…With the rise of AI-driven research agents as a new class of consumers of scientific knowledge, this limitation becomes increasingly consequential, as such agents cannot reliably reconstruct method evolution topologies from…
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…AI-generated summary As large language model (LLM) agents evolve from isolated tool use rs into coordinated teams, reinforcement learning (RL) must optimize not only individual actions but also how work is…
…An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences (2026) GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation (2026) Generation Navigator: A State…
…this line up with differentiable simulation and tool-use work, and a direct comparison here could clarify what external planning actually buys on real visual tasks. Get this paper in your agent…
…Omnimodal World Models for Physical AI Published on Jun 1 Submitted by taesiri on Jun 4 #1 Paper of the day NVIDIA Authors: , , , , , , , , , , , , , , , , , , , , , Abstract Cosmos 3 is an omnimodal world model that…
…Tests the Testers? Systematic Enumeration and Coverage Audit of LLM Agent Tool Call Safety (2026) Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework (2026) CritBench: A Framework for Evaluating…
…AI-generated summary Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective agentic RL remains challenging…