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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 2026-08-12T06:34:41.77262+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:bece2ea8e9ed5637fa73cb760b02122424ad7973faa961747f9dfd9d869195da

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:df6284c56cd3bf2c373eb87de5e24ffaccb1d511104a7d1bfd813594f6e5446c

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:c96f109b97ea6ecf4fddc02d372eb5dd565a2824ac48594caca89e6ae4ae03b2

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=sha256:0a6a0a6fb2647622065e2d5264dad856edce7ced68fef77935b89390b9b6a4b6

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=sha256:3a66d764c9f547d4e5fe6783038fea5142ee34979bed3d27509e7e0c6b26c06b

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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source=pdf_text observed=2026-08-11T05:02:55.843781Z digest=sha256:3ddbd8ca08bb8968ded92023222e2a31fa6f8ceca336e4017931c8c1af737d77

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

Resolution
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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:d67b93333e1cb9cac20caecc72bba81b77dfe0edac1441086bad1c780fc06947

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=sha256:0727d7dcc6fbd15edafa2fba576c6ab6bd8f066ace0dff17a3bee7b9cb3708ea

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

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source=pdf_text observed=2026-08-11T05:02:55.861028Z digest=sha256:f891a8e580b17b6a9644be83c5d83205137a159da90b44653c77a87703680552

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

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source=pdf_text observed=2026-08-11T05:02:55.867097Z digest=sha256:ce18d52b7687590a0ec174e8591bd2dac51322051857e27394859f0c356316f0

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T05:02:55.872708Z digest=sha256:a3372034e1498c5eb49e2b87b222649a8794a104621c6259c547841c20b8783c

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=sha256:2b9fbf0294d0ed090914dce9f1d1a10a6ed97e06d694bf00a862c20a00853e88

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=sha256:08828d29d8dc0c1b53c226ee1459ef22e6d47478b492c04ed83b5cfe811f310a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T05:02:55.892512Z digest=sha256:792279b3d07a8cd2cbfd39b5d007db7b5225ca79b3403e713ac9e77a448706db

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=sha256:07c460c547580039bc6af620dd740c276734e4f59ca7ba56b76f71ab458e6684

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=sha256:6f354b3e04a92ea1a0e0d47edb7427259435602788224d2cc8825c120a302bc3

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:c799b70d339e2bc18097a12645ac1cd4d88b4be7ae32b9113d81721a5238b81b

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=sha256:6bb3f7b61996657c326d35d1e3fa418a0893af210c344dece32f28bd0be1c106

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:ee5b1fb9398f450d9bbaab948f38f1d6821d329297463cb8cb0dcf6630d3aa5c

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T05:02:55.945671Z digest=sha256:9c4d15643f9acf0acea23a598cb1988dc4edeb1736358318b060e22d0c15a1dd

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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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T05:02:55.953057Z digest=sha256:31616c17c3f3bd1b8814c997b45631221cd764c5dd238bcf4b95bd39c027b12a

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=sha256:75bd7870c654f4a68a5da982161c28f05f339299232c4ca90530c366bffb5ce6

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:c5a7d8efdc84930a8a84ae70778ef85e986621924504540980c48d2a3edb7fef

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=sha256:62e05d560b3c427b66d9411031f5fb88c5f5a62322239f444ccd4730903cc2a2

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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unresolved
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source=pdf_text observed=2026-08-11T05:02:55.979647Z digest=sha256:6f05747bf2fed49a316fa166fcb09e02493451a9b919f34d5546cf95f4616336

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:c96a767903ae58dfb572888640b39e83bb7d47c5dd79da895239675e7c6a6c96

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

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:02:55.793564Z digest=sha256:a6abae1662f4edde72d448085e968cf6b4011245df1be4a554eabb28298fe0c2

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T05:02:55.817249Z digest=sha256:e6232bdcafe325867fd2d507f3c22897ef3f2708de4d09973dc6d14ba2faf9af

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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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=sha256:4a35766b86890b4ac1ad377003a0b4d3b3b52c09cbb5b5665e9ea5d9044bec4e

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=sha256:6295c1c72b61f3b5e3b6c855b4e8af9f312b24697cb8a91152edac443b2d3453

Pith citing papers

No inbound Pith citation observations are available.