Pai 的 AI 雷达

今日必看

含近几日仍热话题,按时间衰减排序。今天先看 Build more responsive apps with the Decisions API,其次 Global default for Copilot features generally available。并行还有 frontman-ai/frontman、Meta API endpoints updated for Actions blob storage。

日期
2026/10/23
今日条目
95

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#29

Industry comparisons in the Copilot impact dashboard

The Copilot impact dashboard now includes an “Industry comparison” section. Enterprises, and organizations that aren’t owned by an enterprise, can compare their Copilot agentic adoption and leverage with aggregated benchmarks from participating peers. Participation is opt-in and

推荐理由Industry comparisons in the Copilot:The Copilot impact dashboard now includes an “Industry comparison” section。
Agent与工具GitHub Changelog
#30

Public monitoring now available with data residency

Public monitoring is now available in public preview for GitHub Enterprise Cloud with data residency (GHEC DR). Public monitoring with data residency Public monitoring extends secret scanning beyond internal repositories by monitoring public commits across github.com, attributing

推荐理由Public monitoring now available:Public monitoring is now available in public preview for GitHub Enterprise Cloud。
产品接入GitHub Changelog
#31

Pull requests: Saved whitespace preferences and improved split view

Two improvements make it easier to review pull requests in the “Files changed” page: You can save your whitespace visibility preference across pull requests New and deleted files now use the full width in split view Save your whitespace visibility preference Open Settings → White

推荐理由Pull requests: Saved whitespace:Two improvements make it easier to review pull requests in the “Files changed” page。
产品接入GitHub Changelog
#32

Agentic Video Understanding: A Survey

arXiv:2609.31713v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is moving beyond fixed video-language inference toward agentic systems tha

推荐理由Agentic Video Understanding: A Survey:arXiv:2609.31713v2 Announce Type: replace-cross Abstract: As large language models。
模型与评测arXiv cs.AI 官方
#33

Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI

arXiv:2610.28826v1 Announce Type: new Abstract: Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with

推荐理由Decision language models as affordable:arXiv:2610.28826v1 Announce Type: new Abstract: Automated judges of clinical artificial。
模型与评测生信医疗arXiv cs.AI 官方
#34

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

arXiv:2608.26950v2 Announce Type: replace Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate only final answers. This outcome-oriented evaluatio

推荐理由From Atomic to Agentic: Towards:arXiv:2608.26950v2 Announce Type: replace Abstract: Large Language Models (LLMs) are。
模型与评测arXiv cs.AI 官方
#35

HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning

arXiv:2610.30042v1 Announce Type: new Abstract: Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs. Leading approaches such as the Darwin--Godel Machine and the Hu

推荐理由HGM-Dual: Lineage-Aware Guided Agent:arXiv:2610.30042v1 Announce Type: new Abstract: Harness optimization, which improves。
模型与评测arXiv cs.AI 官方
#36

Inheritance as an Admission Test for LLM Prompt Merge Operators

arXiv:2610.29023v1 Announce Type: new Abstract: Large language model (LLM) agents are often improved by iterating until a fixed verifier is done. Once that verifier saturates, the loop has nothing left to climb. We instead score agents against the current population, and a frozen

推荐理由Inheritance as an Admission Test:arXiv:2610.29023v1 Announce Type: new Abstract: Large language model (LLM) agents are。
模型与评测arXiv cs.AI 官方
#37

LocalAgent: Collaborative Agentic Verification for Fine-Grained Instance-Level Consistency

arXiv:2610.29174v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made rapid progress in general visual understanding, yet they still struggle with fine-grained instance-level consistency verification, where the goal is to determine whether two images

推荐理由LocalAgent: Collaborative Agentic:arXiv:2610.29174v1 Announce Type: new Abstract: Multimodal large language models。
模型与评测arXiv cs.CV 官方
#38

Metamodel-Guided LLM Decoding for Consistent DSL Generation

arXiv:2610.29749v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate software engineering artifacts written in domain-specific languages (DSLs). Correct DSL instances usually must comply with not only the concrete syntax but also the ab

推荐理由Metamodel-Guided LLM Decoding:arXiv:2610.29749v1 Announce Type: cross Abstract: Large language models (LLMs) are。
模型与评测arXiv cs.AI 官方
#39

Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model

arXiv:2610.29139v1 Announce Type: new Abstract: Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they have no internalized knowledge of how that particul

推荐理由Steering Black-Box Large Language Model:arXiv:2610.29139v1 Announce Type: new Abstract: Frontier black-box Large Language Model。
模型与评测arXiv cs.AI 官方
#40

When Agent Evaluation Becomes Unreliable: An Empirical Study of Oracle Failures and Configuration-Dependent Measurement in an LLM Agent Benchmark

arXiv:2610.16067v2 Announce Type: replace-cross Abstract: Agent evaluation harnesses are software: they run a test suite, drive a test input generator, and decide outcomes with a test oracle. We audit them for defects a score table cannot show. First, a systematic oracle-integrit

推荐理由When Agent Evaluation Becomes:arXiv:2610.16067v2 Announce Type: replace-cross Abstract: Agent evaluation harnesses。
模型与评测arXiv cs.AI 官方
#41

VideoGen Model 1: Prompt Expansion Versus Formatting in Audiovisual Generation

arXiv:2610.29407v1 Announce Type: new Abstract: Creative prompt expansion improved overall audiovisual quality scores over raw prompts in a controlled pilot of VideoGen Model 1, our post-trained version of MiniMax H3. We compared the raw, reformatted, and expanded versions of 30

