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SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries — arXiv:2608.05604

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05604
License (as archived): CC0-1.0
Archived: 2026-10-25T21:27:10.198Z
SHA-256 of the archived text: dfd31c88eb886a3c64361c0d80dda6fbf7c7a7021a667c526b1f06bdb8a396c5
Stable URL of this page: https://metraipolis.com/documento/dc_3cfd4fa06eff

How to cite

SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries — arXiv:2608.05604 — arXiv (en), https://arxiv.org/abs/2608.05604 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3cfd4fa06eff (Metraipolis archive dc_3cfd4fa06eff, sha256 dfd31c88eb886a3c64361c0d80dda6fbf7c7a7021a667c526b1f06bdb8a396c5)

BibTeX

@misc{dc3cfd4fa06eff,
  title = {SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries — arXiv:2608.05604},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05604},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3cfd4fa06eff; sha256 dfd31c88eb886a3c64361c0d80dda6fbf7c7a7021a667c526b1f06bdb8a396c5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05604. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time. As skill libraries grow, a central challenge is to expose the smallest sufficient executable context under a limited context budget. Existing systems struggle to reuse routines below the whole-skill level, preserve procedural contracts during compression, keep compressed routines executable and expandable, and update the compressed library as skills evolve. These challenges reveal a unit mismatch: skills are retrieved as packages, compressed as text, and converted into execution graphs only after retrieval, whereas reliable reuse requires a contract-bearing procedural unit. We propose SkillZip, an execution-aware procedural abstraction framework that performs contract-preserving compression over section-level graphs. SkillZip rewrites recurring contract-valid motifs into reversible ported macros while preserving boundary signatures, dependency closure, verifier reachability, and source-level expansion. At inference time, it hydrates a compact, dependency-closed context and expands macros only when required. ReZip further integrates new skills and revises risky macros using execution evidence. Comprehensive experiments1 on technical and embodied agent benchmarks show SkillZip consistently outperforms the strongest baseline by up to 12.2 points, while achieving a 3.46x compression ratio with 99.2% dependency preservation and 98.7% verifier reachability. Scaling analyses further confirm robust retrieval across skill libraries ranging from 200 to 100K skills.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-07 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.05604 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T21:24:38.194Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).