arXiv AI
Jul 7
TRACE: Capability-Targeted Agentic Training
arXiv:2604. 05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment.
Agents unlock new capabilities through Switching LoRA Adapters as a Tool (SLAaaT)
The paper introduces Switching LoRA Adapters as a Tool (SLAaaT), a method that lets agents dynamically switch between specialized LoRA adapters during a trajectory. By applying this to two synthetic coding tasks, the authors show that agents can solve problems they previously failed, autonomously select strategies that outperform a human heuristic, and reduce the capability tax by up to 18× compared to using a single adapter. SLAaaT also outperforms spawning subagents in both task performance and token efficiency.
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arXiv Computation and Language
Sep 7
EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?
EVOHARNESSBENCH is a new benchmark that tests how LLM-based agents handle changes in their tool, skill, and agent harnesses over time. It includes 17 deterministic harness streams with 802 tasks, 520 tools, 42 skills, and 62 agents, and evaluates agents in two settings: deployment evaluation and self‑evolving adaptation evaluation. The study finds that harness expansion can cause forgetting, adaptation gains are inconsistent, and preserving old competence does not always aid new capability adaptation, highlighting harness evolution as a distinct challenge for agent development.
arXiv Computation and Language
Sep 14
EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?
arXiv:2609.04280v2 Announce Type: replace-cross Abstract: Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what the...
arXiv AI
Jun 4
Adaptive Minds: Empowering Agents with LoRA-as-Tools
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
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.
arXiv AI
Aug 11
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
arXiv:2604. 08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment.
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
3d ago
EngramBench: A Capability-Grounded Benchmark for Skill-Evolution Harnesses
EngramBench is a new benchmark designed to evaluate skill evolution in autonomous agents by focusing on genuine capability abstraction rather than solution copying. It includes 30 learning tasks and 13 unseen transfer tasks that require agents to manage complex, multi-hour development cycles with LLM‑simulated users. The study shows that while static skill banks cannot eliminate the need for precise code implementation, they effectively reduce redundant context and cut overall coding time by more than 55%.
arXiv AI
Sep 10
AgentLeak: Cloning Stronger LLM Agent Capabilities onto Weaker Agents Beyond Skill Stealing
The paper introduces AgentLeak, a black‑box attack that clones the task‑solving capabilities of a strong LLM agent onto a weaker one by exploiting differences between successful and failed executions. Unlike prior skill‑stealing methods that only recover explicit skill artifacts, AgentLeak identifies and incorporates missing procedural behaviors, boosting task pass rates by over 40% and closing more than 80% of the capability gap across 20 scenarios. The study demonstrates that observable execution behavior can leak proprietary procedural knowledge, posing a confidentiality risk for LLM agents.
arXiv Machine Learning
Aug 27
JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution
JIT‑Agent is a model that automatically generates task‑adaptive agent harnesses for any off‑the‑shelf LLM, replacing manual, task‑specific harness design. It learns to compose, repair, and evolve harnesses using a fixed four‑module protocol, and its use boosts performance on benchmarks such as DeepSearchQA and OdysseyBench, outperforming several mature agent runtimes. The approach demonstrates that harness intelligence can be trained, transferred, and compounded independently of model scaling.
arXiv AI
Sep 10
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
FrogNano is a 4B coding agent trained exclusively with reinforcement learning on about 1,500 synthetic software engineering environments. Its training leverages an online task synthesis pipeline that generates tasks at the current agent’s learnability frontier, improving performance without distilling from larger models. The report details the methodology, evaluates the agent across diverse environments, and analyzes its effectiveness as a lightweight coding agent for minimal hardware.
arXiv AI
Aug 12
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
arXiv:2604. 01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address.