arXiv AI
VACE: Validation-Gated Alternating Co-Evolution of Agent Models and Harnesses
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arXiv AI
Sep 2
WHALE: A Simple Recipe for Joint Harness-Weight Optimization
The paper introduces WHALE, a method that alternates between updating a language model’s weights and searching for a better harness (the code that manages context and control flow). By iteratively fine‑tuning the model under the current harness and then optimizing the harness under the updated model, WHALE improves performance across search QA, math reasoning, and chess puzzles, outperforming weight‑only, harness‑only, and Fast‑Slow Training by 4.15–24.38 percentage points in mean@8 accuracy. The approach uses either fixed phase lengths or an adaptive patience rule to decide when to switch phases, and the authors provide code on GitHub.
arXiv AI
Aug 21
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
arXiv:2608. 20169v1 Announce Type: cross Abstract: We present a novel approach to efficient LLM agent harness optimization through adaptive validation task selection.
arXiv AI
Jul 17
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
Jul 28
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
arXiv:2607. 22688v1 Announce Type: new Abstract: Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, evaluated, and learned from.
arXiv AI
Jun 15
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
arXiv:2606. 14249v1 Announce Type: new Abstract: AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts.
arXiv AI
Aug 28
Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification
The paper introduces HarnessLens, a budget‑aware framework that evolves language‑model agent harnesses by jointly exploring task spaces and user‑configurable components. It derives candidate modifications from execution trajectories and selectively verifies them on behavior‑relevant tasks using an attributable‑evidence gate, thereby avoiding wasteful rollouts on unrelated behaviors. Experiments on three harnesses and four benchmarks show that HarnessLens improves held‑out performance by 7.6‑13.6% while using less evaluation budget than existing methods.