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Paper Citation Record · LEDGER

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2505.14585.

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

pith.paper-citation-record.v1
2505.14585 v2

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:37:31.442193Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:38:15.928177Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:38:20.641277Z

Reference resolution

66 of 66 outbound references displayed

  • verified exact5
  • verified fuzzy0
  • unresolved60
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39372ee0-310d-4014-9563-c6249e5ce4f3 · outbound

This paper cites Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Reference 1

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metadata mismatch
local_arxiv, observed 2026-08-07T15:37:33.808468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:24.568421Z digest=sha256:b032cf27a45b6f1bd59326037295a6a4532a9168e48b6725e5092425cb93dcfd

Observation 98d5261d-aec7-41d1-87e6-b061f686cbd0 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-07T15:37:24.652966Z digest=sha256:8439bcfd431930caff315e165cc26c1bbf28f29fab56259267f4d6e6d29ab888

Observation e8775409-1e12-4eb4-8e46-f34f00afda2b · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-07T15:37:24.786439Z digest=sha256:6ee263e91aef40b398ab913942eb102c9991b5588c32494ecd2930bc471c6391

Observation ba1cd59e-96f6-4094-8274-c9999a2bab12 · outbound

This paper cites The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

Reference 4

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:37:24.927576Z digest=sha256:411b2433428c9840e21a060c82f426b758490bf9ca2a0aeb7b3ad7fcf4a53b99

Observation 73256e9c-8ad7-463d-8010-e673d7e5b027 · outbound

This paper cites Extracting Training Data from Large Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Extracting Training Data from Large Language Models

Reference 5

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source=arxiv_source observed=2026-08-07T15:37:25.072025Z digest=sha256:aa849bedc54e735d94645e056a3206b47b8ab9f9ee259ad4a8dfb5878cec5d24

Observation 990725b1-be1d-46c1-914d-3fc89abcb71c · outbound

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

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 6

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source=arxiv_source observed=2026-08-07T15:37:25.204373Z digest=sha256:b92f294da8d6493d33bec3437b01901611f08b3ed671727e14ca36d540c7e5f6

Observation 479f46f2-999d-4390-b4b3-e682553bf8d2 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-07T15:37:25.308495Z digest=sha256:dd9d3ed638043cdfb71ad57302f8e8dddc1114838a62cdddf65ca5b4b06200d9

Observation 9465ff60-eece-440f-b092-eaf26ede060d · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T15:37:25.421845Z digest=sha256:cf9252e08269bcf310f2afdf5c40c36ddb59be4784d516506f99dfd34139fe6b

Observation b4c83403-ada1-4f8e-a984-2e5013396b1b · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-07T15:37:25.578725Z digest=sha256:1d6f980f458aa07c2d1a9c4c575be322afb0cf0266b6951cfd32fb86b1d80b4b

Observation 6f69d212-7e44-4e3a-b3e3-18fa67d7dd94 · outbound

This paper cites CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 10

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source=arxiv_source observed=2026-08-07T15:37:25.713365Z digest=sha256:2e9e04717d6c69969387b05004aa39fbcce01768b01f5ea6c0b1b73bc25e8460

Observation fc839245-d41b-4f72-8890-306b4f6ff67d · outbound

This paper cites Process Reinforcement through Implicit Rewards.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Process Reinforcement through Implicit Rewards

Reference 11

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source=arxiv_source observed=2026-08-07T15:37:25.785540Z digest=sha256:9ac15f8910cfad2df6bd28bfdc461f9c87e9340774c8c2752b9018da8a2ae0d8

Observation e587ec70-73d8-4fe2-9bc2-04b660216276 · outbound

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

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

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source=arxiv_source observed=2026-08-07T15:37:25.789933Z digest=sha256:3d0e43bd7ac7170d969682fbcd15127c25cf3d3ad05b351924a953c7ceb059fa

