@inproceedings{chen-etal-2026-skillladder,
title = "{S}kill{L}adder: Multi-User Skill Evolution with Compliance-Oriented Synchronization and Quality-Gated Synthesis",
author = "Chen, Nan and
Wang, Yi and
Zhu, Bin Benjamin and
Xiong, Haoyi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.218/",
pages = "4553--4573",
ISBN = "979-8-89176-462-0",
abstract = "LLM agents accumulate valuable procedural knowledge during task execution, but these traces are session-bound, model-specific, and often entangled with sensitive content, making them unsafe to pool directly across users. Prior approaches to skill discovery often sacrifice cross-user generalization, iterative quality, or compliance-oriented sharing. We propose SkillLadder, a multi-user framework that distills local execution traces into a shared skill library without funneling raw data to a central server. The system evolves user-contributed traces into a multi-level hierarchy (L0{--}L3) using five quality gates that prevent regression below the strongest parent skill under a judge-defined score. To ensure safe sharing, SkillLadder distributes only level-${\geq}1$ skills that have undergone entity scrubbing and lineage sanitization. On $\tau^2$-bench, where each isolated task execution simulates one contributing user{---}a deliberately conservative proxy for many small local contributors, not a model of longitudinal human users{---}evolved skills improve a fixed Claude Sonnet 4.6 agent in three of four domains and generalize to simulated users running different agent models. Compliance audits verify that entity retention reaches zero by L3 and source-attribution accuracy is roughly halved, substantially reducing measured sharing risks."
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<abstract>LLM agents accumulate valuable procedural knowledge during task execution, but these traces are session-bound, model-specific, and often entangled with sensitive content, making them unsafe to pool directly across users. Prior approaches to skill discovery often sacrifice cross-user generalization, iterative quality, or compliance-oriented sharing. We propose SkillLadder, a multi-user framework that distills local execution traces into a shared skill library without funneling raw data to a central server. The system evolves user-contributed traces into a multi-level hierarchy (L0–L3) using five quality gates that prevent regression below the strongest parent skill under a judge-defined score. To ensure safe sharing, SkillLadder distributes only level-\geq1 skills that have undergone entity scrubbing and lineage sanitization. On τ²-bench, where each isolated task execution simulates one contributing user—a deliberately conservative proxy for many small local contributors, not a model of longitudinal human users—evolved skills improve a fixed Claude Sonnet 4.6 agent in three of four domains and generalize to simulated users running different agent models. Compliance audits verify that entity retention reaches zero by L3 and source-attribution accuracy is roughly halved, substantially reducing measured sharing risks.</abstract>
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%0 Conference Proceedings
%T SkillLadder: Multi-User Skill Evolution with Compliance-Oriented Synchronization and Quality-Gated Synthesis
%A Chen, Nan
%A Wang, Yi
%A Zhu, Bin Benjamin
%A Xiong, Haoyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-skillladder
%X LLM agents accumulate valuable procedural knowledge during task execution, but these traces are session-bound, model-specific, and often entangled with sensitive content, making them unsafe to pool directly across users. Prior approaches to skill discovery often sacrifice cross-user generalization, iterative quality, or compliance-oriented sharing. We propose SkillLadder, a multi-user framework that distills local execution traces into a shared skill library without funneling raw data to a central server. The system evolves user-contributed traces into a multi-level hierarchy (L0–L3) using five quality gates that prevent regression below the strongest parent skill under a judge-defined score. To ensure safe sharing, SkillLadder distributes only level-\geq1 skills that have undergone entity scrubbing and lineage sanitization. On τ²-bench, where each isolated task execution simulates one contributing user—a deliberately conservative proxy for many small local contributors, not a model of longitudinal human users—evolved skills improve a fixed Claude Sonnet 4.6 agent in three of four domains and generalize to simulated users running different agent models. Compliance audits verify that entity retention reaches zero by L3 and source-attribution accuracy is roughly halved, substantially reducing measured sharing risks.
%U https://aclanthology.org/2026.findings-emnlp.218/
%P 4553-4573
Markdown (Informal)
[SkillLadder: Multi-User Skill Evolution with Compliance-Oriented Synchronization and Quality-Gated Synthesis](https://aclanthology.org/2026.findings-emnlp.218/) (Chen et al., Findings 2026)
ACL