FedSkillGit is a federated framework that enables large language model agents to evolve reusable natural‑language skills without sharing raw data. Instead of uploading entire skill documents, clients submit bounded patches and local experience summaries, which are filtered for privacy, deduplicated, and merged into a global skill candidate. The server distributes the candidate for client‑side validation, publishing it only when performance improves, and experiments show it outperforms baselines by up to 22% while approaching centralized results.
arXiv:2606. 03143v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement.
arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
arXiv:2606. 01386v1 Announce Type: new Abstract: We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS.
arXiv:2608.21964v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate over evolving software repositories, where success depends on repository-specific procedural kno...
arXiv:2610.40758v1 Announce Type: new Abstract: Self-evolving agents rewrite a persistent state from feedback, but it is largely untested whether this lets them learn a rule nobody told them. An agen...
The paper "It Takes Workflows to Evolve Better Workflows" introduces FloWright, a method that uses a hierarchical, structure‑aware reward system to allow one or more roles in a multi‑agent workflow to self‑evolve without extra models or data. It also proposes DataWright, an adaptive data hardening technique that transforms existing datasets into more challenging workflow‑level tasks. Experiments on document, slide, chart, code, math, and finance tasks show that small open models trained with FloWright can improve performance by up to +7.41%, with co‑evolving multiple roles yielding the largest gains. "whyItMatters":"The work demonstrates that optimizing beyond the workflow generator—by enabling multiple agents to co‑evolve—can substantially enhance the effectiveness of multi‑agent workflows for complex real‑world tasks."
The paper introduces FedRouter, a personalized federated learning framework that clusters tasks rather than clients to build specialized language models. It employs local and global clustering of adapters to associate them with specific tasks and includes an evaluation router that directs test samples to the most suitable adapter. Experiments on a multitask dataset show FedRouter outperforms existing methods, achieving up to 6.1% relative improvement under task interference and 136% under generalization scenarios.
StagedWorkspace is a versioned workspace designed for knowledge‑work AI agents, ensuring that every parsed view, native file edit, and review diff is explicitly tied to a specific version of the workspace state. By binding parsed records and review diffs to content hashes of native files, the system improves performance on tasks such as OfficeQA and APEX‑Agents, achieving higher pass rates and rubric scores compared to single‑view approaches. The study demonstrates that providing dual parsed/native access and visible diffs enhances agent performance, highlighting workspace state as a key experimental variable for future benchmarks.
arXiv:2609.05677v1 Announce Type: cross Abstract: Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usu...
arXiv:2608. 10906v1 Announce Type: cross Abstract: An agent skill is a folder containing a SKILL.
arXiv:2607. 10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model.
arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.