Observation 9dab7cdd-f49e-4fbf-b733-b9dc94183f9e · outbound

This paper cites Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework

Reference 13

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verified exact
local_arxiv, observed 2026-08-07T15:37:33.205897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:25.794251Z digest=sha256:986fda1ca86dc48bab6b4f8b4428b7d2b2eeef42e366315764442a31afdef99d

Observation bacdce3f-568c-49a5-ae88-18e3e8131156 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 14

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source=arxiv_source observed=2026-08-07T15:37:25.866924Z digest=sha256:a08a706cb8def71d585239400a4e7cc2c36d7ce0989f2cd9faac88c7b0c5f91a

Observation cbfdfc53-e9ef-4cc5-a835-1b4a994e05e0 · outbound

This paper cites GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory

Reference 15

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verified exact
local_arxiv, observed 2026-08-07T15:37:32.892279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:26.009555Z digest=sha256:078f8a9b0301f1529d689a2d432bb63cdc854555b589d2ef80e9a44ba87d4d43

Observation b702bf90-def5-4573-a119-0451594a1add · outbound

This paper cites Bias of AI-Generated Content: An Examination of News Produced by Large Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Bias of AI-Generated Content: An Examination of News Produced by Large Language Models

Reference 16

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source=arxiv_source observed=2026-08-07T15:37:26.097347Z digest=sha256:ef5b2fe622da30fe07a71e450d680d457e4334012ef7884471b92f5b51215fe0

Observation 27fb1375-bff2-4cba-a340-09b8db6da751 · outbound

This paper cites LawBench: Benchmarking Legal Knowledge of Large Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning LawBench: Benchmarking Legal Knowledge of Large Language Models

Reference 17

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source=arxiv_source observed=2026-08-07T15:37:26.187600Z digest=sha256:cbf613230b9e7c663d7b7a7509a0e955969baae4b4a03be717b32babd5fe6582

Observation fef6c64a-e147-43c6-a8b0-824ba5572579 · outbound

This paper cites Operationalizing Contextual Integrity in Privacy-Conscious Assistants.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Operationalizing Contextual Integrity in Privacy-Conscious Assistants

Reference 18

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source=arxiv_source observed=2026-08-07T15:37:26.304358Z digest=sha256:8c3a673c9c4b9731b73e0f0be616f98bb33d56a65a8297ff99083c76c19e274e

Observation b7a153d4-0757-4f75-a5ea-f25b1c108195 · outbound

This paper cites Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Reference 19

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source=arxiv_source observed=2026-08-07T15:37:26.435481Z digest=sha256:97daa5c44aa05942583feb34082ed5ecfbbfc1762248b21d5b6f41139e54c22c

Observation 067804ab-144d-4d0d-9762-7e1ba74c4a27 · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 20

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source=arxiv_source observed=2026-08-07T15:37:26.528544Z digest=sha256:fb58929137ac883d757a0b514558446fefa03a8b767b2f02018a74a955f709d2

Observation 6ba1bf51-3967-4541-bdd5-a3c618b1d5cc · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Measuring Massive Multitask Language Understanding

Reference 21

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no resolver link, observed 2026-08-07T15:37:26.628909Z

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source=arxiv_source observed=2026-08-07T15:37:26.628909Z digest=sha256:54d6b8dbc793af94173a709a80eff30c10e4624fb8e30f255626037aa9793007

Observation a2333286-0158-4099-8106-2cacae11a345 · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 22

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source=arxiv_source observed=2026-08-07T15:37:26.704153Z digest=sha256:416a63d06291145b895f87c4c817769d4d1d7e6c45d4e49bef4db912b2506897

Observation ec8375de-bade-4e2e-8ce2-8b5f1a83e83e · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 23

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source=arxiv_source observed=2026-08-07T15:37:26.780649Z digest=sha256:fd71080b4645c028f009112afb4fab18f69736b32a97a8cb26c3a28506f30bbb

