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Paper Citation Record · LEDGER

Understanding intermediate layers using linear classifier probes

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 100 inbound Pith citation observations for arXiv:1610.01644.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1610.01644 v4

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T18:31:44.239360Z

measured 128 of 128 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 100 of 310 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:01:39.648013Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

28 of 28 outbound references displayed

  • verified exact7
  • verified fuzzy6
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

117
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation d6b38447-e0a2-4ce3-bcc0-1f0da35a44e3 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Understanding intermediate layers using linear classifier probes Understanding intermediate layers using linear classifier probes

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-05-11T18:31:44.294465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation b4cb5adf-6e15-4747-8e15-dbcb13f2b423 · outbound

This paper cites Explaining Recurrent Neural Network Predictions in Sentiment Analysis.

Understanding intermediate layers using linear classifier probes Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Reference 2

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verified exact
arxiv_id, observed 2026-05-11T18:31:44.309727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 78b4f757-515f-4f85-9d14-cba793f9e36c · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9b9d9300-17b6-4d5b-b06e-2faa2620d94d · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-11T18:31:44.239360Z digest=sha256:74cb43c259525880544453827578fd4f748d878316f0e62519e4268c864eaff5

Observation 64f10246-a6ad-4124-9b9e-6556530b62e2 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 5

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 69c5edf0-8039-4902-857b-d7100bbb9e90 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 57a70ac0-b4fd-432d-9569-36c14d0500e4 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation faad77a2-00e2-4ebf-9146-c08848e4325e · outbound

This paper cites and Brox, T.

Understanding intermediate layers using linear classifier probes and Brox, T

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e9d8275a-7e15-40af-8be9-c5bd01f1477a · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 85397334-b119-485b-a2a2-51bb01576bd7 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 924c6ef2-8edf-42ae-8af5-ecc02e64d408 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bf723d97-d947-49e5-9dbd-e0e2539d2dbc · outbound

This paper cites Residual Connections Encourage Iterative Inference.

Understanding intermediate layers using linear classifier probes Residual Connections Encourage Iterative Inference

Reference 12

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arxiv_id, observed 2026-07-04T22:36:00.391022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5a545c8d-97b6-4bdc-a810-106931d9ad25 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 49b7c58a-c8d9-403f-b460-20103e65f9ad · outbound

This paper cites FractalNet: Ultra-Deep Neural Networks without Residuals.

Understanding intermediate layers using linear classifier probes FractalNet: Ultra-Deep Neural Networks without Residuals

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2cda7fd5-60a2-4ee0-aaab-bc008e4a96cb · outbound

This paper cites and Vedaldi, A.

Understanding intermediate layers using linear classifier probes and Vedaldi, A

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a853faee-5fd4-40c9-bdb2-7525983a6ad5 · outbound

This paper cites and Vedaldi, A.

Understanding intermediate layers using linear classifier probes and Vedaldi, A

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation db234887-8209-4e95-8742-1987efcc3159 · outbound

This paper cites L., and M \"u ller, K.-R.

Understanding intermediate layers using linear classifier probes L., and M \"u ller, K.-R

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 35ef28c2-cccf-4f87-9e7e-ef8d9b61f4f9 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 25daa382-0919-46a3-97c2-37ed41d80d8e · outbound

This paper cites SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.

Understanding intermediate layers using linear classifier probes SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

Reference 19

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arxiv_id, observed 2026-05-11T18:31:44.344603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a5234b98-5833-4039-badb-812915499326 · outbound

This paper cites C., and Fei-Fei, L.

Understanding intermediate layers using linear classifier probes C., and Fei-Fei, L

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 97818adf-f060-4a01-84e0-817c79850ba2 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 63ec4ce2-af3c-4ccc-91d3-4a9035fe269a · outbound

This paper cites Intriguing properties of neural networks.

Understanding intermediate layers using linear classifier probes Intriguing properties of neural networks

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f5f16a2a-1bc5-4bae-a726-630209828350 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 62f99a41-f6cd-4dc8-9492-c47b194a748b · outbound

This paper cites J., and Belongie, S.

Understanding intermediate layers using linear classifier probes J., and Belongie, S

Reference 24

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2e2aa2d9-ceb0-4471-ace3-c5756adb2af4 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6a53cdda-80c2-4f5b-af65-91ffac1422f0 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5ccbccc0-404e-4856-8481-7da1fec53ad3 · outbound

This paper cites an unresolved cited work.

Understanding intermediate layers using linear classifier probes Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e844b415-520c-43a5-a1ca-d6a00847421e · outbound

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Understanding intermediate layers using linear classifier probes Understanding deep learning requires rethinking generalization

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

Observation d6b38447-e0a2-4ce3-bcc0-1f0da35a44e3 · inbound

Understanding intermediate layers using linear classifier probes cites this paper.

Understanding intermediate layers using linear classifier probes Understanding intermediate layers using linear classifier probes

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 608df017-3f30-4fc8-ad8f-fcc3316ffacc · inbound

Feature selection of neural networks is skewed towards the less abstract cue cites this paper.

Feature selection of neural networks is skewed towards the less abstract cue Understanding intermediate layers using linear classifier probes

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4da4a68a-d6b0-4fd7-b004-7558b37f58ed · inbound

Probing Classifiers: Promises, Shortcomings, and Advances cites this paper.

Probing Classifiers: Promises, Shortcomings, and Advances Understanding intermediate layers using linear classifier probes

Reference 65

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e945462f-c49d-45e4-9bed-5591b379770d · inbound

What learning algorithm is in-context learning? Investigations with linear models cites this paper.

