Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness. Notably absent is a systematic way to probe how models perform as we scale up the difficulty of tasks along various axes. We address this gap by proposing PredicateLongBench, a benchmark that stress-tests long-context reasoning by asking models to identify the longest contiguous subsequence of words in ...
| Authors | Siddhartha Jain, Ameya Velingker |
|---|---|
| Primary category | cs.AI |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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