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Reinforcement learning and autonomy researcher
Reinforcement learning and autonomy researcher with public evidence across Agents & robotics, AI research.
Out of curiosity I had Fable write a response to the article about the weaknesses of LLMs: www.nytimes.com/2026/06/30/o... It's uh, pretty good. claude.ai/code/artifac...
LLMs will likely be the most powerful propaganda tool that has ever existed arxiv.org/abs/2606.16475
This is a really excellent benchmark, pointing to some serious missing capabilities in coding agents: arxiv.org/abs/2603.247.... They don't write code with an eye towards maintenance!
New Paper: arxiv.org/abs/2606.19370 Self-play yields capabilities but requires frustrating cost-function tuning. Surprisingly, just 30 minutes of demonstration data produces much more human-like driving policies! Led by @daphne-cornelisse.bsky.social Website: spiced-self-play.com
New work with Roberta Raileanu: A Compositional Framework for Open-ended Intelligence Open-ended intelligence is the capacity to adapt to novel problems & environments that are substantially different from those seen in training. But most models of open-mindedness don't have c…
"Our most capable agent autonomously resolved 9 of 353 open Erdős problems at the per-problem cost of a few hundred dollars, proved 44/492 OEIS conjectures, and is being deployed in combinatorics, optimization, graph theory, algebraic geometry, and quantum optics research." Ni…
Excited to share our paper! Due Process on Hold: A Queueing Framework for Improving Access in SNAP arxiv.org/abs/2605.15165 Millions of Americans interface with the social safety net via call centers that are too congested. In Holmes v. Knodell, bad operations = procedural due…
These are very odd failures! arxiv.org/abs/2211.00241
Does physical modeling matter or should we just do data-driven stuff? I feel like this paper characterizes things perfectly: arxiv.org/abs/2410.23179. Physics-based modeling is always helpful but the relative gain closes at scale
🔬 This week's KE:SAI research highlight is World Engine, a RL post-training simulator and pipeline for end-to-end driving. 📜 arxiv.org/abs/2606.19836
Agentic coding is genuinely useful now, and there are some impressive reports of AI agents doing science. But how well and how reliably can they handle tasks scientists actually want to hand off, ones that bottleneck progress? How do we even measure that?? New paper🧵 arxiv.org…
Weirdly unnoticed but super interesting paper formalizing what's going on in all the "just imitate successful trajectories" work arxiv.org/abs/2601.18175
Given the concerns about AI writing, be aware that Pangram has a super high false positive rate! It frequently rates my personal writing as 100% LLM written!
A lot of my fears about LLM usage come from using Stackoverflow when I was a wee programmer. It was easy to fall into copy pasting behavior that long term made you worse
My position on AI consciousness is that you really should try to eat less meat if you can
Taking more seriously the claim that recent ML models do not "reason", it still is quite odd the particular ways that superhuman game-playing models fail, suggesting that what they're doing is something quite different from human reasoning about play
The median AI safety person is a weirdo who was into it 10 years before there was any system that appeared to match their concerns. It’s more often correct to treat them as honest than disingenuous
"LLMs mean you don't have to know things, you can just do things" assumes a clean separation that probably doesn't exist. You know what you want to do in part because of what you know / understand
arxiv paper now on hold for almost a week. To all the people submitting LLM slop that brought us to this place, please know that you've appreciably made the world worse
Google's AI search hitting new heights. Unsure how this one happens tbh
Proposed norm: you're allowed to respond to an unedited LLM document with an unedited LLM document that is twice as long
I have already met some number of people who are basically a front-end to an LLM
Center = Eugene Vinitsky. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.
