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Paper Citation Record · LEDGER

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents

As of 13 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2608.09885.

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

pith.paper-citation-record.v1
2608.09885 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:02:55.979647Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

30 of 30 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b47311e-61b9-4240-8360-4b04e597dfc7 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 1

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source=pdf_text observed=2026-08-11T05:02:55.760898Z digest=sha256:f06a78eee9615c177deeed757fcf93d359c85a4b01d83f6fc67316618757e095

Observation 59648144-b4be-4744-ae44-b9277fe4da27 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Constitutional AI: Harmlessness from AI Feedback

Reference 3

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source=pdf_text observed=2026-08-11T05:02:55.777107Z digest=sha256:d78a7858e34bc6897f4890426ef0ece1b37fb30a5aaae1c25554961129c0de8b

Observation e39a29c2-09f3-4466-a8f3-7f3dc3579363 · outbound

This paper cites Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems

Reference 7

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source=pdf_text observed=2026-08-11T05:02:55.810833Z digest=sha256:9a9d76c8feb9f2e101c86871679d91982ea213e8987f5369ab9d16f95f485a81

Observation b386a84e-feb3-401e-b7e6-ab9d42f2a79e · outbound

This paper cites EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systems.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systems

Reference 9

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source=pdf_text observed=2026-08-11T05:02:55.823305Z digest=sha256:2a3b67f853ec10f30dc6d2461946b7d07f23557ad0cc9b2d9ae5bc09de8cf766

Observation 1e307ff1-8b92-45be-a335-77c6387d303f · outbound

This paper cites Defending Against Indirect Prompt Injection Attacks With Spotlighting.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Defending Against Indirect Prompt Injection Attacks With Spotlighting

Reference 10

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source=pdf_text observed=2026-08-11T05:02:55.838094Z digest=sha256:eeef96e656b96d9d1dd8c5bddf81a966f37d5255ae08e7434fd0d269ee3c58e4

Observation e703a554-590f-4871-9e70-36de1de5e10c · outbound

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

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:02:55.843781Z digest=sha256:9240e181c77bd5896cca8a35a10cbecef31c8c4c5043beed15c384781d584ddd

Observation a1cf8647-ed82-4e2d-b2ab-08099c24e1c7 · outbound

This paper cites ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis

Reference 12

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no resolver link, observed 2026-08-11T05:02:55.848941Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:02:55.848941Z digest=sha256:efd91ce5e24e0a39b2bd5a6464e2ba8dba20b3ecafd382b42593e212d77606d6

Observation 2b102ac6-372c-4115-919f-d3a0fb43f1ae · outbound

This paper cites Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

Reference 13

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source=pdf_text observed=2026-08-11T05:02:55.855251Z digest=sha256:e398337d79974490d80b76740e439d759fe2e8231db10d94e4eba3824f894905

Observation 544b7471-3ded-4e37-827c-761d3ffd9ef0 · outbound

This paper cites AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

Reference 14

Resolution
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no resolver link, observed 2026-08-11T05:02:55.861028Z

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source=pdf_text observed=2026-08-11T05:02:55.861028Z digest=sha256:5d154a8b590440c879c15b0dc46cf446665ed0a48e6fe5fae71a3becf0e0e08a

Observation 64be4ab5-3505-42aa-beb3-c152b7422030 · outbound

This paper cites SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety

Reference 15

Resolution
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source=pdf_text observed=2026-08-11T05:02:55.867097Z digest=sha256:741a798fbcb90b8123ba80ae030033c4f2d25b0c9f49763b3d78e94b95488c4a

Observation 0ec64caf-d8cf-421c-b8e3-8cd77229cc6c · outbound

This paper cites Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-11T05:02:56.495472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:02:55.872708Z digest=sha256:9ae288316725600f1969c7db4cc540503407a2bfb411f3935da596a3cb4d5964

Observation 28f757f0-28c9-4a44-8035-1b018ec6cd7a · outbound

This paper cites Tree of Attacks: Jailbreaking Black-Box LLMs Automatically.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

Reference 17

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source=pdf_text observed=2026-08-11T05:02:55.878836Z digest=sha256:da93020acbfc66dbd574024b1ff16b2f9eebfef6603aa1b4f2f03929d1d7fe30

Observation 5f436c7c-442b-414d-a35e-23a55dcc8bc3 · outbound

This paper cites Accessed: 2026-07-26.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Accessed: 2026-07-26

Reference 18

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source=pdf_text observed=2026-08-11T05:02:55.884651Z digest=sha256:ce3eaf0aebfc06c40e239e1101aeb3f04332f0a03e0dc4e4878b3f0940060c17

Observation d1249860-424c-4b7b-88b3-e0cb440aefb1 · outbound

This paper cites Accessed: 2026-07-26.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Accessed: 2026-07-26

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:02:56.863243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:02:55.892512Z digest=sha256:c73b1907826485cb90a74036151508b300d991ba046e29bd692dba7a936072a6

Observation 22c603e3-ee8f-4550-88d1-4f9479adbddf · outbound

This paper cites Progent: Securing AI Agents with Privilege Control.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Progent: Securing AI Agents with Privilege Control

Reference 20

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source=pdf_text observed=2026-08-11T05:02:55.899019Z digest=sha256:56a6d5e7176b37eb390aa75a2d303e56de9d67e5e3c63fb5b7a7593c7634c87f

Observation 2e75b124-9ee9-4da2-a900-3571d20d6532 · outbound

This paper cites The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

Reference 21

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source=pdf_text observed=2026-08-11T05:02:55.905307Z digest=sha256:1b8544e882197a381d2aab95f9cc4ec1ed1a7480d0671007aa2081ae8ad4eb64

