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Paper Citation Record · LEDGER

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt

As of 21 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2607.14250.

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

pith.paper-citation-record.v1
2607.14250 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:43:33.342084Z

measured 49 of 49 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved48
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 990814da-91e8-4b16-ad82-06822294939d · outbound

This paper cites CLAMBER: A benchmark of identifying and clarifying ambiguous information needs in large language models.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt CLAMBER: A benchmark of identifying and clarifying ambiguous information needs in large language models

Reference 1

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source=pdf_text observed=2026-08-02T02:43:28.360816Z digest=sha256:9e4a5da1b3f1640f4f2d91001fcd21da12f73e2cf492e941f2061d0773220f56

Observation a0f2d0e4-bcd6-4281-8d04-3585da275377 · outbound

This paper cites CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models

Reference 2

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source=pdf_text observed=2026-08-02T02:43:28.431108Z digest=sha256:6eda79817cbb175ce921d31ef18610e5f9e57be4c7a6bd9fe2bbab6c014e066f

Observation 8f29ee48-fffa-4e88-8cd0-2e12fb84f5cf · outbound

This paper cites MemGPT: Towards LLMs as Operating Systems.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt MemGPT: Towards LLMs as Operating Systems

Reference 3

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source=pdf_text observed=2026-08-02T02:43:28.606096Z digest=sha256:78378720da0204074c389dabfddfae50bfc7e01238b568d020bab9aa6a9d9fc3

Observation 994bde63-5efb-4ea6-920a-72f1525275a3 · outbound

This paper cites Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Reference 4

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source=pdf_text observed=2026-08-02T02:43:28.694633Z digest=sha256:89ee0058d5d8b016e52cc803e7965ba0bbda1cdc63cb94938b7ab4cc93c35fc3

Observation a831186f-9ed5-4dee-af27-6b7b9e09b7e8 · outbound

This paper cites Memory and new controls for ChatGPT.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Memory and new controls for ChatGPT

Reference 5

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source=pdf_text observed=2026-08-02T02:43:28.842215Z digest=sha256:51df66299f7fa9867aa59a3f3fbec2fa86d31d32e226ca9c8b1fe729407fb04d

Observation e4a18eb0-b327-4e34-af61-308667ea0bb7 · outbound

This paper cites Smithson.Ignorance and Uncertainty: Emerging Paradigms.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Smithson.Ignorance and Uncertainty: Emerging Paradigms

Reference 6

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source=pdf_text observed=2026-08-02T02:43:28.974895Z digest=sha256:4d95017ef27e19c7a4551b8ce2c9a92cf2ede93a1f23b883b05a7676e5d5dc63

Observation 623ec07b-631e-4e74-80d7-ddfe8709efd3 · outbound

This paper cites System card: Claude opus 4 and claude sonnet 4.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt System card: Claude opus 4 and claude sonnet 4

Reference 7

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source=pdf_text observed=2026-08-02T02:43:29.082848Z digest=sha256:acb11f56503f7d78024e7ce42a254815cb916c0fe761d69d9d5637f39c141d9d

Observation 0c2c6740-97d3-4f1c-af20-205cf7b3bdee · outbound

This paper cites Llama 3.3 70B Instruct model card.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Llama 3.3 70B Instruct model card

Reference 8

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source=pdf_text observed=2026-08-02T02:43:29.229848Z digest=sha256:7f43a3648e13275b90a1ca63b8dfee1767cba8ae12081b0b6721ad2e939471e1

Observation 62015736-0267-4ee6-9476-1239b665fe2a · outbound

This paper cites DeepSeek-V3 Technical Report.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt DeepSeek-V3 Technical Report

Reference 9

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source=pdf_text observed=2026-08-02T02:43:29.375173Z digest=sha256:474df9275973fb2f506ec887cbdc9f86f2c891f6c2d40356f4d6a42e48e69a69

Observation 3925df77-895c-4739-8949-d87ccd98b32a · outbound

This paper cites Gemma 4 model card.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Gemma 4 model card

