Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:17:13.027364Z
Paper Citation Record · LEDGER
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
As of 9 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2508.03864.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T18:58:53.183734Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T19:00:30.421345Z
Reference resolution
49 of 49 outbound references displayed
- verified exact0
- verified fuzzy39
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bd9bd894-7267-43b7-8062-5e0173a34f7c · outbound
This paper cites Improving retrieval-augmented generation through multi-agent reinforcement learning, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Improving retrieval-augmented generation through multi-agent reinforcement learning, 2025
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:21.797033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 563fde29-6a1e-49ba-9089-0e7ebe47144d · outbound
This paper cites Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases, 2024
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:21.614175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c6b559a9-3b6a-49c1-b5e5-5dda9b771f89 · outbound
This paper cites Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning, 2025
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:08.292580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3baf1983-0e9d-49ef-9693-4d51b3a54e74 · outbound
This paper cites Multilingual jailbreak challenges in large language models, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Multilingual jailbreak challenges in large language models, 2024
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:21.390561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation be55f796-742a-447e-b510-eb7230a1e39a · outbound
This paper cites A practical memory injection attack against llm agents, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety A practical memory injection attack against llm agents, 2025
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:21.121642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 84c03e86-b527-479e-8d22-668b94824e67 · outbound
This paper cites Peerguard: Defending multi-agent systems against backdoor attacks through mutual reasoning,.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Peerguard: Defending multi-agent systems against backdoor attacks through mutual reasoning,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:20.777722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 00c19c84-6117-4bf4-8b22-ab6470007ec3 · outbound
This paper cites Not what you’ve signed up for: Compromising real-world llm-integrated ap- plications with indirect prompt injection, 2023.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Not what you’ve signed up for: Compromising real-world llm-integrated ap- plications with indirect prompt injection, 2023
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:20.414779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a8e70d4c-72fb-4780-a4f6-80e62addbec8 · outbound
This paper cites Llm multi-agent systems: Challenges and open problems, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Llm multi-agent systems: Challenges and open problems, 2025
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:20.053410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 438d1589-163d-4f7c-865f-9bedbfe11013 · outbound
This paper cites Red-teaming llm multi-agent systems via commu- nication attacks, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Red-teaming llm multi-agent systems via commu- nication attacks, 2025
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:19.752036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c67e3a34-d65b-43b3-b3c5-4a5afc1c68e9 · outbound
This paper cites Measuring mathematical problem solving with the math dataset, 2021.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Measuring mathematical problem solving with the math dataset, 2021
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:19.453212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2a341dd6-e818-45f6-8273-9fa6e85da5a1 · outbound
This paper cites Llama guard: Llm-based input-output safeguard for human-ai con- versations, 2023.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Llama guard: Llm-based input-output safeguard for human-ai con- versations, 2023
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:19.144675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c633c3ff-51e4-466e-ba03-ead9e4384812 · outbound
This paper cites Search- r1: Training llms to reason and leverage search engines with reinforcement learning, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Search- r1: Training llms to reason and leverage search engines with reinforcement learning, 2025
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:18.829033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b8bf145c-aa84-451a-86ca-f7c230af4dd8 · outbound
This paper cites an unresolved cited work.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T04:17:18.524994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d2312e4f-b055-4103-a084-17f8bd5ffb13 · outbound
This paper cites Trust re- gion policy optimisation in multi-agent reinforcement learn- ing, 2022.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Trust re- gion policy optimisation in multi-agent reinforcement learn- ing, 2022
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:18.351198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3abee350-f220-43c3-a556-2c46afef842e · outbound
This paper cites Prompt infection: Llm-to- llm prompt injection within multi-agent systems, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Prompt infection: Llm-to- llm prompt injection within multi-agent systems, 2024
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:18.131372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0d027fcb-cd73-457d-815b-59f282d5bedf · outbound
This paper cites Deepinception: Hypnotize large language model to be jailbreaker, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Deepinception: Hypnotize large language model to be jailbreaker, 2024