Observation 4487fc13-7444-4f37-89e3-8d559c0be63a · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 24

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source=arxiv_source observed=2026-08-07T15:37:26.913997Z digest=sha256:2ac2f9568a9a2bf033031b5c7a950ab81d7fa3eeafb955e4b44a349e12b2cf29

Observation d1853068-8c23-40ec-bdd7-29e702130f21 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-07T15:37:27.029278Z digest=sha256:fde4c8240edb74799ead4a27d62e99ae4089cac316944d9169e071fc71dfbeff

Observation c59c179e-81a5-45a2-afc1-87ed334df0df · outbound

This paper cites Privacy in Large Language Models: Attacks, Defenses and Future Directions.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Privacy in Large Language Models: Attacks, Defenses and Future Directions

Reference 26

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source=arxiv_source observed=2026-08-07T15:37:27.148812Z digest=sha256:84cf9877dd6b13f94788e38bcd66aa111d2142dc68939f9af4a84497b38d4a1a

Observation 58eb9242-5b8c-4f41-9903-b5fdadd27428 · outbound

This paper cites Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory

Reference 27

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source=arxiv_source observed=2026-08-07T15:37:27.233556Z digest=sha256:49d3ca1464bdb367a1425b2d43f3a75909e428b4f774cc2ab458aaa0e5e4efdf

Observation f7e18a7c-af09-43e7-a55d-7dc2e77a5963 · outbound

This paper cites Multi-step Jailbreaking Privacy Attacks on ChatGPT.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Multi-step Jailbreaking Privacy Attacks on ChatGPT

Reference 28

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source=arxiv_source observed=2026-08-07T15:37:27.312304Z digest=sha256:ae76e82ed66f687f963c8bdb09bcb913aa25b246d1897099d80fdfefae7c75c8

Observation 7ba95d35-7c49-42cb-9484-dd87661f6893 · outbound

This paper cites PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance

Reference 29

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source=arxiv_source observed=2026-08-07T15:37:27.419956Z digest=sha256:a1f175949883f60179f9daa19d7f3f73a04b2bd4268a925ba98038576160d279

Observation 5980dbd6-2928-4bbf-800a-fc9376c9a236 · outbound

This paper cites DeepInception: Hypnotize Large Language Model to Be Jailbreaker.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning DeepInception: Hypnotize Large Language Model to Be Jailbreaker

Reference 30

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source=arxiv_source observed=2026-08-07T15:37:27.502326Z digest=sha256:4a48eab946aba5632abe91f576f63766350d8d502e76bf48c55085b82068cfab

Observation 5094c26d-9c13-4d18-bba0-2b2d99d36fcb · outbound

This paper cites Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Reference 31

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source=arxiv_source observed=2026-08-07T15:37:27.632302Z digest=sha256:72f2d4ed37be7c9eca7b35c96cc39385360bb5840cded4da0064c1582ffada3c

Observation b87d7704-760f-4733-9942-2bbf1c04246f · outbound

This paper cites Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism

Reference 32

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source=arxiv_source observed=2026-08-07T15:37:27.735776Z digest=sha256:cf66183e55e307e99b088524c8cb351f9f2c840217cc601edf8d74f5e69d6ad0

Observation c30a8834-e4ad-45dd-a616-498d642e8474 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 33

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source=arxiv_source observed=2026-08-07T15:37:27.835124Z digest=sha256:6cc4e4d14b02337cc7a57cd6a41e818a80714c6c7900c703c39fe04c00844029

Observation aa9fa9bf-f5d7-4496-8632-368ff503d553 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Prompt Injection attack against LLM-integrated Applications

Reference 34

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source=arxiv_source observed=2026-08-07T15:37:27.924319Z digest=sha256:68b8e58a9475709de7dc569543c80a1db548d4fa0f3ed9db66d9ba96edf05379

