Paper Citation Record · LEDGER

Symbolic Learning Enables Self-Evolving Agents

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2406.18532.

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

pith.paper-citation-record.v1
2406.18532 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:03:44.895276Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 51f32ff8-bcf3-4f4c-ae14-9b999077721b · inbound

Automated Design of Agentic Systems cites this paper.

Automated Design of Agentic Systems Symbolic Learning Enables Self-Evolving Agents

Reference 239

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:07:54.959274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44682949-fc98-48f6-a81c-71fe2e0456df · inbound

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning cites this paper.

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning Symbolic Learning Enables Self-Evolving Agents

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T11:03:44.895276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36d9c9fb-a405-4c4c-acf1-63ea8512166f · inbound

WebDancer: Towards Autonomous Information Seeking Agency cites this paper.

WebDancer: Towards Autonomous Information Seeking Agency Symbolic Learning Enables Self-Evolving Agents

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:02.084175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 316ddb9f-b80b-4011-a4f6-ebbfb3832251 · inbound

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems cites this paper.

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems Symbolic Learning Enables Self-Evolving Agents

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T05:39:58.999437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cb9647f-f075-40d0-9f76-129851fcdb05 · inbound

TaskCraft: Automated Generation of Agentic Tasks cites this paper.

TaskCraft: Automated Generation of Agentic Tasks Symbolic Learning Enables Self-Evolving Agents

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:42:20.014137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 752036d6-2d75-4ae0-8470-afc36563930b · inbound

Scaling Test-time Compute for LLM Agents cites this paper.

Scaling Test-time Compute for LLM Agents Symbolic Learning Enables Self-Evolving Agents

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:48.421514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13ef49e-0936-4778-bcdb-029bb6b7089a · inbound

OAgents: An Empirical Study of Building Effective Agents cites this paper.

OAgents: An Empirical Study of Building Effective Agents Symbolic Learning Enables Self-Evolving Agents

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T00:13:51.869032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921e0e69-98b1-45e1-a491-8a42fd88bd16 · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence Symbolic Learning Enables Self-Evolving Agents

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:23:15.505257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d81235d-624b-42cd-af34-ed281e30b754 · inbound

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL cites this paper.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Symbolic Learning Enables Self-Evolving Agents

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:37.538262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7de181-9284-441c-ad4a-033716d4d98e · inbound

Memory in the Age of AI Agents cites this paper.

Memory in the Age of AI Agents Symbolic Learning Enables Self-Evolving Agents

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:18:20.122930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e47e0da2-060b-4058-afbb-235234477df6 · inbound

SEVerA: Verified Synthesis of Self-Evolving Agents cites this paper.

SEVerA: Verified Synthesis of Self-Evolving Agents Symbolic Learning Enables Self-Evolving Agents

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:38:23.486844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1cc7718c-060d-4f75-a193-0fcde0d1e519 · inbound

FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification cites this paper.

FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification Symbolic Learning Enables Self-Evolving Agents

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.995724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c8d8743f-cf01-4d82-b4f1-e01c11a5b5f9 · inbound

FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification cites this paper.

FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification Symbolic Learning Enables Self-Evolving Agents

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T18:43:47.943521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b4429a4-15f2-4184-a49f-07127935de2a · inbound

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses cites this paper.

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses Symbolic Learning Enables Self-Evolving Agents

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:46:46.170133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6155fa02-e31c-4200-ac32-20ebb4871f95 · inbound

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses cites this paper.

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses Symbolic Learning Enables Self-Evolving Agents

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:53:51.687272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df9f0d1c-31b1-43cf-a0d7-15501a60a070 · inbound

Reinforced Collaboration in Multi-Agent Flow Networks cites this paper.

Reinforced Collaboration in Multi-Agent Flow Networks Symbolic Learning Enables Self-Evolving Agents

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:57.119783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2fb67c0c-7330-4780-a275-d8429fd2d83b · inbound

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation cites this paper.

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation Symbolic Learning Enables Self-Evolving Agents

Reference 146

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:48:56.392493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 539c83e6-55ec-4f3e-aa9a-c7bcd2b267c3 · inbound

Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration cites this paper.

Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration Symbolic Learning Enables Self-Evolving Agents

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T22:37:40.381525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.