Reference 10

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source=pdf_text observed=2026-08-02T02:43:29.487894Z digest=sha256:4f08ce0a8e6c551000814260a6b6cf77424c263093929f2ee7668c0bb7d8493d

Observation d7e18852-4e79-4943-b035-95ae01ef206d · outbound

This paper cites Qwen3 Technical Report.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Qwen3 Technical Report

Reference 11

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source=pdf_text observed=2026-08-02T02:43:29.599354Z digest=sha256:c5295fdeafa32f4cc0c1728b383ddd8baa6cb09c24449e021302b1c99ec136b0

Observation 090f1c9d-f2f7-4e2a-bdaa-20f804b7afce · outbound

This paper cites Update to GPT-5 system card: GPT-5.2.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Update to GPT-5 system card: GPT-5.2

Reference 12

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source=pdf_text observed=2026-08-02T02:43:29.744794Z digest=sha256:59a0e651f04536a44b3fc5f2c94ce6b680eece60d09eef05fa6319218b236ce5

Observation 8d261a53-95db-42ab-85a4-23ad7167a9fc · outbound

This paper cites Language Models (Mostly) Know What They Know.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Language Models (Mostly) Know What They Know

Reference 13

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source=pdf_text observed=2026-08-02T02:43:30.026093Z digest=sha256:5507aa059fd751ee8386599baf4ea259827d767359e6a74a2026cdd4e518d988

Observation 030d744f-975e-4af8-9421-d36b40756ed4 · outbound

This paper cites Teaching models to express their uncertainty in words.Transactions on Machine Learning Research, 2022.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Teaching models to express their uncertainty in words.Transactions on Machine Learning Research, 2022

Reference 14

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source=pdf_text observed=2026-08-02T02:43:30.165840Z digest=sha256:9aede8211ec227b6c21cfec2070f792bef3ab98024f824a187b2112ecaedbd46

Observation b89dc18d-b62a-45ee-af59-35b5b6a0f5da · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.Nature, 630(8017):625–630, 2024.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Detecting hallucinations in large language models using semantic entropy.Nature, 630(8017):625–630, 2024

Reference 15

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source=pdf_text observed=2026-08-02T02:43:30.232369Z digest=sha256:99226c4832afe34673d8758fb2356577d705ea9ead3178dae6a7d54806d62f37

Observation 70d61ba3-a143-4d42-82c0-f50acc205227 · outbound

This paper cites Do large language models know what they don’t know? InFindings of the Association for Computational Linguistics, 2023.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Do large language models know what they don’t know? InFindings of the Association for Computational Linguistics, 2023

Reference 16

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source=pdf_text observed=2026-08-02T02:43:30.290177Z digest=sha256:05726d3c2624d1d9163de2acb1e4a93ca61b9768418e712a871bad375296b03f

Observation fe4aeaad-4618-46a7-816f-a4d70be87768 · outbound

This paper cites Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models

Reference 17

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source=pdf_text observed=2026-08-02T02:43:30.389343Z digest=sha256:a78cdc602eab128db3d99356f172982a725dd453049607cc09198485c537ea3e

Observation 14ef2e2d-b076-425e-bd94-03a0408e16df · outbound

This paper cites Do llms estimate uncer- tainty well in instruction-following? InInternational Conference on Learning Representations (ICLR), volume 2025, pages 95951–95974, 2025.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Do llms estimate uncer- tainty well in instruction-following? InInternational Conference on Learning Representations (ICLR), volume 2025, pages 95951–95974, 2025

Reference 18

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source=pdf_text observed=2026-08-02T02:43:30.476397Z digest=sha256:c5320a4956da7d47c70f94f45d65a158bce35745440a262288d14a9151f18c11

Observation cc9a20df-b458-4424-95f4-e520576d3573 · outbound

This paper cites Evaluating large language models in theory of mind tasks.Proceedings of the National Academy of Sciences, 121(45):e2405460121, 2024.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Evaluating large language models in theory of mind tasks.Proceedings of the National Academy of Sciences, 121(45):e2405460121, 2024

