arXiv AI
Jul 7
Unsupervised Features Mining via Activation Geometry
arXiv:2607. 04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs).
Fast Multi-dimensional Refusal Subspaces via RFM-AGOP
arXiv:2607. 02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability.
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arXiv Machine Learning
Jun 2
Step-Level Sparse Autoencoder for Reasoning Process Interpretation
arXiv:2603. 03031v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning.
arXiv AI
4d ago
Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning
The paper investigates whether large language models (LLMs) follow Occam's Razor when performing inductive and abductive reasoning. It introduces a synthetic framework for generating questions that require both types of reasoning and a new automated metric to evaluate the simplicity and correctness of generated hypotheses. Experiments show that while LLMs can handle simple scenarios, they struggle with complex world models and producing high‑quality, simplest hypotheses, even when using advanced reasoning techniques.
arXiv AI
Aug 13
Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selection
arXiv:2512. 13374v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their internal representations capture problem structure or algorithmic behavior.
arXiv AI
Jun 15
Implicit Reasoning for Large Language Model-based Generative Recommendation
arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.
arXiv AI
Jun 3
X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes
arXiv:2603. 05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood.
arXiv Machine Learning
Aug 7
Reasoning Errors Have a Region and a Direction in the Residual-Stream Trajectory of LLMs
arXiv:2608. 05660v1 Announce Type: new Abstract: As language models are increasingly used for tasks that require verifiable reasoning, reliably distinguishing sound reasoning from flawed reasoning has become an important practical problem.
arXiv AI
Jul 7
Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models
arXiv:2503. 06269v3 Announce Type: replace-cross Abstract: Traditional white-box methods for creating adversarial perturbations against LLMs typically rely only on gradient computation from the targeted model, ignoring the internal mechanisms responsible for attack success or failure.
arXiv AI
Jul 29
Controllable LLM Reasoning via Sparse Autoencoder-Based Steering
arXiv:2601. 03595v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) exhibit human-like cognitive reasoning strategies (\eg backtracking, cross-verification) during the reasoning process, which improves their performance on complex tasks.
arXiv Machine Learning
Jul 3
PARTREP: Learning What to Repeat for Decoder-only LLMs
arXiv:2607. 01792v1 Announce Type: cross Abstract: While decoder-only LLMs excel at a vast array of natural language tasks, it suffers from an asymmetric information flow induced by causal attention: later tokens are richer in contextual grounding than earlier ones.
Hugging Face Trending Papers
Jun 25
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs). However, existing methods for curating high-quality SFT data rely heavily on strong reasoning models to filter examples based on diversity and difficulty, making the curation process costly while often yielding suboptimal data quality.
arXiv Machine Learning
Jun 26
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
arXiv:2606. 26797v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs).