Paper Citation Record · LEDGER

ProBench: Benchmarking Large Language Models in Competitive Programming

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2502.20868.

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

pith.paper-citation-record.v1
2502.20868 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:24.239110Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T18:47:17.150367Z

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 26a851d1-438b-4508-b7e4-9317aeee407b · inbound

SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation cites this paper.

SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:24.239110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427a19f3-d228-419f-9323-740974b83fa5 · inbound

Evaluating and Improving Large Language Models for Competitive Program Generation cites this paper.

Evaluating and Improving Large Language Models for Competitive Program Generation ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:03:31.682090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72edc84c-a6b1-46de-a8e0-41b3081fe2eb · inbound

Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization cites this paper.

Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:20:46.022614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89bf09dc-5846-4083-aff6-635875be5000 · inbound

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning cites this paper.

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T20:33:16.612337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 68678faa-388a-446d-babc-7b37464b7f5f · inbound

ARIADNE: Agentic Reward-Informed Adaptive Decision Exploration via Blackboard-Driven MCTS for Competitive Program Generation cites this paper.

ARIADNE: Agentic Reward-Informed Adaptive Decision Exploration via Blackboard-Driven MCTS for Competitive Program Generation ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:55:42.794123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01d2dc28-86ca-42fc-b9d0-db5f3b4fc4df · inbound

CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization cites this paper.

CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T18:50:02.070125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5171926c-7e4f-4347-9df8-52751d823c78 · inbound

AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation cites this paper.

AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation ProBench: Benchmarking Large Language Models in Competitive Programming

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:47:17.151766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.