Attention-Aligned Reasoning for Large Language Models
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The paper introduces COMPASS, an inference-time steering technique that enhances large language model performance on mathematical reasoning tasks. By using the correctness of a model’s direct answer attempts as a latent signal, COMPASS identifies a small subset of attention heads whose activations can be steered to elicit reasoning. Experiments across three model families and multiple math benchmarks show that COMPASS improves GSM8K accuracy by an average of 16 percentage points and achieves near Chain‑of‑Thought performance while generating 20–70% fewer tokens.
The paper surveys efficient reasoning in large language models, contrasting fast intuitive (System 1) and slow deep (System 2) reasoning. It analyzes why System 2 is computationally costly yet more accurate, and why System 1 is efficient but less effective. The survey covers causes of inefficiency, patterns of reasoning behavior, and potential solutions to balance performance and computational budgets, offering actionable insights and an open‑source repository for ongoing research.
The paper introduces a lightweight evaluation method for logical reasoning in transformer-based language models, using query-key alignments within attention heads to compute a QK-score. This single forward-pass technique identifies valid versus invalid inferences and is validated across multiple reasoning benchmarks, showing robustness to distractors and deeper reasoning. Experiments span models from 1.5B to 70B parameters, demonstrating scalability.
arXiv:2605.06165v2 Announce Type: replace Abstract: As the widespread adoption of Large Language Models (LLMs) accelerates, token consumption from intermediate reasoning traces increasingly contribut...
The paper proposes Layer-Informed Fine-Tuning (LIFT), a method that identifies and updates only the most functionally critical layers of large language models (LLMs) using a bottleneck identification mechanism based on sensitivity analysis. By focusing on layers that handle conceptualization, reasoning, and textualization, LIFT aims to accelerate training and enhance performance on reasoning tasks. Experiments demonstrate that this selective fine-tuning approach both speeds up the training process and yields significant performance gains.
The paper introduces Causal Attribution Pruning (CAP), a training‑free technique that identifies critical attention heads in large language models by measuring their causal impact on reasoning tasks. CAP estimates performance loss when each head is masked on a small calibration set, then translates these causal scores into weight‑level importance for pruning. Compared to magnitude‑only or activation‑based methods, CAP achieves up to 61% relative accuracy gains over Wanda on ARC‑Challenge at 20% sparsity and improves performance on GSM8K, StrategyQA, and ARC‑Challenge across Llama‑3‑8B‑Instruct and Mistral‑7B‑Instruct at 10%, 20%, and 50% sparsity.