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Paper Citation Record · LEDGER

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2505.21800.

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

pith.paper-citation-record.v1
2505.21800 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:28:02.896098Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:01:47.847228Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

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

This paper cites Understanding intermediate layers using linear classifier probes.

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:27:59.847789Z digest=sha256:dfc742a5588c82b9a7231bd9fceb37380a50e2c7326545b25f96f0d01735e13f

Observation 04ea8199-8400-4fc9-8941-c6a83134ec4e · outbound

This paper cites Refusal in language models is mediated by a single direction.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Refusal in language models is mediated by a single direction

Reference 2

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T13:27:59.950402Z digest=sha256:a52ebea4c13423766c7d1db053bba92d8c974ed69dba3907979e62bda35deae8

Observation c6c2acd4-5291-4e4c-b9fd-6709abd76870 · outbound

This paper cites The Internal State of an LLM Knows When It's Lying.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs The Internal State of an LLM Knows When It's Lying

Reference 3

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source=arxiv_source observed=2026-08-07T13:28:00.057643Z digest=sha256:c23b12ae4bc69a76c3559bddbf5d66803f4ce2979e599239604390b48eefef6d

Observation ace53ab5-3a7b-4301-9a5c-d2e4c154bbc1 · outbound

This paper cites Probing classifiers: Promises, shortcomings, and advances.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Probing classifiers: Promises, shortcomings, and advances

Reference 4

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raw_fallback, observed 2026-08-07T13:28:05.706903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T13:28:00.153974Z digest=sha256:39e109af7de5559de2cf64bbe15f8fa49571109f8861f7dcf966603c3624ee8a

Observation 0078724c-dbbf-45b1-9ae4-2bb508860bee · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Mechanistic Interpretability for AI Safety -- A Review

Reference 5

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source=arxiv_source observed=2026-08-07T13:28:00.211642Z digest=sha256:b9ac680c216dbb92aaeb340177774262aea26cf969cc923795e48bdde5c43fb6

Observation b69bf6c7-8d5b-4304-9614-16b029e23e91 · outbound

This paper cites Language models are few-shot learners.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Language models are few-shot learners

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T13:28:00.262504Z digest=sha256:e8fedcec777ff1226fc9072c3c83cb886640d4c8754b46ae59f8a45ff1624cc0

Observation 81b65b78-40d9-4935-942f-cae148cbe40c · outbound

This paper cites Truth is Universal: Robust Detection of Lies in LLMs.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Truth is Universal: Robust Detection of Lies in LLMs

Reference 7

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source=arxiv_source observed=2026-08-07T13:28:00.302815Z digest=sha256:c5eefcd4baf461bc43901e23cd6e8352335f7d3f93a6eb29a7d880672ef20cdf

Observation 47991700-91e9-4e72-a7a8-5e1b57044454 · outbound

This paper cites an unresolved cited work.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T13:28:00.391564Z digest=sha256:0b45caab5614d75f31b95d7dd978e12f198d0dcb1aea23fedea6b52ab9b6fbc8

Observation 150ca2d8-b181-44ed-bc01-89d9371d6a02 · outbound

This paper cites C., Lundberg, S.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs C., Lundberg, S

Reference 9

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source=arxiv_source observed=2026-08-07T13:28:00.514448Z digest=sha256:0b4727aaeb92b6fd6e547a26974f20ade18a25ba7a37733b9758e04afeaad455

Observation 8307d388-da22-4023-bb8a-0b27a7263393 · outbound

This paper cites From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

Reference 10

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source=arxiv_source observed=2026-08-07T13:28:00.576395Z digest=sha256:6484111979b9f2b25c4992f9d3f6facd23a54fec03d2750888ea7107b8dc9fbe

Observation 2d62b86c-66df-4e49-8179-8f6d8daeb7ab · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 11

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Observation 879689ee-382a-4dd8-8543-74caa120033b · outbound

This paper cites Toy Models of Superposition.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Toy Models of Superposition

Reference 12

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source=arxiv_source observed=2026-08-07T13:28:00.704798Z digest=sha256:cdeeb787a2d429a1bf11dbcc09763026ab36ee4200b6b6adea223d73f34d0fa0

Observation 44a87f73-8562-4a2b-98f8-8fb523dc1847 · outbound

This paper cites Not All Language Model Features Are One-Dimensionally Linear.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Not All Language Model Features Are One-Dimensionally Linear

