Sign in

Maxime Peyrard

@peyrardmax.bsky.social
601 followers 178 following 12 posts

Junior Professor CNRS (previously EPFL, TU Darmstadt) -- AI Interpretability, causal machine learning, and NLP. Currently visiting @NYU peyrardm.github.io

PostsRepliesMedia
Reposted by Maxime Peyrard
Thomas Palmeira @thomaspalmeira.bsky.social · 21/08/2026
🔍 Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models Latent thoughts are hidden computation, not explanations. Causality should come first, then we can interpret the geometry of the revealed behavior-influential spaces. arxiv.org/abs/2606.12689
arxiv.org
Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models
Latent reasoning models (LRMs) replace explicit chain-of-thought with continuous thoughts. Recent work treats observable latent-state patterns, such as BFS-like frontiers and decodable arithmetic comp...
111
Reposted by Maxime Peyrard
Maxime Méloux @maximemeloux.bsky.social · 10/07/2026
I'm very happy to give a spotlight at the Mechanistic Interpretability Workshop @ ICML on our work: "Validating Causal Abstraction Metrics on Simulated Complex Systems" Which metrics actually tell you if an explanation is valid? We built a benchmark to find out. 1/n
melouxm.github.io
Validating Causal Abstraction Metrics on Simulated Complex Systems
142
Maxime Peyrard @peyrardmax.bsky.social · 08/01/2026
To take a step in this direction, we propose a statistical perspective of XAI, focusing on inference, uncertainty quantification and hypothesis testing. But it is only a start! (with @maximemeloux.bsky.social , Giada Dirupo, François Portet) @cnrs.fr @getalp.bsky.social @cnrsalpes.bsky.social
arxiv.org
The Dead Salmons of AI Interpretability
In a striking neuroscience study, the authors placed a dead salmon in an MRI scanner and showed it images of humans in social situations. Astonishingly, standard analyses of the time reported brain re...
010
Maxime Peyrard @peyrardmax.bsky.social · 08/01/2026
Psychology, econometrics, or neuroscience, have faced similar difficulties and reacted by adopting methodological reforms and rigorous statistical (causal) frameworks. We believe, it is now our turn to build the methodological guardrails turning XAI into a pragmatic science.
120
Maxime Peyrard @peyrardmax.bsky.social · 08/01/2026
This affects feature attribution, probing, SAEs, and even causal analyses Taking a statistical view, we argue that most interpretability queries are non-identifiable: multiple incompatible explanations fit the same computation, leading to false positives and poor generalization
100
Maxime Peyrard @peyrardmax.bsky.social · 08/01/2026
New preprint: The Dead Salmons of XAI Standard fMRI pipelines once detected predictive brain regions in a dead salmon! A warning about poor statistical methodology Now, XAI faces its own issues: many methods can yield plausible explanations even for randomized networks arxiv.org/abs/2512.18792
arxiv.org
The Dead Salmons of AI Interpretability
In a striking neuroscience study, the authors placed a dead salmon in an MRI scanner and showed it images of humans in social situations. Astonishingly, standard analyses of the time reported brain re...
1125
Reposted by Maxime Peyrard
Kristina Gligoric @IC2S2 @gligoric.bsky.social · 05/11/2025
I'm recruiting multiple PhD students for Fall 2026 in Computer Science at @hopkinsengineer.bsky.social 🍂 Apply to work on AI for social sciences/human behavior, social NLP, and LLMs for real-world applied domains you're passionate about! Learn more at kristinagligoric.com & help spread the word!
03017
Reposted by Maxime Peyrard
Maxime Méloux @maximemeloux.bsky.social · 26/04/2025
I'm very happy to present our work "Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?" this afternoon at #ICLR2025! Come have a chat at stand #439 :)
0101
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
What can be done? 👉 Stricter validity criteria? 👉 Maybe interpretability is inherently underdetermined? and we can only get control and predictability but not "understanding" This is a fascinating topic, and we keep investigating. If you're interested, come and chat at ICLR!
010
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
We find a lot of identifiability issues: - Multiple explanatory algorithms exists - Even for one algorithm, there are many localizations in the network Identifiability problems remain across scenarios: changing levels of over-parametrization, progress in training, multi-tasks, model size.
110
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
In our work, we stress-test the identifiability of research programs of MI with small MLPs and simple boolean logic tasks. Why? It allows us to enumerate all possible explanations and see how many pass various MI testing criteria.
100
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
This brings us to identifiability. In statistics a property is identifiable if a unique value is compatible with the data. Identifiability matters because it is a prerequisite for doing statistical and causal inference. Interpretability is also an exercise in causal inference!
110
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
Mechanistic Interpretability aims to produce statements like: "Model M solves task T by doing X." To do so, many causal manipulations are performed to validate an explanation. But what if (many) other, incompatible explanations also pass the causal tests?
Illustration of different strategies for mechanistic interpretability
100
Maxime Peyrard @peyrardmax.bsky.social · 21/04/2025
Our paper "Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?" will be presented at #ICLR2025! It's also the first paper of my first PhD student, congrats @maximemeloux.bsky.social ! 🎉 blog: melouxm.github.io/MI-identifia... An explanatory thread 🧵:
Abstract of the paper
1179
Reposted by Maxime Peyrard
Tom Aarsen @tomaarsen.com · 10/03/2025
An assembly of 18 European companies, labs, and universities have banded together to launch 🇪🇺 EuroBERT! It's a state-of-the-art multilingual encoder for 15 European languages, designed to be finetuned for retrieval, classification, etc. Details in 🧵
57820
Reposted by Maxime Peyrard
kyunghyuncho.bsky.social @kyunghyuncho.bsky.social · 18/02/2025
bc i haven't done so yet, i decided to burn any remaining bridge to the land of statistics. it wasn't statisticians nor statistics but it was me. i am simply not good enough to do statistics myself. so, @peyrardmax.bsky.social and i decided to turn statistical estimation into supervised learning.
3309
Reposted by Maxime Peyrard
Kristina Gligoric @IC2S2 @gligoric.bsky.social · 04/12/2024
Check out our new paper on social determinants of on-campus food choice, now out in @pnasnexus.org! academic.oup.com/pnasnexus/ar...
academic.oup.com
0123
Maxime Peyrard @peyrardmax.bsky.social · 24/11/2024
Hey, thanks for making it, can you also add me
020
Reposted by Maxime Peyrard
Julian Skirzynski @jskirzynski.bsky.social · 23/11/2024
I tried to find everyone who works in the area but I certainly missed some folks so please lmk... go.bsky.app/BYkRryU
325318
Maxime Peyrard @peyrardmax.bsky.social · 23/11/2024
Thanks for creating the pack, I am also working on this topic :)
010