arXiv Machine Learning
Jul 10
Prompt Compression via Activation Aggregation
arXiv:2607. 08399v1 Announce Type: cross Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response.
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.
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arXiv AI
Aug 5
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.
arXiv AI
Jul 2
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization
arXiv:2511. 05747v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, limiting deployment in resource-constrained settings.
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.
arXiv AI
Jun 16
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.
arXiv AI
Jul 22
Fluid Reasoning Representations
arXiv:2602. 04843v2 Announce Type: replace Abstract: Frontier large language models increasingly solve complex tasks involving abstract concepts through extended test-time thinking.
arXiv AI
Jun 4
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression
arXiv:2510. 08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance.
arXiv AI
Jul 28
Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models
arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.
arXiv Machine Learning
Aug 12
The Parser Already Knows: Lightweight Bias Correction in Constrained Decoding
arXiv:2608. 10137v1 Announce Type: cross Abstract: Grammar Constrained Decoding (GCD) forces Language Models (LMs) to produce syntactically valid outputs by masking out non-conforming tokens at each step.
arXiv AI
Jun 9
MixReasoning: Switching Modes to Think
arXiv:2510. 06052v2 Announce Type: replace Abstract: Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answer.
arXiv AI
Jun 9
TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering
arXiv:2606. 08408v1 Announce Type: cross Abstract: We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept.
Hugging Face Trending Papers
Aug 10
UNSPECIFIC: General Constraint Synthesis for Breaking Copy-and-Paste Shortcut in LLM Instruction Following
Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a reference document (i. e.