Tag
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.