Paper Citation Record · LEDGER

Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2402.17574.

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

pith.paper-citation-record.v1
2402.17574 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:08:58.313670Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T17:20:00.052495Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9f5f84bd-24de-4a88-9145-f96f8fbda7d2 · inbound

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms cites this paper.

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T19:10:14.548500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af16f172-6a01-4b14-89ea-2613ee381586 · inbound

Training Agents with Weakly Supervised Feedback from Large Language Models cites this paper.

Training Agents with Weakly Supervised Feedback from Large Language Models Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T10:08:13.259699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee55fb2e-2672-42cd-867c-e86ed65458d8 · inbound

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization cites this paper.

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T22:52:15.192227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 958420c8-eaf0-4178-b1c4-216ed9ea6eac · inbound

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios cites this paper.

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 141

Resolution: unresolved; no resolver link, observed 2026-08-11T21:58:46.979827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02515358-adfb-4cac-bd7d-727bbcf74640 · inbound

On the Structural Memory of LLM Agents cites this paper.

On the Structural Memory of LLM Agents Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T14:03:01.161778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0e7372d-3567-431b-b450-16628c4db821 · inbound

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework cites this paper.

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T00:08:16.295396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5656fe9e-a0d2-46c9-a02b-a2042eca476d · inbound

WebWalker: Benchmarking LLMs in Web Traversal cites this paper.

WebWalker: Benchmarking LLMs in Web Traversal Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:30.399995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2afa31b-fd8f-4fb2-bccf-c07d480cb1e6 · inbound

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models cites this paper.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 190

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:20:59.435553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ee24071-c004-481e-9291-358b5be18f86 · inbound

Regression and Forecasting of U.S. Stock Returns Based on LSTM cites this paper.

Regression and Forecasting of U.S. Stock Returns Based on LSTM Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T14:34:58.948949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a004e354-8cac-4039-bc1a-1aab93c0a57e · inbound

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences cites this paper.

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:12:28.469302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b692b4a-1c69-4a2f-a7d9-4ce1ebfd1d7f · inbound

Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability cites this paper.

Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:08:58.313670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d72ca18-692f-4d09-bf5a-7c5ddb887e1c · inbound

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation cites this paper.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.906880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0259a033-897e-4c0e-8a1c-2a357cedce90 · inbound

Reasoning Capabilities of Large Language Models on Dynamic Tasks cites this paper.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.515887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1037613c-34e7-4cf4-993a-c89ec1469acc · inbound

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding cites this paper.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:05.260604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa4919cb-4133-466b-8fa6-ad50041b38f7 · inbound

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines cites this paper.

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:54.047516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c480e29b-1511-4bab-80e1-30443739c009 · inbound

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives cites this paper.

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 160

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:04.611203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69e25ffc-54ad-4ef8-b964-6658e1d7f3e8 · inbound

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents cites this paper.

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:42.695008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b0d1496-ebbc-45ad-9857-a97331296eca · inbound

SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models cites this paper.

SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:33:48.946358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 798c817d-f13b-4592-aeba-3f94cb13d2c6 · inbound

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning cites this paper.

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 221

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:56.343748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75198911-985f-42d3-a3e2-5020a92af878 · inbound

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers cites this paper.

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:37:52.799830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36a7ab9f-b5b7-450a-af13-c5a3a5958309 · inbound

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving cites this paper.

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:28:26.110501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 703355d8-a7fb-41a5-9489-2d637eff7ba7 · inbound

SAGE: A Service Agent Graph-guided Evaluation Benchmark cites this paper.

SAGE: A Service Agent Graph-guided Evaluation Benchmark Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:21:00.927739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aa262c24-ccc9-4d99-957a-a4b013562bcb · inbound

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts cites this paper.

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 133

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:21:11.091887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1bd5d1e7-1590-46d7-a587-802b9acbf478 · inbound

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts cites this paper.

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 133

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T23:33:50.618069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d904238-7149-45db-aaa3-adb0f33c42e7 · inbound

Agent System Operations: Categorization, Challenges, and Future Directions cites this paper.

Agent System Operations: Categorization, Challenges, and Future Directions Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:16:24.986196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8bd71679-c7ed-4447-84a7-ee36f63cefc4 · inbound

Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning cites this paper.

Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T17:20:00.053906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61fcf741-39ad-4f0a-a781-88dcae1d4211 · inbound

Self-Evolving Coding Agents cites this paper.

Self-Evolving Coding Agents Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T19:47:58.926605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.