arXiv AI
Scale and Selection: What Makes Automatic Harness Evolution Work for Visual-Interface Robot Agents
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arXiv Machine Learning
Sep 22
CHART: A Harness-Rotation Curriculum for Harness-Robust Search Agents
The paper introduces CHART, a curriculum that rotates harnesses during training to teach search agents parallel search strategies robustly across different harness configurations. Unlike static harness augmentation, CHART gradually consolidates behavior by graduating learned harnesses and replacing them, maintaining a reward gap that drives learning. Experiments show CHART enables agents to parallelize on 89% of held‑out harnesses, improves performance on a new QA task by 5.6pp, and benefits more from meta‑harness search than baselines.
arXiv AI
Sep 25
HarnessPAI: An Evolving Harness for Physical AI
HarnessPAI is a model‑ and embodiment‑agnostic framework that treats code as an executable, evolvable interface for Physical AI. It separates short‑term open‑loop program execution from long‑term closed‑loop evolution, using feedback to refine programs and distill reusable skills. Across various robots, HarnessPAI outperforms pure action models and code‑as‑policy baselines, achieving significant gains on tasks like LIBERO‑PRO and RoboCasa without retraining the underlying model.
arXiv AI
Jul 15
Rethinking the Evaluation of Harness Evolution for Agents
arXiv:2607. 12227v1 Announce Type: new Abstract: We revisit the evaluation of automatic harness evolution for LLM agents.
arXiv AI
Sep 21
Evolving Skill Modules under a Fixed Planner: Versioning, Rollback, and Runtime Governance for Long-Lived Robot Systems
arXiv:2604.07799v3 Announce Type: replace-cross Abstract: Robots deployed for long periods keep improving their skills, and each update changes a released system. We treat this as a software-lifecycl...
arXiv Computation and Language
Aug 25
Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report
arXiv:2608.15763v2 Announce Type: replace Abstract: AI-powered digital avatar streamers must answer product questions, engage viewers, and execute marketing strategies in real time, demanding low lat...
arXiv Computation and Language
Sep 7
EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?
EVOHARNESSBENCH is a new benchmark that tests how LLM-based agents handle changes in their tool, skill, and agent harnesses over time. It includes 17 deterministic harness streams with 802 tasks, 520 tools, 42 skills, and 62 agents, and evaluates agents in two settings: deployment evaluation and self‑evolving adaptation evaluation. The study finds that harness expansion can cause forgetting, adaptation gains are inconsistent, and preserving old competence does not always aid new capability adaptation, highlighting harness evolution as a distinct challenge for agent development.