Reference 13

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no resolver link, observed 2026-08-07T13:28:00.819179Z

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source=arxiv_source observed=2026-08-07T13:28:00.819179Z digest=sha256:f6fc3fecf85a8d62b7cd47ea9f2d6d247f64a4d8255a40e43bda21fca17a0766

Observation b640db2b-7daf-4a19-9da9-e08abb8412bc · outbound

This paper cites Sequential integrated gradients: A simple but effective method for explaining language models.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Sequential integrated gradients: A simple but effective method for explaining language models

Reference 14

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source=arxiv_source observed=2026-08-07T13:28:00.901866Z digest=sha256:f1c81c22c739a9dca427150d934c2cfbbc4a76af8fbc90d8fcbba2a24a212d21

Observation 45f21513-9c7a-4bc1-a9ee-dca34c421494 · outbound

This paper cites and Tegmark, M.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs and Tegmark, M

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bec5cdc0-ee92-4958-b014-b4345c467e14 · outbound

This paper cites X-Risk Analysis for AI Research.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs X-Risk Analysis for AI Research

Reference 16

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Observation 7d2c3955-327f-44b5-88eb-8603bc507b99 · outbound

This paper cites An Overview of Catastrophic AI Risks.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs An Overview of Catastrophic AI Risks

Reference 17

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no resolver link, observed 2026-08-07T13:28:01.117390Z

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Observation 189a364b-e6a2-44fa-ad62-842051410f85 · outbound

This paper cites Do LLMs "know" internally when they follow instructions?.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Do LLMs "know" internally when they follow instructions?

Reference 18

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no resolver link, observed 2026-08-07T13:28:01.184746Z

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source=arxiv_source observed=2026-08-07T13:28:01.184746Z digest=sha256:ce3371edd64234a5f3e2a07255b01059d51579e342196f45436caf955e3cacb8

Observation c1e00696-6307-4123-8077-4a8cfe9e000c · outbound

This paper cites and Manning, C.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs and Manning, C

Reference 19

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Observation 20956f71-d4aa-4f50-a5e4-5565a7e8d1a0 · outbound

This paper cites Refusal Behavior in Large Language Models: A Nonlinear Perspective.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Refusal Behavior in Large Language Models: A Nonlinear Perspective

Reference 20

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source=arxiv_source observed=2026-08-07T13:28:01.376676Z digest=sha256:3e8724219f7b0d9c66226bb89237331f668b23acba00f1ca1129a5d47fe7cf9f

Observation 3adb0171-8940-4338-af2c-280c251ded82 · outbound

This paper cites Linear representations of political perspective emerge in large language models.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Linear representations of political perspective emerge in large language models

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6be96987-204d-4e99-bb48-22ea76ceb463 · outbound

This paper cites Generating Wikipedia by Summarizing Long Sequences.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Generating Wikipedia by Summarizing Long Sequences

Reference 22

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no resolver link, observed 2026-08-07T13:28:01.546143Z

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source=arxiv_source observed=2026-08-07T13:28:01.546143Z digest=sha256:f8c62e41042c02aed3ac6d0b472fe656444f17e77b1cd28886461c5f02c4761c

Observation 7521b11d-60ca-42cf-b55c-9cb52c43d05b · outbound

This paper cites Cones: Concept Neurons in Diffusion Models for Customized Generation.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Cones: Concept Neurons in Diffusion Models for Customized Generation

Reference 23

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source=arxiv_source observed=2026-08-07T13:28:01.636198Z digest=sha256:0534b892c3d5db6ec24c781f5e06edc67c105627b616bc010d2121d6a8240c3b

Observation 534da651-1afe-434e-94d6-4f91f0eed78a · outbound

This paper cites and Tegmark, M.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs and Tegmark, M

Reference 24

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raw_fallback, observed 2026-08-07T13:28:05.039213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 31bef590-c80b-4d06-a4c4-dc138bf82863 · outbound

This paper cites Linguistic regularities in continuous space word representations.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Linguistic regularities in continuous space word representations

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7ff1b04c-c0e8-4365-9969-ff6cb2bf4baf · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Progress measures for grokking via mechanistic interpretability

Reference 26

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Observation 69e0e3d4-2040-4c0b-8b68-1c21e8c6a2e8 · outbound

This paper cites The Alignment Problem from a Deep Learning Perspective.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs The Alignment Problem from a Deep Learning Perspective

Reference 27

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Observation 3f14723c-d7dd-4310-8bdf-9fe7907fc36c · outbound