Observation 3d6c3987-3ffe-4eb9-9cc3-45ea47f9aea6 · outbound

This paper cites Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

Reference 35

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source=arxiv_source observed=2026-08-07T15:37:28.028759Z digest=sha256:8bea492d0c2439901a3f3af5472a012e427af4460e73c7e2fb4fb2ea6f1b3f06

Observation 11479c46-247c-49e9-a952-b96ccdbb76b9 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 36

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raw_fallback, observed 2026-08-07T15:37:34.235599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:28.162230Z digest=sha256:aa71d7b7055e5758752351b1fc47a65e1033e25ba78c5e7ebcd8fc44a8aac813

Observation de1ba10a-b695-469b-8824-53816953cf6f · outbound

This paper cites GPT-4o System Card.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning GPT-4o System Card

Reference 37

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source=arxiv_source observed=2026-08-07T15:37:28.278624Z digest=sha256:b5edf33961dc1115b9013e51b21ba4b5da11684acdf24575ac116882f3f4fc8d

Observation 62b6b9ae-bcd5-4c87-8601-37f27a930680 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-07T15:37:34.042678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:28.400443Z digest=sha256:eded8b1f1a696560638dbe8c5d422ce4f03553f5731ab6300101b91461289b93

Observation 6618858b-b737-4f8a-92d8-c33b401ca31a · outbound

This paper cites Training language models to follow instructions with human feedback.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Training language models to follow instructions with human feedback

Reference 39

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source=arxiv_source observed=2026-08-07T15:37:28.515613Z digest=sha256:50d37ba5ba3f1e06ddb385c0e6d18048db865b7455d2fc064e9e6beb14cbb11a

Observation 486809dd-1a11-4bb1-8fa9-8b0e89c82e63 · outbound

This paper cites Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta

Reference 40

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source=arxiv_source observed=2026-08-07T15:37:28.688518Z digest=sha256:d77697d76fa07d26ca29a4cd0fe9bbe0f8b37cb605beb995f009309c2b0e1c39

Observation 5cb2798a-9944-4a40-8031-4ef46e772760 · outbound

This paper cites Qwen2.5 Technical Report.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Qwen2.5 Technical Report

Reference 41

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source=arxiv_source observed=2026-08-07T15:37:28.876330Z digest=sha256:3426072dfbd48347e47ff979535e84f5e7a62d6ba28e3edbcc7d479d482f483e

Observation f3a04e38-52b1-4441-8be7-adac42b2cf39 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 42

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no resolver link, observed 2026-08-07T15:37:29.013764Z

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source=arxiv_source observed=2026-08-07T15:37:29.013764Z digest=sha256:87df637196e1f14a40fe9eab4dffca9776b4fb5451b536592ab7ebb6758108ab

Observation 62e3c68a-c131-4125-af26-f5768d6306aa · outbound

This paper cites Trust Region Policy Optimization.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Trust Region Policy Optimization

Reference 43

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source=arxiv_source observed=2026-08-07T15:37:29.124059Z digest=sha256:709603534194b72ec896c179b8983246dd579549037039e5f7fade2deb68a673

Observation 92e376a2-5fe9-4744-831c-413a80925506 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 44

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source=arxiv_source observed=2026-08-07T15:37:29.274966Z digest=sha256:ac463ec3ead007cf3b2421cca60e1dd6dd4d2bae5edcac8f45e2e33a93198557

Observation 66cd6366-c2e6-4c65-8bf3-be3220eeb346 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 45

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source=arxiv_source observed=2026-08-07T15:37:29.386957Z digest=sha256:62aca7738f55ca309f55e4c138ab6ea7e79aa19b24e7f017b98d6ddbf966a7d2

Observation 1576fe6c-af4c-432b-af4e-5c1b6d7860b0 · outbound

This paper cites Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Reference 46

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:29.522948Z digest=sha256:a07b9cfb7d89f18b615518c965c66392c98308a645270d6b67927000639fc417

