arXiv AI July 7, 2026 By Alex Kwon

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.

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

arXiv Machine Learning

5d ago

A Probe Direction Is a Property of Its Prompt

arXiv:2608. 13329v1 Announce Type: new Abstract: A model that behaves differently when it senses it is being tested would undermine the evaluations we rely on, so recent work has sought to read that sense directly from a model's activations.

By Valentin No\"el

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

arXiv Machine Learning

Jun 30

Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring

arXiv:2606. 30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated.

By Max Fomin, Elad David, Amit LeVi

llms agents fine-tuning

arXiv AI

Jul 23

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations

arXiv:2607. 20379v1 Announce Type: new Abstract: Natural-language autoencoders score explanations of hidden activations by reconstruction: an explanation is deemed faithful if the activation can be regenerated from it.

By Hiskias Dingeto

safety

arXiv Machine Learning

6d ago

Orientation, not magnitude: the causal structure of task-vector interference in merged language models

arXiv:2608. 11797v1 Announce Type: new Abstract: Model merging by task arithmetic works until it doesn't, and the field diagnoses why with magnitudes: layerwise representation bias, deviations from cross-task linearity, parameter overlap.

By Chencheng Zhu

llms efficiency safety

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

llms safety

Related stories

A Probe Direction Is a Property of Its Prompt

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

Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations

Orientation, not magnitude: the causal structure of task-vector interference in merged language models

Verbalizable Representations Form a Global Workspace in Language Models