Richard Antonello @NeuroRJ
Postdoc in the Mesgarani Lab at Columbia University. Studying how the brain processes language by using LLMs. (Formerly @HuthLab at UT Austin) Joined May 2020-
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1/ The biggest problem in video understanding today isn't the models. It's that we can barely run them. Introducing StateKV: an inference-time method that makes pretrained video VLMs scale linearly with video length.🧵 🔗 ceyzaguirre4.github.io/StateKV
Our fast language localizer paper is published! tinyurl.com/2rwf4yx8 In brief: You can identify fronto-temporal language regions using just a few minutes of fMRI scanning with speeded reading. Findings + code in thread below! With @elizj_lee* and @aloxatel @ev_fedorenko
1/ New work! Localizing the human language network in ~3.5 minutes using speeded reading. Co-led with @elizj_lee, and with @aloxatel @ev_fedorenko Paper: biorxiv.org/content/10.110… Code: rb.gy/x2rjzj
Excited to share that our paper: ‘Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age’ is finally published! mcgovern.mit.edu/2026/05/17/lan… nature.com/articles/s4146… @Amanda_M_OBrien @ev_fedorenko
A developmental “truth” is that the language system starts bilateral and left-lateralizes w/ age. @Amanda_M_OBrien & I co-led a large-scale 🧠study tinyurl.com/LangNetDevPrep & found lateralization is already adult-like by age 4! w/ @ev_fedorenko, @gabrieli_john, @rebecca_saxe🧵 1/n
1. we gave AI models “brain damage” to learn how they process language (so you don't have to!) it turns out they produce the same symptoms as humans with aphasia, but in distinct distributions... (joint with @coryshain, Jill Kries, and @GwilliamsL )
What if a hearing device could identify who you want to listen to, directly from your brain signals? Today in Nature Neuroscience, we demonstrate real-time brain-controlled hearing that improves speech perception in noisy settings. Paper, Demos and Code: vishalchoudhari.com/real-time-aad
How is uncertainty in LLMs output reflected in internal representations? In our new work (to appear at ICML 2026), we show that the shape of internal token trajectories provides a direct geometric link to behavioral uncertainty (output entropy). 🧵(1/n)
2 emerging interpretability trends I'm excited about from this paper: (1) agent-facing interp & (2) interp objectives for autoresearch 🧵
NEW paper from Microsoft Research. (bookmark it) The entire interpretability literature is built around human readers. As more analysis gets delegated to agents, the right target of interpretability shifts. This paper is a recipe for designing tools that agents can actually
How can manifold theory help us understand why representations learned by AI models🤖 are aligned to the brain🧠? We expanded our UniReps Best Short Paper on this topic into a full paper at @icmlconf! Now extended to ECoG and with new brain-tuning results! Check it out👇
I’m excited to share my newest work with the Hayden lab, and the work I’m most proud of to date, on characterizing semantic coding in single-neuron hippocampal activity in patients with autism during natural language comprehension! biorxiv.org/content/10.648…
Language, Intelligence & Thought lab is looking for a lab manager! This is a 2-year postbac position that will allow you to gain experience in human neuroscience, cognitive science, and AI research prior to applying to PhD programs. Express interest here: forms.gle/289sLgZdJ2bQr1…
🧠New at #neurips2025! TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain. 🔗arxiv.org/abs/2510.228...
As our lab started to build encoding 🧠 models, we were trying to figure out best practices in the field. So @NeuroTaha built a library to easily compare design choices & model features across datasets! We hope it will be useful to the community & plan to keep expanding it! 1/
🚨 Paper alert: To appear in the DBM Neurips Workshop LITcoder: A General-Purpose Library for Building and Comparing Encoding Models 📄 arxiv: arxiv.org/abs/2509.09152… 🔗 project: litcoder-brain.github.io
Human auditory cortex integrates information in speech across absolute time (e.g., 200 ms), not phonemes, syllables, words, or any other time-varying speech structure: nature.com/articles/s4159…
If you're at #SNL2025 and curious about speech and music perception or its representation in developing brains, stop by my poster in Session C – #77! :)
The #SNL2025 Annual Meeting Program Booklet and Abstract book are now available for download in PDF format. See: 2025.neurolang.org/2025-program-p…
*Harnessing the Universal Geometry of Embeddings* by @rishi_d_jha @jxmnop @shmatikov With the proper set of losses, text embeddings from different models can be aligned with no paired data (what they call the "strong" Platonic hypothesis). arxiv.org/abs/2505.12540
@sanmking At the moment, the model instantiates an embedding space where semantic concepts that have the same answers to the questions are quantized to a point and are identical in the eyes of the model.
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
@schulzb589 At present, it wouldn’t be able to distinguish between these cases, because it operates on the text and not the raw audio. However, if the model was adapted for AudioQA, it’s possible this could become relevant.
@NeuroRJ Very cool video. I created an infographic to try to visualize the full study in more depth studyvisuals.com/artificial-int…
@aryaman2020 This means that the set of questions that we arrive at is itself useful for understanding what the most critical semantic characteristics of information are in the brain.
@aryaman2020 Yes, definitely! The main difference here is that we use some tricks to figure out what the (near-optimal) hand-coded embedding should be for predicting brain responses, instead of relying on our intuition alone to pick out the correct pieces of information.
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Juanjo do Olmo - e/ac... @Claxterix
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ttlalala @TTLaps
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Society for Neuroscie... @SfNtweets
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Nature Neuroscience @NatureNeuro
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Roshan Rao @proteinrosh
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