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Paper Citation Record · LEDGER

Evaluating Language Model Reasoning about Confidential Information

As of 17 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2508.19980.

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

pith.paper-citation-record.v1
2508.19980 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:52:47.572246Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

32 of 32 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation f42fe708-dd30-4539-80ac-723e359d7cb4 · outbound

This paper cites Prompt leakage effect and mitigation strategies for multi-turn LLM ap- plications.

Evaluating Language Model Reasoning about Confidential Information Prompt leakage effect and mitigation strategies for multi-turn LLM ap- plications

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T16:52:47.399495Z digest=sha256:ff8e6f20640dff28e929174cd0c9a4a0b0eb01ac4753ec83e5fd6376d3902e23

Observation 2e0e3fe2-f9c5-4427-930f-18edc4e2f26c · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Evaluating Language Model Reasoning about Confidential Information Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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source=pdf_text observed=2026-08-15T16:52:47.411807Z digest=sha256:cfdd08959acdcaa256c6d1d40f887d917c1bf1cc3b238b43a327828754f2fc7a

Observation 6669c185-6eed-49d6-8c25-434a3bc8cafb · outbound

This paper cites Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models.

Evaluating Language Model Reasoning about Confidential Information Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models

Reference 6

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source=pdf_text observed=2026-08-15T16:52:47.432140Z digest=sha256:2c5501f555d7b7e958d6a34ea85c93a5f6c3e70aed1c123baa2046138a86951a

Observation 1648d5bb-b74f-45b2-a6d9-bea32523d993 · outbound

This paper cites Deliberative Alignment: Reasoning Enables Safer Language Models.

Evaluating Language Model Reasoning about Confidential Information Deliberative Alignment: Reasoning Enables Safer Language Models

Reference 8

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source=pdf_text observed=2026-08-15T16:52:47.443369Z digest=sha256:2b76ad5583eac83190a616f743ef4f5a11f26ef55e9e37eef52f2f45332c3eb0

Observation 96214bf6-1ba4-4227-b11d-cb9418ba1322 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Evaluating Language Model Reasoning about Confidential Information DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-15T16:52:47.448604Z digest=sha256:6ffde723a68ca7792abe826a69b192b778cec90af2b12a73c6419085050f8b9c

Observation 8f73a527-36f8-4945-900e-21f1b8fb4fc0 · outbound

This paper cites GPT-4o System Card.

Evaluating Language Model Reasoning about Confidential Information GPT-4o System Card

Reference 10

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source=pdf_text observed=2026-08-15T16:52:47.455685Z digest=sha256:55ceacc34312b1902b87166d2f288517f9ac5ea7d3c67b555d83db76e4e5ec2b

Observation c758c7d7-f648-4164-b243-fede705e2b77 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Evaluating Language Model Reasoning about Confidential Information Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 11

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source=pdf_text observed=2026-08-15T16:52:47.461071Z digest=sha256:6f87226d20df0bb0099f0674d695f96ad6a403b457aecb363d3f7fbf8c5d556a

Observation a71410a8-08ee-4239-b6c5-5d7cd5c2f155 · outbound

This paper cites Safety pretraining: Toward the next generation of safe ai.

Evaluating Language Model Reasoning about Confidential Information Safety pretraining: Toward the next generation of safe ai

Reference 12

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source=pdf_text observed=2026-08-15T16:52:47.467250Z digest=sha256:96b40c80583d561dbddf897f424a0a49ea585cefbcc3880835f2d2b3d99db47f

Observation 75a303ba-dd04-419d-bd9a-39f6c3661454 · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

Evaluating Language Model Reasoning about Confidential Information HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 13

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source=pdf_text observed=2026-08-15T16:52:47.472824Z digest=sha256:16a65671c57b1285a2ff984944bc0b4c54362fa40d9200349916239b5a510886

Observation a3e913d6-27af-4af8-a544-46b7653a9ca4 · outbound

This paper cites Can LLMs Follow Simple Rules?.

Evaluating Language Model Reasoning about Confidential Information Can LLMs Follow Simple Rules?

Reference 14

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source=pdf_text observed=2026-08-15T16:52:47.477922Z digest=sha256:630c767990e1a2c07f1435a72d0dfee4d3558bcf2529c090cc533ca281e28259

Observation 36dc7f6c-2c5f-4a6f-8fd9-cb615e8658c4 · outbound

This paper cites A Closer Look at System Prompt Robustness.

