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arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.
arXiv:2608.29530v1 Announce Type: cross Abstract: Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modele...
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.
The study investigates whether large language models (LLMs) develop internal representations similar to cognitive maps used by animals for flexible behavior. Using relational reasoning tasks and causal mosaic experiments, the authors found that LLMs beyond ten billion parameters can infer unstated relationships, generalize to synthetic entity names, and form abstract relational structures that combine with entity identities. These relational representations are stored as pairwise links and composed retrospectively, mediated by a sparse, distributed key‑value system that can be perturbed to implant false memories and alter behavior predictably.
arXiv:2608.29034v1 Announce Type: cross Abstract: A wide range of methods have been proposed for interpreting language models, delivering important insights into their inner workings. However, differ...
arXiv:2608.28980v1 Announce Type: cross Abstract: Can the specialized architectures that machine learning has traditionally built for structured data be replaced by language-based models? This questi...