1 citations · 2 across the 18 of their papers we have counts for
19 papers
Attention Sinks in VLMs Are Borrowed, Not Seen
Dengjia Zhang, Alexander Martin, Pengyu Yan +6
We study how to detect and mitigate visual attention sinks in vision-language models (VLMs). Visual attention sinks are image tokens that absorb a large share of attention over the…
LAYERSCOPE: A Layerwise Characterization of Video and Multimodal Learned Representations
Sandra Arcos-Holzinger, Debashish Chakraborty, Rohita Mocharla +7
We propose LAYERSCOPE, a label-free, layerwise framework that aims to characterize a model's learned representations in video and multimodal settings. Evaluating downstream perform…
MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos
Reno Kriz, David Etter, Alexander Martin +11
Online information is increasingly consumed in video format. Much of this comes in the form of *raw video*: continuous footage taken on a cell phone, with a hand-held camera, or vi…
Findings of the MAGMaR 2026 Shared Task
Alexander Martin, Dengjia Zhang, Joel Brogan +7
This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task partic…
ColBERTSaR: Sparsified ColBERT Index via Product Quantization
Eugene Yang, Andrew Yates, Dawn Lawrie +3
While ColBERT is an effective neural retrieval architecture, it requires a heavy index structure to support candidate set retrieval based on approximated token embeddings, gatherin…
Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability
Jia-Huei Ju, Eugene Yang, Trevor Adriaanse +2
Long-form Retrieval-Augmented Generation (RAG) brings the challenge of coverage-based ranking, because ranking methods must ensure the inclusion of comprehensive relevant nuggets (…
CoverageBench: Evaluating Information Coverage across Tasks and Domains
Saron Samuel, Andrew Yates, Dawn Lawrie +4
We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Inf…
Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage
Saron Samuel, Alexander Martin, Eugene Yang +5
Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relat…
NeuCLIRTech: Chinese Monolingual and Cross-Language Information Retrieval Evaluation in a Challenging Domain
Dawn Lawrie, James Mayfield, Eugene Yang +6
Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collectio…
Overview of the TREC 2025 RAGTIME Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +3
The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has cr…
RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation
Eugene Yang, Andrew Yates, Dawn Lawrie +2
Retrieval models are key components of Retrieval-Augmented Generation (RAG) systems, which generate search queries, process the documents returned, and generate a response. RAG sys…
FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG
Maxime Dassen, Rebecca Kotula, Kenton Murray +5
Retrieval-Augmented Generation (RAG) models are critically undermined by citation hallucinations, a deceptive failure where a model cites a source that fails to support its claim.…
NeuCLIRBench: A Modern Evaluation Collection for Monolingual, Cross-Language, and Multilingual Information Retrieval
Dawn Lawrie, James Mayfield, Eugene Yang +6
To measure advances in retrieval, test collections with relevance judgments that can faithfully distinguish systems are required. This paper presents NeuCLIRBench, an evaluation co…
Augmenting Researchy Questions with Sub-question Judgments
Jia-Huei Ju, Eugene Yang, Trevor Adriaanse +1
The Researchy Questions dataset provides about 100k question queries with complex information needs that require retrieving information about several aspects of a topic. Each query…
HLTCOE at LiveRAG: GPT-Researcher using ColBERT retrieval
Kevin Duh, Eugene Yang, Orion Weller +2
The HLTCOE LiveRAG submission utilized the GPT-researcher framework for researching the context of the question, filtering the returned results, and generating the final answer. Th…
Rank-K: Test-Time Reasoning for Listwise Reranking
Eugene Yang, Andrew Yates, Kathryn Ricci +4
Retrieve-and-rerank is a popular retrieval pipeline because of its ability to make slow but effective rerankers efficient enough at query time by reducing the number of comparisons…
Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval
Arun Reddy, Alexander Martin, Eugene Yang +7
In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval…
MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion
Saron Samuel, Dan DeGenaro, Jimena Guallar-Blasco +13
Videos inherently contain multiple modalities, including visual events, text overlays, sounds, and speech, all of which are important for retrieval. However, state-of-the-art multi…
Rank1: Test-Time Compute for Reranking in Information Retrieval
Orion Weller, Kathryn Ricci, Eugene Yang +3
We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language mo…