Linjie (Lindsey) Li

Everyone’s asking whether AI will replace researchers. I’m more interested in a different question: what will research look like when every researcher has an AI collaborator? I’ve been trying Lattice by @leoliuym, a workspace that brings papers, notes, LaTeX, and AI together. As AI evolves, our research workflows should too.

We ran GPT-6 Astra on HumanCLAW-Bench, using the same open-source harness and motion generator. It is a big step over the previous best: - FindSR: 64.9% → 75.5% (+10.6) - NavSR: 42.4% → 57.1% (+14.7) - InteractSR: 16.8% → 46.6% (+29.8) The gains compound: Find-to-Nav conversion rises from 65% to 76%, and Nav-to-Sit from 39% to 72%. Astra also solves 147/507 (29%) episodes missed by all nine previous models . Three things stood out. 1/ Better embodied spatial awareness GPT-6 Astra's dDtb—how far the objects it disturbs end up moving—is 0.65 m, the lowest on the leaderboard, while its #Dtb, the number of objects it touches, remains in line with the other models. It still bumps into things; it just does not keep shoving them. 2/ Stronger spatial memory Sitting is the hard case: the agent has to turn away from the target, so the seat leaves its ego view and it must rely on its own history. Earlier models often sat on the wrong object or on thin air. GPT-6 Astra converts 72% of successful approaches into a correct sit, up from 39%. Once it reaches the target, it never forgets to sit (0 of 108 failures). 3/ Better agentic decision continuity Earlier VLMs often jump between decisions: sit down, stand up again, then wander off. GPT-6 Astra commits. Among episodes where a sit lands on the target, 97% end still seated with a clean stop, compared with 78% for Gemini 3.1. It knows what it is doing and finishes it. ⚠️ Caveats: this is a single run in the low-thinking setting, and 53% of sit episodes still fail. Embodied self-awareness remains the bottleneck we described in the paper; the ceiling has simply moved up. Leaderboard: human-claw.github.io All 1,218 episode videos: huggingface.co/datasets/Choi…

Robotics felt different this month. Google DeepMind and Anthropic made the same question hard to ignore: can a reasoning model decide what a whole body/robot should do next? Gemini Robotics 2 put an embodied reasoning model above a VLA for whole-body control. Anthropic tested general-purpose reasoning models at several levels of control. - deepmind.google/blog/gemini-… - anthropic.com/research/claud… The bad news is clear. General-purpose reasoning models still cannot control a humanoid on their own. They need a pretrained policy underneath. Still, I think this might actually work. A strong model (w/ excellent multimodal and reasoning capabilities) could really do it one day. HumanCLAW is our attempt to get an early glimpse of that future.

🦾HumanCLAW: Can Vision-Language Models Act Through a Body? A VLM can spot the sofa in a second. But can it get a body there and sit down? We call it Action Intelligence, and our work decouples it from motor control so it can finally be measured. Today’s best models turn out to be surprisingly bad at it. 🔻human-claw.github.io/