RHO

Headlines · 2

1. Oct 18
   HarnessPro recursively evolves agent harnesses with Pareto cost-performance tuning

   The paper introduces HarnessPro, a label-free method that evolves AI agent harnesses through recursive self-improvement, aiming to improve task performance while reducing execution cost. Candidate harnesses are evaluated using LLM-generated tests without access to ground-truth solutions or official tests; specialized agents diagnose execution trajectories, plan updates with different performance-cost priorities and implement them, and normalized weighted squared-distance selection yields cost-focused (lite), balanced (medium) and performance-focused (heavy) harnesses. The authors report that on Terminal-Bench 2, SWE-Bench Pro and ProgramBench, HarnessPro's heavy mode with GPT-5.5 and Codex achieved an average pass-rate gain of 2.0 percentage points and a 26.1% average per-task cost reduction relative to RHO, and say the advantage holds with GLM-5.2 and Claude Code; code is available and the 41-page paper is under review.

   Hugging Face · Papers · 🔥 0
2. Oct 17
   General LLMs are rewriting robotics R&D, from code to benchtop experiments

   A TMTPost analysis describes two ways general-purpose LLMs are entering robotics. On Oct 16, MicroFactory founder Igor Kulakov said a single customer deployment used Claude Opus and GPT-6 Astra to generate control plans and code while dedicated programs executed the work, running 1.5 hours of autonomous electronics assembly with zero errors (a company claim). Two days earlier, Poke & Wiggle let Claude Opus 5.5 handle the full loop of reading papers, writing algorithms, training models and running real-robot evaluations to reproduce 10 robotics papers; an agent integrated and trained Microsoft's ~5B-parameter open-weight dual-arm Rho model the next day, reaching 87% success on a screw-driving task, and once diagnosed and rolled back its own inference-code bug in under 35 minutes. The piece also recounts a DSD reproduction where the agent widened the action-normalization range, flattening DSD's edge (45.3% vs 46.0%); restoring the paper's parameters gave DSD 45.3% against a 32.0% baseline, a 13.3-point lead. The author concludes R&D and integration workflows are shifting, but the evidence does not show general LLMs broadly replacing VLA models.

   钛媒体 · 🔥 0

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