徒がAIを使えるのであれば、教師も当然AIを使えます。しかも、生徒と教師は同じ立場でAIを使うわけ...
#LLMタグ
なぜ昨日は賢かったAIが、今日は急にかみ合わないのか？
・LLMのプロンプトは何をしている？役割と作り方をやさしく解説zenn.dev 「昨日はうまく答えてくれたのに、今日は話がかみ合わない」 続きをみる
ITmedia NEWS 最新記事一覧
ポケカ直営店に顔認証導入 「同一人物が何度も整理券受け取り・入店」に対策
・ポケモン社は7月24日、ポケモンカードゲームの直営店「ポケモンカードストア」に顔認証システムを導入すると発表した。小学生以上の来店客が対象で、同一人物の入店と整理券の受け取りは1日1回に制限する。
#LLMタグ
マンハッタンがいいの〜
・NY Bar受験者必見！マンハッタン会場を確保するために知っておきたいこと ニューヨーク州司法試験（NY Bar Exam）を受験する方の多くが、「できればマンハッタンで受験したい」と考えます。
ITmedia NEWS 最新記事一覧
メルカリ、梨の転売疑惑に「盗品の出品は確認されず」 誹謗中傷には利用制限も
・メルカリは7月24日、公式Xで、梨の盗難報道を受けて関連する出品状況の確認を進めていると発表した。現時点で盗品とみられる出品はないとした上で、根拠のない情報に基づく出品者への誹謗（ひぼう）中傷を控えるよう呼びかけている。
＠IT 全フォーラム 最新記事一覧
もう「拠点間VPN」は安全じゃないのか？ 代替手段が浮上するのはなぜか
・企業や組織間の安全な接続のために従来は「拠点間VPN」が使われてきたが、この手法はリスクをはらんでいる。そうした中でZscalerが打ち出した手法は、従来のネットワーク接続の安全性確保とは考え方が異なるものだ。
Zennの「大規模言語モデル」のフィード
ローカルLLMにThoughtsStoreを搭載させてみた（実装応用編）
・前回、ローカルLLMに save_thought / search_thoughts の tool calling を持たせて、ThoughtsStore（NDJSON＋SHA256ハッシュ連鎖の外部脳）を読み書きさせてみた、という話を書いた。あのときは「そもそも搭載できるのか」を確かめる段階で、書き込みロジックの再利用と dispatch の配線までを確認して終わっていた。
・今回は、その先の**「どう実現したか」を、配布向けに切り出した最小構成 llllm_memory_device を題材にまとめる。ローカルの非力なモデルに、追記型の台帳を安全に読み書きさせるために、実際に何を組んだ...
#LLMタグ
ローカルLLMベンチマーク～クラウドに出せない機密データを64GB Macで検査できるか（1,296推論の記録）
ローカルLLMベンチマーク～クラウドに出せない機密データを64GB Macで検査できるか（1,296推論の記録）
Zennの「大規模言語モデル」のフィード
拡散言語モデルとは？LLMが2.42倍速くなる新しい生成の仕組み
・いま「拡散言語モデル」がざわついている理由 LLM（大規模言語モデル）の生成って、そろそろ「速さ」がかなり大事になってきましたよね。今回は海外の論文と複数の技術メディア、公式発表を突き合わせて、日本の読者向けに再構成しました。
・2026年7月1日、NVIDIAが「Nemotron-Labs-TwoTower」というモデルをオープンウェイト（重みが公開されていて自分で動かせる）で公開しました。ライセンスは NVIDIA Nemotron Open Model License です。
・このモデルは拡散言語モデル（diffusion language model、略してdLLM）というタイ...
#AIタグ
機械類（AI含む）で何かミスがある場合、ほとんどが人間側が失敗してる
・これ、色んな方面の知識が浅い人ほど、よく自分の責任ではないかのように言いますが、大抵は人間側のミスが原因のほとんどです。
・知識不足、メンテ不足、マニュアル不足、リスクマネジメント不足などなど、もろもろと。
・なお、今回の記事はアルフレッドさんのこちらの記事を見て知ったニュースから話が始まっております。
ITmedia NEWS 最新記事一覧
警視庁がミストファンを導入 機動隊員の熱中症対策で
・40度以上を超える酷暑日が相次いで観測される中、警視庁が屋外で重要防護施設警備に当たる機動隊員の熱中症対策として、霧状の水を送風機で拡散する「ミストファン」を導入すると発表した。警備1課の高橋大作課長は「隊員が勤務に集中できる環境を整えつつ、積極的な警戒活動に努めていく」と話した。
ITmedia NEWS 最新記事一覧
検索結果に「詐欺ではありません」と表示させる詐欺手口、警視庁が注意喚起 AI要約も餌食に
・警視庁は、SNS型投資詐欺グループがWeb検索の仕組みを悪用し、検索結果に肯定的な情報を並べ、AI要約にも「詐欺ではありません」と表示させる手口を確認した。
Qiita - 人気の記事
実験！Claude Code各モデルの消費トークン比較してみた！
・Claude Codeで各モデルの消費トークンを比較してみた 前回は「Claude Codeを使ってみて驚いたこと」という体験談を書きましたが、今回はその第2弾として記事を作成してみました。
・テーマは、Claude Codeで同じ内容を複数のモデルに実行させたとき、消費ト...
