arXiv AI
Aug 11
The Transformer Revolution, Part 1: Dynamic Processing through Output-Weight Interconnections
arXiv:2608. 03921v2 Announce Type: replace Abstract: This paper offers a new interpretation of the Transformer during inference.
The Transformer Revolution, Part 1: Dynamic Processing through Output- Weight Interconnections
arXiv:2608. 03921v1 Announce Type: new Abstract: This paper offers a new interpretation of the Transformer during inference.
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arXiv AI
Jul 8
Transformers converge to invariant algorithmic cores
arXiv:2602. 22600v2 Announce Type: replace-cross Abstract: Training selects for behavior, not circuitry: many weight configurations can implement the same function.
arXiv Machine Learning
Jul 15
Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
arXiv:2603. 06592v2 Announce Type: replace-cross Abstract: Contemporary studies in mechanistic interpretability have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models, such as induction heads, function vectors, and the Hydra effect.
arXiv Machine Learning
Jun 4
Transmuting prompts into weights
arXiv:2510. 08734v3 Announce Type: replace Abstract: A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly modifying their internal states, either through vector additions to their activations or through updates to their weight matrices.
Hugging Face Trending Papers
Jul 13
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics have so far been mostly tied to specific tasks, we study a generalized class of inductive tasks that unifies several synthetic tasks known in the literature, including in-context n-grams and multi-hop reasoning.
arXiv Machine Learning
Aug 27
The Von-Neumann State-Space Transformer for neural decoding
The paper introduces the Von‑Neumann State‑Space Transformer (VN‑SST), a memory‑augmented Transformer that replaces the standard feed‑forward block with a low‑rank instruction bank. By decoding token‑specific operators from a low‑dimensional state‑space memory, VN‑SST achieves higher data‑efficiency and parameter‑efficiency on motor‑cortex neural‑decoding tasks and on small language‑model benchmarks. The model demonstrates that a compact instruction set can act as a control channel, improving performance without increasing accuracy through larger parameter counts.
arXiv Machine Learning
Sep 16
Attention Mean Fields Predict Average Representation Dynamics and Reveal Context-Specific Computation
The paper presents a mean‑field analysis of attention in language models, defining an average attention kernel that propagates representations layer by layer. When conditioned on a whole corpus, the kernel predicts the average evolution of representation geometry; when conditioned on a single context, it predicts the expected geometry for that context. The difference between actual attention and the mean‑field prediction—called the mean‑field deviation—captures context‑specific computation, revealing how models diverge from average behavior during training and in few‑shot tasks.
arXiv AI
Jul 14
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
arXiv:2607. 11875v1 Announce Type: cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models.
arXiv AI
Jul 20
Verbalizable Representations Form a Global Workspace in Language Models
arXiv:2607. 15495v1 Announce Type: cross Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning.
arXiv Machine Learning
Jun 16
Emergent retokenization symmetry in large language models: phenomenology and applications
arXiv:2606. 15521v1 Announce Type: cross Abstract: Tokenization introduces representational redundancy: under a fixed token vocabulary, every byte string admits many valid token encodings, or segmentations, that decode to the same surface string.
arXiv AI
Jun 2
Prototype Transformer: Towards Language Model Architectures Interpretable by Design
arXiv:2602. 11852v2 Announce Type: replace Abstract: While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and hallucination.
arXiv Machine Learning
Sep 17
Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence
The paper studies GLU-based neurons in large language models by measuring the cosine similarity between each neuron's input and output weight vectors. A strong negative similarity identifies a "weakening neuron," which tends to appear in late layers, activates frequently, and exerts a large influence on model behavior. The authors also find that weakening neurons significantly affect outputs when gate values are negative, contrary to expectations.