AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28695v1 Announce Type: new Abstract: Here is the plain text version optimized for arXiv's submission form. Custom macros (like \CV and \SI) have been converted to standard text/math so they render correctly on the webpage: Evaluating the fatigue life of structural steels conventionally requires mechanical testing lasting tens to hundreds of hours, making it impractical for rapid quality control. We present CV, a computer vision framework that estimates the fatigue life ($\log N_f$) of lightweight alloy steels directly from optical micrographs without physical testing.The pipeline features a seven-stage OpenCV preprocessing routine to remove artifacts, a 28-dimensional physics-informed feature extractor (quantifying crack morphology, grain…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28693v1 Announce Type: new Abstract: Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on residential data. This work adopts a one-to-many, multi-task disaggregation setting, in which a single network estimates multiple industrial machine loads from aggregate power. Under a unified evaluation protocol on IMDELD, we benchmark Seq2Seq, Seq2SubSeq, Seq2Point, GRU, and WaveNet using energy-estimation metrics and the accuracy-delay criterion. While Seq2Point offers a stronger accuracy-delay balance than Seq2Seq/Seq2SubSeq, GRU and WaveNet achieve higher accuracy at markedly higher computational cost. To close this gap, we propose SEDR-Seq2P, a lig…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28687v1 Announce Type: new Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs. This article critically synthesizes recent technological advances for detecting and managing cognitive impairment in older adults, spanning neurophysiological signals (chiefly electroencephalography, EEG), structural and molecular neuroimaging (MRI and amyloid/tau PET), blood-based biomarkers, and digital markers, integrated through artificial intelligence (AI), machine learning (ML), and deep learning (DL). Beyond summarizing, it contributes a cross-disciplinary taxonomy, a methodological-rigor lens foregrou…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28681v1 Announce Type: new Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNNs) for analysis of brain functional connectivity have shown great potential for the classification of brain disorders, such as Alzheimer's disease (AD). However, these methods often assume a preset number of functional modules across all subjects, which overlooks inter-subject variability. In addition, the discovered modules are rarely used to directly guide the learned connectivity patterns. Here, to address these issues, we propose a Meta Probabilistic Pooling GNN (MPP-GNN). We frame the model's task as a coupled, bilevel optimization that performs adap…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28672v1 Announce Type: new Abstract: When a neural network predicts a physical system accurately, has it learned the governing law as formal, structured knowledge, and if so, does the network's internal computation actually use that representation throughout the law's domain of validity? We identify four interpretability gaps that limit answering these questions for {\em physics laws over continuous variables}: the absence of a coverage-aware causal-consistency measure over continuous counterfactuals; of a domain-of-validity test for the identified circuit; of a verification of the law's invariants and forbidden behaviors; and of a quantification of how a derived physical quantity flows through the circuit. We develop a foundational frame…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28670v1 Announce Type: new Abstract: A stochastic Gumbel-Top-$K$ router defines, for every token of a mixture-of-experts (MoE) model, a \emph{routing law}: a distribution over ordered expert lists and mixture weights. We ask which \emph{joint} distributions over the routing choices of different tokens are reachable while every individual token's complete routing law is held exactly fixed. We give a two-sided construction, \emph{Hierarchical Copula-Gumbel-Top-$K$} (\CGA{}). Within a group of related tokens, an exchangeable Gaussian copula positively correlates the Gumbel perturbations at each expert coordinate, which can increase within-group expert-set coherence. Across disjoint pairs of groups, a tunable antithetic construction introduce…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28669v1 Announce Type: new Abstract: We present LARA (Lightweight Additive Residual Adaptation), a method for efficient adaptation that operates in the residual stream of a frozen model rather than in its weights. Where LoRA adds an update of low rank to weight matrices, LARA reads the hidden state at a small set of layers and adds a correction of low rank back to the residual stream, leaving all base weights untouched. On a code fine-tuning task and on preference optimization (DPO), LARA matches LoRA at equal parameter counts. Because adaptation is a frozen base plus a residual, LARA exposes a scale {\gamma}, applied at inference, that interpolates smoothly between base and