Observation eb02cbac-84df-4a97-b20f-a9093090f566 · outbound

This paper cites "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models

Reference 47

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source=arxiv_source observed=2026-08-07T15:37:29.632571Z digest=sha256:dd0099857d9082545199520dbfbb5651e9934abd8f1c118145d3757b5989038e

Observation c1636e9e-1bc9-45b0-b391-92f2a876e4e8 · outbound

This paper cites INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling

Reference 48

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verified exact
local_arxiv, observed 2026-08-07T15:37:32.326115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:29.724188Z digest=sha256:cea61f918df1f224898104593436313ccad04503b0a55b07b2366c7b173032ba

Observation 4c6097ab-0a34-4a5b-9dee-9d50b7b73b76 · outbound

This paper cites Membership Inference Attacks against Machine Learning Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Membership Inference Attacks against Machine Learning Models

Reference 49

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source=arxiv_source observed=2026-08-07T15:37:29.827094Z digest=sha256:13ddb4829afb467b7fc3e56feb05a891a6a56790f8463c7839ef761c07a63aca

Observation fa043073-fcc7-4bcc-a37c-2c236a655372 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 50

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verified exact
raw_fallback, observed 2026-08-07T15:37:32.205258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:29.969859Z digest=sha256:b219d5a113a16a332f1d2f58faa15ccde74bcbbfc68813f33ce76b3f420895de

Observation 6d6c5988-c25c-4d7a-9a8e-699ac05660b6 · outbound

This paper cites Certified Defenses for Data Poisoning Attacks.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Certified Defenses for Data Poisoning Attacks

Reference 51

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source=arxiv_source observed=2026-08-07T15:37:30.109588Z digest=sha256:c377931a3d9675b6a9b38238f9d9ccb7699dc90a6e4e2b224c2eb193d6040ded

Observation a820e140-31ed-415a-bd95-f9abb0ac5a3b · outbound

This paper cites Data Poisoning Attacks Against Federated Learning Systems.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Data Poisoning Attacks Against Federated Learning Systems

Reference 52

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verified exact
local_arxiv, observed 2026-08-07T15:37:31.937855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T15:37:30.222364Z digest=sha256:49e064494a4e2b9793bcf32a880f02345c3ab92993f522ce4bfd4949831eefba

Observation b1eb8f5e-018f-4d87-a30f-b8e88e93bda9 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning LLaMA: Open and Efficient Foundation Language Models

Reference 53

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source=arxiv_source observed=2026-08-07T15:37:30.290138Z digest=sha256:03e4493690ca03e86fd1f2fb8e4454bbf7e23270188ee9e50c53b66797800757

Observation a4b6069e-05b6-4e2b-b0f4-b60cb5c7be29 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-07T15:37:30.379542Z digest=sha256:94232ef79af84f8e8d13f6b766e7571212076ff49288ffa5956e103c8910e62e

Observation 1fbee1af-5bd7-48d0-a783-857fd091681a · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 55

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no resolver link, observed 2026-08-07T15:37:30.498244Z

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source=arxiv_source observed=2026-08-07T15:37:30.498244Z digest=sha256:b36bc5fb425d6893a4c48a53b90fa84c9f2330f20f80d7dca8244c5b40ee0e4f

Observation bbcee3d7-8de2-4d4f-a310-df21ca0b17e2 · outbound

This paper cites Efficient Adversarial Training in LLMs with Continuous Attacks.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Efficient Adversarial Training in LLMs with Continuous Attacks

Reference 56

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source=arxiv_source observed=2026-08-07T15:37:30.568678Z digest=sha256:6e669e625c1f8970e74c7da3c3325f8668bb85ad5ed81110c0d0990d2d94eb7e

Observation 90cd7ff6-f1eb-4947-b4cd-1eaf29f02015 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 57

