activity
20242026
most citedAgents of Chaos

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

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Showing 2025Show all

7 papers · 1 filter

cs.AI2025

Internal states before wait modulate reasoning patterns

Dmitrii Troitskii, Koyena Pal, Chris Wendler +2

Prior work has shown that a significant driver of performance in reasoning models is their ability to reason and self-correct. A distinctive marker in these reasoning traces is the…

cs.CL2025

Discovering Forbidden Topics in Language Models

Can Rager, Chris Wendler, Rohit Gandikota +1

Refusal discovery is the task of identifying the full set of topics that a language model refuses to discuss. We introduce this new problem setting and develop a refusal discovery…

cs.CL2025

zip2zip: Inference-Time Adaptive Tokenization via Online Compression

Saibo Geng, Nathan Ranchin, Yunzhen yao +4

Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized on general-purpose co…

cs.AI2025

The Geometry of Self-Verification in a Task-Specific Reasoning Model

Andrew Lee, Lihao Sun, Chris Wendler +2

How do reasoning models verify their own answers? We study this question by training a model using DeepSeek R1's recipe on the CountDown task. We leverage the fact that preference…

cs.CL2025

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models

Veniamin Veselovsky, Berke Argin, Benedikt Stroebl +5

Just as humans display language patterns influenced by their native tongue when speaking new languages, LLMs often default to English-centric responses even when generating in othe…

cs.CV2025

Controlling Latent Diffusion Using Latent CLIP

Jason Becker, Chris Wendler, Peter Baylies +2

Instead of performing text-conditioned denoising in the image domain, latent diffusion models (LDMs) operate in latent space of a variational autoencoder (VAE), enabling more effic…

cs.CL2025★ 1 cited

Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages

Jannik Brinkmann, Chris Wendler, Christian Bartelt +1

Human bilinguals often use similar brain regions to process multiple languages, depending on when they learned their second language and their proficiency. In large language models…