Research Papers Institution
Public research university in Hong Kong; its computer science group co-authored this work on video reasoning.
AI Agents · City University of Hong Kong
RHO tunes an LLM agent harness from past unlabeled trajectories using self-consistency and pairwise self-preference, lifting SWE-Bench Pro from 59% to 78% in one round with no external grading.
AI Agents · TokenRhythm Technologies
Claw-SWE-Bench evaluates OpenClaw-style coding-agent harnesses on 350 GitHub issue tasks. OpenClaw jumps from 19.1% to 73.4% Pass@1 with a full adapter.
Video Generation · Kuaishou Technology
Instead of asking a video model to reason directly, a VLM grades its in-progress frames and fine-tunes a per-instance LoRA. The trick lifts RULER-Bench from 46.4 to 68.2.