arXiv AI

Vernier: Probing Representational Misalignment Behind Lexical Gaps in Causal Reasoning

arXiv:2606. 15733v1 Announce Type: cross Abstract: Instruction-tuned language models can answer the same causal-reasoning question differently after its English variable names are replaced by type-preserving placeholders, although the structural causal model and the gold answer are unchanged.

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.

By Dmitrii Kharlapenko, Terry Jingchen Zhang, Arth Singh, Alessandro Stolfo, Arthur Conmy, Mrinmaya Sachan, Zhijing Jin
arXiv Computation and Language
Sep 7

Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs

The paper investigates how large language models (LLMs) organize reasoning operations—such as problem formulation, goal decomposition, and deduction—within their hidden representation spaces. It shows that these operations are separable in held‑out representations, with peak separability in middle layers, and that token‑wise alignment of operations becomes more distributed across spans as layers deepen. Attention‑masking experiments reveal that representations aligned to operations at chunk onsets depend on prior reasoning context, indicating a geometric correspondence between linguistic reasoning expressions and internal model structure.

By Seogyeong Jeong, Jaehui Hwang, Dongyoon Han, Geonmo Gu, Alice Oh, Taekyung Kim
arXiv AI
Jul 20

Verbalizable Representations Form a Global Workspace in Language Models

arXiv:2607. 15495v1 Announce Type: cross Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning.

By Wes Gurnee, Nicholas Sofroniew, Adam Pearce, Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, Jack Lindsey
arXiv Machine Learning
Aug 27

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

The paper critiques a recent NLI benchmark that tests the imperfective paradox, arguing that the benchmark suffers from conceptual and evaluation mis-specifications, notably Aspectual Reduction and a lack of strict NLI standards. The authors re-evaluate the benchmark, identify mis-specifications, and construct lexically matched minimal pairs to control for lexical variation. Their experiments reveal that models often exhibit a Sufficiency Bias, accept simple‑past hypotheses without affirming culmination, and that prompting interventions shift label decisions without improving true semantic understanding, highlighting additional failure modes such as compositional aspectual classification errors and surface‑form attraction.

By Kaiqiao Han, Yizhou Sun
arXiv Machine Learning
Sep 2

How Do Language Models Choose Between Context and Memory?

The paper investigates how language models decide between contextual information and their internal memory when the two conflict. By estimating "authority directions" from agreement prompts and swapping these directions between matched prompts, the authors show that such interventions can reproduce 30–68% of the shift in source choice across Qwen, Llama, and OLMo models. Cross‑task experiments reveal that authority directions learned on one task transfer only modestly (≈9%) to another, indicating that authority computations are largely task‑specific.

By Benjamin Shih, John Winnicki, Arianna Cao