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no resolver link, observed 2026-08-07T15:37:30.637518Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:37:30.637518Z digest=sha256:efd8d4accac385fc5df272f344ef6b4246213b286a6fb48be874cf311fb90280

Observation 69f8b6ff-4719-4c93-b8b6-64d6c4e0eca4 · outbound

This paper cites Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

Reference 58

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no resolver link, observed 2026-08-07T15:37:30.734019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:30.734019Z digest=sha256:fe126ae5dda73df887e98be0f7b81e2139704051545cba8326dd971f9a72bb46

Observation 04bd2685-fe14-4ac5-a7de-b0ab2e454d5e · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 59

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source=arxiv_source observed=2026-08-07T15:37:30.835377Z digest=sha256:6fd59c6d4e15e198c90ca07ecad062225b3ac635918e0d376945112dcb1a9fea

Observation 39ef9557-10bf-46a0-a162-fdd838650712 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 60

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source=arxiv_source observed=2026-08-07T15:37:30.925687Z digest=sha256:add9217631f3b23b8e5e87906a1efb9a4e92a56bcb039266d9ea8355354f530e

Observation c7e0e76c-dfb0-42dd-a0ac-40850cb5cca2 · outbound

This paper cites Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 61

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no resolver link, observed 2026-08-07T15:37:31.014205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.014205Z digest=sha256:fb82451dd3c886e7c69642a685905747ca0c3f56645b35e3d795081fd7628321

Observation bde7067d-2eb5-44ca-bf9c-a58bf81fefe1 · outbound

This paper cites Differentially Private Fine-tuning of Language Models.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Differentially Private Fine-tuning of Language Models

Reference 62

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no resolver link, observed 2026-08-07T15:37:31.106379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.106379Z digest=sha256:4499993f264ca6f3419937f83ce255a2c4cb925d6e308fc484d81a1e6ce5f4df

Observation fd6fddfe-7dd6-4593-9f8e-389c59ea9d33 · outbound

This paper cites an unresolved cited work.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Unresolved cited work

Reference 63

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no resolver link, observed 2026-08-07T15:37:31.194994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.194994Z digest=sha256:0745d8a6ea41d5778f2d89b0bbaa44148ce9dfffec1dc062b1810cb22b664ec3

Observation 97a2a604-06e6-4a7c-ba26-a760461be9bd · outbound

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

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.261817Z digest=sha256:00ed9b9e0bbe2daf3f23e44085d39ba230267837c6c3dacc085f1ac81aef8568

Observation 1563a4df-d57c-4a8b-a89d-2a75f427ff5f · outbound

This paper cites online" 'onlinestring :=.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning online" 'onlinestring :=

Reference 65

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no resolver link, observed 2026-08-07T15:37:31.367911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.367911Z digest=sha256:d600e2a7501505f494722527935d29dc9d28483c590d01f3eec263e8c32625c4

Observation 2c0c20cf-47b3-4045-9497-cdbe27181b09 · outbound

This paper cites write newline.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning write newline

Reference 66

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no resolver link, observed 2026-08-07T15:37:31.442193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:31.442193Z digest=sha256:89324a92d92a802ee8851beb34d1da0189038f4021f95fbcf62330648b8da32f

Pith citing papers

Observation 48e6a4e1-d5b3-4f4b-bdc1-1603a6fae1b6 · inbound

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong cites this paper.

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning

Reference 21

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verified exact
local_arxiv, observed 2026-08-06T17:38:20.745079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:38:15.928177Z digest=sha256:9be02a2dd36a6ae05da80d4b04447e8eb43cbf0fb5608315c9da4889448b3f23

Observation b83fe0b0-dcaa-4c11-b003-c3021ae15ad2 · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning

Reference 156

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no resolver link, observed 2026-08-05T10:39:07.039355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:39:07.039355Z digest=sha256:5fe4aa45f8c9d0a54f5a1714dacc9b55dadbf1b4cb8bf9de30d39c8b8c36b0c7