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Pierre Beckmann

@pierrebeckmann.bsky.social
13 followers 40 following 16 posts

DL researcher who turned to philosphy. Epistemology of AI.

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Pierre Beckmann @pierrebeckmann.bsky.social · 17/04/2026
"Mechanistic indicators of Understanding in LLMs" is finally out in Philosophical studies! link.springer.com/article/10.1...
link.springer.com
Mechanistic indicators of understanding in large language models - Philosophical Studies
Philosophical Studies - Large language models are often portrayed as merely imitating linguistic patterns without genuine understanding. We argue that recent findings in mechanistic...
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Pierre Beckmann @pierrebeckmann.bsky.social · 23/02/2026
A couple years back, DL models were often described as feature combinators. Turns out that they can also recall features. This explains for example how LLMs can retrieve the bibliography of someone. Check out my Phil of AI preprint: philpapers.org/rec/BECDLM-2
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Reposted by Pierre Beckmann
Rahul @trsam97.bsky.social · 10/01/2026
A discussion on the philosophy of deep learning, mechanistic interpretability and the epistemology of LLMs. @pierrebeckmann.bsky.social @matthieu-queloz.bsky.social youtu.be/1_0ttM8zp9o?...
youtu.be
Mechanistic Interpretability and How LLMs Understand
YouTube video by Rahul Sam
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Reposted by Pierre Beckmann
Jeff Yoshimi @jyoshimi.bsky.social · 12/01/2026
One of the best discussions of AI I've seen in a while, because it's deeply informed by philosophy AND computer science. LLM’s are more than just “stochastic parrots”, but their understanding is still nonhuman. The discussion of concepts, understanding, and world models is especially informative.
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Pierre Beckmann @pierrebeckmann.bsky.social · 28/11/2025
Find our more in the paper: link.springer.com/article/10.1...
link.springer.com
New horizons in machine understanding: explanatory and objectual understanding in deep learning video generation models - Synthese
Synthese - OpenAI has recently released SORA, a deep learning model that can generate highly realistic videos. Its creators claim that it “understands the physical world in motion.” In...
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Pierre Beckmann @pierrebeckmann.bsky.social · 28/11/2025
This is because deep learning models learn to form putative connections concerning the domain they are trained on. This grasp of connections is essential for explanatory and objectual understanding.
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Pierre Beckmann @pierrebeckmann.bsky.social · 28/11/2025
I thus synthesise this literature into a set of conditions for understanding-of-the-world and submit it to SORA and deep learning models in general. I conclude that deep learning models are capable of such understanding!
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Pierre Beckmann @pierrebeckmann.bsky.social · 28/11/2025
In recent epistemology literature, philosophers work with the concepts of explanatory and objectual understanding. I've found these to be more appropriate to tackle the question of SORA's understanding than the typical semantic understanding often discussed for LLMs.
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Pierre Beckmann @pierrebeckmann.bsky.social · 28/11/2025
Does SORA "understand" the world? For example, does it understand the movement of the ship in the coffee cup below? In my latest Synthese article I tackle this question!
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Reposted by Pierre Beckmann
Jeff Yoshimi @jyoshimi.bsky.social · 21/10/2025
We’ve recently updated our collaborative open-access book, “Neural Networks in Cognitive Science”, adding a few new authors, chapters, and lots of content. downloads.jeffyoshimi.net/NeuralNetwor...
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
Curious? Read the full paper: arxiv.org/abs/2507.08017 It doubles as an accessible introduction to the field of mechanistic interpretability! (9/9)
arxiv.org
Mechanistic Indicators of Understanding in Large Language Models
Recent findings in mechanistic interpretability (MI), the field probing the inner workings of Large Language Models (LLMs), challenge the view that these models rely solely on superficial statistics. ...
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
In short, LLMs build internal structures that echo human understanding—relying on concepts, facts, and principles. But their “understanding” is fundamentally alien: sprawling, parallel, and unconcerned with simplicity. Philosophy of AI now needs to forge conceptions that fit them. (8/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
Strange minds. LLMs exhibit the phenomenon of parallel mechanisms: instead of relying on a single unified process, they solve problems by deploying many distinct heuristics in parallel. This approach stands in stark contrast to the parsimony typical of human understanding. (7/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
Level 3: Principled understanding At this last tier, LLMs can grasp the underlying principles that connect and unify a diverse array of facts. Research on tasks like modular addition provides cases where LLMs move beyond memorizing examples to internalizing general rules. (6/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
But LLMs aren’t limited to static facts—they can also track dynamic states. OthelloGPT, a GPT-2 model trained on legal Othello moves, encodes the board state in internal representations that update as the game unfolds, as shown by linear probes. (5/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
Level 2: State-of-the-world understanding LLMs can encode factual associations in the linear projections of their MLP layers. For instance, they can ensure that a strong activation of the “Golden Gate Bridge” feature leads to a strong activation of the “in SF” feature. (4/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
How does the model use these features? Attention layers are key. They retrieve relevant information from earlier tokens and integrate it into the current token’s representation, making the model context-aware. (3/9)
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
Level 1: Conceptual understanding Emerges when a model forms “features” as directions in latent space, allowing it to recognize and unify diverse manifestations of an entity or a property. E.g., LLMs subsume “SF’s landmark” or “orange bridge” under a “Golden Gate Bridge” feature.
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Pierre Beckmann @pierrebeckmann.bsky.social · 15/07/2025
New preprint: “Mechanistic Indicators of Understanding in LLMs” with @matthieu-queloz.bsky.social Building on mechanistic interpretability, we argue that LLMs exhibit signs of understanding—across three tiers: conceptual –, state-of-the-world –, and principled understanding. 🧵(1/9)
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