What learning algorithm is in-context learning? Investigations with linear models Understanding intermediate layers using linear classifier probes

Reference 1

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verified exact
local_arxiv, observed 2026-05-17T13:40:34.013043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-17T13:40:33.889712Z digest=sha256:ac6a420dd27ebcd33032f3189ac4eece6d566ca9abbbeb96c3fc4a59a57c0abd

Observation 7e9f250c-da3f-4870-99ef-caf1bc426044 · inbound

Eliciting Latent Predictions from Transformers with the Tuned Lens cites this paper.

Eliciting Latent Predictions from Transformers with the Tuned Lens Understanding intermediate layers using linear classifier probes

Reference 3

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local_arxiv, observed 2026-05-12T16:54:37.498913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f1eb6100-59c3-4b36-98b8-2cc0e7f6c2af · inbound

When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations cites this paper.

When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations Understanding intermediate layers using linear classifier probes

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 89f497aa-daf8-4c8b-9de5-0a89aa81c1cc · inbound

LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states cites this paper.

LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Understanding intermediate layers using linear classifier probes

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8b710cbf-ab86-4b3f-bcfb-c14b113e4887 · inbound

Linear Probe Penalties Reduce LLM Sycophancy cites this paper.

Linear Probe Penalties Reduce LLM Sycophancy Understanding intermediate layers using linear classifier probes

Reference 1

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no resolver link, observed 2026-08-12T04:53:06.372154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:53:06.372154Z digest=sha256:40e6847e37151b1a3509dacd45bb8825de51df00e27b1e697452989462195a63

Observation 6908a6f0-3bda-4593-ae08-18c89028451a · inbound

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey cites this paper.

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey Understanding intermediate layers using linear classifier probes

Reference 234

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no resolver link, observed 2026-08-11T23:54:24.321298Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:54:24.321298Z digest=sha256:d83ca6dbd7d21675496501cc37ed8624a64f21f06422121ff6f7abe2d870e068

Observation a15f435f-53d3-4836-9e1b-7165bb085139 · inbound

Obfuscated Activations Bypass LLM Latent-Space Defenses cites this paper.

Obfuscated Activations Bypass LLM Latent-Space Defenses Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-11T16:59:11.409609Z digest=sha256:51834cd11da81d45f9e1426c829372329981a4024d991fd6c60b96de1752b6b4

Observation ff4c5108-159f-49e5-bfc8-c254c50082e4 · inbound

Financial Fine-tuning a Large Time Series Model cites this paper.

Financial Fine-tuning a Large Time Series Model Understanding intermediate layers using linear classifier probes

Reference 38

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source=pdf_text observed=2026-08-11T16:43:21.456274Z digest=sha256:b9e652a72e1c1e1d01620148d0f4c1db499bc5b043bdd6dfe3060692f990ec32

Observation ad6820a7-19e1-403c-b153-3420259e9e13 · inbound

Transformers Use Causal World Models in Maze-Solving Tasks cites this paper.

Transformers Use Causal World Models in Maze-Solving Tasks Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-11T14:35:02.214193Z digest=sha256:0e8954b68703652c8dcb903cb70fd8cac9702713f2fc6330c398830be67deb6e

Observation 6e0e83c8-487a-42e0-aa99-2748ad46e4c2 · inbound

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective cites this paper.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Understanding intermediate layers using linear classifier probes

Reference 5

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source=arxiv_source observed=2026-08-11T14:20:23.538909Z digest=sha256:9edc9cb80bec6c1912036675eb8f11156187fac88425467e2d4c3f17e5570019

Observation 621aba30-d957-4494-9157-56eb913fcd43 · inbound

Lightweight Safety Classification Using Pruned Language Models cites this paper.

Lightweight Safety Classification Using Pruned Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-11T13:11:27.209218Z digest=sha256:aa1e3f0a8a45338e49636d7f2e922319e4cb62be813b9a1c15ed07387fa46f56

Observation a20896bd-eb2c-4f37-b4a3-c15722d4b43e · inbound

LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer cites this paper.

LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer Understanding intermediate layers using linear classifier probes

Reference 5

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source=pdf_text observed=2026-08-11T12:46:59.359870Z digest=sha256:f46e9aef0880cf95a35e81b6829a542a0cd93cda593916f3d534d19f1e2c7cf5

Observation 76740fb5-3213-4d10-9b9c-56e1e9ea2187 · inbound

Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report cites this paper.

Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report Understanding intermediate layers using linear classifier probes

Reference 125

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source=pdf_text observed=2026-08-11T12:31:56.551283Z digest=sha256:c95f61ad98b74e7585e755b606726299b9aa521d934253a804a01c0e586e02a8

Observation 358590ac-b63b-4dac-b539-496b4f244482 · inbound

Sentiment trading with large language models cites this paper.

Sentiment trading with large language models Understanding intermediate layers using linear classifier probes

Reference 2016

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source=pdf_text observed=2026-08-11T00:50:57.859341Z digest=sha256:709ab56a0c5d9c81693d4a7dcccb0db900f89110290d0ee7804dc1a8418e47b5

Observation bef35f44-1437-4d74-aa20-76cd32aa75d1 · inbound

Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models cites this paper.

Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models Understanding intermediate layers using linear classifier probes

Reference 2

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source=pdf_text observed=2026-08-10T22:28:31.919773Z digest=sha256:7bc43c1744550b84120eb6cb72bc049ac88437950896d50abb44509769038b21

Observation 1ddc2ebb-5481-48fb-bbe7-4e3b629f7a38 · inbound

Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach cites this paper.

Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-10T18:47:26.128834Z digest=sha256:b2f7bcf26fdaa802cea9a9fefbca1d16fab6a3ebe239fad979b98606233a2f4b

Observation 9015422d-b7ad-4ce4-8101-07aa05a192a1 · inbound

Unraveling Token Prediction Refinement and Identifying Essential Layers in Language Models cites this paper.

Unraveling Token Prediction Refinement and Identifying Essential Layers in Language Models Understanding intermediate layers using linear classifier probes

Reference 2021

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source=pdf_text observed=2026-08-10T14:43:14.733628Z digest=sha256:c7e0a99f02af2cab84234ed9cc3bb7382711010a219023423c4c6e7e240a8700

Observation af7d1eb7-9eae-4398-a32d-2dc231825daa · inbound

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models cites this paper.

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-09T20:34:01.172410Z digest=sha256:e2af8e9f650653584d78b2aee0e1ab55f6a1e096d03ae4ef7bc8538cb1ca19e5

Observation 20457c54-18ff-4dd8-909d-fbb96efd1522 · inbound

Latent Action Learning Requires Supervision in the Presence of Distractors cites this paper.

Latent Action Learning Requires Supervision in the Presence of Distractors Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-09T19:18:44.681015Z digest=sha256:17b3a9783aed3749c01591a531c24366fb545e916860312b5235e987e8c8bcb5

Observation a4a9e301-b854-4202-898d-afdb1cd6bb45 · inbound

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning cites this paper.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Understanding intermediate layers using linear classifier probes

Reference 4

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source=pdf_text observed=2026-08-09T17:58:33.774391Z digest=sha256:dd148a1c09958763fdd06ddd9500ada676e6361a0f9801aa75c45c7fe6472eaa

Observation d433ce8b-345f-4cb2-bfd5-82621f8d205e · inbound

What is a Number, That a Large Language Model May Know It? cites this paper.

What is a Number, That a Large Language Model May Know It? Understanding intermediate layers using linear classifier probes

Reference 2018

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source=pdf_text observed=2026-08-09T15:05:03.703196Z digest=sha256:9875420a0e70fa87dcef14577d94ea86ee82d64ac828f600c7bce990d39413ff

Observation 7a43bb7e-da09-47de-8160-e23d72ea698d · inbound

Grokking vs. Learning: Same Features, Different Encodings cites this paper.

Grokking vs. Learning: Same Features, Different Encodings Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-09T14:44:26.975066Z digest=sha256:22fa1d4c9eca0081e8dab9929a52e4cb9d967e5c91c5e03b5a4f50fbd24468ff

Observation 0fa5db58-a585-4d2d-8338-1a896c4d37d4 · inbound

Parameter Symmetry Potentially Unifies Deep Learning Theory cites this paper.

Parameter Symmetry Potentially Unifies Deep Learning Theory Understanding intermediate layers using linear classifier probes

Reference 2

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source=pdf_text observed=2026-08-08T19:58:25.840740Z digest=sha256:6324298191303d82b17d5c10a45b7741bb91dfc223c5efe105aff3fccec6b81a

Observation a877984e-bb44-4910-8304-89fc8beed9f0 · inbound

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks cites this paper.

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-08T15:22:44.782384Z digest=sha256:25af6b628631ec2462d670c5ac166da0a442013bbefad1a53b9ea7b242b4c0eb

Observation ba94dd62-519a-4efa-aaf8-e9e7a1472e39 · inbound

We Can't Understand AI Using our Existing Vocabulary cites this paper.

We Can't Understand AI Using our Existing Vocabulary Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-08T12:17:45.914983Z digest=sha256:114f57c18e68348a91e3422871b7bc82506efd9c4f92a944e516d844ad85098f

Observation 19b302f5-386f-47ac-a198-9ec14dbec680 · inbound

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs cites this paper.

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-08T00:04:57.000885Z digest=sha256:13e4a0460741dafd44da5c97352f35a07b7245f05243bd1c48306e649740370e

Observation e835392a-c22d-4090-acb5-1881d69c7645 · inbound

Solving Empirical Bayes via Transformers cites this paper.

Solving Empirical Bayes via Transformers Understanding intermediate layers using linear classifier probes

Reference 2018

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source=pdf_text observed=2026-08-07T20:21:58.214293Z digest=sha256:e5c40f7b9599729cf1ebe1c80c4e63323656ce90bd56943f2ff83192089b805f

Observation da685c7d-6460-4f2b-be84-b125ec9432a2 · inbound

Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward cites this paper.

Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward Understanding intermediate layers using linear classifier probes

Reference 4

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source=arxiv_source observed=2026-08-16T11:01:39.648013Z digest=sha256:f1692e70b2cdda4ec7bf36df282a4f82fe1863f4f4c39b294edc7b9dc7902227

Observation 8e4bacab-cdbb-4a29-8665-0cc36d4740ad · inbound

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies cites this paper.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Understanding intermediate layers using linear classifier probes

Reference 5

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source=arxiv_source observed=2026-08-16T11:00:20.305699Z digest=sha256:2d61faceccaccd707fafea543a0e14cbe55f70f334ea089d79056d9ee3e9e3a7

Observation 31425d2e-627d-4bbc-9e6c-5835df5f3dfd · inbound

Investigating task-specific prompts and sparse autoencoders for activation monitoring cites this paper.

