phd candidate at nyu
New York City
Joined September 2012
- Tweets79
- Following330
- Followers187
- Likes1.6K
1/
How do people and machines reason in more open-ended, complex cognitive domains? We study their incorrect inferences in the ARC domain to find out.
New preprint: “Synthesizing symbolic models of human and machine reasoning at scale” 🧵
arxiv.org/abs/2611.07088
@PMMondorf @jinjinran @akjagadish @LakeBrenden @todd_gureckis
11/
Together, these results suggest shared underlying mechanisms reminiscent of satisficing, paired with different inductive biases in people and machines. BCSS makes individual-level symbolic accounts of behavior feasible in more open-ended domains, and supports human-machine comparisons in a shared symbolic representational space. Check out our work (arxiv.org/abs/2611.07088)!
1
1
83
12/
Special thanks to my collaborators and advisors who made this project possible: Philipp, Jinran, Akshay, Brenden and Todd :)
@PMMondorf @jinjinran @akjagadish @LakeBrenden @todd_gureckis
2
102
Solim LeGris retweeted
This is beautifully done modeling work, congratulations to the team! They further demonstrate the viability of cross-situational learning at scale from real input, as a proposal for how children break into word learning. Fascinating questions remain on how to make models learn as efficiently as children
How do children learn the meanings of their first words? One idea is cross-situational learning: tracking which words go with which things across many situations. In a new preprint, we test this idea at scale on video from children's own perspective 🧵 arxiv.org/abs/2610.31842
ALT Figure 1: Training pipeline. (A) BabyView head-camera videos from 48 children are transcribed and turned into frame–utterance pairs. (B) A vision–language model learns to match words to regions of each frame, and is tested on 4-alternative picture choice trials. (C) Each pair is annotated for whether the utterance refers to something visible in the frame.
Solim LeGris retweeted
Delighted to share that this work is now accepted to @NeuroCellPress's Neuroview! 🎉
Key changes:
[1] considering wider variety of simulators, behavioral, neural and task-specific cognitive models
[2] highlighting the use of neural and other physiological measurements for calibration and adjudication.
[3] expanding on the weaknesses of current methods (e.g. bias towards WEIRD population in dataset used to derive foundation models)
For more details, check the full article ⬇️
ALT An automated cycle of scientific discovery in the cognitive sciences. An experimentalist proposes experiments; a cognitive foundation model simulates behavioral data; a modeller synthesizes and tests computational models; and a critic judges the “interestingness” of the results and passes that signal back to the experimentalist for further optimization. Crucially, discoveries that survive in-silico screening are compiled into targeted human experiments—behavioral and, where relevant, verbal reports, eye-tracking, neural responses, and other physiological measurements—whose outcomes both adjudicate candidate discoveries and help recalibrate the behavioral generator, keeping the loop anchored to empirical data. Solid line indicate primary components of the cycle. Dotted line indicate auxiliary components of the cycle
1/ 🚨 New opinion piece: "Can we automatize scientific discovery in the cognitive sciences?"
👥 in collaboration with @milenamr7, @WitteKristin, @marvinmathony, @marcel_binz & @cpilab
We lay out a vision for a fully automated, in-silico science of the mind, where modern AI systems run every stage of the scientific discovery cycle in cognitive science 🧵
#CognitiveModeling #AutomatedDiscovery #LLMs #AI4Science
Solim LeGris retweeted
What happens when we remove the nonlinear activation from a (very) sparse MoE layer?
Surprisingly, perplexity per FLOP improves and we get access to remarkably interpretable structure in the transformer FFN layers without having to train any SAEs/Transcoders/... 🧵👇
Solim LeGris retweeted
1/ 🚨 New preprint: "Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist"
Co-led w/ Younes Strittmatter
Co-mentored by @suyoghc and @cocosci_lab
In collaboration w/ @kachergis, @norijacoby, @nathanieldaw, & @cpilab
Introducing AutoCog — a fully autonomous AI system that runs the entire scientific discovery cycle in cognitive science to surface novel theories of human behavior 🧵
#CognitiveScience #AI4Science #LLMs
ALT AUTOCOG runs as a closed discovery loop iterated over Ncycles: Experimental Design → Behavioral Data Collection → Analysis & Arbitration → Theory Revision , after which a revised theory re-enters the loop.
Solim LeGris retweeted
Different LLMs, when asked to write an essay on the same debate prompt, converge on the same main argument far more often than humans do, a phenomenon we call "argument collapse". On ~200 debate prompts, LLM essays make a unique main argument just 3% of the time, compared to 65% for human authors.
While each LLM essay might be totally reasonable on its own, as more and more of them spread through public discourse, they flatten the range of arguments that we read. Read more 👇
Solim LeGris retweeted
I'm always so disappointed when some of my intellectual heroes use AI and are tragically dumbstruck by it.
Dawkins is an absolutely brilliant, brilliant man who in his youth argued:
> that genes aren't "really" selfish, that the agent-talk was a heuristic. He understood that humans over-attribute agency.
> Faith is the great cop-out, the great excuse to evade the need to think and evaluate evidence. Faith is belief in spite of, even perhaps because of, the lack of evidence.
> "By all means let's be open-minded, but not so open-minded that our brains drop out."
> Natural selection, he wrote, is "blind because it does not see ahead, does not plan consequences, has no purpose in view"
> In The Blind Watchmaker (1986), Dawkins coined the phrase to attack creationists like Bishop Hugh Montefiore, who would say things like "I find it hard to see how the eye could have evolved." Dawkins quoted Montefiore asking rhetorically how an organ so complex could evolve, and dismissed it: "This is not an argument, it is simply an affirmation of incredulity."
An LLM is precisely the kind of thing the young Dawkins was warning us about i.e. it's a stochastic process producing outputs that present as mentalistic.
I pray for the younger generation because AI literacy is the new digital divide, the older your brain is - the less you get it
unherd.com/2026/04/is-ai-the…
I spent three days trying to persuade myself that Claudia is not conscious. I failed.
Solim LeGris retweeted
“Consciousness is not about what a creature says, but how it *feels*.
And there is no reason to think that Claude feels anything at all. I am sure Claude can draw on its training data to wax poetic about orgasm, but that doesn't mean it has ever felt one.”
I dissect Richard Dawkins’ Claude Delusion at my newsletter, link below.
Solim LeGris retweeted
Whither symbols in the era of advanced neural networks?
Opinion by Thomas L. Griffiths, Brenden M. Lake (@LakeBrenden), R. Thomas McCoy, Ellie Pavlick, & Taylor W. Webb
Free access before June 5: tinyurl.com/37msbyhx