Reference 19

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Observation 60ace4c0-58ee-40fa-a436-d4e569ff3a88 · outbound

This paper cites Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

Reference 20

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source=pdf_text observed=2026-08-02T02:43:30.604719Z digest=sha256:9ac6b12f7e9ad447f4191d6fd71ee416bf66d433421cd9ccaf37d168c5003c18

Observation ccc56e81-e17d-41da-a592-bc3400e35169 · outbound

This paper cites Neural theory-of-mind? on the limits of social intelligence in large LMs.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Neural theory-of-mind? on the limits of social intelligence in large LMs

Reference 21

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source=pdf_text observed=2026-08-02T02:43:30.661451Z digest=sha256:1feb5c3a52df29beb12ed529cd48e92c882a6bd980502ffe89c3ad0b4bd44398

Observation 39cfb614-c08a-461c-9374-f09786c0a13e · outbound

This paper cites Me, myself, and AI: The situational awareness dataset (SAD) for LLMs.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Me, myself, and AI: The situational awareness dataset (SAD) for LLMs

Reference 22

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Observation 2f4c38b7-7bd1-4e2e-a006-35b8884f0305 · outbound

This paper cites Tell me about yourself: LLMs are aware of their learned behaviors.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Tell me about yourself: LLMs are aware of their learned behaviors

Reference 23

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Observation d692f618-6e12-485c-81da-054f0d93a10b · outbound

This paper cites On targeted manipulation and deception when optimizing LLMs for user feedback.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt On targeted manipulation and deception when optimizing LLMs for user feedback

Reference 24

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source=pdf_text observed=2026-08-02T02:43:30.842157Z digest=sha256:2734aefa9c6a404f282dc691bd1494adacb933c2023aba31156116dc850945d9

Observation 49fd367e-fb71-4a8e-8cab-e4abc8cccfeb · outbound

This paper cites an unresolved cited work.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Unresolved cited work

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source=pdf_text observed=2026-08-02T02:43:30.890355Z digest=sha256:9e650f85c12b877315e121d5a379457d5600fcbdf00d59e9c7ce68e2ef35ae6e

Observation 8155f19e-cfab-435b-bae9-2937dbda5ddf · outbound

This paper cites The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses

Reference 26

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source=pdf_text observed=2026-08-02T02:43:30.971890Z digest=sha256:9b019f3d835e9d463abd41bd96dbbdd21d8209cac26c5e2b0933d74d37bdf716

Observation a33b2cdf-56d3-4b54-9251-6c884952bd8e · outbound

This paper cites an unresolved cited work.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-02T02:43:31.066437Z digest=sha256:9a9ea291ce8cd54c54f67ac22c3e96773a79c570373dddf8c768c1aeeed75f92

Observation 33a64fc0-e224-428b-94c9-344804c7f43a · outbound

This paper cites Gui, Tianyi Peng, Daniel J.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Gui, Tianyi Peng, Daniel J

Reference 28

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source=pdf_text observed=2026-08-02T02:43:31.147517Z digest=sha256:6f87ddf00bb08f2405d491fb5e3c571ff387483b94d4e87c5578a2835fdd533d

Observation dc6c7141-19ad-4a16-8ec7-f336c4558600 · outbound

This paper cites O’Brien, Carrie J.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt O’Brien, Carrie J

Reference 29

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source=pdf_text observed=2026-08-02T02:43:31.240101Z digest=sha256:27e6804cdba9ebad8afe48b78cb195d384559e30a59e1a612b38ef72567f130b

Observation b0e755fe-4558-4b35-9b51-529ff5b9c9d3 · outbound

This paper cites LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

Reference 30

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source=pdf_text observed=2026-08-02T02:43:31.335385Z digest=sha256:edc0247e1ad6cd0a04c7088803dbc4724e5fa28f850a69674511c4182ce56d9f

