appworld

This paper identifies that naive skill accumulation in LLM agents can cause performance regressions, as skills beneficial for some tasks hurt others. The authors propose Assay, a framework that measures per-skill causal contributions and applies per-task masking, achieving state-of-the-art results on AppWorld and τ-bench without weight updates.

MIND-Skill is a new framework introduced in this research paper that automates the generation of high-quality, reusable agent skills using multi-agent induction and deduction with quality guarantees via TextGrad optimization.

This paper introduces HCL-GP, a dynamic policy-learning framework that integrates generalized planning and hierarchical task decomposition to enable LLM-based agents to learn and reuse executable policy components, significantly improving performance on the AppWorld benchmark.