arXiv Machine Learning
Jul 13
Mach-Mind-4-Flash Technical Report
arXiv:2607. 09375v1 Announce Type: new Abstract: We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters.
Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering
arXiv:2606. 30911v1 Announce Type: new Abstract: ML engineering agents waste compute rediscovering known techniques because every competition is a cold start.
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arXiv AI
Jun 9
Skill Retrieval Augmentation for Agentic AI
arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.
arXiv AI
Aug 21
Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees
arXiv:2608. 19993v1 Announce Type: new Abstract: Loading reusable skill documents into a bounded context window is now the primary way large language model (LLM) agents acquire task-specific capabilities, which makes skill selection a first-order determinant of task performance and token cost.
arXiv AI
2d ago
Dynamic Expert Pruning for Multi-Agent Systems
Dynamic Expert Pruning for Multi-Agent Systems introduces DEP, a method that generates a per-request mask of experts based on an agent’s system and task prompts. Unlike static pruning, DEP uses a lightweight predictor trained on workflow transcripts to activate only the experts needed for each specific request, requiring no per-configuration calibration. Experiments across various tasks, models, and MoE architectures show DEP outperforms static pruning and merging baselines, especially when few experts are retained, and it generalizes to unseen workflows.
arXiv AI
6d ago
SkillSeek: Revisiting Agent Skill Retrieval at Marketplace Scale
arXiv:2609.38822v1 Announce Type: cross Abstract: Anthropic's Agent Skills package reusable procedural know-how for an LLM agent into SKILL.md directories, and open-source aggregations have grown pas...
arXiv AI
Sep 30
SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation
SkillGym is an automatic pipeline that generates verifiable environments for training skill-use agents. It crawls internet skills, filters for reproducible workflows, and uses a builder‑reviewer process to create difficulty‑controlled tasks with reference solutions and verifiers. The system builds 6.8k environments, collects 19k successful trajectories, and fine‑tunes LLMs from 2B to 122B parameters, improving performance and skill invocation rates.
arXiv AI
Sep 3
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
The paper introduces Repo-To-Skill, a method for converting GitHub repositories into reusable AI skills. By distilling operational knowledge from over 1,000 machine‑learning repositories, the authors build the AREX‑Skill Library with more than 5,000 verified skills across 20 areas. Integrating these skills into a research agent—DisCo—yields significant performance boosts on multiple benchmarks, demonstrating the value of reusable, task‑agnostic knowledge.
Hugging Face Trending Papers
Sep 2
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills introduces DisCo, a research agent that extracts and verifies operational knowledge from GitHub repositories to create reusable AI skills. The agent produces both task‑agnostic skills—compiled into the AREX‑Skill Library of over 5,000 verified skills from 1,000 repositories—and task‑oriented skills tailored to specific research tasks. When equipped with these skills, the agent achieves significant performance gains across multiple benchmarks, outperforming a skill‑free version by 134.3% on MLE‑bench, 34.4% on PaperBench, 9.2% on FrontierCS, and 14.0% on PassNet.
arXiv AI
Jul 21
From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?
arXiv:2604. 01608v4 Announce Type: replace Abstract: Multi-agent systems (MAS) tackle complex tasks by distributing expertise, though this often comes at the cost of heavy coordination overhead, context fragmentation, and brittle phase ordering.
arXiv AI
Sep 15
The Router Within: Eliciting Native Skill Routing from a Frozen LLM
arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by pre...
arXiv AI
Aug 21
AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement
arXiv:2608. 20318v1 Announce Type: new Abstract: Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inherits the improvement.
arXiv AI
Jun 16
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
arXiv:2602. 12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time.