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AdaExploreBench benchmarks LLM agents on proactive exploration and adaptation to rule drift
The benchmark hides environment rules and tests whether agents can explore to find them and detect when they change. 9% calibrated accuracy, and supplying sufficient evidence or removing rule drift lifts average performance by close to 10%.
OR-License benchmark tests LLM agents on real operations research engagements
Across 11 frontier LLMs in coding-agent harnesses, several beat the expert reference on objective quality, but none matches its Deployment-Adjusted Quality score. 87 against the expert reference of 1.
Self-HPO framework alternates harness and policy optimization for coding agents
Self-HPO lets a coding agent propose its own harness candidates and then update its policy with reinforcement learning, raising Qwen3.5-35B-A3B's average resolve rate from 57.8% to 64.8% across three coding benchmarks. The paper also reports gains with a stronger external model, such as Opus 4.8, a…
Hugging Face paper introduces CoworkEnv for synthesizing training environments for AI coworker agents
The framework generates executable training environments for AI coworkers and trains PocketWorker models. 9-point gain on multi-turn tasks.
Interlock framework synthesizes long-horizon agent tasks that improve agent pass rates after training
Interlock represents tasks as completion graphs and generates workspaces, requests and rubrics. 16 points.
OpenVLHarness adds persistent multimodal memory and tool use to vision-language models
The harness gives multimodal models a tool capability layer and persistent memory for images, data and web references. 6 point average gains over no-tool baselines across 22 benchmarks and eight backbones, including GPT-6 Luna at lower token cost.
Brain encoding method BrainLoRA sets Algonauts 2025 record using parameter-efficient fine-tuning
The paper adapts backbones up to 104B parameters with LoRA and reports the best published result on the Algonauts 2025 Challenge using Qwen3-Omni. An ensemble of 200+ adapted models ranks first on the public post-challenge leaderboard.
Paper introduces AnyGoal benchmark testing world model generalization beyond expert rollouts
AnyGoal is a benchmark of goal-shifted tasks built from common world-model environments, with an open-source library. The authors report that current world models consistently fail when familiar state and action factors are recombined.
Hugging Face paper introduces HarnessPro for cheaper, stronger AI agent harnesses
HarnessPro evolves agent harnesses without ground-truth tests and outputs lite, medium and heavy variants. 1% versus RHO.