Investigating task-specific prompts and sparse autoencoders for activation monitoring Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-16T05:38:12.140523Z digest=sha256:d10a8c99a3f72406c39366abcb6dd59570fdfa5a00dd2e83f417f9a51b711e7a

Observation a18391a9-f22c-473a-a2b8-6b730dc2e87a · inbound

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation cites this paper.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-16T01:07:18.870889Z digest=sha256:278d2c383a6a180fb66decd41ad1ae0d896075474f9779fda89922e83c6a952d

Observation 9bdaa413-5218-4268-b5ff-2d0900576d25 · inbound

When Bad Data Leads to Good Models cites this paper.

When Bad Data Leads to Good Models Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-15T23:25:00.101334Z digest=sha256:3ca403d6c09875464a21f58d7bc046df51dc2c0d53514309f3d37c96b23ae3c9

Observation 126dee83-562e-41cc-a021-dc3826e693a1 · inbound

OMGPT: A Sequence Modeling Framework for Data-driven Operational Decision Making cites this paper.

OMGPT: A Sequence Modeling Framework for Data-driven Operational Decision Making Understanding intermediate layers using linear classifier probes

Reference 2

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source=pdf_text observed=2026-08-15T20:23:51.010219Z digest=sha256:fd275b329db45152bfc44dc0de17369407ad3b6c9bd745b4b55b2e47c3d964c3

Observation fa4bd15f-d49d-4209-8da2-9ded7e238591 · inbound

Void in Language Models cites this paper.

Void in Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-07T15:36:58.593864Z digest=sha256:9f1d47998e65a5e5522d040451f4bc1b0f3f0314d152a5cd058fda934cf207c2

Observation 53088e9f-6b06-4a54-ba53-5f2f223dbd75 · inbound

Concept Incongruence: An Exploration of Time and Death in Role Playing cites this paper.

Concept Incongruence: An Exploration of Time and Death in Role Playing Understanding intermediate layers using linear classifier probes

Reference 3

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source=pdf_text observed=2026-08-07T15:31:31.350949Z digest=sha256:b2eb747ced049017550b70492bfbfe78feab5bcea1571b47253fe16b372933db

Observation c4a512aa-97f5-420f-847b-66e00b28f24a · inbound

Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering cites this paper.

Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-07T15:30:20.574425Z digest=sha256:4bc5d167d5369d5105afa224e201194d7aa67abebd029c4e94413a2f9dd9aa5b

Observation 14eed45a-2f05-4f1e-aa3e-9277e560210a · inbound

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing cites this paper.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-07T15:32:11.215971Z digest=sha256:86dc5cde78fae63d0a24d4f470d769713f5893d2d03e6420fdb9273404ad3d31

Observation 6f746dfd-1b2c-4f01-99f7-7248c6eb08d1 · inbound

Are the Hidden States Hiding Something? Testing the Limits of Factuality-Encoding Capabilities in LLMs cites this paper.

Are the Hidden States Hiding Something? Testing the Limits of Factuality-Encoding Capabilities in LLMs Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-07T15:04:04.462024Z digest=sha256:439fa9a5d51941d8d43f1a01f2e7fc52c51de6062925159dc0b1761896353a76

Observation 9a02851f-305b-48d0-81b0-d0c0a4809ffe · inbound

Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models cites this paper.

Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models Understanding intermediate layers using linear classifier probes

Reference 2006

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source=pdf_text observed=2026-08-07T14:44:41.885452Z digest=sha256:3bebfee0699d16f5ffd3aa82d460f27303d13538c17e820d4ebdd519099c96df

Observation 3ac98076-51a9-44e8-8d3f-2c4d2357ddac · inbound

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks cites this paper.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-07T14:43:32.884776Z digest=sha256:3c5ee3e9093c1e2000600cddf315cbe62c0bee6e794ffc988e3f2181c0e5d655

Observation 0875bfc1-21c5-468d-b028-6d8889357607 · inbound

Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? cites this paper.

Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-07T14:50:09.727392Z digest=sha256:0737b530646524646177482da29f9e85380a91978c2338de52eaeb49f4c49a8b

Observation 39471ad9-2149-4212-ab77-4c84f03b3515 · inbound

Can Visual Encoder Learn to See Arrows? cites this paper.

Can Visual Encoder Learn to See Arrows? Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-07T14:07:17.749975Z digest=sha256:50228ac96b466f9c9632cb2988a0893bb63751b0178ad5ca88f131eb3de25803

Observation a3f445fb-28fa-4a33-a1b1-5ef0ae9d4ac0 · inbound

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs cites this paper.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-07T13:27:59.847789Z digest=sha256:e2c29592849c98532c855618f717874cebbec9c8e4bb1da2322660d44d55373e

Observation 8e4bb419-2c6b-4ebb-87fe-22d65f9aa43a · inbound

BIRD: Behavior Induction via Representation-structure Distillation cites this paper.

BIRD: Behavior Induction via Representation-structure Distillation Understanding intermediate layers using linear classifier probes

Reference 49

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source=pdf_text observed=2026-08-07T12:45:23.495814Z digest=sha256:cdec26355580f30eb9ad85762d7e14738b8199e672601893c193ea161f84c8d1

Observation cf076e2a-4030-4733-9ae2-36cde59fd1fc · inbound

LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs cites this paper.

LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs Understanding intermediate layers using linear classifier probes

Reference 23

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source=pdf_text observed=2026-08-07T12:28:29.025394Z digest=sha256:ccc6fc40040dbb86f10d4959f627c3f6c17865476922424429234ff2447826d4

Observation 81fd5c2b-7541-4c26-bf4d-2a5ea42699cb · inbound

Beyond the Black Box: Interpretability of LLMs in Finance cites this paper.

