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

Hugging Face Daily Papers Papers
attention-mechanism latent-variables language-models interpretability neural-networks

Summary

This paper explores how attention mechanisms in language models render latent variables accessible without a selective gate, identifying a demand-specific mid-depth window where attention-mediated gathering occurs.

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. What causes a representation to enter that form is open, and the word workspace invites an admission story: a gate that decides what gets in. Testing it on open-weight models with Jacobian lenses, over a benchmark whose five arms share an identical context, we find no gate where it predicts one. Demand raises a concept's lens visibility beyond what applying an operator to a supplied value produces: +0.050 [+0.045, +0.057] in percentile rank on our primary checkpoint, positive on all four we measure, though that arm answers at ceiling and the accuracymatched contrast is stronger under that readout. At the same time one shared linear map decodes the variable from every arm, the control included, at 6.4-9.0x its selection-corrected floor. What produces the later readable form at the queried position is attention-mediated gathering inside a mid-depth window: separating patch depth from readout depth puts transport there at least 17x above anywhere shallower under non-saturating readouts, with no tested MLP output contributing positively inside it. Under the saturating percentile rank the same grid does not localise the window, which is a fact about that measure. An arm that needs the variable for nothing concentrates sevenfold less, so the window is demand-specific. That window has two measured edges, a survival failure below and destruction above, and it falls at the same fractional depth in a 64-layer hybrid and a 62-layer dense model from another family. We localise where the variable is installed and read, not the route from the passage, which transports nothing. But the readout is not a calibrated measure of use: three components move it to within 12% of one another and differ 7.4x in what they do to the answer.
Original Article
View Cached Full Text

Cached at: 08/18/26, 07:53 AM

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

Source: https://huggingface.co/papers/2608.15022

Abstract

In language models, flexible reuse demands attention-mediated gathering at a mid-depth window to make latent variables readable, without a selective gate, and readout measures poorly reflect actual use.

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. What causes a representation to enter that form is open, and the wordworkspaceinvites an admission story: a gate that decides what gets in. Testing it onopen-weight modelswithJacobian lenses, over a benchmark whose five arms share an identical context, we find no gate where it predicts one. Demand raises a concept’s lens visibility beyond what applying an operator to a supplied value produces: +0.050 [+0.045, +0.057] inpercentile rankon our primary checkpoint, positive on all four we measure, though that arm answers at ceiling and the accuracymatched contrast is stronger under thatreadout. At the same time one sharedlinear mapdecodes the variable from every arm, the control included, at 6.4-9.0x its selection-corrected floor. What produces the later readable form at the queried position isattention-mediated gatheringinside amid-depth window: separatingpatch depthfromreadoutdepth puts transport there at least 17x above anywhere shallower under non-saturatingreadouts, with no testedMLPoutput contributing positively inside it. Under the saturatingpercentile rankthe same grid does not localise the window, which is a fact about that measure. An arm that needs the variable for nothing concentrates sevenfold less, so the window is demand-specific. That window has two measured edges, a survival failure below and destruction above, and it falls at the same fractional depth in a 64-layer hybrid and a 62-layerdense modelfrom another family. We localise where the variable is installed and read, not the route from the passage, which transports nothing. But thereadoutis not a calibrated measure of use: three components move it to within 12% of one another and differ 7.4x in what they do to the answer.

View arXiv pageView PDFProject pageGitHub0Add to collection

Get this paper in your agent:

hf papers read 2608\.15022

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2608.15022 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2608.15022 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2608.15022 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

Emergent Structure in the Marginal Attention Space of Language Models

arXiv cs.CL

The paper introduces a marginal attention space for language models, showing that token-wise reduction yields a text-intrinsic signal conserved across 60+ LLMs while head-wise reduction forms a model-private signature, enabling offline per-head KV-cache eviction budgets that perform competitively with per-document methods.

Rethinking the Role of Efficient Attention in Hybrid Architectures

arXiv cs.CL

This paper systematically analyzes the role of efficient attention modules in hybrid language model architectures, finding that different designs converge in long-context performance under sufficient training, and that long-range retrieval is primarily carried by full attention while efficient attention shapes the optimization trajectory, revealing a 'Large-Window Laziness' phenomenon.

The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

Hugging Face Daily Papers

This survey analyzes the evolution of attention mechanisms in large language models through a five-dimensional lens of contextual memory (representation, update, access, readout, integration), drawing on 59 release-level records across 14 model lineages, and proposes a stateful multidimensional memory-routing hypothesis for future sequence architecture design.

Language Models Can Control Their Own Attention

Hugging Face Daily Papers

This paper introduces Declarative Attention, a method that enables language models to declare relevant context regions during inference, significantly reducing KV cache reads with small accuracy trade-offs in long-context tasks.