LongCoT is a benchmark dataset designed to measure a model's ability to sustain coherent reasoning across long chains of thought. The data covers multiple domains including logic, computer science, chemistry, chess, and mathematics. It is released by LongHorizonReasoning on Hugging Face, with the canonical codebase hosted on GitHub.
Use Cases
- Benchmarking model performance on long-context reasoning tasks based on the described multi-domain problems.
- Training models for improved chain-of-thought reasoning based on the structured benchmark data.
- Analyzing failure modes in logical and mathematical reasoning across extended sequences.
- Developing specialized verifiers or evaluation harnesses for long-horizon tasks.
Strengths
- Focuses on a specific and challenging AI task: long-horizon reasoning.
- Covers a diverse set of domains including logic, computer science, chemistry, chess, and mathematics.
- Data is provided in a viewer-friendly Parquet format for easy browsing and loading.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and dataset size are unknown, which may limit suitability assessment.
Provenance
- Source
- LongHorizonReasoning
- Collection Method
- Likely constructed as a benchmark for evaluating AI models.
- Time Range
- null
- Freshness
- Last updated 2026-04-16 16:17:15; freshness should be verified.
- Geography
- null