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 sickn33/agentic-awesome-skills,其次 TabularisDB/tabularis。并行还有 Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw、How to Evaluate AI Agents From Tool Calls to Task Completion。

日期
2026/09/22
今日条目
90

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#28

A visual large language foundational model for medical image recognition using clinician-contributed online resources

arXiv:2609.06914v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual ques

推荐理由A visual large language foundational:arXiv:2609.06914v3 Announce Type: replace Abstract: Large language models (LLMs) have。
模型与评测生信医疗arXiv cs.AI 官方
#29

CESBench: Benchmarking Large Language Models on Cryptographic Engineering Security for IoT Devices

arXiv:2609.21344v1 Announce Type: cross Abstract: For Internet of Things (IoT) devices, a secure algorithm alone is not enough: an attacker with physical access can attack the implementation directly, and its flaws are hard to fix once deployed. Large language models (LLMs) are n

推荐理由CESBench: Benchmarking Large Language:arXiv:2609.21344v1 Announce Type: cross Abstract: For Internet of Things (IoT) devices。
模型与评测arXiv cs.AI 官方
#30

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

arXiv:2609.18120v2 Announce Type: replace-cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, automated testing unaffordable for the smaller organisations that need it

推荐理由PentestChain: A Cost-Aware,:arXiv:2609.18120v2 Announce Type: replace-cross Abstract: AI-driven penetration testing。
模型与评测arXiv cs.AI 官方
#31

PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design

arXiv:2609.21493v1 Announce Type: new Abstract: Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whether an engineering design will work when executed. Existing benchmarks assess sp

推荐理由PolyBridgeBench: Benchmarking:arXiv:2609.21493v1 Announce Type: new Abstract: Multimodal large language models。
模型与评测arXiv cs.AI 官方
#32

QwenVLConnector: A Fast, Unified Medical VLM Chatbot for Fine-Grained Clinical Perception and Text Generation

arXiv:2609.23139v1 Announce Type: new Abstract: Most medical vision-language models (VLMs) excel at open-ended report generation and VQA but provide limited support for structured, fine-grained clinical perception within a unified interface. We present QwenVLConnector, a Qwen2.5

推荐理由QwenVLConnector: A Fast, Unified:arXiv:2609.23139v1 Announce Type: new Abstract: Most medical vision-language models。
模型与评测生信医疗arXiv cs.CV 官方
#33

Runtime Authorization for Resources Acquired by AI Agents

arXiv:2609.14744v2 Announce Type: replace Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, budget, OAuth, mandate, and fulfillment checks can validate transaction conditions w

推荐理由Runtime Authorization for Resources:arXiv:2609.14744v2 Announce Type: replace Abstract: By acquiring compute, credentials。
模型与评测arXiv cs.AI 官方
#34

SLICEChat: Progressive In-Encoder Token Pruning for Whole-Slide Pathology Language Models

arXiv:2609.24894v1 Announce Type: new Abstract: Whole-slide pathology images (WSIs) contain gigapixel-scale visual content, creating a major scalability challenge for slide-level multimodal large language models (MLLMs). Existing approaches process thousands of patch tokens and t

推荐理由SLICEChat: Progressive In-Encoder Token:arXiv:2609.24894v1 Announce Type: new Abstract: Whole-slide pathology images (WSIs)。
模型与评测arXiv cs.CV 官方
#35

SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?

arXiv:2609.21190v1 Announce Type: cross Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check correctness with held-out test suites, which are inherently incomplete and increasing

推荐理由SWE-Proof: Can Language Models Resolve:arXiv:2609.21190v1 Announce Type: cross Abstract: Ensuring the correctness。
模型与评测arXiv cs.AI 官方
#36

Verify, Don't Trust: Agentic Model Development for Video Discovery Retrieval at Scale

arXiv:2609.21257v1 Announce Type: cross Abstract: Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-scale iterations on a self-contained program. Online autoresearch instead spans a

推荐理由Verify, Don't Trust: Agentic Model:arXiv:2609.21257v1 Announce Type: cross Abstract: Large language model (LLM) agents can。
模型与评测arXiv cs.AI 官方
#37

SHAFT: A Slack-Compensating, Helical-Buckling-Attenuating Flexible-Shaft Transmission for Lightweight Multi-DoF Manipulation

arXiv:2609.22677v1 Announce Type: new Abstract: Lightweight and slim manipulators enable safe operation in human living environments. Proximal actuation using remote transmission mechanisms, such as wire-driven or Bowden cables, effectively reduces inertia and arm size by relocat

推荐理由SHAFT: A Slack-Compensating,:arXiv:2609.22677v1 Announce Type: new Abstract: Lightweight and slim manipulators。
模型与评测机器人具身arXiv cs.RO 官方
#38

AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation

arXiv:2609.23578v1 Announce Type: new Abstract: As AI agents become increasingly capable, agent-driven robotic control is emerging as a compelling paradigm. However, prevailing vision-language-action (VLA) models and world action models (WAMs) still rely on natural-language instr

推荐理由AR-WAM: A Visual-Conditioned:arXiv:2609.23578v1 Announce Type: new Abstract: As AI agents become increasingly。
模型与评测arXiv cs.RO 官方
#39

ARCGym: Benchmarking Deep Reinforcement Learning in Autonomous Robotic Colonoscopy

arXiv:2609.22803v1 Announce Type: new Abstract: Simulations for learning-based autonomous colonoscopic navigation focus mainly on fully actuated capsule robots, failing to capture the contact-rich navigation of long and flexible clinical colonoscopes. We present the Autonomous Ro

推荐理由ARCGym: Benchmarking Deep Reinforcement:arXiv:2609.22803v1 Announce Type: new Abstract: Simulations for learning-based。
模型与评测生信医疗arXiv cs.RO 官方
#40

ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation

arXiv:2609.24563v1 Announce Type: new Abstract: Adapting visuomotor policies to new manipulation tasks often requires substantial manual engineering or teleoperated data collection. Simulation can provide task-specific data at scale, but constructing the scene, designing expert b

推荐理由ARSTAG: An Agentic Real2Sim2Real System:arXiv:2609.24563v1 Announce Type: new Abstract: Adapting visuomotor policies to new。
模型与评测arXiv cs.RO 官方
#41

DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation

arXiv:2609.23103v1 Announce Type: new Abstract: While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess physical plausibility after generation, leaving an object's simulated response unused a

推荐理由DiagGen: Agentic Generation:arXiv:2609.23103v1 Announce Type: new Abstract: While simulation-ready deformable。
模型与评测arXiv cs.RO 官方
#42

Distributed Infrastructure Sensors and Cues for Robotic Fire-Fighting and Safety System on a Lunar Base

arXiv:2609.22786v1 Announce Type: new Abstract: There are renewed efforts to build a lunar base and house a team of astronauts on the Moon for extended periods. However, important challenges remain, such as a rapid-response fire-hazard management system. In the event of a fire or

推荐理由Distributed Infrastructure Sensors:arXiv:2609.22786v1 Announce Type: new Abstract: There are renewed efforts to build。
模型与评测arXiv cs.RO 官方
#43

Robot World Models Are Not Invariant to How the Actions Are Written

arXiv:2609.23252v1 Announce Type: new Abstract: A robot policy is trained with one of two action parameterizations: absolute joint targets, or deltas relative to the current state. The choice is a live engineering decision in robot learning, and a world model conditioned on actio

推荐理由Robot World Models Are Not Invariant:arXiv:2609.23252v1 Announce Type: new Abstract: A robot policy is trained with one。
模型与评测arXiv cs.RO 官方
#44

Structured World-State Reasoning for Agentic Robotic Search

arXiv:2609.23841v1 Announce Type: new Abstract: Long-horizon robotic search must resolve natural language against heterogeneous, incomplete, and often ambiguous evidence: textual information, prior maps, and observations arriving over time. The core challenge is to contextualize

推荐理由Structured World-State Reasoning:arXiv:2609.23841v1 Announce Type: new Abstract: Long-horizon robotic search must。
模型与评测arXiv cs.RO 官方
#45

TS-MAMP: A Remanufactured Agricultural Robot with Second-Life EV Components and NMS-Free On-Device Weed Detection

arXiv:2608.02270v2 Announce Type: replace Abstract: Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agriculture. Meanwhile, a growing number of retired low-speed electric-vehicle (LSEV

推荐理由TS-MAMP: A Remanufactured Agricultural:arXiv:2608.02270v2 Announce Type: replace Abstract: Agriculture 4.0 robotic systems。
模型与评测arXiv cs.RO 官方
#46

3D Gait-Based Autism Classification Using Attention-Enhanced Deep Learning with Cross-Fold Statistical Stability Analysis

arXiv:2609.14159v2 Announce Type: replace Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments are often subjective, time-consuming, and require expert evaluation. Gait provi

推荐理由3D Gait-Based Autism Classification:arXiv:2609.14159v2 Announce Type: replace Abstract: Autism Spectrum Disorder (ASD) is。
模型与评测生信医疗arXiv cs.CV 官方
#47

AI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture

arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe for a use case to deliver its intended outcome while meeting

推荐理由AI-GRACE: A Use-Case Operationalization:arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic。
模型与评测arXiv cs.AI 官方
#48

AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos

arXiv:2609.24487v1 Announce Type: new Abstract: In this work, we present a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis. Unlike prior methods which first estimate dense pixel correspondences and then recover object motion from them, ou

推荐理由AgentSTAR: Agentic Shape Tracking:arXiv:2609.24487v1 Announce Type: new Abstract: In this work, we present a method。
模型与评测arXiv cs.CV 官方
#49

An Agentic Just-in-Time Adaptive Intervention System for Personalized Sleep Support: Proof-of-Concept Study with N of 1 Data

arXiv:2609.21805v1 Announce Type: cross Abstract: Background: Just-in-time adaptive interventions (JITAIs) can use behavioral data to adapt support to changing contexts, but many rely on predefined rules and manual configuration. Objective: We developed a proof-of-concept sleep J

推荐理由个人 AI 代理 面向个人场景的代理产品。
模型与评测arXiv cs.AI 官方
#50

An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models

arXiv:2609.23586v1 Announce Type: new Abstract: The rapid development of video generative models (VGMs) has enabled the generation of highly realistic synthetic videos, raising concerns about the intellectual property rights (IPR) of these models. In particular, two closely relat

推荐理由An Efficient and Effective Watermarking:arXiv:2609.23586v1 Announce Type: new Abstract: The rapid development of video。
模型与评测arXiv cs.CV 官方
#51

AI 巨头智能体产品竞速与递归自我改进 (RSI) 动态

📌 一句话摘要 OpenAI 计划发布对标 Grok Bot 的个人智能体产品,同时呼吁制定前沿 AI 的全球技术标准,重点关注递归自我改进 (RSI) 的安全。 📝 详细摘要 OpenAI 预计在 DevDay 前发布暂名 "Codex Bot" 的个人智能体产品,旨在追赶 SpaceXAI 的 Grok Bot 和 Meta 的 Muse,实现 AI 队友跨应用工作。同时,OpenAI 呼吁制定前沿 AI 全球标准,聚焦递归自我改进 (RSI),尽管公开称其尚未实现,但内部数据显示 AI 智能体已能高效产出研究量并完成轻量模型的后训练。此外,文中还提

推荐理由📌 一句话摘要 OpenAI 计划发布对标 Grok Bot 的个人智能体产品,同时呼吁制定前沿 AI 的全球技术标准,重点关注递归自我改进 (RSI) 的安全。
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