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Venktesh V

SIGIR 2026×2效率与系统评测与基准推理检索与知识
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一作贡献40
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1 一作 · 2 篇 · SIGIR 2026

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2026 顶会论文

2
作者位次 #2/4SIGIR 2026Oral· Information Retrieval (Reproducibility)

Reproducing Adaptive Reranking for Reasoning-Intensive IR

Mandeep Rathee · Venktesh V · Sean MacAvaney · Avishek Anand

摘要

The classical cascading pipeline of retrieve--rerank suffers from a bounded recall problem, stemming from limitations of the first-stage retriever. Most current approaches address the bounded recall problem by improving the first-stage retriever, but this incurs substantial training and inference...

推理效率与系统评测与基准OpenReview
一作SIGIR 2026Oral· Information Retrieval (Full)

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback

Venktesh V · Mandeep Rathee · Avishek Anand

摘要

Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces a fundamental tradeoff between recall and query drift. Generating many reformulated queries can substantially increa...

评测与基准效率与系统检索与知识OpenReview

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