Paper Citation Record · LEDGER

Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2310.08118.

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

pith.paper-citation-record.v1
2310.08118 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:11:28.174002Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T11:05:42.217787Z

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 0eaa7b4b-6f74-45f9-a1be-e544b5295761 · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 232

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:35.544552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3cc974f2-a2c4-4171-8590-00b868f03bd0 · inbound

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? cites this paper.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.174002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d07930e-325d-4ba0-a1bd-68fc220040b9 · inbound

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency cites this paper.

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T21:33:37.640825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4360466-1e79-4357-9803-c21c70600514 · inbound

Exchange of Perspective Prompting Enhances Reasoning in Large Language Models cites this paper.

Exchange of Perspective Prompting Enhances Reasoning in Large Language Models Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:00.034064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1839f655-5ea1-40b2-87e4-919933da9d28 · inbound

Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platforms cites this paper.

Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platforms Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:29.395654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc1db1c5-5c14-49db-8e7c-7a23c02e4b59 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 174

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.287151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 62d2848f-8c45-4134-a8c1-ca9a5060a426 · inbound

End-to-end PDDL Planning with Hardcoded and Dynamic Agents cites this paper.

End-to-end PDDL Planning with Hardcoded and Dynamic Agents Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:38:41.621289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5218f3c7-8cc0-4bda-93fa-e2492323df5e · inbound

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding cites this paper.

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T04:06:11.908439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5258a308-80a1-47b3-9b9d-f41dc493e3a2 · inbound

OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces cites this paper.

OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:01:18.655422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ae7bdbd-a4bb-46d7-9beb-345465abd922 · inbound

Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning cites this paper.

Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:55:04.650977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67c87912-15ca-415d-864e-66c22e0171fe · inbound

Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers cites this paper.

Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:42:46.127304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b6a978cc-5345-4543-bba1-919efe96c1e3 · inbound

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models cites this paper.

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-01T11:05:42.219249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.