Beyond the Black Box: Interpretability of LLMs in Finance Understanding intermediate layers using linear classifier probes

Reference 2016

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Observation 03512336-1c9f-4724-b15c-69f75a2b3196 · inbound

Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment cites this paper.

Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-05-19T11:52:36.688263Z digest=sha256:7b3eb004f2ce58c85b7816689ddf4b18a604faabc92a126c3a9e82fd6c8e8014

Observation 7de31c2a-3748-41af-83e5-97b57e0084c2 · inbound

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models cites this paper.

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-07T05:44:34.811248Z digest=sha256:1338928afce8cccee7acaac55fa0c6246ce2ff35126dd0a1c3d134a45d58cb2d

Observation d414bc41-689e-4392-afdf-d0f6b6415096 · inbound

Resa: Transparent Reasoning Models via SAEs cites this paper.

Resa: Transparent Reasoning Models via SAEs Understanding intermediate layers using linear classifier probes

Reference 2018

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source=pdf_text observed=2026-08-07T04:45:42.680950Z digest=sha256:06a2cd6f67d02250e0069ee4515e8787e647e65bcac0fab4ceaef328288aa8b4

Observation 19d21acc-d8df-4775-bd28-51742fbb8140 · inbound

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning cites this paper.

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning Understanding intermediate layers using linear classifier probes

Reference 29

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source=pdf_text observed=2026-08-07T15:11:29.812575Z digest=sha256:578274e5ea210391ee40b8c017381bc3198bf2ee6679e4fac265cfc29960c148

Observation 175e16e9-35e5-48c6-a9b6-601090a8c876 · inbound

Collaborative Prediction: To Join or To Disjoin Datasets cites this paper.

Collaborative Prediction: To Join or To Disjoin Datasets Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-07T04:20:52.074496Z digest=sha256:3ce1d85b43f2f7a116bfa5905e9ddf1f137fcbfa1d1a5feb20e18801acce192b

Observation bdcba208-0abf-479e-90c7-da7673d8cef1 · inbound

How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks cites this paper.

How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Understanding intermediate layers using linear classifier probes

Reference 2018

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source=pdf_text observed=2026-08-06T20:55:06.033898Z digest=sha256:7d84948f203c309b0654522215a89bdeb50c4a09d77dfb270657f4fc087c716f

Observation 6d595cf1-56ec-47b9-ba72-ddeadc186849 · inbound

How Do Vision-Language Models Process Conflicting Information Across Modalities? cites this paper.

How Do Vision-Language Models Process Conflicting Information Across Modalities? Understanding intermediate layers using linear classifier probes

Reference 2

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source=arxiv_source observed=2026-08-06T20:47:03.208420Z digest=sha256:1e35ef77f40ee9f7d1d735f01c1686a269d1f35c298cfc113c66a18210491d31

Observation fa24f752-8b96-414c-af72-91354341285f · inbound

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models cites this paper.

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models Understanding intermediate layers using linear classifier probes

Reference 110

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source=pdf_text observed=2026-08-07T10:18:59.137707Z digest=sha256:9d9d2ab603f1cbab4f6b26611edc76837a716a6634d3856cfc994db442bc8785

Observation 72fcb7d1-1878-4a5b-987a-7a8956bbd1b7 · inbound

On the rankability of visual embeddings cites this paper.

On the rankability of visual embeddings Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-06T20:11:48.623426Z digest=sha256:2571c941ab3374b9770b84a46f8669aa38e6ee2e07121592a889880b3d1a7750

Observation ec88b170-7272-40ce-aa7d-c56353fefef7 · inbound

Word stress in self-supervised speech models: A cross-linguistic comparison cites this paper.

Word stress in self-supervised speech models: A cross-linguistic comparison Understanding intermediate layers using linear classifier probes

Reference 9

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source=pdf_text observed=2026-08-06T19:44:26.773199Z digest=sha256:55a50585dd05d8ef375116b23f19ef7fc9f6c63cffb2fa804fa7b56f07d08d89

Observation 20224d9a-c3ee-4a11-a890-c72f24505d77 · inbound

The Generalization Ridge: Information Flow in Natural Language Generation cites this paper.

The Generalization Ridge: Information Flow in Natural Language Generation Understanding intermediate layers using linear classifier probes

Reference 14

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-19T05:30:38.612759Z digest=sha256:696348cbe2f27e26415fdf3d8e77b301da58379b59e9592bcaf67a4beffaf2c0

Observation a6f99e68-2dc0-42e8-9c9d-f2aebe854de5 · inbound

BlueGlass: A Framework for Composite AI Safety cites this paper.

BlueGlass: A Framework for Composite AI Safety Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-06T17:46:15.466161Z digest=sha256:6ca8c0d89e3e7ee238c974b8de94342a88acf717c3dfa713166a3b2c9ed2a91b

Observation 9f2de84e-ace9-4260-86b0-f7e15904959a · inbound

CLA: Latent Alignment for Online Continual Self-Supervised Learning cites this paper.

CLA: Latent Alignment for Online Continual Self-Supervised Learning Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-06T17:37:52.574317Z digest=sha256:ae3a520c9f6a1622d5b0f0969d7e1ac22c5e0283cb417b1f89db2447aa31abb4

Observation 7b93434d-5be4-4eaa-a64a-557f5634a9b5 · inbound

The Other Mind: How Language Models Exhibit Human Temporal Cognition cites this paper.

