SkillsBench

2026-10-21

RESEARCH · 1 SOURCE · arXiv cs.AI

SPEM: structure-aware procedural memory for efficient skill use in LLM agents (arXiv preprint)

The paper introduces SPEM, a lightweight, structure-aware procedural memory framework that stores Atomic Skill Memory for individual skills and Skill-DAG Memory for reusable skill compositions; it uses Skill-GNN for state- and structure-aware selection and writes successful executions back as new Skill-DAGs. On ALFWorld and SkillsBench, SPEM achieves an 87.1% ALFWorld success rate and 38.38% SkillsBench reward while reducing token and runtime costs compared to strong baselines.

7.0

2026-10-21

RESEARCH · 1 SOURCE · arXiv cs.AI

PROVISO: provenance-informed controller for secure LLM agents (arXiv:2610.27115v1)

PROVISO is a controller that decides whether to commit an LLM agent's proposed action by encoding trusted observations, external tool outputs, the proposed action, and an alternative into provenance-typed memory and selecting the action with the highest calibrated value learned from outcomes. Evaluated on four agent security tasks, PROVISO outperforms baselines — improving F1 by 13.84 points on SkillsBench and 7.07 points on ToolSafe, and achieving 88–99 F1 on three additional skill detection benchmarks.

7.0

2026-10-16

RESEARCH · 1 SOURCE · arXiv cs.AI

SkillNEO: non-elitist evolutionary optimization of agent skills (arXiv:2610.20926v1)

arXiv:2610.20926v1 presents SkillNEO (Non-Elitist Evolutionary Optimization for Agent Skills), which decouples which skill version is executed from which versions are retained for further edits so that low-scoring intermediates can be revised rather than discarded. On 86 SkillsBench tasks across eight domains (e.g., software engineering and office work), SkillNEO raises GPT-5.4 Mini's average reward from 49.1% to 66.4%; reused modified skills improve GPT-5.6 Luna by 10.5 percentage points on 41 modified tasks and place GPT-5.5 10.6 points above its published original-skill reference.

7.0

2026-10-15

RESEARCH · 1 SOURCE · arXiv cs.AI

ClawLoop: framework for self-improving harness-based LLM agents (arXiv:2610.14118v1)

arXiv preprint arXiv:2610.14118v1 introduces ClawLoop, a closed training loop for harness-based LLM agents that iteratively synthesizes tasks, trains agents (SFT/RL), and diagnoses failures to generate next-round training data without relying on an external teacher model. Using Qwen3.6-27B as the loop model, the authors report consistent gains on SkillsBench, Claw-Eval-Live, and ClawGym-Bench, and show the synthesized tasks transfer performance improvements to smaller Qwen3.5 4B and 9B models.

7.0