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Paper Citation Record · LEDGER

Evaluating Language Model Reasoning about Confidential Information

As of 16 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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T16:52:47.399495Z digest=sha256:e84eb9e1fec2c40ba5ffa2235722f96a77ea7ef0d822c3462a7e14ab13e891e2

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=sha256:1457a6ff585fd06f740c62f22505df5847af1a8b31878e4bd30e297a28ee4036

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=sha256:8c081fe9e108c0f56902444641430a8ec2a68a85937110dd7ab2883b2383faa0

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=sha256:5e3a160fd1e2da2fd28c7b7eea800d27f79c1206688def901d893589083b0a4d

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:f89d340e90eedbca70b5dd52db9fd9a3f0effad0a9d40e00a94cc7abf8486272

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:f50220a05f7a0d7bb64d4d995493a3dc3aab50545626c6296c68847ffeac5f9b

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:ff4bbe8b8409daedf2918dadaefc5910ccdd1b893f8e433d619be2f1ac234414

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=sha256:51313b4afe274415fcf3d418271568fe6be403a964946e7adbd665084d189d37

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:ede0f0930eeb3ec203909ea68fe50810cabe2865b1866fe196efc2321b6d45b9

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:fce6b47dae2400a926e1792f835baad3753c8e97b8b08ef144236a3f4f646c72

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=sha256:22f454f4ea25ec2ac3e4b1d5f73fcf404f031ef65fb9c24f5dc14101e7b36ffa

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=sha256:44bc8280bd46fbaabe771c552aca44b3aa9edb0b6f032d527eec252ecd230530

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=sha256:9602d6e9f88fd5bc032de1c598039b47de72ae14a616da16d51453dbb74f6aa2

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=sha256:73bde828c2c1a01ebe0dabd3d13dcd40bf54e5233de8c5711579eb0a2e9a9930

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=sha256:7e0c18801ecf5af759cea1b1f4cec112e7f2b46078953e9a393d1dccb35dd1f1

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:b1911e57a4c5cf11506083c8963fe6470517faab97d11f561629bcb3e7dd8230

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:b90c3c3398305a6cdb7301d5d8d4a9743f845b9cde470aa48b7b83725d8d47cb

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=sha256:78a45a45c8a5a6fb990b26e9ff09abf5d7fac04c0ed749f868ea45a362b74441

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=sha256:3f41dcd327fd03e528f8138ad46753bafc56672954bd6688425f8f519d58c292

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=sha256:3fe783f86d7244089603b2e1058a2706121855f3019014809ac123f9f630366c

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=sha256:93ec87b651df6d71b7e9c55fec4b86439c8fd8803d5932c6675172a119dc6a83

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:dbbc8a917e42c3f99b3532b29cdf14f73ce3d4b1d31bb2fa050d3d2b6212c769

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=sha256:5e5770e1c391353365bd9e97e5c3eb47d1668e66c47df9d5737b50c1506190ad

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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source=pdf_text observed=2026-08-15T16:52:47.561700Z digest=sha256:181b7d73469c750440b70a2bcad128807f6948beff97ea6a7a9e2e28d1f7068c

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

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source=pdf_text observed=2026-08-15T16:52:47.572246Z digest=sha256:b1209d5f0322cb143c7922308e30861728918a7870c690dd63b2ddf892169a46

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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source=pdf_text observed=2026-08-15T16:52:47.566252Z digest=sha256:6746ac687f3b79a8bbe086b96c945d8afe8ae7b3b019ee965c6e41a626119904

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=sha256:232329fe96f4dd60131c0feb4cfdcbec5bb7c09d6d8f4a3f63769a7ed7a6bf9b

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=sha256:2fbee052a5545c7c143cbb554200dcf4cb39ac25788f579f79a7eea1157283bb

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=sha256:4c279eef05c910377aa249ea785c1209335c0eed738b4574d9c2d1c75e846081

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=sha256:18e8f3cf482617fc537d0e298ba003bf44060e05dd6c7dd1c7b947de20cf8b13

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=sha256:78ff6c8ceb13da6668a6187456ab9046739a6648e65c395100fb8e857655e912

Pith citing papers

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