Paper Citation Record · LEDGER

Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.06290.

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

pith.paper-citation-record.v1
2404.06290 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:52.314623Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T21:03:26.396066Z

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 4a653bf3-8902-415c-82fa-63e7e9be261b · inbound

Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement cites this paper.

Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:03:26.399515Z

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 eab5d896-e04d-409e-b0bc-c2d1884e39c3 · inbound

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model cites this paper.

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:45:04.013914Z

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 71b0cbff-f8d6-4a13-aaee-826a1c96dee0 · inbound

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs cites this paper.

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:52.314623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b52d83c6-e543-4f97-a1a8-e3e0de718af0 · inbound

On the Convergence of Large Language Model Optimizer for Black-Box Network Management cites this paper.

On the Convergence of Large Language Model Optimizer for Black-Box Network Management Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:33:42.409678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04efd822-a808-411d-9584-e088e8423d05 · inbound

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving cites this paper.

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T20:55:41.231755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7002c4b0-efdb-4c68-87de-95fa1c80e74a · inbound

SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization cites this paper.

SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:15:34.204047Z

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 e226a3c4-7dcc-4030-9cf6-4a601100c364 · inbound

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models cites this paper.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 119

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:40:54.601926Z

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 c1de0f7c-2683-4aef-afa0-ba2252c3fabc · inbound

The Problem of Dynamic Spatial Sampling and Geofence Surveillance cites this paper.

The Problem of Dynamic Spatial Sampling and Geofence Surveillance Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T16:01:44.895474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42443ebf-8058-43e1-8e22-6e87388b2373 · inbound

ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract) cites this paper.

ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract) Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T05:08:05.264335Z

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 3360b63f-e5b8-49d5-83e2-0760f6b6c759 · inbound

Better Together, in the Right Order: Classical-then-LLM Optimization for SE cites this paper.

Better Together, in the Right Order: Classical-then-LLM Optimization for SE Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T09:35:19.604418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.