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Skill2Real introduces an agentic skill learning framework using a Proposer-Verifier-Governor loop that learns executable robot skills in simulation via a shared API and transfers them zero-shot to real-world manipulation without fine-tuning, achieving strong success rates on LIBERO benchmarks and real-robot tasks.
该论文提出CASE——一系列用于证据搜集的临床智能体，结合工具使用框架与智能体后训练方法，在基于UK Biobank构建的纵向多模态医学基准上进行评估，其基于Qwen3-VL-8B的智能体在匹配评估条件下答案准确率相对GPT-5.4和Claude Opus 4.8分别提升超16%和10%。
The paper presents AHL Studio, a tool using agentic heuristic learning to create executable, inspectable policies for human activity recognition without traditional training, targeting edge deployment.
The paper introduces a multi-agent agentic graph learning framework (MAAGL) that partitions graphs into communities and uses structural signatures to improve graph reasoning tasks, outperforming state-of-the-art methods.
Agent-G^2 introduces a Gaussian guidance framework for hint depth in reinforcement learning, enhancing performance on long-horizon agentic tasks without extra probing rollouts, with superior results on ALFWorld and WebShop benchmarks.
This paper proposes a framework for evaluating agentic learning harnesses in cybersecurity without labeled benchmarks, using a teacher-student model based on the scaling hypothesis to proxy performance improvements.
SERL-SQL proposes a selective execution-grounded reinforcement learning framework for multi-turn Text-to-SQL agents, using teacher-student likelihood gaps to reweight GRPO advantages on SQL action tokens. It achieves strong results on BIRD and Spider benchmarks.
This paper investigates whether LLM agents can infer hidden world models through interaction, finding that they struggle to build stable internal models as complexity increases.