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.931362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3aaee5ab-09c9-41fd-a037-a51cb2150694 · outbound
This paper cites Tf-attack: Transferable and fast adversarial attacks on large language models, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Tf-attack: Transferable and fast adversarial attacks on large language models, 2024
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.769154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9ea0f7f3-6939-47e1-af4d-b57dbbb0846e · outbound
This paper cites Autodan: Generating stealthy jailbreak prompts on aligned large language models, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Autodan: Generating stealthy jailbreak prompts on aligned large language models, 2024
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.613247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dc5de2ff-03af-4183-821d-9d360b7cd6cc · outbound
This paper cites Jailbreakv: A benchmark for assessing the robustness of multimodal large language models against jail- break attacks, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Jailbreakv: A benchmark for assessing the robustness of multimodal large language models against jail- break attacks, 2024
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.448546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 6b73d23f-c53a-4e18-9395-705fc64b6b13 · outbound
This paper cites Codechameleon: Personalized encryption frame- work for jailbreaking large language models, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Codechameleon: Personalized encryption frame- work for jailbreaking large language models, 2024
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.286041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fc82ba90-e4b7-4cd4-a863-28a7e9356d1e · outbound
This paper cites Harm- bench: A standardized evaluation framework for automated red teaming and robust refusal, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Harm- bench: A standardized evaluation framework for automated red teaming and robust refusal, 2024
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.210645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 172ba3d1-7225-4121-877c-619737d097b6 · outbound
This paper cites Metaspatial: Reinforcing 3d spa- tial reasoning in vlms for the metaverse.arXiv preprint arXiv:2503.18470, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Metaspatial: Reinforcing 3d spa- tial reasoning in vlms for the metaverse.arXiv preprint arXiv:2503.18470, 2025
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:10.290349Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcfa71a7-5459-452b-b77c-4839d76b6df8 · outbound
This paper cites Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:10.380062Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9cd9caf-e8d6-4c21-8bd6-cf11e8a8ad7b · outbound
This paper cites Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:10.475488Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d953f70-da50-44d8-913c-25ea686208ab · outbound
This paper cites Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:10.574459Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26f531b3-0c99-4667-9d91-3e65103ea2e0 · outbound
This paper cites Do code llms understand design patterns? In2025 IEEE/ACM Inter- national Workshop on Large Language Models for Code (LLM4Code), pages 209–212.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Do code llms understand design patterns? In2025 IEEE/ACM Inter- national Workshop on Large Language Models for Code (LLM4Code), pages 209–212
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:17.070627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dd25c245-3dfc-493f-ac18-7da47383aca2 · outbound
This paper cites Yu, Manling Li, and Han Liu.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Yu, Manling Li, and Han Liu
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.906588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation afb8e609-e62e-4f80-8620-b874fa7db811 · outbound
This paper cites Maporl: Multi-agent post-co-training for collaborative large language models with reinforcement learning, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Maporl: Multi-agent post-co-training for collaborative large language models with reinforcement learning, 2025
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.737349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0fbbc2b8-4635-4abb-9151-e7ad4b34d2a3 · outbound
This paper cites Red teaming the mind of the machine: A systematic evaluation of prompt injection and jailbreak vul- nerabilities in llms, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Red teaming the mind of the machine: A systematic evaluation of prompt injection and jailbreak vul- nerabilities in llms, 2025
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.581769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 30435057-2a00-48ea-bb19-38d223ce91a7 · outbound
This paper cites Qwen2.5 technical report, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Qwen2.5 technical report, 2025
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.462728Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f5f5cdb3-ce4f-4001-aad7-796e434fe262 · outbound
This paper cites Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning, 2018.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning, 2018
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.270429Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation cd0c0ce3-4ff0-46e8-9a8c-a4a2626487e5 · outbound
This paper cites Proximal policy optimization algo- rithms, 2017.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Proximal policy optimization algo- rithms, 2017
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:16.069560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7313c8ca-3796-41ce-866b-0df1da0a3aac · outbound
This paper cites Exfiltration of personal information from chatgpt via prompt injection, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Exfiltration of personal information from chatgpt via prompt injection, 2024
