arXiv AI
WeAct: Dynamic Weight Switching as Agent Actions
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arXiv Machine Learning
Aug 19
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.
arXiv AI
Sep 30
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
arXiv:2609.36887v1 Announce Type: new Abstract: Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component o...
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
Oct 16
Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
arXiv:2605. 22166v3 Announce Type: replace Abstract: LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feedback interpretation, and trajectory control.
arXiv AI
Jun 2
SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
arXiv:2606. 01311v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks.
arXiv AI
Oct 28
EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis
EMMA is a framework that treats the configuration of a multi‑agent system—controller routing, specialist prompts, and procedural skills—as structured episodic memory. It explicitly assigns ownership and limits updates through a reflector and backbone‑independent guards, ensuring edits are scoped, incremental, and bounded. Experiments on KramaBench, AppWorld, and BBEH show EMMA outperforms four baselines, achieving up to 32.5% higher performance and reducing training executions by 17–38%.