62 citations · 226 across the 45 of their papers we have counts for
49 papers
Seek: Self-Evaluative Exploration for Knowledge Retrieval
Amin Bigdeli, Radin Hamidi Rad, Negar Arabzadeh +4
LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set, leaving re…
Judging a Review by its Cover: A Reliability Analysis of LLM-based Peer Review Evaluation Metrics
Shakiba Amirshahi, Sajad Ebrahimi, Hai Son Le +2
Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high score because it is fluent, org…
Route Me If You Can: A Benchmark for Query Reformulation Selection
Hai Son Le, Negar Arabzadeh, Amin Bigdeli +4
LLM-based query reformulation can improve retrieval, but no single reformulation strategy is consistently optimal across queries, domains, retrievers, or model backbones. This crea…
Question's Gambit: The First Move Matters in Agentic Deep Search
Radin Hamidi Rad, Amin Bigdeli, Negar Arabzadeh +4
Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmarks such as BrowseComp-Plus sh…
Peerify: Benchmarking Peer-Review Claim Verification
Alireza Daghighfarsoodeh, Sajad Ebrahimi, Ali Ghorbanpour +4
Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely manual and time-consuming pro…
What Happens When the Model Eats the Stack? Rethinking the Research Agenda for Data Agents to Withstand the Bitter Lesson
Liana Patel, Siddharth Jha, Negar Arabzadeh +3
The bitter lesson poses an existential question for the data systems community, whereby large language models (LLMs) trained end-to-end are rapidly internalizing new capabilities t…
Multi-Agent Transactive Memory
To Eun Kim, Xuhong He, Dishank Jain +3
Decentralized deployment of LLM agents with diverse capabilities motivates infrastructure for knowledge sharing across heterogeneous agent populations. Just as search engines index…
ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback
Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad +3
LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-document…
Natural Language Query to Configuration for Retrieval Agents
Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob +3
Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and ser…
RAG over Thinking Traces Can Improve Reasoning Tasks
Negar Arabzadeh, Wenjie Ma, Sewon Min +1
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as ma…
A Reproducibility Study of LLM-Based Query Reformulation
Amin Bigdeli, Radin Hamidi Rad, Hai Son Le +4
Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substantial effectiveness gains. Howeve…
PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality
Sajad Ebrahimi, Soroush Sadeghian, Ali Ghorbanpour +6
The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools to assess review quality. We…
Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
Negar Arabzadeh, Andrew Drozdov, Michael Bendersky +1
Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling the generation of multiple se…
Peerispect: Claim Verification in Scientific Peer Reviews
Ali Ghorbanpour, Soroush Sadeghian, Alireza Daghighfarsoodeh +4
Peer review is central to scientific publishing, yet reviewers frequently include claims that are subjective, rhetorical, or misaligned with the submitted work. Assessing whether r…
PeerPrism: Peer Evaluation Expertise vs Review-writing AI
Soroush Sadeghian, Alireza Daqiq, Radin Cheraghi +3
Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, expansion, and refinement. However, existing peer-review LLM detec…
ReFormeR: Learning and Applying Explicit Query Reformulation Patterns
Amin Bigdeli, Mert Incesu, Negar Arabzadeh +2
We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits…
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
Measuring Agents in Production
Melissa Z. Pan, Negar Arabzadeh, Riccardo Cogo +22
LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful. We present the first syst…
QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation
Amin Bigdeli, Radin Hamidi Rad, Mert Incesu +3
We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent…
Adversarial Attacks against Neural Ranking Models via In-Context Learning
Amin Bigdeli, Negar Arabzadeh, Ebrahim Bagheri +1
While neural ranking models (NRMs) have shown high effectiveness, they remain susceptible to adversarial manipulation. In this work, we introduce Few-Shot Adversarial Prompting (FS…
DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
Liana Patel, Negar Arabzadeh, Harshit Gupta +4
The ability to research and synthesize knowledge is central to human expertise and progress. A new class of AI systems--designed for generative research synthesis--aims to automate…
Benchmarking LLM-based Relevance Judgment Methods
Negar Arabzadeh, Charles L. A. Clarke
Large Language Models (LLMs) are increasingly deployed in both academic and industry settings to automate the evaluation of information seeking systems, particularly by generating…
A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment
Negar Arabzadeh, Charles L. A . Clarke
Large Language Models (LLMs) are increasingly used to automate relevance judgments for information retrieval (IR) tasks, often demonstrating agreement with human labels that approa…
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem
Sajad Ebrahimi, Sara Salamat, Negar Arabzadeh +2
The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual…