arXiv Machine Learning
AlignSAE: Concept-Aligned Sparse Autoencoders
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A Geometric Unification of Concept Learning with Concept Cones
arXiv:2512. 07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge.
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Characterizing Model-Native Skills
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DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
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A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM
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When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities
arXiv:2607. 08605v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large models, each of which encodes a distinct concept.
arXiv Machine Learning
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PermuFormer: Multi-Task Pretraining for Permutation Representation in Algebraic Combinatorics
arXiv:2609.25438v1 Announce Type: new Abstract: Diverse pretraining has been shown to be an effective method for learning reusable, domain-aware representations that provide a starting point for fine...