SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xiaofan Bai, Hongqiang Lin, Chao Liu, Yantao Zhang, Xuan Jin, Xipeng Cao, Yuhong Li
• Venue: arXiv: cs.AI
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.11079v1
• PDF: https://arxiv.org/pdf/2608.11079v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper presents SkillZip, an evaluation-free method for compressing skills used by self-evolving agents. SkillZip aims to make skill compression more efficient by finding the shortest faithful structural explanation of a skill, thus reducing redundancy and making it easier to maintain.

What the Document Says

• Self-evolving agents accumulate skills over time, but this can lead to expensive skills due to redundancies.
• A skill is not a flat passage but has a name, description, workflow, tool and output contracts, and rare exceptions that need to be considered for compression.
• Evaluation-guided compression introduces rollouts, cost, and dependence on the compression-time evaluation set.
• SkillZip is an evaluation-free method that compresses a skill by finding its shortest faithful structural explanation.
• The intuition behind SkillZip is to explain a rule once at its scope, factor repeated action sequences into shared procedures, and keep only the differences as explicit exceptions.
• SkillZip uses a typed minimum description-length objective over a skill contract and a residual, subject to a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field.
• SkillZip has a one-shot mode and a continual Zip-on-Write mode that integrates each self-evolution patch without replaying tasks or reparsing the full history.
• Through experimental evaluations, it was demonstrated that SkillZip is effective in compression performance, generalizability, and cost overhead.

Key Concepts Mentioned

• self-evolving agents
• skill compression
• redundancy
• evaluation-free method
• structural explanation
• typed minimum description-length objective
• one-shot mode
• continual Zip-on-Write mode

Claims Made by the Document

• SkillZip can make skill compression more efficient.
• SkillZip reduces redundancy and makes it easier to maintain skills.
• SkillZip preserves unique rare rules by construction.

Evidence Used

• The paper presents SkillZip as a solution to the problem of expensive and difficult-to-maintain skills in self-evolving agents.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are copied rather than reused. The resulting skill becomes expensive to inject and difficult to maintain. Generic prompt compression is ill-suited to this setting because a skill is not a flat passage: its name and description define when it applies, its workflow controls execution, its tool and output contracts constrain validity, and rare exceptions may remain essential even when no sampled task activates them. Evaluation-guided compression can test these behaviors, but it introduces rollouts, cost, and dependence on the compression-time evaluation set. We present SkillZip, an evaluation-free method that compresses a skill by finding its shortest faithful structural explanation. The intuition is explain once, reference many: state a repeated rule once at the scope where it applies, factor a repeated action sequence into a shared procedure, and keep only the differences as explicit exceptions. We formalize this intuition as a typed minimum description-length objective over a skill contract and a residual, subject to a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field. The formulation provides simple sharing thresholds, preserves unique rare rules by construction, and supports efficient local updates. SkillZip has a one-shot mode with one structured extraction call and deterministic optimization, and a continual Zip-on-Write mode that integrates each self-evolution patch without replaying tasks or reparsing the full history. Through comprehensive experimental evaluations, we demonstrate the effectiveness and superiority of SkillZip in compression performance, generalizability, and cost overhead.