The Other Mind: How Language Models Exhibit Human Temporal Cognition Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-06T15:30:31.687982Z digest=sha256:67d257e659ffd8fa385a30c3c60a2917f9e5436bd49cce26f476574def13f31a

Observation 869c6913-c7a2-4334-bdd0-7d95e39d5e19 · inbound

What Does it Mean for a Neural Network to Learn a "World Model"? cites this paper.

What Does it Mean for a Neural Network to Learn a "World Model"? Understanding intermediate layers using linear classifier probes

Reference 2021

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source=pdf_text observed=2026-08-06T12:44:44.766290Z digest=sha256:ae396b713eb710f03700de8210f70e289183333ae3d468b5d6a0d90c2f1434ba

Observation 25dac620-b13b-4dcb-80fe-451c71a158d6 · inbound

A Single Direction of Truth: An Observer Model's Linear Residual Probe Exposes and Steers Contextual Hallucinations cites this paper.

A Single Direction of Truth: An Observer Model's Linear Residual Probe Exposes and Steers Contextual Hallucinations Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-06T11:01:14.932935Z digest=sha256:85bf9c6271aa59630b91bd36071f88f72056effb2e65c8586174426bb50171f0

Observation 47edcf96-f82f-46ed-b619-bda8b0379b74 · inbound

Balancing Stylization and Truth via Disentangled Representation Steering cites this paper.

Balancing Stylization and Truth via Disentangled Representation Steering Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-05T23:59:13.262269Z digest=sha256:16f1499279f4342f5070cfce1f574d55ce35e2b678a7f95c7c6f5dd85d03c968

Observation 951453fe-d3fb-4bc5-b51a-5e27af2a5bb5 · inbound

Activation Steering for Bias Mitigation: An Interpretable Approach to Safer LLMs cites this paper.

Activation Steering for Bias Mitigation: An Interpretable Approach to Safer LLMs Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-15T17:34:31.001583Z digest=sha256:b307c868af8bbf999690d8f5cc6a3914373d573ce742dff9616d174e74cceebe

Observation 75710bb6-36ba-4585-9d68-8dc48a6379fd · inbound

Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images cites this paper.

Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images Understanding intermediate layers using linear classifier probes

Reference 80

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source=pdf_text observed=2026-08-05T16:41:45.665313Z digest=sha256:d4d5226194107fef9954ad5e0c4a13a3c45a86860acb3e25927cd3d731e92662

Observation 02e91ae1-ea28-4716-ab2a-25eae3614bd1 · inbound

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models cites this paper.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-15T16:47:43.554649Z digest=sha256:ab0eea4f11da8230559c894f6a13f0028d50f75c095b0a94007960a2e55ae249

Observation 6c2076f5-61fe-4dec-95f5-b9a80fd21c31 · inbound

Online incremental learning for audio classification using a pretrained audio model cites this paper.

Online incremental learning for audio classification using a pretrained audio model Understanding intermediate layers using linear classifier probes

Reference 22

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source=pdf_text observed=2026-08-05T14:55:32.884243Z digest=sha256:b1686398ab9a00a111c5e7e6ef17f9e46548e1881d80110ae21d36f60c2e7580

Observation 6ef37baa-8274-4882-b354-40e5a3640629 · inbound

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition? cites this paper.

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition? Understanding intermediate layers using linear classifier probes

Reference 3

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source=arxiv_source observed=2026-08-05T05:53:21.191664Z digest=sha256:b9d63dd5a754774495c599de966537ebb8834317e9ea794c1ce6e24db37623fc

Observation be38cfb8-0332-48a8-889e-6c0e415459ae · inbound

Evaluating the Efficiency of Latent Spaces via the Coupling-Matrix cites this paper.

Evaluating the Efficiency of Latent Spaces via the Coupling-Matrix Understanding intermediate layers using linear classifier probes

Reference 16

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source=pdf_text observed=2026-08-15T16:22:07.795817Z digest=sha256:4822476a92f50d221a387d575da7083b27b57775c4216333fc74dafb241a4f21

Observation 823a344f-ad58-42cb-a3b9-d92c754b5bf0 · inbound

RAPTOR: A Foundation Policy for Quadrotor Control cites this paper.

RAPTOR: A Foundation Policy for Quadrotor Control Understanding intermediate layers using linear classifier probes

Reference 45

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local_arxiv, observed 2026-05-18T17:31:41.754842Z

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source=pdf_text observed=2026-05-18T17:28:32.778343Z digest=sha256:b850d3164c9bbacc68a6625675881fac98f0f8ab50f5c80cdd35fe370cdd6b1e

Observation 9c544c4c-571c-43a7-a704-d3c8136b689d · inbound

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models cites this paper.

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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local_arxiv, observed 2026-05-18T16:21:36.526141Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-18T16:21:20.463222Z digest=sha256:62fffa18dfe46379b12f3a7c946a5c400d4420ce3763953f4c9cdf4a2ca19d9b

Observation 179a57e9-4947-4c45-a243-73666df31a0b · inbound

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation cites this paper.

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation Understanding intermediate layers using linear classifier probes

Reference 22

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source=pdf_text observed=2026-08-04T15:18:04.265355Z digest=sha256:2dfbc6e5fdd7ffa3b8ddbddc553d4d6aec5f5deefb905186dc30fa72ef483818

Observation a5f6e6c4-f3c6-4c0b-b60f-de3dd9e163f4 · inbound

Prophecy: Inferring Formal Properties from Neuron Activations cites this paper.

