arXiv AI July 29, 2026 By Diego Salda\~na Ulloa

Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code

arXiv:2607. 24797v1 Announce Type: cross Abstract: In the literate human brain, reading and writing are two doubly-dissociable systems: a ventral decoding route (impaired in pure alexia) and a fronto-parietal encoding route (impaired in pure agraphia), sharing a partial orthographic core.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jul 7

Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study

arXiv:2607. 04926v1 Announce Type: cross Abstract: How does the way information reaches a transformer -- as symbolic tokens, a clean per-factor "oracle" code, or an entangled perceptual vector -- shape whether it binds that information compositionally?

By Yoshiyuki Ootani

llms safety

arXiv AI

Jul 7

They Infer What You Meant: Models Represent Communicative Intent More Reliably Than They Act On It

arXiv:2607. 03598v1 Announce Type: cross Abstract: When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by sending it: share a finished project and it critiques the code; share a raw late-night line and it runs a wellness check.

By Alex Kwon

llms safety

arXiv AI

Jul 9

Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix

arXiv:2607. 06925v1 Announce Type: new Abstract: Compact world models that condition on a language goal promise to ground relations such as ``put the red block left of the blue block'' using a sparse set of explicit \emph{reference anchors}.

By Yufeng Wang, Lu Wei, Haibin Ling

benchmarks

arXiv AI

Aug 7

Answer First, Reason Later: Commitment Order in Diffusion LLMs

arXiv:2608. 05687v1 Announce Type: cross Abstract: Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding.

By Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Hwiyeong Lee, Taesup Kim

llms diffusion

arXiv AI

Jul 29

Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners

arXiv:2607. 25532v1 Announce Type: new Abstract: Consider a model trained at a single hospital to predict patient recovery, where the measured feature $X$ bundles the patient's true health signal ($C$) with a systematic artefact from that hospital's equipment ($S$).

By Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz, Sotirios Tsaftaris

benchmarks

arXiv AI

1d ago

Gathered, Not Admitted: How Attention Brings a Latent Variable into Verbalizable Form

arXiv:2608. 15022v1 Announce Type: new Abstract: Language models hold latent quantities in a form they can report on, and more of a quantity is present in that form when the task requires reusing it flexibly.

By Parsa Mazaheri

llms benchmarks

Related stories

Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study

They Infer What You Meant: Models Represent Communicative Intent More Reliably Than They Act On It

Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix

Answer First, Reason Later: Commitment Order in Diffusion LLMs

Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners

Gathered, Not Admitted: How Attention Brings a Latent Variable into Verbalizable Form