Chandan Singh @csinva
Seeking superhuman explanations. Senior researcher @MSFTResearch, PhD from @Berkeley_AI csinva.io Seattle, WA Joined February 2018-
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Title: Advice for a young investigator in the first and last days of the Anthropocene Abstract: Within just a few years, it is likely that we will create AI systems that outperform the best humans on all intellectual tasks. This will have implications for your research and…
Teaching a new course @Stanford this quarter on explainable AI, motivated by neuroscience. I have curated a paper list 4 pages long (link in comment). What are your favorite papers on explainable AI/mechanistic interpretability that I am missing? Please comment or DM. thanks!
“Bayesian concept bottleneck models using LLM priors” (BC-LLM) was accepted at #neurips2025! BC-LLM tackles a key clinical AI problem: how do we find interpretable features for predicting an outcome when there are ♾ possibilities from the electronic health record (e.g. notes)
🚀 ARE: scaling up agent environments and evaluations Everyone talks about RL envs so we built one we actually use. In the second half of AI, evals & envs are the bottleneck. Today we OSS it all: Meta Agent Research Environment + GAIA-2 (code, demo, evals). 🔗Links👇
😺Glad to share that our mixture-of-inputs paper is accepted at NeurIPS, see you in San Diego!
🚨 New paper alert 🚨 Using LLMs as data annotators, you can produce any scientific result you want. We call this **LLM Hacking**. Paper: arxiv.org/pdf/2509.08825
Google presents an AI system to write expert-level scientific software. Using LLMs + tree search, it invented novel methods in bioinformatics, epidemiology, geospatial analysis & more, often surpassing human SOTA. (1/4)
New Open dataset alert: 🧠 Introducing "Spacetop" – a massive multimodal fMRI dataset that bridges naturalistic and experimental neuroscience! N = 101 x 6 hours each = 606 functional iso-hours combining movies, pain, faces, theory-of-mind and other cognitive tasks! 🧵below
Instructions/reasoning are now everywhere in retrieval - we want embeddings to do it all! 🚀 But... is it even possible? 🤔 Turns out, it's not possible for single-vector models 😱 theoretically and empirically! To make it obvious we OSS a simple eval SoTA models flop on! 🧵
Glad to share that our paper was accepted the main EMNLP 2025 Conference! x.com/Sahil1V/status…
Glad to share that our paper was accepted the main EMNLP 2025 Conference! x.com/Sahil1V/status…
This is (imo) the single most important result in any modern language encoding study. 35 interpretable yes/no questions gets the same neural predictivity as an LLM. No deep mechanistic similarity to the brain required; any model that can represent these features would do.
This is (imo) the single most important result in any modern language encoding study. 35 interpretable yes/no questions gets the same neural predictivity as an LLM. No deep mechanistic similarity to the brain required; any model that can represent these features would do.
New paper with @NeuroRJ, @csinva, @SunaGuo, Gavin Mischler, Jianfeng Gao, & @NimaMesgarani: We use LLMs to generate VERY interpretable embeddings where each dimension corresponds to a scientific theory, & then use these embeddings to predict fMRI and ECoG. It WORKS!
New paper with @NeuroRJ, @csinva, @SunaGuo, Gavin Mischler, Jianfeng Gao, & @NimaMesgarani: We use LLMs to generate VERY interpretable embeddings where each dimension corresponds to a scientific theory, & then use these embeddings to predict fMRI and ECoG. It WORKS!
In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6
Evaluating scientific theories as predictive models in language neuroscience biorxiv.org/content/10.110… #biorxiv_neursci
Are you compositionally curious 🤓 Want to know how to learn embeddings using🌲? In our new #ICML2025 paper, we present Banyan: A recursive net that you can train super efficiently for any language or domain, and get embeddings competitive with much much larger LLMs 1/🧵

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j @JC_9_1_1
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𝙷𝚒𝚖𝚊 𝙻... @hima_lakkaraju
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