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:15.898471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e234953e-7426-45a9-ab15-f4820562e85d · outbound
This paper cites Sciscigpt: Advancing human- ai collaboration in the science of science.arXiv preprint arXiv:2504.05559, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Sciscigpt: Advancing human- ai collaboration in the science of science.arXiv preprint arXiv:2504.05559, 2025
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:11.579252Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ee7eff76-68f7-44ac-b859-43b5a7ce914d · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:17:11.673708Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5bc28a52-9f95-45d8-be1a-dfaabc07693a · outbound
This paper cites Prompt injection attack to tool selection in llm agents, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Prompt injection attack to tool selection in llm agents, 2025
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:15.712033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8ce99069-7b5a-4ac8-afea-8b737881ab91 · outbound
This paper cites an unresolved cited work.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Unresolved cited work
Reference 37
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T04:17:15.484083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c2759c17-5d28-48fd-a776-64d70bbd1d72 · outbound
This paper cites Multi- agent systems execute arbitrary malicious code, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Multi- agent systems execute arbitrary malicious code, 2025
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:15.296371Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a735f8f2-8ca2-4fb3-b43f-be9f120ba091 · outbound
This paper cites Lyu, and Maarten Sap.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Lyu, and Maarten Sap
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:15.122813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8534057d-95d5-4384-b11b-5f76d7b748fb · outbound
This paper cites Rema: Learning to meta-think for llms with multi-agent reinforcement learning, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Rema: Learning to meta-think for llms with multi-agent reinforcement learning, 2025
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:14.972763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5f09d10d-b5ea-470b-9983-6738b143438c · outbound
This paper cites G- safeguard: A topology-guided security lens and treatment on llm-based multi-agent systems, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety G- safeguard: A topology-guided security lens and treatment on llm-based multi-agent systems, 2025
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:14.794982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f5b6ad89-c513-4b99-82eb-a695d9e10541 · outbound
This paper cites Unleashing the emergent cogni- tive synergy in large language models: A task-solving agent through multi-persona self-collaboration, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Unleashing the emergent cogni- tive synergy in large language models: A task-solving agent through multi-persona self-collaboration, 2024
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:14.643499Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5db696b9-5838-47c4-b9da-cf88ac3b0ded · outbound
This paper cites an unresolved cited work.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Unresolved cited work
Reference 43
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T04:17:14.448121Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ecd6e62b-a10c-4132-826c-e8bee689369d · outbound
This paper cites Beyond self-talk: A communication-centric survey of llm-based multi-agent systems, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Beyond self-talk: A communication-centric survey of llm-based multi-agent systems, 2025
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:14.190529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a476655e-230b-413d-96f5-3ce7b67cff24 · outbound
This paper cites Jailbreak attacks and defenses against large language models: A survey, 2024.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Jailbreak attacks and defenses against large language models: A survey, 2024
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:14.002521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 75ea36b8-32cb-4a7b-88be-1357fe5c1fc2 · outbound
This paper cites The surprising effec- tiveness of ppo in cooperative, multi-agent games, 2022.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety The surprising effec- tiveness of ppo in cooperative, multi-agent games, 2022
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:13.852226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 52a24b10-38cc-42b5-88fb-d5aa4bd7049b · outbound
This paper cites Agent security bench (asb): Formalizing and benchmarking attacks and defenses in llm-based agents,.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Agent security bench (asb): Formalizing and benchmarking attacks and defenses in llm-based agents,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:13.688356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bc6697c0-445f-4a7b-8189-e958168c64c1 · outbound
This paper cites Corba: Contagious recur- sive blocking attacks on multi-agent systems based on large language models, 2025.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Corba: Contagious recur- sive blocking attacks on multi-agent systems based on large language models, 2025
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:13.539126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 54529aa9-6f4a-4b55-b105-ef3c47614e96 · outbound
This paper cites Successful defense on JailBreakV 1.
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety Successful defense on JailBreakV 1
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:17:13.390325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
Observation 76e6783f-e53e-49ae-baf6-8a3465b90fb8 · inbound
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs cites this paper.
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T19:00:30.423456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.