arXiv AI
Recursive Environment Scaling for General Long-Horizon Agents
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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
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Beneath the Surface: What We Overlook When We Talk About Agent Evolution
The paper investigates how agent harness evolution supports recursive self‑improvement without changing model weights, assessing four methods across general and coding tasks. Repeated tests and component interventions show that all evolved agents acquire new tasks while losing some previously mastered ones, with reliability gains sometimes offset by higher deployment costs. The study highlights that the highest‑scoring agent does not always deliver the greatest mean improvement, and that stricter admission criteria can eliminate useful alternatives; it proposes preservation‑aware evaluation and a follow‑up tool, EvoAudit, to refine agent design.
arXiv AI
Oct 19
EvalResearchBench: Can AI Agents Build Their Own Feedback for Recursive Self-Improvement?
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arXiv AI
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Reward-Free Evolving Agents via Pairwise Validator
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
arXiv AI
Aug 7
Recursive Synthesis for Long-Horizon Terminal Tasks
arXiv:2608. 05466v1 Announce Type: new Abstract: High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent.
arXiv AI
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Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.