Structurally distinct circuits for literal sequence copying across token frequency bands implement the same computation, shown by broad transfer of band-specific edges, a shared core recovering 99% performance, and interchangeable representations via causal interventions.
Michael Polanyi.The Tacit Dimension
7 Pith papers cite this work. Polarity classification is still indexing.
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Energy-navigated trajectory shaping during training produces 8-step discrete flow matching students that achieve 32% lower perplexity than 1024-step teachers on 170M language models with unchanged inference cost.
Repetition of training data produces a systematic eval loss peak at intermediate repeat counts whose location scales with model size, quantifiable as large compute-equivalent loss even at modest repetition fractions.
ARTS improves automated scientific discovery by using reasoning LMs with test-time training to separate hypothesis merit from execution quality in tree search, achieving 15.3% relative gains on 22 MLGym and MLEBench tasks.
Explicitly disentangling semantic and positional streams in a Transformer encoder reveals that absolute positional representations collapse to a 2D document-structure manifold, attention heads specialize by role, and the approach improves linguistic probing performance on 49 of 65 phenomena.
A 355M-parameter byte-level LM on 80B multilingual tokens exhibits UTF-8 validity converging after 4.2B tokens versus 2.1B for perplexity, with higher validity on rare characters than common ones.
At least fifteen independent dimensions of taste, texture, geography, processing, and culture can be recovered from curated FlavorGraph ingredient embeddings.
citing papers explorer
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Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery
Structurally distinct circuits for literal sequence copying across token frequency bands implement the same computation, shown by broad transfer of band-specific edges, a shared core recovering 99% performance, and interchangeable representations via causal interventions.
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Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
Energy-navigated trajectory shaping during training produces 8-step discrete flow matching students that achieve 32% lower perplexity than 1024-step teachers on 170M language models with unchanged inference cost.
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Internal Data Repetition Destroys Language Models
Repetition of training data produces a systematic eval loss peak at intermediate repeat counts whose location scales with model size, quantifiable as large compute-equivalent loss even at modest repetition fractions.
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Learning the ARTS of Search for Automated Discovery
ARTS improves automated scientific discovery by using reasoning LMs with test-time training to separate hypothesis merit from execution quality in tree search, achieving 15.3% relative gains on 22 MLGym and MLEBench tasks.
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Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders
Explicitly disentangling semantic and positional streams in a Transformer encoder reveals that absolute positional representations collapse to a 2D document-structure manifold, attention heads specialize by role, and the approach improves linguistic probing performance on 49 of 65 phenomena.
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Beyond Perplexity: UTF-8 Validity in Byte-aware Language Models
A 355M-parameter byte-level LM on 80B multilingual tokens exhibits UTF-8 validity converging after 4.2B tokens versus 2.1B for perplexity, with higher validity on rare characters than common ones.
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Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings
At least fifteen independent dimensions of taste, texture, geography, processing, and culture can be recovered from curated FlavorGraph ingredient embeddings.