Long-Context QA

Long-context QA asks language and vision-language models to answer questions using evidence distributed across books, reports, conversations, and multimodal documents. Approaches include long-context fine-tuning, retrieval and agentic document navigation, structured memories, evidence selection and compression, and efficient attention or KV-cache methods. Benchmarks such as LongBench, HotpotQA, LongMemEval, and long-document QA suites test multi-hop reasoning, answerability, citation support, and evidence spread across pages or narrative events. A central challenge is that larger context windows do not guarantee usable evidence: systems must balance accuracy and faithfulness against token, latency, and memory costs, while evaluation must distinguish retrieval failures from reasoning failures.