Observation 47eb48d5-dee7-4c2e-b4ec-6d858008d583 · outbound

This paper cites How far are LLMs from being our digital twins? a benchmark for persona-based behavior chain simulation.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt How far are LLMs from being our digital twins? a benchmark for persona-based behavior chain simulation

Reference 31

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source=pdf_text observed=2026-08-02T02:43:31.422258Z digest=sha256:924f2c8b300fb4cc5826bdb67f657ba7ca7af75357cc875f3a44f851063c418c

Observation 88b977ad-5713-4934-98d1-a467f035f938 · outbound

This paper cites KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation

Reference 32

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source=pdf_text observed=2026-08-02T02:43:31.512863Z digest=sha256:21f742f44d31d966aa93576fed1f7830fba8d14e2b01c7329bb4207a80b52039

Observation a00d0885-1a0b-4160-afb5-8b4345a0fb33 · outbound

This paper cites LaMP: When large language models meet personalization.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt LaMP: When large language models meet personalization

Reference 33

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source=pdf_text observed=2026-08-02T02:43:31.676776Z digest=sha256:d982ffa237a99b853f8a25c9dd6248b564fe40b8d5017a0fe2d33b50c64f39b5

Observation 749f6a9c-0e88-4bb0-97f8-9fbb299924e6 · outbound

This paper cites PersonalLLM: Tailoring LLMs to individual preferences.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt PersonalLLM: Tailoring LLMs to individual preferences

Reference 34

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source=pdf_text observed=2026-08-02T02:43:31.782850Z digest=sha256:5fdd841e8f410f41788b607ed327c72f0946f61a1b241e2be5b51b21857ccb53

Observation e44a0cfe-6850-4677-807b-d242b9faa6b3 · outbound

This paper cites Whose opinions do language models reflect? InInternational conference on machine learning, 2023.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Whose opinions do language models reflect? InInternational conference on machine learning, 2023

Reference 35

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source=pdf_text observed=2026-08-02T02:43:31.918874Z digest=sha256:fccc4d0342694cc52e8edf8b08d71df6ba579b7d6a1551cb8998690497adf391

Observation 5a6df131-643d-484f-81d4-9742b6be6902 · outbound

This paper cites Uncertainty of thoughts: Uncertainty-aware planning enhances information seeking in LLMs.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Uncertainty of thoughts: Uncertainty-aware planning enhances information seeking in LLMs

Reference 36

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no resolver link, observed 2026-08-02T02:43:32.057514Z

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source=pdf_text observed=2026-08-02T02:43:32.057514Z digest=sha256:f2bf66360323f4868448f39140f884a6a9c9b812ae061f91d9ee54e26b65ef2c

Observation 2644567b-bea9-492a-8388-06372bbc23ed · outbound

This paper cites Bradley Knox, and Eunsol Choi.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Bradley Knox, and Eunsol Choi

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.166412Z digest=sha256:e063ff06092dcfca5d6a01a4e167eb20b55c0a8b1f8f8cc771309cc676e7d29e

Observation 30ed1ae5-3b34-4d69-9019-907ee9e147e3 · outbound

This paper cites Li, Alex Tamkin, Noah Goodman, and Jacob Andreas.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Li, Alex Tamkin, Noah Goodman, and Jacob Andreas

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.271204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.271204Z digest=sha256:0f5676002af5662c54c49dfb51b912eea8e48692bb5048a7fd56235286a15c40

Observation 505dea10-97be-4fb1-b95c-67f044370ca2 · outbound

This paper cites STar- GATE: Teaching language models to ask clarifying questions.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt STar- GATE: Teaching language models to ask clarifying questions

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.337980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.337980Z digest=sha256:cecd2b41806defff2ed5df2456f92b8a67f4898ed9da8104d5cd9e5e2a22a836

Observation 3cafd394-b31e-44f3-9ec6-616d6d3e5535 · outbound

This paper cites Artificial intelligence, values, and alignment.Minds and machines, 30(3): 411–437, 2020.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Artificial intelligence, values, and alignment.Minds and machines, 30(3): 411–437, 2020