Evaluating Language Model Reasoning about Confidential Information A Closer Look at System Prompt Robustness

Reference 15

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source=pdf_text observed=2026-08-15T16:52:47.483616Z digest=sha256:2cbcfb15a123fcbd5c8195fe41f131419418dad9432a8b7c1227bb09182a3365

Observation 0a7f098c-9bc3-4288-9c08-67444ce3c695 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Evaluating Language Model Reasoning about Confidential Information SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 17

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source=pdf_text observed=2026-08-15T16:52:47.493891Z digest=sha256:636a9b55302d2ec39422feb24624a45d07be468e1c18bb00be66cdc27152415e

Observation 98454188-99f4-4c45-9a2c-f51f54524fb9 · outbound

This paper cites Jailbreaking LLM-Controlled Robots.

Evaluating Language Model Reasoning about Confidential Information Jailbreaking LLM-Controlled Robots

Reference 18

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source=pdf_text observed=2026-08-15T16:52:47.498632Z digest=sha256:d160b28b4c1ffa24c129cfbd8e9379e611fa1ff21f621d8d569116922e263b41

Observation 2795196e-45e6-458d-8120-f39063b2c735 · outbound

This paper cites Predicting the performance of black-box llms through self-queries.

Evaluating Language Model Reasoning about Confidential Information Predicting the performance of black-box llms through self-queries

Reference 19

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source=pdf_text observed=2026-08-15T16:52:47.503742Z digest=sha256:27ae683e13c471033b934015ab7f4980af7d312bd62695979d80d989c5870ed3

Observation 84c66cd3-aaeb-4702-9cf6-0ee28df97a34 · outbound

This paper cites Antidistillation sampling.

Evaluating Language Model Reasoning about Confidential Information Antidistillation sampling

Reference 20

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source=pdf_text observed=2026-08-15T16:52:47.509662Z digest=sha256:cd36a991b035d5f67039273a64841d6d72799b91b265919d73edd7ac996aae7c

Observation 4c401e31-80fd-42d7-98a8-32896ec4d246 · outbound

This paper cites Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming.

Evaluating Language Model Reasoning about Confidential Information Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Reference 21

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source=pdf_text observed=2026-08-15T16:52:47.514445Z digest=sha256:7214d051f51b9b8063d78acca698361c0031c6a42d3ba3ed4d11a5641631c0c9

Observation 47d29d1f-de95-49ea-acde-df0167319f14 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Evaluating Language Model Reasoning about Confidential Information Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 22

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source=pdf_text observed=2026-08-15T16:52:47.519835Z digest=sha256:8b5ea60c247aad55fbe39a5e90715e2c4d3f75671c2f42488634a6a8c0263ab0

Observation 35815867-dbe3-4cb1-b11d-5136d5061d30 · outbound

This paper cites Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models.

Evaluating Language Model Reasoning about Confidential Information Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models

Reference 23

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source=pdf_text observed=2026-08-15T16:52:47.524750Z digest=sha256:b46ef063d5bc931700c4cb6863e35e2ba86c7dea2597024710079d218eef9a49

Observation 1f7fe58f-43e0-481c-8450-15a2d8b072f8 · outbound

This paper cites The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions.

Evaluating Language Model Reasoning about Confidential Information The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

Reference 24

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Observation e94dfdbb-0546-42e0-9f77-aca3bab65642 · outbound

This paper cites Qwen3 Technical Report.

Evaluating Language Model Reasoning about Confidential Information Qwen3 Technical Report

Reference 25

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source=pdf_text observed=2026-08-15T16:52:47.535411Z digest=sha256:d4251d6101ba1d24e148d6089e9e84094ce75e1cf7bbf8a0d2c0b45547a4c5ea

Observation 90908afd-a11e-436f-8dff-d9fc765213f3 · outbound

This paper cites Trading Inference-Time Compute for Adversarial Robustness.

Evaluating Language Model Reasoning about Confidential Information Trading Inference-Time Compute for Adversarial Robustness

Reference 26

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source=pdf_text observed=2026-08-15T16:52:47.541094Z digest=sha256:52829200d386530eab87480b823b9e3c0693839b673fd6bddd4b147c1b5e132d

Observation d1a7b0b7-37ba-47da-9ea0-588d5c74fecd · outbound

This paper cites Backtracking Improves Generation Safety.

