CORTEXA
← Browse
arxivcs.CLcs.LG2026-07-11

Belief-reality separation lives in routing over a shared value slot in language models

Oliver Steele, Jiangtao Wen, Yuxing Han

Capable language models hold what a character believes apart from what is true: told "Anna believes the cup is blue; in reality it is red," they answer blue about Anna and red about the world. Where in the computation does that separation live? We show it rests on two separable mechanisms at two positions. A generic value slot binds the attributed value. A router at the query position selects which frame, the character's belief or reality, a query reads out. Two routes fill the slot: an asserted belief, whose value the text supplies, binds in directly; a derived belief, whose value must be inferred from what the character could see, arrives by a visibility-gated lookback. A subspace trained on either route steers the other, and only the derived route depends on described visibility. The slot itself carries no belief-reality tag: intervening on it moves a reality readout as strongly as a belief one. The separation lives instead in a dissociated pair of routing subspaces, which flip a query between frames without injecting the donor's value. These results hold across three architectures, on stimuli de-confounded against theory-of-mind-benchmark shortcuts; the behavior itself emerges between 3B and 7B across five model families. This paper develops the single belief-reality axis in depth; a companion paper shows the same slot-and-router format is shared across the other non-actual contexts a sentence can open (counterfactual, fictional, temporal).

View free PDFSource page

Related papers

arxivcs.CLcs.LG2026-07-11

One mechanism for many mental spaces: a shared router over a value slot in language models

Oliver Steele, Jiangtao Wen, Yuxing Han

Language builds discourse contexts other than the actual: a painting, a belief, a memory, a hypothetical. Each is a mental space in which the same entity can take a different value, as when a flower is red in reality but purple in a portrait. Formal semantics keeps these contexts…

View free PDFSource page
arxivcs.AIcs.CLcs.LGcs.SIphysics.soc-ph2026-07-13

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, et al.

Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns. However,…

View free PDFSource page
arxivcs.LGcs.AIcs.CLcs.DB2026-07-22

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

Yurong Liu, Yeye He, Haoyu Dong, Junjie Xing, Shi Han, Dongmei Zhang, et al.

Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in tables, by reasoning holistically across rows and columns, they are costly to deploy at scale and t…

View free PDFSource page
arxivcs.LGcs.CL2026-07-09

Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models

Bishmoy Paul, Youngmin Yi, Hoeseok Yang

Efficient inference in Large Language Models (LLMs) requires deciding where computation can be reduced while preserving model quality. We study this problem through multilayer perceptron (MLP) activation sparsification and token-level conditional routing. We first propose Sensiti…

View free PDFSource page