Alexey Koshevoy

@alexeykoshevoy.bsky.social

Postdoctoral researcher | LSCP, ENS-PSL | Interested in how communication and cultural evolution jointly shape languages https://alexeykosh.github.io

I am reading a lot of modelling papers lately, and I have one advice to everyone creating figures with large amounts of observations. Please, rasterize your images, it takes a while to load 1000+ vectorized scatter points in a pdf!

My lab has been really interested in topological perception the past two years. In a new paper, we ask: How does a topological perspective (re-)shape our understanding of visual complexity? Work led by one of our many wonderful undergraduate students, Ashna Shah! link.springer.com/article/10.3...

Topological structure and the creation of visual complexity - Psychonomic Bulletin & Review

What does it mean for something to be complex? Fundamentally, complexity is a cue that something is less easily represented by our minds. With this fact in view, complexity becomes a tool: By understa...

link.springer.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].

Schrodinger’s language models LLMs randomly choose between possible words, and certain choices can lead to wildly different results Goodfire’s latest work shows that those decisions largely end in roughly the same result. arxiv.org/abs/2608.19611

Diagram featuring text with orange highlighting and branching lines leading to alternate text lines below. The top line reads "...ar is 2024. We also know that Queen Elizabeth was", with curved orange lines extending downward from the words "2024", "that", and "was" to lower text fragments including "2021. We also know that for the past..." and "who the current p...".

I am currently recruiting for a 2-year postdoc/research fellow role at the University of Auckland here in New Zealand, focusing on auditory perception, hearing, and music in older adults, including people with Alzheimer's disease or other dementias ~NZ$100k/yr, flexible start date, please apply!

Postdoc in The Music Lab // Expression of interest

Note. The below text is a general overview of the sorts of postdocs we often recruit. We are currently recruiting candidates for a 2-year position focusing on auditory perception in aging populations,...

forms.gle

"CLDF Meta" (meta.clld.org) links to data from over 800 CLDF datasets that have been released on Zenodo (e.g. WALS, APiCS and Grambank). There are links to data on over 9000 languoids, e.g. 98 entries on Ambulas (to take a random language). Great work by my colleague Johannes Englisch!

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