Observation 48f29f00-2001-444b-a4eb-cd40fd6e5ded · outbound

This paper cites ABSTRAL: Automatic design of multi-agent systems through iterative refinement and topology optimization.arXiv preprint arXiv:2603.22791,.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents ABSTRAL: Automatic design of multi-agent systems through iterative refinement and topology optimization.arXiv preprint arXiv:2603.22791,

Reference 22

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source=pdf_text observed=2026-08-11T05:02:55.910843Z digest=sha256:1e1fcf8d9391b97d219f75263228e2f84d150fc3d7e8aa0a2ce2a87db5529bd5

Observation 2ebbe66a-ac36-4289-ab7e-84375012b328 · outbound

This paper cites SkillOpt: Executive Strategy for Self-Evolving Agent Skills.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Reference 23

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source=pdf_text observed=2026-08-11T05:02:55.917946Z digest=sha256:62478b5afda8c295f993dd575a50ba7ae56f6d4344531d9a5ebfce9c82f0ab68

Observation 36977ecf-757f-4ff2-833c-282a924a43cd · outbound

This paper cites GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Reference 24

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source=pdf_text observed=2026-08-11T05:02:55.936859Z digest=sha256:940012cb2f9bdf6d2c35541957c5d1078ade34bb3d089e261cae1cdac53459b0

Observation de2969d2-cf4e-44be-9acd-9fedf27c5c64 · outbound

This paper cites R-Judge: Benchmarking safety risk awareness for LLM agents.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents R-Judge: Benchmarking safety risk awareness for LLM agents

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:02:56.836258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:02:55.945671Z digest=sha256:ec6ba6b5249341ebb39919e512b371269f5e3a59335251e98a7ec063c886e859

Observation cb546907-9ed8-4430-a3fd-2df7347e8166 · outbound

This paper cites an unresolved cited work.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:02:55.953057Z digest=sha256:83b5a32e251547c01e946837363b05c62b84d8475a5f9ad11b3b194e96d4c8eb

Observation 7eef1df5-5708-423a-8ac9-7b3b48e5d629 · outbound

This paper cites ShieldGemma: Generative AI Content Moderation Based on Gemma.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents ShieldGemma: Generative AI Content Moderation Based on Gemma

Reference 27

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source=pdf_text observed=2026-08-11T05:02:55.960250Z digest=sha256:de921b39fc76e19c1bfcd9fe237388081dddb10620628908e6bdc3b1ef1affab

Observation fc926af3-7cca-48b9-8cbd-ea7b61c90c9e · outbound

This paper cites Self-Harness: Harnesses That Improve Themselves.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Self-Harness: Harnesses That Improve Themselves

Reference 28

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source=pdf_text observed=2026-08-11T05:02:55.966211Z digest=sha256:be8e8c01cf258dee01eef5146fd5319ab8c79d4c5f1b6bef108343f9c80647e0

Observation f211ff1c-57d9-4883-b775-10c8a52fb53c · outbound

This paper cites Agent-SafetyBench: Evaluating the Safety of LLM Agents.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Agent-SafetyBench: Evaluating the Safety of LLM Agents

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-11T05:02:55.973774Z

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source=pdf_text observed=2026-08-11T05:02:55.973774Z digest=sha256:0f6481aa98bd0504ac6d7bf898fda05c4966f26f9d91e7a5b24accf41fe30462

Observation 478cb12e-20c3-4b5d-b3a8-2fa1705c798b · outbound

This paper cites ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection

Reference 30

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source=pdf_text observed=2026-08-11T05:02:55.979647Z digest=sha256:29f48bdd47329e563653187efb22aa1e0af536a8b7fcbf9b64d16adf46e8d689

Observation dea3bf96-73ea-4260-a878-3545167f9d19 · outbound

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

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 2022

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source=pdf_text observed=2026-08-11T05:02:55.784125Z digest=sha256:dc7b9361fad088316894a2d576024ec98482f0ce0231450afb016fc417913c53

Observation e5f63d2f-133a-4c2a-aedc-a302ffd42ca7 · outbound

This paper cites The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

Reference 2023

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no resolver link, observed 2026-08-11T05:02:55.793564Z

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source=pdf_text observed=2026-08-11T05:02:55.793564Z digest=sha256:4ee6e7a080e3e946ff007c19f1cc55386ebc58fb6e75d5a7e5eddf2f93c4c0e6

Observation edbd400d-218b-4fe3-8f05-4bead4d81db1 · outbound

This paper cites 10 Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, and Yuning Mao.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents 10 Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, and Yuning Mao

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:02:56.883564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:02:55.817249Z digest=sha256:57cb9b1b52ce0085826f6eb92f9f6cb02b472482d89830f0e68c1545d8df96b5

Observation 26eb5d9d-7740-4ba0-8bf1-e85c74f8eda8 · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 2025

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unresolved
no resolver link, observed 2026-08-11T05:02:55.770978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:02:55.770978Z digest=sha256:3499b2cecd71e1cec3e0f5e82d28d6c28e85912c29da9862ca54a2f1a2d38d90

Observation d37f90ff-5756-43b2-83a3-478c45d07777 · outbound

This paper cites LlamaFirewall: An open source guardrail system for building secure AI agents.

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents LlamaFirewall: An open source guardrail system for building secure AI agents

Reference 2026

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no resolver link, observed 2026-08-11T05:02:55.803576Z

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source=pdf_text observed=2026-08-11T05:02:55.803576Z digest=sha256:d68632479d9a362e49796793bec307303596ea231db35477f1ce696b63ece649

Pith citing papers

No inbound Pith citation observations are available.