Category

Has GPT-6-Astra Achieved Embodied Intelligence?

Evaluates GPT-6-Astra as an embodied policy across 12 robotic benchmarks, examining in‑context learning, hybrid System‑2/System‑1 control, and real‑world deployment gaps; finds strong benchmark competence but practical limits from demonstration cost, latency, and failure recovery.

Self-Evolving Coding Agents via Harness-Policy Optimization

Alternates harness and policy optimization so a coding agent uses its own policy to propose harness revisions that target evolving failure modes. Combines harness search (H-step) with RL-based policy updates (P-step), improving resolve rates on multiple coding benchmarks and generalizing out-of-distribution.

OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory

Provides a training-free multimodal reasoning harness that combines heterogeneous visual tools, persistent multimodal memory, and code execution so MLLMs can iteratively acquire and compose grounded evidence. Improves accuracy on 22 benchmarks by about 9.7–12.6 points while cutting comparative token costs.