Paper Citation Record · LEDGER

Empowering Large Language Model Agents through Action Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.15809.

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

pith.paper-citation-record.v1
2402.15809 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:26:27.036884Z

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

0
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 c235c46a-220d-4ab4-9133-4ae714471215 · inbound

POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval cites this paper.

POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval Empowering Large Language Model Agents through Action Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:27.036884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53795e59-98ae-4552-9f91-01f1dcf8c4ae · inbound

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning cites this paper.

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning Empowering Large Language Model Agents through Action Learning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T10:28:24.359078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a58d94cc-b730-494f-99f9-d87bf3669e23 · inbound

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment cites this paper.

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment Empowering Large Language Model Agents through Action Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:55:50.923657Z

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 078f6b8a-5a5f-48d0-868d-e0abfa8a32cd · inbound

Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation cites this paper.

Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation Empowering Large Language Model Agents through Action Learning

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T08:49:15.283428Z

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 a4038a35-a867-433e-94c3-10cbf301569b · inbound

LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents cites this paper.

LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents Empowering Large Language Model Agents through Action Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-01T03:18:44.699383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6465e1b8-3d1d-4bea-9b96-a76182a1bb92 · inbound

LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents cites this paper.

LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents Empowering Large Language Model Agents through Action Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T01:43:01.110364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.