Research Project
Single-Pass Document Scanning for Question Answering
Training state space models for long-context question answering that scan documents once and answer efficiently.
Overview
This project studies retrieval-augmented question answering with self-trained Mamba-2 state space models. The goal is to answer questions over very long documents while reducing repeated document passes and inference cost.
What This Page Can Include
- Research motivation and core problem.
- Model or system diagram.
- Dataset, training, and evaluation details.
- Selected results and qualitative examples.
- Links to paper, code, demos, or related talks.
Current Summary
We trained state space models for long-context question answering, reaching performance comparable to strong frontier baselines on extremely long documents while improving computational efficiency.