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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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=sha256:38c7d6824976f8e367ac1de6fc34e4767508da262084ecdc8d7b442051c6a95b

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:f90bd0bf784a413c73d430555e7cdbb150e14dfd9696907507ea1c652b9e8a39

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=sha256:933014bf8150f36c05ef6e0c715314b77d65ac098103dc4d13cf842ff09d6903

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=sha256:60874809c27cfb2e25cb2c509ac9fd609704ab828583f20c2207f39aa94aeb82

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=sha256:44083cf680317ae12bca360387b64611bb8bc167c90cc320c58e29231f5346ec

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=sha256:4ddd32326003f3383814dcc20e314791db2a277215cef5fda91155063051db87

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=sha256:8804cb7b490897a0f909a78db00e0a1fd9d8020d132176a75318b41b2665b8e7

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:c5db8b7cb66c7e279e0abcd91e2066a4bb4f508411be4696c3a9b4f95944dca9

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=sha256:787eb8e475eb3867efef616fa346893fadafce27a46da9f931aae8a0264865a0

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=sha256:8fdd7cb449f1c241e4a30aba63029e702acdd5afea2a87957c0887ed0f0fe561

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=sha256:95f2516fc8189875dbf66ce957a9f728864115fcaac5e40b1acf15f10e614aec

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=sha256:8c8ccb704d960528eca88c9578f147d275a9f328ab4f55e937243e8e24618675

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=sha256:7afc630759f4acf1ca2d00bc3af1cbfc77950289577d60d4ee112f2c4b64ef31

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=sha256:725956ab3874090eaf24348a347d74c2085889a1ce25b362312085564d796e6e

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=sha256:6f1eacde30c1f8fdc5270ea161ff6121f43a9fedc130ec768a379de77ee76277

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:d3810a368cbb1ee26ab685676fec73f2b11296dd8bf132a060dbb83e98dc3511

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:f254685a5390933405bd47e0a8ce3c9cc3fc85c864e57e31ae8be9d31232ab2c

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=sha256:643be73ef95a7862065214923975abea5ef242d5473132d5ffd6bdd9e1ff4c0f

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:d0a1e661f6614b4d11e0389cc9f72f3e117fa5e12987e2327a7318bb6e213da4

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:cfaa80d6c4594020265b717fa4d8fcdd3e06a3fa7852656d6a402da178298920

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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source=pdf_text observed=2026-08-02T02:43:30.724747Z digest=sha256:9121ec8b1e87158729fdf41de8ecacd5016cf23f92dd1c18bc7e7f13767f551a

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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source=pdf_text observed=2026-08-02T02:43:30.778372Z digest=sha256:1aa32f159c128720920148fa0015338511f872c1bdd4e60431637d1aab001a41

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:c671de01285fbf265dc1dcb642220a183e1d6de4869a95484d26a4657f085f54

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

Reference 25

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source=pdf_text observed=2026-08-02T02:43:30.890355Z digest=sha256:c87e7199eaf87a7b907d374a7f9c88f08dc287981a6873c99cc409792040651e

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:d8caa1bf78c603b834b75639eb7b7198c805b8b281f03c6fc2fa561b1749e321

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=sha256:768b8c286c3a752c7fdc7e043e2aa00c414dfe19ac8896fff05d3948e87a5459

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=sha256:40e808b7669f984688ad4470d33e2fcf9728688c6157e0e321d010d6bffd94f3

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:ca00df1d595d5a69e16cd36aa13ff3786f672b0cb6c56035126ed69dd07b96fe

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:c6e591f91b8f20ecba5f7a58941c2c1e38690a49ba9c2d3ae8a7c19f35dc530a

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=sha256:971e423cf71e92822d5c73ffa8c7d0577adb6b16762c9ace594e5e45da4f4640

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:fd472a600be8a64408eb1af6a08cb047f161bab0f27fc1c625a80e32fe2cb6ee

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=sha256:29833c92aaf4586e0b5ab375971f5540209dfc7b33828d9fe32fcbde23840518

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:aa84ee9cbf2aaf3d1df81bbff476ea30de4af1ca2599836f042cd5132f0ad1b6

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=sha256:1dc6084f7451e574d0d01a1dad1741832d4bfdcda9d713f26ef6e292f197c568

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:f4ffa60d654ffb0672996b3ccea688ee4e6be04025ca17a659b125c7e9559957

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=sha256:3b6dc94a64d261976f581f21d34e3867cc24596269cafa2b9ceedf28ff88f4f9

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=sha256:1859500e1100042d47302c33945faea2bbc882ca839c9acb0f8b5a1a96a1624b

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=sha256:999bae1a5639829d778efa9a08a854b8bb800c039a3440bfae54360951ed1df5

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=sha256:448bd8e61acec399e1cd3ae8ec7d45eceafedc75a04ec16a0d7cfa7433fb4f35

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:ee05c9c7cd1af2bbfaeb157311a8eaa92a9742d82c135c3762fed45b25da28a7

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=sha256:44e265001f21c9003917ec0476f3350fdff12ff4fd3c83068ffd46d14123f1fc

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=sha256:5a106599c54bbf4ce58089f9b7c7126c3415483c56e523c3a308ef7d95c878cd

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=sha256:07213c171b6ef6b9e3d0a98ce9e7b63edc4571daa637f743a670008ada9ba2ac

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:ef4dcdd3e321692048b53886c4704689f5427ffe23cb0d2e5b9155eb619ad40a

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:e3c614a21730db907c328d752ecd15ff9b86d1da07bc6d99a2328c1e22f12acc

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:e5b807f302619415e6f1cebbf96c569540695b01e490a3b76bac0949ad91c64b

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:c9030206fcd67c3f24d42a1bc1fc75e5381957c745de64e86a9e01c8d57ee150

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:c51adc4d605c54e2cf5672c96b6b91f884b9a4271d38a56b9e7efe49b36ea538

Pith citing papers

No inbound Pith citation observations are available.