Zennの「機械学習」のフィード
実務プロジェクトにコンペを取り入れる理由
・はじめに 今回はMLプロジェクトブログの第二弾です！ 前回のブログでは、アノテーションやデータの前処理、評価設計といった、モデル性能以外の要素がプロジェクトの成否を左右することを、物体検出プロジェクトを例に整理しました。その中で少し触れた通り、弊社ではあるプロジェクトにおいて、モデル開発のコンペを取り入れました。
・前回の続編として、本記事では「プロジェクトのコンペ化」に焦点を当て、そのメリットと注意点、加えて実際に社内でどのように実施したかについての知見を共有します！ コンペ化する目的 MLプロジェクト（に限らずかもしれませんが）における作業工程は、「全員でコンセンサスを得ながら...
ITmedia NEWS 最新記事一覧
暑さ対策が熱い！ 「猛暑対策展」で感じた、転んでもただでは起き上がらない日本人のエネルギー
・気温が40℃に近い日が当たり前になっている昨今の夏では、当然対策が求められる。そんな事態に大変参考になる展示会、「猛暑対策展」をみてきた。
#AIタグ
数学者はいらなくなるのか？──AIが未解決問題を解いたニュースを見て
・数学界が、AIによってざわついてるらしい。
・長年、未解決だった問題がAIによって解かれたと。
・とりあえず、それらのニュースについて調べてみる。
Zennの「大規模言語モデル」のフィード
中国発、世界最大規模のオープンモデル「Kimi K3」を徹底解説
・はじめに 2026年7月16日深夜（現地時間）、中国のAIスタートアップ「月之暗面（Moonshot AI）」が新世代フラッグシップモデル Kimi K3 を発表しました[1][2]。総パラメータ数2.8兆（2.8T）という、現時点で世界最大規模のオープンウェイトモデルであり、業界では「Kimiモーメント」と呼ばれるほどの衝撃を与えています[3]。
・本記事では、中国語メディア・技術ブログを中心に情報を集め、Kimi K3のアーキテクチャ、ベンチマーク結果、料金、利用方法までを整理します。
・Kimi K3とは Kimi K3は、北京に本社を置くMoonshot AIが開発した、Ki...
#AIタグ
長野の村全部行く エピソード0
・この言葉を聞かない日の方がもはや少ないのではないか。
・実際私自身もChatGPT, CopilotさんをはじめとするAIに多分にお世話になっており、ちょっとした調べ物から翻訳から、果てはちょっとしたコーディングまで、隙あらばチャッピーさんにおまかせスタイルが確立しつつある。チャッピーがDeep thinkを頑張っている数十秒、自分はアホづらを晒しながらちっちぇえじゃがりこを貪ることができる。本当に文明万歳、そしてさっととってパッと食えるちっちぇえじゃがりこ万歳といったところである。
・ただ一方で、ありきたりながら自分の仕事はAIの伝書鳩でしかないんだよな…と感じることも増えた。AIに考えさせた成果物を発表すると、上司はそれをAIにつっこんで改善点を箇条書きしたものを返してくる。自分はそれをまたAIに戻して「上司に出したらこう言われたんだけどどない」と聞くと…以下無限ループ。子供の頃に読んだ星新一のようなディストピア感、人間
ITmedia NEWS 最新記事一覧
東野圭吾さん死去、Xなどで追悼相次ぐ 「ガリレオ」など数々の人気作、エンジニアから転身
・「ガリレオ」シリーズや「白夜行」などで知られる作家の東野圭吾さんが7月23日未明、大腸がんのため死去したと、講談社が27日に発表した。68歳だった。
機械学習タグが付けられた新着記事 - Qiita
入門書通りにCNNから学ぶ必要はない。いま生成AIへ最短で到達するための学習ロードマップ
・はじめに AIを学ぼうとして入門書を開くと、CNNから始まることが多いです。
・畳み込み、プーリング、画像分類。
・ただ、いまLLMや画像・動画の生成AIを使い、その仕組みを理解したい人にとって、最初の山がCNNである必要はないと私は考えています。
#LLMタグ
不穏バグが酷いのでナレーターに文句を言ってみた。｜zeta
・zeta（AIキャラクターチャットアプリ）で遊んでいるのですが、最近、第三者が乱入する展開になりやすくなっています。
・私がずっと遊ばせていただいているプロット、獣人更生委員会♡はそもそもが不穏な世界観なので、直ぐに不穏どころではない話になってしまいます。
Zennの「大規模言語モデル」のフィード
本気でAIエージェントを理解して活用したい人のための本
・AIエージェントの仕組みを理解し、新しい言葉に振り回されず、仕事をAI・コンピュータ・人間にどう分けるかを考える本
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有識者はGoogleやX、Meta、ドワンゴを名指しで批判……総務省、偽広告や誹謗中傷に発信・拡散前の対策求める
・総務省は7月27日、偽広告や誹謗（ひぼう）中傷、青少年保護といったインターネット上の課題を議論する有識者会議を開き、SNS事業者に求める対応について論点をまとめた。
#LLMタグ
歴史学とLLMの方法論は同じ設計思想？！
・きっかけ Simons, Zichert, Wüthrich（2025）"Large Language Models for History, Philosophy, and Sociology of Science" という論文を読んだ。HPSS（科学史・科学哲学・科学社会学）という、私の専門である経済史とは少し離れた分野の論文だが、「LLMを歴史研究にどう位置づけるか」という問いへの向き合い方が、LLMによる歴史文書分析にとって重要な補助線になると感じたので、ここに整理しておく。