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
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
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.
arXiv AI
Jun 18
Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
arXiv:2606. 18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.
arXiv AI
Jul 7
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
arXiv:2607. 05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability.
arXiv AI
Aug 12
Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization
arXiv:2608. 10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.
arXiv AI
Jul 7
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
arXiv:2607. 05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification.
arXiv AI
Jun 26
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
arXiv:2604. 15877v2 Announce Type: replace Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck.
arXiv AI
Aug 5
EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners
arXiv:2608. 03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS).
arXiv AI
Jun 17
A Framework for Evaluating Agentic Skills at Scale
arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.
Hugging Face Trending Papers
Jul 6
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer.