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Paper Citation Record · LEDGER

Looking Inward: Language Models Can Learn About Themselves by Introspection

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2410.13787.

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

pith.paper-citation-record.v1
2410.13787 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:19:50.984661Z

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

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 17a09530-ceb9-458b-b1e0-29ece5a7e5ff · inbound

AI Awareness cites this paper.

AI Awareness Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 151

Resolution
unresolved
no resolver link, observed 2026-08-16T10:19:50.984661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:19:50.984661Z digest=sha256:c221c3ff40baa5e66b9629ef363208615e34730870c96f7a7f63ab952c82ace1

Observation b1541934-f8ce-451a-8264-cd84e081727e · inbound

Does It Make Sense to Speak of Introspection in Large Language Models? cites this paper.

Does It Make Sense to Speak of Introspection in Large Language Models? Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T10:30:37.543618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:30:37.543618Z digest=sha256:5251e1a352978fd925274815943cb0484adaab520303bd02e60ab4081d076bc7

Observation 27d2542c-aa3b-455a-8ca7-9bdcd003c23f · 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 Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-03T09:31:50.927568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:31:50.927568Z digest=sha256:9bfc130342e137daf9877e58886e6ea451830c262a16ccf93a9e3635a7053495

Observation 52ac047a-b088-489d-ac56-15718f2c8676 · inbound

When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models cites this paper.

When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:16:10.913678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-10T15:02:06.434650Z digest=sha256:0b38dffd3c76dd16ccff29cc0bc602948c2e7cb467accac5c2a4fe8f31d1bed7

Observation 8df3a7e6-f921-4b33-a6cf-4a9079ca4dd2 · inbound

Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power cites this paper.

Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-09T22:54:16.435789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-09T22:50:39.184499Z digest=sha256:61f8973ff00f451a2a720f5c26791d9b9491830a0cd1b14664b021ce29215b9e

Observation 28355d39-a12c-4508-8d0d-ebb7d6a0fbf8 · inbound

Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models cites this paper.

Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:23:26.953983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T23:18:34.388467Z digest=sha256:c3d19d629f454f1e1df8f14381e96ca615b96c7f6f2fa364daf9d718be846b99

Observation 5acbb37e-2bc2-418d-adaf-363f32f67357 · inbound

Characterizing the Consistency of the Emergent Misalignment Persona cites this paper.

Characterizing the Consistency of the Emergent Misalignment Persona Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T10:01:29.631530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-07T08:06:58.635103Z digest=sha256:11e0db7c60396c5a5e56ad795f91dd86410eb9299979b210a10aa7cfc08d0a33

Observation 1cf8dfab-8c7b-4f15-a0a9-a039c9f6e1a4 · inbound

The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences cites this paper.

The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:46:15.975763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T17:08:29.027351Z digest=sha256:f85825e2b42b6fafa442d9570b0ceab4514d68da9459f63c06e2f84a496c3099

Observation d0c5d734-ac2f-43b0-a9c4-43ad5b7550fa · inbound

Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry cites this paper.

Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T00:33:52.231046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-21T00:29:53.240959Z digest=sha256:bc70f635fcfb7c55686ebabb379881f59b2248a58c1a00b87cf350a433a6ef80

Observation 99d16cf5-ba57-4952-8731-0731716c189f · inbound

Some[Body] Must Receive That Pain for Agent Accountability cites this paper.

Some[Body] Must Receive That Pain for Agent Accountability Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-19T19:47:44.425558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-19T19:46:03.728266Z digest=sha256:30913b0ba5d7c60aeaf55f15be0f9ba161e4fc8110a5ef49f4375743bdc96986

Observation b4aec935-bdc1-4315-a9ad-33ce0dd5a5e4 · inbound

Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs cites this paper.

Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-21T07:34:02.845037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-21T07:30:27.297971Z digest=sha256:2de511675a3ad3b531c9dfcad8f976d3a88afe34545ef008a520bb2eb2f83ec2

Observation ad0428f1-bcee-4759-9f61-e7ad5dafdbbc · inbound

Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs cites this paper.

Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-06-30T18:04:58.141031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-30T18:00:29.237972Z digest=sha256:071a0da35eef698447fe4491660a258a454e367f141c0edd0641d685a86bbda2

Observation 01b212bb-1da9-4eec-9275-067060916b1d · inbound

ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions cites this paper.

ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:34:48.451516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-30T15:17:37.904831Z digest=sha256:f7c317745219c2e14f30afaaa25ccc2bd4961b6d2f35e05ee951deb9b54a6170

Observation b121fd25-0392-446c-919c-4c9b7021fb32 · inbound

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition cites this paper.

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T19:42:35.860988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-28T19:34:27.009061Z digest=sha256:38063f0374660dfc009ef1a819af08e45c38decbb9b8964f768b1f7cd194b238

Observation d0a05eb9-25ff-4eaf-9be1-9dc206104170 · inbound

When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty cites this paper.

When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:16:57.140927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-28T02:16:46.396133Z digest=sha256:20aef0d1eb9e81ec898cea47bb742d7464a5382e1637a4e1f7098817d2b1a5eb

Observation 9c7c9ebf-113e-456c-bfb7-3715a8b77c8d · inbound

MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games cites this paper.

MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 30

Resolution
unresolved
no resolver link, observed 2026-07-14T10:15:59.479435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T10:15:59.479435Z digest=sha256:15d3dbfd569a8a70dba563763cd23f1caed357a9da241ecbb233e2b5620172ee

Observation 03735aee-4cea-47e3-85c8-a112486f23a9 · inbound

MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games cites this paper.

MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-02T07:15:01.993544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T07:15:01.993544Z digest=sha256:13194364632b1211d8cd718127fa3a61a475271ce6cc2362683e126aa8374ab7

Observation 6d64bd45-f16b-498f-b4b2-83caec73200d · inbound

Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect cites this paper.

Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-02T14:41:01.060123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:41:01.060123Z digest=sha256:ed6bcecd58a655862bca851ab607f25e8a6499d7175b67b2e582d1347122e23b

Observation e354eae5-30fa-43bf-a1c7-00604cc20572 · inbound

Verbalizable Representations Form a Global Workspace in Language Models cites this paper.

Verbalizable Representations Form a Global Workspace in Language Models Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-01T23:15:18.474238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:15:18.474238Z digest=sha256:19d87d8dd7d78c44ba95a65c734bfad274f6e3f78e2de6002df39b02a4a94962

Observation b55c7487-df42-4a6c-9c8a-85b7ea894d76 · inbound

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary cites this paper.

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T15:08:36.595363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:08:36.595363Z digest=sha256:af48a622143cb49b39b6c6988980b9d547c8b022705bab6b4018f09b75bec56a

Observation 2011e836-e6b8-425d-a35f-91a0ff7e857d · inbound

Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory cites this paper.

Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-31T23:35:43.173563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:35:43.173563Z digest=sha256:1c541cce6e725736a863c9ed71f220347f0b9555048f914bd0626badade93c89

Observation 18de718f-09dc-42c6-98f9-d936ada2e720 · inbound

Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models cites this paper.

Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-01T00:38:40.804086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:38:40.804086Z digest=sha256:77901f8add1913609c63afbf1e120efc2603a5c5d2aa73cb518799b5f8d0b437

Observation 8e474134-1a2f-4145-bfa0-7c155d077a0a · inbound

Asymmetric Communication: Large Language Models and Language Games cites this paper.

Asymmetric Communication: Large Language Models and Language Games Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 44

Resolution
unresolved
no resolver link, observed 2026-07-31T16:43:58.844236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T16:43:58.844236Z digest=sha256:9a8a501c9869288e9904ce57883564f0f8651efc112b1f0f2d88a2c8960daa35

Observation 35155c98-0973-46b5-8e3a-f506d0b29627 · inbound

Position: It's Time to Optimize LLMs for Self-Consistency cites this paper.

Position: It's Time to Optimize LLMs for Self-Consistency Looking Inward: Language Models Can Learn About Themselves by Introspection

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T01:00:03.398770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T01:00:03.398770Z digest=sha256:f9f02a4eaf61b855a6d15ee8c1c79c9d29ed223c51882f6efcc1cc024b988ce2