This paper cites Zoom in: An introduction to circuits.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Zoom in: An introduction to circuits

Reference 28

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source=arxiv_source observed=2026-08-07T13:28:01.971422Z digest=sha256:96ab69a6105e9c851ed01c2f69301c7e9ecee06ab190ae866924420b9ece8e56

Observation 280d9657-6672-4751-aa26-de164f877fec · outbound

This paper cites Introducing chatgpt.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Introducing chatgpt

Reference 29

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raw_fallback, observed 2026-08-07T13:28:04.603896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c182496d-3a5d-4581-bbbb-3a7b3b4b7f8a · outbound

This paper cites GPT-4 Technical Report.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs GPT-4 Technical Report

Reference 30

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source=arxiv_source observed=2026-08-07T13:28:02.152213Z digest=sha256:0fe8d2dae063dabd3ed2f8d71eb0ec94ed3597f992ece5547b8601ac114b46d2

Observation b880fcf9-89e8-40eb-98bf-2124960c0a40 · outbound

This paper cites Steering Llama 2 via Contrastive Activation Addition.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Steering Llama 2 via Contrastive Activation Addition

Reference 31

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source=arxiv_source observed=2026-08-07T13:28:02.231534Z digest=sha256:dfdc95c0e8f9906f0725c27ddc842a87ecc2a4f93eb44fa18f6f8531680ebe7d

Observation b2b3b2bd-9093-41ae-885c-7e3d3ad3923b · outbound

This paper cites J., and Veitch, V.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs J., and Veitch, V

Reference 32

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raw_fallback, observed 2026-08-07T13:28:04.344325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6fe8b3b4-6cf5-4d74-8ba9-d3f3299f07a6 · outbound

This paper cites J., and Veitch, V.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs J., and Veitch, V

Reference 33

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raw_fallback, observed 2026-08-07T13:28:04.198623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T13:28:02.404746Z digest=sha256:943d3faa2fc162ade5cab537418083604338c996bc60d49dc1166660c5bd6e77

Observation 02d4f182-f88c-4868-9eaa-4269cb04c415 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 34

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raw_fallback, observed 2026-08-07T13:28:04.004943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c6868f84-7684-41fa-96b3-74aaba9385cf · outbound

This paper cites Taking features out of superposition with sparse autoencoders.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Taking features out of superposition with sparse autoencoders

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T13:28:03.864329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T13:28:02.522092Z digest=sha256:bf29f2faf7e18b0bbda8c310773c77573079aa6560d656af73773a7219a9c214

Observation 6d86993c-a819-4464-a0ea-f01d9452022c · outbound

This paper cites Linear Representations of Sentiment in Large Language Models.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Linear Representations of Sentiment in Large Language Models

Reference 36

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Observation adcd7c41-c26a-4d98-b165-315dda00ac13 · outbound

This paper cites Steering Language Models With Activation Engineering.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Steering Language Models With Activation Engineering

Reference 37

Resolution
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Unavailable: canonical work link unavailable.

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This paper cites and Pinter, Y.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs and Pinter, Y

Reference 38

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5322eb23-947a-465a-b16b-07f53fafbde0 · outbound

This paper cites a ger, T., Elstner, J., Geisler, S., Cohen-Addad, V., G \.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs a ger, T., Elstner, J., Geisler, S., Cohen-Addad, V., G \

Reference 39

Resolution
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no resolver link, observed 2026-08-07T13:28:02.852685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c854ce80-596c-465a-be17-4825780764a1 · outbound

This paper cites an unresolved cited work.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:28:03.692562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers

Observation f5c4d94f-9caa-4c5e-8b5b-e722938ad976 · inbound

The Geometry of Harmfulness in LLMs through Subconcept Probing cites this paper.

The Geometry of Harmfulness in LLMs through Subconcept Probing From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs

Reference 26

Resolution
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Unavailable: canonical work link unavailable.

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Observation 00337ba7-24a4-419a-acf8-a0fe04d10442 · inbound

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations cites this paper.

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs

Reference 20

Resolution
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arxiv_id, observed 2026-06-29T12:43:25.524172Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation ad0b3e54-1d75-4654-8f30-753db6ba14cc · inbound

ToxiREX: A Dataset on Toxic REasoning in ConteXt cites this paper.

ToxiREX: A Dataset on Toxic REasoning in ConteXt From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs

Reference 296

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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