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20242026
most citedEfficient Causal Graph Discovery Using Large Language Models

6 citations · 12 across the 15 of their papers we have counts for

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cs.CL2026

Scaling Latent Reasoning via Looped Language Models

Rui-Jie Zhu, Zixuan Wang, Kai Hua +30

Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…

cs.CL2025

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin, François Lespinasse, Philipp Thölke +5

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilities. This idea has sparked a blend of exci…

cs.CL2025

Geometric Signatures of Compositionality Across a Language Model's Lifetime

Jin Hwa Lee, Thomas Jiralerspong, Lei Yu +2

By virtue of linguistic compositionality, few syntactic rules and a finite lexicon can generate an unbounded number of sentences. That is, language, though seemingly high-dimension…

cs.CL2025

A Complexity-Based Theory of Compositionality

Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio +1

Compositionality is believed to be fundamental to intelligence. In humans, it underlies the structure of thought, language, and higher-level reasoning. In AI, compositional represe…

cs.CL2025

Learning diverse attacks on large language models for robust red-teaming and safety tuning

Seanie Lee, Minsu Kim, Lynn Cherif +8

Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing ef…

cs.CL2025

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models

Seanie Lee, Haebin Seong, Dong Bok Lee +6

Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world appli…