Prophecy: Inferring Formal Properties from Neuron Activations Understanding intermediate layers using linear classifier probes

Reference 3

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local_arxiv, observed 2026-05-18T13:31:24.709427Z

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source=pdf_text observed=2026-05-18T13:31:04.425374Z digest=sha256:d8f12c48fea9893c582270641bd0e11d42116d8714a8bf856ca3080a9faa05c8

Observation b44232bd-70ed-46be-bfe2-ada2297930d8 · inbound

Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks cites this paper.

Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks Understanding intermediate layers using linear classifier probes

Reference 1

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source=arxiv_source observed=2026-08-04T14:42:35.513314Z digest=sha256:29f0d6b0cc2a9c89db83bc4105f59ea95216d0612d016998fa86157dd741ef68

Observation 154c9b8e-d347-4be7-874d-cd2befea2ede · inbound

Feature Identification via the Empirical NTK cites this paper.

Feature Identification via the Empirical NTK Understanding intermediate layers using linear classifier probes

Reference 2

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local_arxiv, observed 2026-05-18T11:01:16.953582Z

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source=pdf_text observed=2026-05-18T11:01:14.230977Z digest=sha256:016cf63678cfbf82309632d112cb89b7133c3a2029a11ad64690c5d414096f17

Observation 764f5240-69ca-4e02-9fc6-0f15bcfa5bec · inbound

Foundation Models for Discovery and Exploration in Chemical Space cites this paper.

Foundation Models for Discovery and Exploration in Chemical Space Understanding intermediate layers using linear classifier probes

Reference 126

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source=pdf_text observed=2026-05-18T05:52:10.848118Z digest=sha256:92ca000391e55aafffbffdd749ecede514331bf98e68a940f8883be571db1cdd

Observation d6dc1fce-9051-41ea-9d4d-7f7a3f42b3de · inbound

MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains cites this paper.

MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains Understanding intermediate layers using linear classifier probes

Reference 4

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local_arxiv, observed 2026-05-17T23:20:27.604441Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-17T23:19:11.439175Z digest=sha256:ec43680b2ff3cf6357264471e66f675ce9cb634f1c4784e9ac7e72fab6eeb3e0

Observation 22f43ec8-f984-435e-ad08-ae767c184a84 · inbound

fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding cites this paper.

fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding Understanding intermediate layers using linear classifier probes

Reference 1

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local_arxiv, observed 2026-05-17T05:39:06.254039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-17T05:34:32.444507Z digest=sha256:514a4b7bdac928265163ef5a966ddd65bd5f59ffe4498e458276249b1d76de0c

Observation 31611ee4-7200-40a1-9464-552f8d695a5d · inbound

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail cites this paper.

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail Understanding intermediate layers using linear classifier probes

Reference 2018

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source=pdf_text observed=2026-08-03T18:33:00.774153Z digest=sha256:93619a69fa76ce25d1a518ad61bff7c98a9a4811e88f992a13182f35084d7a4f

Observation 0655eb43-bfe3-498d-b4a5-96f13125a585 · inbound

Quantitative Analysis of Proxy Tasks for Anomalous Sound Detection cites this paper.

Quantitative Analysis of Proxy Tasks for Anomalous Sound Detection Understanding intermediate layers using linear classifier probes

Reference 39

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source=pdf_text observed=2026-08-03T10:53:15.278767Z digest=sha256:302413cc7a0be2684c722790dcf60b55f3de4f80501437d1d41d7187e839831d

Observation e61f02dc-8fff-4b25-a88a-345f6708e699 · inbound

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models cites this paper.

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models Understanding intermediate layers using linear classifier probes

Reference 2016

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source=pdf_text observed=2026-08-03T09:31:52.284743Z digest=sha256:50a95d64c04f5f1b5eac27b981e807a1ee230e9f23ced41cb73a87b371ba2803

Observation 87069aac-61aa-44c6-a6bc-c2419586eb33 · inbound

Adapter Merging Reactivates Latent Reasoning Traces: A Mechanism Analysis cites this paper.

Adapter Merging Reactivates Latent Reasoning Traces: A Mechanism Analysis Understanding intermediate layers using linear classifier probes

Reference 2017

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source=pdf_text observed=2026-08-03T08:03:24.221945Z digest=sha256:057bfaa090dd15d7d3ffaf2daec7da67cd364bf6b3effa5ef926bcbc0e2f7785

Observation 96f89125-1781-4d92-88dd-9e8e75a16511 · inbound

Representation Unlearning: Forgetting through Information Compression cites this paper.

Representation Unlearning: Forgetting through Information Compression Understanding intermediate layers using linear classifier probes

Reference 1

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source=pdf_text observed=2026-08-03T06:59:17.743673Z digest=sha256:314fb2b430b0403c6fd51dc8b5793413521d068463ad67341763c8fa3f315611

Observation 0af79a6e-3b15-499c-8465-28e4b7c6cabf · inbound

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits cites this paper.

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits Understanding intermediate layers using linear classifier probes

Reference 3

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source=pdf_text observed=2026-08-03T05:00:54.907724Z digest=sha256:c94efd73e61cabab906d97476abbf9b8fedd468f62fe447db1b2051881994d78

Observation 90fae6d2-d80e-453c-bdcc-8343d1713c80 · inbound

Emergent Causal-Geometric Dynamics Across Depth in Large Language Models cites this paper.

Emergent Causal-Geometric Dynamics Across Depth in Large Language Models Understanding intermediate layers using linear classifier probes

Reference 2018

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Unavailable: canonical work link unavailable.

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Observation c576b98e-0ef9-4f8d-b023-fbbc144df584 · inbound

From Features to Actions: Explainability in Traditional and Agentic AI Systems cites this paper.

