Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks
Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not control positional placement of target tasks in long contexts. We audit 11 long-context benchmarks and find none jointly controls task position, filler content, and context length for reasoning. An audit of four flagship long-context releases finds no main result-table entry for NIAH, RULER, or LongBench-family benchmarks, while agentic and coding benchmarks appear in main result-tables across all four. We propose Context Rot Evaluation (CRE), a controlled framework varying all three factors, and evaluate nine LLMs on GSM8K and ARC-Challenge acr...
| Authors | Chuyifei Zhang, Hongyu Cui, Xiaowen Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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