adapted behavior, a form of graded control that adaptation in we…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28667v1 Announce Type: new Abstract: Recent work has shown that classifying large language models (LLMs)' responses can be distinguished by modeling token embeddings as trajectories of a black-box dynamical system (DS) and comparing prediction residuals of two DSs. Despite the empirical success of this dynamical approach, a theoretical understanding of why it works, how well it scales as a function of the token sequence, and when it transfers across embedding models remains lacking. We address these questions by formalizing the classification task as a binary hypothesis test between two stochastic linear DSs. We show that the total variation distance between the stationary marginal distributions of the two DSs can be arbitrarily small eve…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28665v1 Announce Type: new Abstract: Automated driving systems (ADSs) are becoming ubiquitous. Future Software Defined Vehicles (SDVs) may be able to run multiple ADSs, both native and aftermarket such as Comma.ai's Openpilot. Monitoring systems to independently verify which automated driving system is active are important for safety monitoring, regulatory compliance, insurance assessment, and anomaly detection. In this paper, we first evaluate the effectiveness of three sequence-based classification models: Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and a Transformer encoder model for identifying Level 2 automated driving systems using vehicle telematics data alone: Comma Openpilot, Tesla Autopilot, and Cadillac…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28633v1 Announce Type: new Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly. When prefill and decode run on separate GPU pools, the KV cache must be transferred between them. For a 70B model this is 2.6 GB per request, exceeding 100 GB/s aggregate at production scale. Yet DistServe, Splitwise, and Mooncake all use uniform RDMA, ignoring that bandwidth between two GPUs varies by 72x depending on their physical relationship: 900 GB/s via NVLink within a domain, 50 GB/s via InfiniBand across nodes, 12.5 GB/s via TCP across data centers. We design a topology-aware transfer orchestrator that discovers interconnect hierarchy at startup and selects optimal transport per transf…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28678v1 Announce Type: new Abstract: Multimodal agents operating in long-horizon environments must build and continually update multimedia memories to support entity-consistent, temporally grounded reasoning. However, existing agentic memory approaches often discard fine-grained dentity cues under aggressive compression and segment-wise processing. They also rely heavily on vector similarity retrieval, which can surface semantically related yet identity-mismatched evidence, leading to entity confusion, error propagation, and hallucinated answers. We propose ViSAGE, a multimodal agentic memory framework that constructs self-correcting, entity-centric memories. Specifically, ViSAGE anchors entity identity via cross-modal binding over long t…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the use of LLMs for symptom assessment and clinical decision support in diagnostic and treatment guidance, administrative documentation, and rules-based alert enhancement. This Perspective concerns the most consequential of these applications: the autonomous triage of self-presenting, undifferentiated patients, with little or no clinician in the loop. For that task, the evidence of safety does not yet exist. The gap is not in medical knowledge but in the fidelity of clinical evaluation: a model optimized to continue the most probable text is not optimized…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28674v1 Announce Type: new Abstract: Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpretability methods rely on output-level signals or collapse processing depth into a single trajectory-level scalar, leaving step-wise effort opaque. We propose Step-Aware Reasoning Energy (SARE), a geometric framework that quantifies effort at the granularity of individual CoT steps via Centered Kernel Alignment (CKA) between Gram matrices of token hidden states across adjacent transformer layers, capturing inter-token relational structure without requiring eigenvector alignment or cluster correspondence. SARE further contextualizes this energy within re…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2607.28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across documents, the same person surfaces under several name variants, relationships duplicate, and distinct individuals who share a name risk silent conflation. This paper presents the design, implementation, and empirical refinement of a production extraction layer that converts a live document stream into a validated knowledge graph aligned to a formal ontology. The system consumes document metadata from Kafka, routes PDF, spreadsheet, Office, and image content through handlers built for each format, and extracts entities and relationships in two passes using…

