01 / Context
This project explores how ListT5-style listwise reranking behaves under compute-aware inference strategies.
02 / Role
Describe the implementation, experiment design, debugging, and result analysis responsibilities here.
03 / System
Use this section to explain BM25 candidate retrieval, grouping, first-round caching, tournament reranking, and final output selection.
04 / Technical Depth
Good future details include duplicate-index handling, forward-call budgets, dataset differences, and quality-latency trade-offs.
05 / Product / Research Angle
Frame this as a search-quality versus compute-efficiency study, not just a leaderboard exercise.
06 / Result
Add the final table, best configurations, and a short discussion of where the method helped or hurt.
07 / Reflection
Explain what you would test next if there were more compute or time.