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.385125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.385125Z digest=sha256:2216a6138a96e0433aa53a9fdac913dbff3f3978ca881754f7ac7df41bffd409

Observation 012718d1-0371-434e-b123-7cb81c13cd4b · outbound

This paper cites LLM alignment should go beyond harmlessness-helpfulness and incorporate human agency.Cognitive Computation, 18(1):26, 2026.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt LLM alignment should go beyond harmlessness-helpfulness and incorporate human agency.Cognitive Computation, 18(1):26, 2026

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.481612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.481612Z digest=sha256:6d0b4eb3354eca7cf3572f5d43c2dae50d0788428ab1e6feaa88d5277c98fb4f

Observation 170df78d-4c2c-43dc-801c-85cb8efb59b1 · outbound

This paper cites The benefits, risks and bounds of personalizing the alignment of large language models to individuals.Nature Machine Intelligence, 6(4):383–392, 2024.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt The benefits, risks and bounds of personalizing the alignment of large language models to individuals.Nature Machine Intelligence, 6(4):383–392, 2024

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.546319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.546319Z digest=sha256:3117e335745a7ea44259c1f69136d07972d790a8cd1acb428550cf9a73363794

Observation 2afe72f4-ae31-4d6a-bae1-229040d6f11e · outbound

This paper cites Discovering language model behaviors with model-written evaluations.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Discovering language model behaviors with model-written evaluations

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.677034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.677034Z digest=sha256:0d44cc14a5a7f556d38ebea194efa8af67ca28d63077015c1d3c13a36d437d30

Observation a981535e-3f02-403e-93b7-46f94cb1fceb · outbound

This paper cites Syco- phantic ai decreases prosocial intentions and promotes dependence.Science, 391(6792): eaec8352, 2026.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Syco- phantic ai decreases prosocial intentions and promotes dependence.Science, 391(6792): eaec8352, 2026

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.787925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.787925Z digest=sha256:c4d11c1a1c2f2665ca6b5f16297c5a7738a421547c0616225f3d1ba9ad4f2930

Observation 4972b3c8-dfad-4ff7-b9c8-1e278f1ea5ff · outbound

This paper cites Your agent, their asset: A real-world safety analysis of openclaw.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Your agent, their asset: A real-world safety analysis of openclaw

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:32.900924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:32.900924Z digest=sha256:882dec0e121352060f9d5e036cda901555335959c00cdea5d1bd1f67b362d991

Observation 44d376a1-1a6d-4fe5-8a55-6cf15a8d0fa1 · outbound

This paper cites Openagentsafety: A comprehensive framework for evaluating real-world AI agent safety.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Openagentsafety: A comprehensive framework for evaluating real-world AI agent safety

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:33.044867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:33.044867Z digest=sha256:197520947d1536a617b6aa34f905c75f0946acecb39fbc7c3a72b896759eff79

Observation 121a8029-a52b-4c56-be39-78eefdd4890d · outbound

This paper cites Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:33.223740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:33.223740Z digest=sha256:c4ebedd3f841124f32a74b5390b4583a8c1391385189a13d08e88a9c4dd5f56b

Observation f1ce11a8-031a-4a9e-9dd1-9dc8ea5af0e0 · outbound

This paper cites I want to start training for a marathon. How should I begin?.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt I want to start training for a marathon. How should I begin?

Reference 48

Resolution
malformed identifier
no resolver link, observed 2026-08-02T02:43:33.342084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:33.342084Z digest=sha256:c8f58d76f138d2e9cee5665a44e91794c92b6255a79de225f912b3d5d047f0c0

Observation 634a3fd9-fa6d-4336-8898-f7f1e416473d · outbound

This paper cites an unresolved cited work.

The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt Unresolved cited work

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T02:43:29.894934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:43:29.894934Z digest=sha256:6939993684fb5218dbb4e361c51405765b79adc7ca59a72435e3d938451820b6

Pith citing papers

No inbound Pith citation observations are available.