Alireza Karami | علیرضا کرمی

@alirezakr.bsky.social

Cognitive neuroscientist | AI 🤖 × Neuroscience 🧠 | NeuroSpin • CIMeC • SUT https://linktr.ee/alirezakr

Terence Tao says AI companies are harming mathematics by dropping new results without engaging with academia. “These companies are dumping carcasses of raw meat onto our table and saying ‘here you go, I solved your food problem’ and then they just leave." www.newscientist.com/article/2588...

Terence Tao: AI companies are harming mathematics | New Scientist

AI companies are making new mathematical discoveries at a rapid pace but not sticking around to help unpick the new proofs. That is not the way to advance our understanding, says mathematician Terence...

newscientist.com

🤖🧠NEW PAPER🧠🤖 (The result of an 8-year project!) LLMs seem very different from symbolic systems. Yet LLMs excel in symbolic domains (e.g., language/code/math). How do they do it? Our finding: LLM representations have implicit symbolic structure! Link in thread ⬇️ 1/n

Overview of the paper. 
Title: The Emergent Symbolic Structure of Artificial Neural Networks
Authors: Tom McCoy, Paul Soulos, Tal Linzen, Paul Smolensky
Left: Neural networks encode information in vectors (there is then an image of a vector), yet they excel at tasks long thought to require symbolic structure (there is then an image of a symbolic representation, specifically a syntax tree). How do LLMs do it?
Right: We find that LLM representations can be closely approximated with symbolic structures. This approximation lets us edit the structure of an LLM’s output by editing the structure of its internal representations, as shown. There is then an image of two edits to LLMs. In the first one, the original input is 3 + 6 * 8, with an answer of 51. But if we swap the positions of the 3 and the 6, the output becomes 30. In the second one, the original input is a Python command repeating the list [Z, U] three times, producing [Z, U, Z, U, Z, U]. But if we edit the input in a way that adds a Q at the end of the input, the output becomes [Z, U, Q, Z, U, Q, Z, U, Q].

It has been painfully clear since Fall’24 that US science funding would tank, universities would be targeted, and any assertions about a new “gold standard” disingenuous. The question has not been “will it be that bad,” but “how bad will it be.”

Nicole Rust@nicolecrust.bsky.social · 2mo ago

US scientists: Going into fall 26, how are you thinking about things? Following Fall 24 events and a tumultuous spring 25, I remember a convo with a colleague in which we agreed we just needed to wait things out until fall 25 and we'd have some clarity about the landscape (eg for funding). /1

Great paper! could someone please point me to a paper or two that has seriously pushed for ideas that would be in contradiction with this decoder? I know @benhayden.bsky.social, @pessoabrain.bsky.social et al have done that on bluesky but an actual research paper? Feels like a total straw man

Shahab Bakhtiari@shahabbakht.bsky.social · 3mo ago

Important paper: "The response profiles of neurons in different brain regions differ enough that a decoder can determine the region to which a particular neuron belongs." Sounds like the perfect antidote to the "everything is everywhere" view.

❗3x scholar-at-risk positions (2 post MSc, 1 postdoc) at Uni Trento (several departments incl. CIMeC)❗ 1 year + possibility of 1 year extension. Must hold asylum seeker / refuge or SAR status. Please contact me for more info. Please share!

Missed my talk at #MCLS2026? Here's a brief thread on our work combining fMRI, MEG, and representational similarity analysis (RSA) to investigate how numerosity (the number of items in a set) is represented across space and time in the human brain. @mcls-official.bsky.social

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Alireza Karami | علیرضا کرمی@alirezakr.bsky.social · 4mo ago

Back in Italy after two years for my talk at @mcls-official.bsky.social , “How Our Brain Sees Number.” The first day was already inspiring, with fascinating talks by David Burr, @gvallortigara.bsky.social , and Marco Zorzi.