このarXiv論文は、大規模言語モデルを用いて主要な数学的予想を系統的に生成・検証する3段階パイプラインを提案し、Lean 4とMathlibによる形式検証を行います。実験では20個の候補予想すべてが構文解析と型チェックを通過し、重複や近似重複も見つかりませんでした。

新しいarXiv論文は、エージェンティックAIのための階層的アーキテクチャを提案し、OllamaをLLM推論層、OpenClawをエージェントランタイムのオーケストレーション層とするフルスタックシステムを分析しています。実験では、永続的記憶やツール利用、適応的な意思決定がシステム統合から創発することが示され、スケーラビリティ、セキュリティ、プライバシー、ガバナンス、評価といった課題も論じられています。

Lerobit は、LinkedIn、Facebook、Instagram、TikTok 向けに AI を活用したソーシャルメディア管理機能を開発中です。アカウント接続からパフォーマンス追跡までを6つのステップで行い、ブランドに一貫した投稿運用を実現します。現在は AI SEO コンテンツ生成サービスを利用でき、Google や ChatGPT などの AI 検索での可視性向上に役立ちます。

Qwen3.8-Max は、Qwen のコーディングと共同作業向け最強モデルで、Product Hunt で紹介され、ディスカッションと公式リンクが提供されています。

アンドリュー・ン氏が中国、オープンソース、AI競争力の交差点について議論し、オープンな協力がAIの展望をどう形作るかを探ります。

本研究は、AIがレガシーCOBOLプログラムをJavaへ移行した結果を決定的に検証するためのエージェント型テスト合成手法「Locksmith Loop」を提案する。モックを計装したCOBOLとJavaの環境を通常のハードウェア上で実行し、反復的なウィットネス探索とパリティ維持ミューテーションで分岐探索を深める。430〜4,114行の3事例でオープンソースはほぼ完全な分岐カバレッジ、本番相当は91.90%を達成し、受理された全テストケースが決定論的パリティ検証に合格した。

ChatGPT、Gemini、Claude、Perplexityが推薦の質問に対して自社ブランドを挙げるかを無料で確認できるツール。AI可視性スコア、欠落プロンプト、引用元、競合比較などを提供。

Murmellは、チームとAIエージェントが同じ空間で協力できるクラウドベースのキャンバスです。Product Huntで議論が始まっています。

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

Zenith は機械工場向けのAI自動見積もりエンジン。2D PDF図面から幾何形状・材料・公差を読み取り、2分以内に項目別見積もりを生成します。ショップキャリブレーション、高度な板金見積もり、計画中のオンプレミス安全展開などの機能を備えています。

gnt は、AI エージェント向けのオープンソースのガバナンス層です。ルールを Markdown ファイルとして Git リポジトリに置き、通常の PR マージで承認します。エージェントは MCP 経由で check_action などのツールを呼び出し、返金や削除などの高リスク操作前に許可・ブロック・人間の承認が必要の判定を取得できます。セルフホストは無料、ホスト型は gntai.dev で提供。

SFに拠点を置くWeaveは、AIコーディングツールが開発者の生産性を実際に向上させているかを測定するソフトウェアを開発し、1350万ドルのシリーズAを調達した。アウトプットスコアにより、経営幹部はAI支出の投資対効果を評価でき、AIの利用量を生産性の指標とする「tokenmaxxing」を抑制する。現在、Robinhood、Reducto、PostHogを含む500以上の企業で2万人のエンジニアを測定している。

AI推論モデルは、長年未解決だった数学上の予想を次々と解決し、数学者たちの役割や研究のあり方に深刻な問いを投げかけています。本記事では、AIの数学的能力の進化、具体例、そして数学者が「問題解決者」から「結果のキュレーター」へと移行する可能性について考察します。

アンドリュー・スタイン作の『Disruption』がロンドン初演から3年を経てニューヨークのオフ・ブロードウェイで上演。ジョン・デイヴィッド・ワシントン主演。個人データがテック企業に悪用される可能性を描くが、俳優陣の熱演や洗練された舞台にもかかわらず、AIに関する考察は時代遅れで、新鮮な洞察に欠ける。

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Circles uses the OpenAI API and Codex to power AI-native telco experiences, increasing ARPU by 22%, reducing churn by 9%, and improving development efficiency.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by producing responses that are inconsistent with the image content. A primary objective of alignment for MLLMs is to encourage these models to align responses more closely with image information. Recently…