RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents

arXiv:2609.15684 · cs.CL · Submitted 2026-09-14 · Read on arXiv

cs.CL

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: Accepted to EMNLP 2026 (Main Conference)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 18× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Affordance — 4× in this paper · explained in 1 episode
• Ablation — 2× in this paper · explained in 1 episode
• Abstraction — 2× in this paper · explained in 1 episode
• Agent Skills — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Admissibility — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?
• Uncertainty-Aware Clarification in LLM Agents with Information Gain
• Inner Monologue: Embodied Reasoning through Planning with Language Models
• SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
• Odyssey: Empowering Minecraft Agents with Open-World Skills
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support
• How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings
• SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
• SkillGen: Verified Inference-Time Agent Skill Synthesis
• ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
• SkillX: Automatically Constructing Skill Knowledge Bases for Agents
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
• SkillOS: Learning Skill Curation for Self-Evolving Agents
• Large Language Models Can Self-Improve At Web Agent Tasks
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
• EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
• SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
• AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution

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