Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.11393.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:43:58.486504Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:41:36.721453Z
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 d041a5cf-2076-47c1-88dd-0b99bdee0745 · inbound
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation cites this paper.
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:41:36.724097Z
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 abe9e047-01dc-4272-a012-e5869a9c0d20 · inbound
Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code cites this paper.
Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:43:58.486504Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.