From Features to Actions: Explainability in Traditional and Agentic AI Systems Understanding intermediate layers using linear classifier probes

Reference 2

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no resolver link, observed 2026-08-03T03:50:14.472520Z

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source=pdf_text observed=2026-08-03T03:50:14.472520Z digest=sha256:d14051a46a9df2c8bb27dd24d5a8b358f59770c58156479e21af47a856b70040

Observation 38e62d9b-833c-4caf-bc31-4b413c9153d9 · inbound

Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning cites this paper.

Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning Understanding intermediate layers using linear classifier probes

Reference 2021

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no resolver link, observed 2026-08-03T04:23:24.508265Z

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source=pdf_text observed=2026-08-03T04:23:24.508265Z digest=sha256:91302676bb84780eedbedec74873c2c3d6e391840050b82435393ff7efcd264b

Observation 616d90a8-ca8e-4514-a55e-bb722bf690ad · inbound

The Information Geometry of Softmax: Probing and Steering cites this paper.

The Information Geometry of Softmax: Probing and Steering Understanding intermediate layers using linear classifier probes

Reference 1

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no resolver link, observed 2026-08-02T22:58:05.011126Z

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source=pdf_text observed=2026-08-02T22:58:05.011126Z digest=sha256:3e9c04204c52ec861b7e9892d2fcd68e2c08bb25552652f4b3e9d86dc2026f13

Observation 9c54a5ff-1d40-4d9b-a540-6ece6a80a2c3 · inbound

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning cites this paper.

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning Understanding intermediate layers using linear classifier probes

Reference 1

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verified exact
local_arxiv, observed 2026-05-15T20:20:17.730365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-15T20:18:20.610845Z digest=sha256:063b39795e3e58002d4f469f9ba3376409302edf24528a5e458451bdf741e56f

Observation a0825d20-f9bc-4197-8a86-ee6bd28b1461 · inbound

RAE-NWM: Navigation World Model in Dense Visual Representation Space cites this paper.

RAE-NWM: Navigation World Model in Dense Visual Representation Space Understanding intermediate layers using linear classifier probes

Reference 38

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no resolver link, observed 2026-07-15T12:07:05.640150Z

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source=pdf_text observed=2026-07-15T12:07:05.640150Z digest=sha256:345aec00a0ed11dd2129c89b887268b551125812169dd3fa1326db36c47d79e8

Observation 3bfdcc87-6f45-4c77-a29a-6ca84487e821 · inbound

SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation cites this paper.

SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation Understanding intermediate layers using linear classifier probes

Reference 1

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malformed identifier
no resolver link, observed 2026-07-13T21:52:18.253648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T21:52:18.253648Z digest=sha256:a83a46e62410b3a871e45069e5851c3856efc31b02841c99de55c32fab122062

Observation 833c757a-e13b-4a76-8c89-8ae9dbbeb84c · inbound

Do Audio-Visual Large Language Models Really See and Hear? cites this paper.

Do Audio-Visual Large Language Models Really See and Hear? Understanding intermediate layers using linear classifier probes

Reference 2

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verified exact
local_arxiv, observed 2026-05-13T20:58:15.866491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T20:56:19.815569Z digest=sha256:8e662d19a6aecbb9924802ec01d53d5b59561205a55a9cb4cdba59b1f01d239d

Observation 1a693058-cc28-4d2a-8c0f-15e09b7f571d · inbound

A Model of Understanding in Deep Learning Systems cites this paper.

A Model of Understanding in Deep Learning Systems Understanding intermediate layers using linear classifier probes

Reference 1

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verified exact
local_arxiv, observed 2026-05-13T16:53:00.156229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T16:50:24.142103Z digest=sha256:e0018e2500dbad6c9cdc03dc97dafa609aacb1ed8fbf61265e523d3a9ed7e093

Observation cbc7f616-8629-4b31-985a-e55dece523d6 · inbound

Beamforming Feedback as a Novel Attack Surface for Wi-Fi Physical-Layer Security cites this paper.

Beamforming Feedback as a Novel Attack Surface for Wi-Fi Physical-Layer Security Understanding intermediate layers using linear classifier probes

Reference 1

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unresolved
no resolver link, observed 2026-07-13T11:07:39.016814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T11:07:39.016814Z digest=sha256:d990eea9a17da1794e1e16f0a22d5681f345bbe1c7decd6a5e2318e34611d499

Observation db0c9a21-6055-40d9-bcb7-5dc1ddab0a23 · inbound

Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification cites this paper.

Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification Understanding intermediate layers using linear classifier probes

Reference 2

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verified exact
arxiv_id, observed 2026-05-11T05:21:00.937795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T18:11:13.523021Z digest=sha256:fbf973b305404ccf6f6c851cc41aa3366f7a89c52062e9521b2fa5fec060654a

Observation 85a0fd9e-d9c3-4c60-9412-5c5e021112ca · inbound

Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies cites this paper.

Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies Understanding intermediate layers using linear classifier probes

Reference 4

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verified exact
arxiv_id, observed 2026-05-11T00:20:52.000539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T18:36:20.397623Z digest=sha256:e37c2afdae7a9ba59696a8c9010ebb2a6c141109f9fa478ceb326fa2045dafe7

Observation 92f894ac-59fd-4407-aa20-dfdafca32e04 · inbound

Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR cites this paper.

Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR Understanding intermediate layers using linear classifier probes

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:00:58.948180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-10T16:21:52.761909Z digest=sha256:0f27bdbd2bd1484d7467700a0a4cb7e45c19be0564cb4d96b47ae9b2f7765bbd