LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 consists of 503 challenging multiple-choice questions, with contexts ranging from 8k to 2M words, across six major task categories: single-document QA, multi-document QA, long in-context learning, long-dialogue history understanding, code repository understanding, and long structured data understanding. To ensure the breadth and the practicality, we collect data from nearly 100 highly educated individuals with diverse professional backgrounds. We employ both automated and manual review processes to maintain high quality and difficulty, resulting in human experts achieving only 53.7% accuracy under a 15-minute time constraint. Our evaluation reveals that the best-performing model, when directly answers the questions, achieves only 50.1% accuracy. In contrast, the o1-preview model, which includes longer reasoning, achieves 57.7%, surpassing the human baseline by 4%. These results highlight the importance of enhanced reasoning ability and scaling inference-time compute to tackle the long-context challenges in LongBench v2. The project is available at https://longbench2.github.io.
Key facts. Cited by 379 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 51 of its 68 references that are in the map. First posted on arXiv 19 Dec 2024.
Builds on
51 of 68 references.
Closest ancestors · 2
Same direction, newest first
- 2024Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries19 Sep
- 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack14 Jun
Other references · 49
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