推荐理由VideoGen Model 1: Prompt Expansion:arXiv:2610.29407v1 Announce Type: new Abstract: Creative prompt expansion improved。
模型与评测arXiv cs.CV 官方
#42

Claude 网页、桌面和移动端将默认启用 auto mode

Claude 官方宣布,下周起 Pro、Max 和 Team 套餐在 Claude 网页、桌面和移动端应用中的会话将默认以 auto mode 运行。auto mode 不再逐步请求用户批准,而是由自动检查拦截高风险操作,用户可随时切回手动模式。其引用的 Claude Code 更新提到 auto mode 用独立分类器审查 shell 命令,测试中拦截了 89% 的危险命令,手动批准为 14%。 🔗 阅读原文 via AIHOT · https://aihot.news/items/ybo2q5ug2e71tfuab9m1t128o

推荐理由Claude 官方宣布,下周起 Pro、Max 和 Team 套餐在 Claude 网页、桌面和移动端应用中的会话将默认以 auto mode 运行。
编码代理AIHOT feed.xml
#43

OpenAI 正式发布 Decisions API,面向低延迟低成本小决策

OpenAI 宣布基于 GPT-6 Luna 的 Decisions API 正式可用,用于分类、路由、评分等有界答案问题,速度最高比通过 Responses API 使用 GPT-6 Luna 快 10x。 🔗 阅读原文 via AIHOT · https://aihot.news/items/xffhf8h0e0r145getmi9k1zgs

推荐理由OpenAI 宣布基于 GPT-6 Luna 的 Decisions API 正式可用,用于分类、路由、评分等有界答案问题,速度最高比通过 Responses API 使用 GPT-6。
产品接入AIHOT feed.xml
#44

Extending Affordance Segmentation for Wearable Robots to Multi-Object Images

arXiv:2610.29474v1 Announce Type: new Abstract: This paper proposes a refinement to existing vision-based portable pipelines for the recognition of graspable object parts. Such pipelines can effectively support automatic grasp selection and preshaping in upper-limb prosthetic dev

推荐理由Extending Affordance Segmentation:arXiv:2610.29474v1 Announce Type: new Abstract: This paper proposes a refinement。
模型与评测arXiv cs.CV 官方
#45

Physical AI for Intralogistics: Benchmarking the NVIDIA Sim-to-Real Ecosystem for Cosmos-Enhanced COMPASS Navigation on Industrial Mobile Robots

arXiv:2610.28432v2 Announce Type: replace Abstract: Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site. Learning-based navigation shifts this effort from hand-tuned parameters to data generation and training, where a p

推荐理由Physical AI for Intralogistics:arXiv:2610.28432v2 Announce Type: replace Abstract: Classical robot navigation stacks。
模型与评测arXiv cs.RO 官方
#47

An Evidence-Driven Multi-Agent Compound AI System for Head and Neck Proton Radiotherapy Planning

arXiv:2610.29010v1 Announce Type: new Abstract: Radiation treatment planning requires balancing competing objectives to satisfy clinical criteria while accommodating physicians' preferences. This study aims to develop an evidence-driven, multi-agent compound AI system that integr

推荐理由An Evidence-Driven Multi-Agent Compound:arXiv:2610.29010v1 Announce Type: new Abstract: Radiation treatment planning requires。
模型与评测生信医疗arXiv cs.AI 官方
#48

Arbitrary-Scale Anisotropic 3D MRI Super-Resolution with Gaussian Prototypes

arXiv:2610.24858v2 Announce Type: replace Abstract: Reducing MRI acquisition time improves patient comfort, reduces motion artefacts, and increases scanner throughput. Clinical protocols often shorten acquisitions by sacrificing spatial resolution, particularly along the slice ax

推荐理由Arbitrary-Scale Anisotropic 3D MRI:arXiv:2610.24858v2 Announce Type: replace Abstract: Reducing MRI acquisition time。
模型与评测生信医疗arXiv cs.CV 官方
#49

Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents

arXiv:2610.28934v1 Announce Type: new Abstract: Coding agents now complete tasks autonomously, modifying real-world state such as artifacts, software environments, and configurations along the way. Yet they show limited backup awareness: recognizing that an action will modify sta

推荐理由Back Up Before You Regret: Measuring:arXiv:2610.28934v1 Announce Type: new Abstract: Coding agents now complete tasks。
模型与评测arXiv cs.AI 官方
#50

Beyond Masks and Trajectories: Flow-Guided Latent Action Injection for Stable Surgical Video Generation

arXiv:2610.09800v2 Announce Type: replace Abstract: Surgical video generation holds substantial potential for surgical education, simulation, and data augmentation, yet generating surgical videos with realistic and clinically plausible motion remains challenging. Most existing me

推荐理由Beyond Masks and Trajectories:arXiv:2610.09800v2 Announce Type: replace Abstract: Surgical video generation holds。
模型与评测生信医疗arXiv cs.CV 官方
#51

Brittle by Design? Multi-Scenario Reinforcement Learning for Robust Mission Coordination in Systems of Systems

arXiv:2610.28818v1 Announce Type: cross Abstract: As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in SE. Mission environments are uncertain and dynamic, and outcomes depend on how mis

推荐理由Brittle by Design? Multi-Scenario:arXiv:2610.28818v1 Announce Type: cross Abstract: As systems engineering (SE) shifts。
模型与评测arXiv cs.AI 官方
#52

CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

arXiv:2608.12002v2 Announce Type: replace Abstract: While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of real-world telecom environments. We introduce CTBench, a pu

推荐理由CTBench: Evaluating Troubleshooting:arXiv:2608.12002v2 Announce Type: replace Abstract: While autonomous agents show。
模型与评测arXiv cs.AI 官方