Evaluating Language Model Reasoning about Confidential Information Backtracking Improves Generation Safety

Reference 27

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source=pdf_text observed=2026-08-15T16:52:47.546538Z digest=sha256:d2cd85bf787263d4cb3d989e1a12956396425b7b0dc936ad3bce8ecd13e685c5

Observation bb9024e3-ba0c-4e5d-b643-bb0ff2c21409 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Evaluating Language Model Reasoning about Confidential Information Instruction-Following Evaluation for Large Language Models

Reference 28

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source=pdf_text observed=2026-08-15T16:52:47.551289Z digest=sha256:efba83e8e0bf8fcf91a4a5092dc3725e809b269e211ad58d10a01ef655ebfb44

Observation 1be5d31f-5cc8-4aea-94b0-27b1d288a608 · outbound

This paper cites Large Language Models can Learn Rules.

Evaluating Language Model Reasoning about Confidential Information Large Language Models can Learn Rules

Reference 29

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source=pdf_text observed=2026-08-15T16:52:47.556541Z digest=sha256:90c67cd6c7c46074b75bc50e2b9d3feb3dff3c89a975f210d63cb77b9787e892

Observation 68b5ed83-e030-443a-99be-4129d2ea2b33 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Evaluating Language Model Reasoning about Confidential Information Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 30

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Observation add381a1-d232-4eea-96cb-6a2a6f52daf3 · outbound

This paper cites Rating: [[rating]].

Evaluating Language Model Reasoning about Confidential Information Rating: [[rating]]

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T16:52:47.572246Z digest=sha256:c495bc56f0ed7fe13e1ac54c710c0bf33b317b5f5cbfb3627bf8586c864c6405

Observation 9963bfef-e6de-4133-819e-e0164ac93a29 · outbound

This paper cites However, we focus on cases where we do not have knowledge of the specialized target string.

Evaluating Language Model Reasoning about Confidential Information However, we focus on cases where we do not have knowledge of the specialized target string

Reference 256

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T16:52:47.566252Z digest=sha256:9a5903df14505f83a34f224b8581a07d58a305b12061bc96d4635deee474796d

Observation dcb888b3-01ac-4a48-af50-5c066515b242 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

Evaluating Language Model Reasoning about Confidential Information Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 2020

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source=pdf_text observed=2026-08-15T16:52:47.419836Z digest=sha256:d817f06135ef7cac41f461bc4c16eb2191e7d55a7156a16e1b01bd4840e6f5fe

Observation bce381b0-ab56-4b71-8529-27ee8027dfce · outbound

This paper cites Safety Alignment Should Be Made More Than Just a Few Tokens Deep.

Evaluating Language Model Reasoning about Confidential Information Safety Alignment Should Be Made More Than Just a Few Tokens Deep

Reference 2022

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source=pdf_text observed=2026-08-15T16:52:47.488631Z digest=sha256:e4b5435656a2b864d4f74cb2ca76566e53fb89f64512b31aa535a81d5ecd43c7

Observation 1595a515-9db7-48a2-bbe1-426554394451 · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

Evaluating Language Model Reasoning about Confidential Information JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-15T16:52:47.426749Z digest=sha256:600c54799449bbb6dec3033e0803ca0ddb15fbadadb39f51717c51769527ccce

Observation 572586c9-8a08-469f-8814-8ada343975f8 · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Evaluating Language Model Reasoning about Confidential Information Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2024

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source=pdf_text observed=2026-08-15T16:52:47.406169Z digest=sha256:ccb9508e1ecbd18ab695d2117096467b6f494d6ae5282228384acb21622da0c4

Observation d8400fb9-4462-45b1-9ed0-8d82afcc5b6f · outbound

This paper cites Stress-Testing Capability Elicitation With Password-Locked Models.

Evaluating Language Model Reasoning about Confidential Information Stress-Testing Capability Elicitation With Password-Locked Models

Reference 2025

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source=pdf_text observed=2026-08-15T16:52:47.437872Z digest=sha256:295c5fc75c6780fa6bf3cd453346ed20633bba50f011871fc4e306bf79ccd1be

Pith citing papers

No inbound Pith citation observations are available.