Papers by Sahel Sharifymoghaddam
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models (2025.findings-naacl)
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| Challenge: | Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding and MM generation. |
| Approach: | They propose a plug-and-play technique that adds relevant retrieved information to prompts as few-shot examples during inference. |
| Outcome: | The proposed method significantly improves the output quality of large vision language models when input prompts are augmented with relevant information retrieved by Vision-Language retrievers like UniRAG. |
Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents (2026.findings-acl)
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| Challenge: | Recent work emphasizes improving efficiency in LLM-based systems, especially for longcontext and multi-step reasoning. |
| Approach: | They analyze the role of listwise reranking in deep search pipelines and compare their results to a novel ETC metric to determine model scale and reasoning effort. |
| Outcome: | The proposed model scale, reasoning effort, reranking depth, and total token cost (ETC) metric improve retrieval and end-to-end accuracy and moderate reranked agents achieve comparable accuracy at substantially lower cost. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |