模型与评测

Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment

arXiv:2609.01202v2 Announce Type: replace-cross Abstract: Clinical trials are essential for evidence-based medicine and drug development, but many fail because of insufficient patient enrollment. Although there is growing interest in using AI to support patient recruitment, exist

TrussTutor-Bench: Benchmarking large language models on solution accuracy, sycophancy, and error detection in statics

arXiv:2610.38917v1 Announce Type: cross Abstract: Large language models (LLMs) have rapidly become a default source of help for students in science and engineering courses. In foundational engineering mechanics courses such as statics, students routinely use these tools to check

Well-Specified and Well-Tested? Assessing SWE-bench-Style Datasets with a Self-Evolving Agent

arXiv:2610.39417v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance on software engineering (SE) tasks, driving the need for high-quality datasets for reliable training and evaluation. SWE-bench-style datasets (SWE datasets) are widely use

When Is Smaller Greener? Right-Sizing Language Models for Agentic AI

arXiv:2610.19345v2 Announce Type: replace Abstract: AI agents use a large language model (LLM) to plan and act over many steps. Which model should an agent use, so that its tasks are completed reliably with the least energy, cost and time? We treat this as a multi-criteria decisi

Whom Do AI Agents Serve? Role-Induced Sponsorship Bias in Decision Models and Generative LLMs

arXiv:2609.17989v3 Announce Type: replace-cross Abstract: Consumers increasingly delegate product search to AI shopping agents powered either by generative large language models (LLMs) that deliberate in natural language or by non-generative decision models that output calibrated

Event2Reward: Learning Robotic Rewards from Progress Relations

arXiv:2610.39212v1 Announce Type: new Abstract: Learning task progress from robot demonstrations provides a practical way to construct dense shaping rewards for robotic reinforcement learning. A common approach uses normalized demonstration time as progress labels, implicitly ass

Humanoid Dodgeball: Learning Fast Dynamic Obstacle Avoidance Motions for Humanoids

arXiv:2610.40003v1 Announce Type: new Abstract: Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and physically feasible whole-body mo

启元 CEO 田华：启元机器人接入WorkBuddy，不仅看见你的世界，还能走得到你身边

Google’s satellite AI server transmits its first response to Earth

Google's AI chip-equipped satellite has generated its first response from space. In a blog post on Friday, Google says it successfully booted up the satellite's Tensor Processing Units (TPUs), allowing the on-board Gemma 4 model to follow through on a prompt to create a haiku abo

Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

Fleet-wide computer vision model lifecycle management is a problem that shows up the moment a quality inspection proof of concept succeeds. One camera and one model work fine. Then the plant asks for the same result on 40 lines, each with several cameras, and every camera sees th