arXiv AI
Prime Agent: A Self-Improving RLM Harness
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arXiv AI
Aug 19
Agent Lightning v1.0: Towards Harnessed Agentic RL
Agent Lightning v1.0 is a lightweight framework that enables harnessed agentic reinforcement learning, where the agent harness—managing tools, context, and control flow—directly participates in model post‑training. It supports arbitrary agent harnesses and addresses challenges such as retokenization, sample merging, and advantage calculation, providing a reproducible pipeline for instruction‑following, search, and coding agents. In experiments, RL training on 6K examples improved Qwen3.5‑9B’s performance on SWE‑bench from 41.8% to 56.4%.
arXiv AI
1d ago
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
arXiv:2609. 20519v1 Announce Type: new Abstract: As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback.
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
Aug 26
Beyond Executable Models: The Pufibara Agent Harness and the Modelica Agent Workflow Benchmark for Physical System Modeling
The paper introduces Pufibara, an agent harness designed to maintain engineering state and evidence across revisions in Modelica-based physical system modeling. It also presents a 232-task Modelica Agent Workflow Benchmark covering model repair, generation, and tuning, evaluated by an external benchmark-owned evaluator. Experiments show Pufibara outperforms Claude Code in task success and resource efficiency across two LLM backends.
arXiv AI
Sep 7
MaxKernel: Agentic Kernel Generation for TPUs
MaxKernel is a multi‑agent system designed to generate high‑performance custom kernels for TPUs. It offers three paradigms: a Human‑in‑the‑Loop agent for collaborative design, an Autonomous agent that runs a fully automated optimization loop, and a Graph‑Based Autonomous Search for global exploration. All paradigms share specialized sub‑agents for planning, implementation, debugging, testing, and profiling, and the system consistently matches expert hand‑tuned baselines on the JaxBench suite and real‑world workloads.
Hugging Face Trending Papers
1d ago
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedb...