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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:f1ffcbdf9e29a8a1d19f9d7cbafb4da8ade2947666e067fb6405fc4e5b26e1f8

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=sha256:78a50c5715b14fdd1490a6c7ef9b93b6a8dff5e5984e54554c0ba05a4da0a4f4

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=sha256:813d5f86c70015241ba3d6bcd480106c2eb6144fb0474016c5139164c7da5c53

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=sha256:5c4e2d002b530520aaf70cfd42fe66887631da800b26a5c5610f10f863dc0bed

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=sha256:1496741f4c4984fc1f387df2bd802ac8a888e029f6d6810769c9cec43dc86eba

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=sha256:51765d83c78c17337447ef7d0cbb864a3887393dbc88432dcb4b17d2985e57e6

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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source=pdf_text observed=2026-08-11T05:02:55.848941Z digest=sha256:639d1b48ea248ee669354f99323c1450acb6269f0a75d201a03205009d8ef627

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:f45f3546157d61aa6ad7b42786393fb2ed31ecf2a9e132cee14b30ce3dfd8c17

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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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:d624b0f2d37f7e48ba11ee5610ba2b40fa1cf15c9f96c6644a592ee5afb95cb9

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=sha256:1e68dd5100a1339c09c5e4e9111a24a3299d79bb66ed04c623652a0a61864212

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=sha256:7d577ea0485aef929b57f7bd6105d4fd5c73b7abd44f63f7e074935dd94b2a7b

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=sha256:4a351390526c6867a3657079355fd0adc33fb2a86d47ca618dc4a15390073c24

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=sha256:28b4632748b85d321786db13ae33320656ccf2df0bfd86ac852aa18ff699814f

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=sha256:0087d2f8a289e054024a16be474e82667d90e394f59aac66954c01d2487cc206

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=sha256:38940f10d8a41d0be5d0420a9b7c10d0346fe4f146c7cc3bd6cc3bfe902c5916

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=sha256:8d4903433a44d1325ced4e18a4b1f1cf650993168b24af8488aaee04648674f1

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=sha256:7feb9db2bbf9e7bdd99fb9f63cbd52310d436736e69f8f8d0e41f1bd604b195a

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=sha256:5fc8ca262c355952de84782cf7df305d812f693b8f592ef358a3c536fdf29941

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=sha256:008f890046dd078c7f8125ceb89839c7d4fa9b4a84a610632a636d5d0baf88b7

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=sha256:86735a3692c0bba334a58521459baf22e0175c759a941d43a6a32ca11d427b72

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

source=pdf_text observed=2026-08-11T05:02:55.953057Z digest=sha256:df18adadb3c00af3e95dcd4b69674ca782796fda0bf0562211a8804b08a080a4

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=sha256:33e443a2854264f8455881f084c4bd2377632b10a0249e8bbf01b58bd25a8a48

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=sha256:048dcc0915407fe8e605958744beab44d9aebe81000e33ed0e4fceecc6f5d0b6

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

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unresolved
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source=pdf_text observed=2026-08-11T05:02:55.973774Z digest=sha256:0483156cdce7ff68ff345041eb4e040f7d3c9fdae35f30438ae47448aef5f2b5

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=sha256:947205848c0066e7cf704b8857f43599a0a9df6c7b6841b2a561d890464b79c0

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=sha256:1f0f149edef36001bbf86b4010f21e5f7822f00eb457507682b4086c308f286a

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
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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:e22036ed8d2c9374ebf5a809614eea08628e1a42cef2366fc37266f1078d8879

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=sha256:21f93ff91820423d6486c2d3ff37755ea13e88ce4460c63bfa070f041a8923fc

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:02:55.770978Z digest=sha256:a4e71fbd7030d6923e66ecf54f6ba33e24db1e5a5cf37d36a49febc1cfcdf77f

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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source=pdf_text observed=2026-08-11T05:02:55.803576Z digest=sha256:0ece72fa0585eb5ecd188b626e4222e19d026964e4517ef81347cd5b00260f64

Pith citing papers

No inbound Pith citation observations are available.