Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-09T23:42:00.548132Z
Paper Citation Record · LEDGER
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
As of 3 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 2 inbound Pith citation observations for arXiv:2604.20714.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T07:21:27.065956Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
18 of 18 outbound references displayed
- verified exact7
- verified fuzzy5
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch5
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c8197416-7ca2-4bd2-b704-5e1b39d0b469 · outbound
This paper cites PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:01:07.098180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f10b1d48-0791-4223-8f8c-29402ef1aae9 · outbound
This paper cites Proceedings of the 31st International Conference on Computational Linguistics , pages=.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Proceedings of the 31st International Conference on Computational Linguistics , pages=
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T12:37:59.422811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 50fb5681-4f0b-4408-af8c-a34ffa9e5950 · outbound
This paper cites TextGrad: Automatic "Differentiation" via Text.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization TextGrad: Automatic "Differentiation" via Text
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T11:27:59.484812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 4fe32321-2f8f-4937-b977-32404fa3df60 · outbound
This paper cites Narasimhan and Yuan Cao , title =.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Narasimhan and Yuan Cao , title =
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T12:37:59.418977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 4e268226-f37f-472e-b9e7-46d56997a10c · outbound
This paper cites Chawla and Olaf Wiest and Xiangliang Zhang , title =.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Chawla and Olaf Wiest and Xiangliang Zhang , title =
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T12:37:59.402779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1401ba74-2aae-48ad-8e56-1585e230bcce · outbound
This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Multi-Agent Collaboration Mechanisms: A Survey of LLMs
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T15:54:55.141401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 14cf9c57-c47a-45da-a52f-eb481fb19a6e · outbound
This paper cites O'Brien and Carrie Jun Cai and Meredith Ringel Morris and Percy Liang and Michael S.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization O'Brien and Carrie Jun Cai and Meredith Ringel Morris and Percy Liang and Michael S
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T23:44:44.631580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 41a17b6e-8176-48ea-a826-edc845bdeff2 · outbound
This paper cites MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:01:07.064100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 60418032-e56e-4900-9b92-397ee0fbc428 · outbound
This paper cites Creativity in LLM-based Multi-Agent Systems: A Survey.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Creativity in LLM-based Multi-Agent Systems: A Survey
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:01:07.076136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation eadd0064-da00-4c12-885f-00dc23598ad9 · outbound
This paper cites Exploring collaboration mechanisms for.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Exploring collaboration mechanisms for
Reference 10
- Resolution
- verified exact
- doi, observed 2026-05-09T23:44:44.626299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1ff3b866-ab3d-4aa5-8c91-bba386a7dde6 · outbound
This paper cites The Twelfth International Conference on Learning Representations , year=.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization The Twelfth International Conference on Learning Representations , year=
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T12:37:59.409374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 655a4ae8-3cce-4af4-8345-63cbf2fcafaa · outbound
This paper cites MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T14:01:07.105970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8ebfedda-cdc5-46a6-8489-872b286a101d · outbound
This paper cites an unresolved cited work.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T12:37:59.398722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 56d7749e-62b7-404a-9d27-a2ae22018e8d · outbound
This paper cites The Twelfth International Conference on Learning Representations , year=.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization The Twelfth International Conference on Learning Representations , year=
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T12:37:59.414795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 04044b64-5a51-4ba2-a541-c4d7a2b066ce · outbound
This paper cites Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T14:01:07.035142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation dc4861cc-2d0f-4f2c-b0af-4dbecc5be481 · outbound
This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T14:01:07.052979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 31d31b94-9e13-4f42-9db6-780119fc8a4b · outbound
This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization Multi-Agent Collaboration Mechanisms: A Survey of LLMs
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T15:54:55.141401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation dae6b833-aa3d-4c0e-a30f-9844d507591c · outbound
This paper cites A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems.
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
Reference 18
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T23:21:42.844044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
Observation 2a51a2f3-d655-43c7-b305-936c163bdbab · inbound
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory cites this paper.
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T12:26:27.446079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 929893f6-4209-4647-ac4f-d8a8f0d82141 · inbound
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory cites this paper.
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:21:27.065956Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.