40 citations · 124 across the 62 of their papers we have counts for
11 papers · 1 filter
Small sums of roots of unity: polylogarithmic exceptional sets and conditional separation
Qi Cheng
We prove a polylogarithmic exceptional-set estimate for symmetric five-term sums of roots of unity of arbitrary order. More precisely, at most points of the $N…
Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation
Yuxin Yue, Yingchen Zhang, Ruqing Zhang +3
Analytical charts in multimodal deep research encode quantitative claims, requiring every visualized value to be faithfully grounded in supporting evidence. Unlike retrieved images…
A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models
Catherine Chen, Maarten de Rijke, Carsten Eickhoff
While gender bias in dense retrieval models is well documented, with prior work showing that models often score male-gendered documents higher than female or neutral variants, the…
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2
Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…
Cold-Starts in Generative Recommendation: A Reproducibility Study
Zhen Zhang, Jujia Zhao, Xinyu Ma +3
Cold-start recommendation remains a central challenge in dynamic, open-world platforms, requiring models to recommend for newly registered users (user cold-start) and to recommend…
Model Editing for New Document Integration in Generative Information Retrieval
Zhen Zhang, Zihan Wang, Xinyu Ma +6
Generative retrieval (GR) reformulates the Information Retrieval (IR) task as the generation of document identifiers (docIDs). Despite its promise, existing GR models exhibit poor…
Contrastive Learning for Diversity-Aware Product Recommendations in Retail
Vasileios Karlis, Ezgi Yıldırım, David Vos +1
Recommender systems often struggle with long-tail distributions and limited item catalog exposure, where a small subset of popular items dominates recommendations. This challenge i…
Orcheo: A Modular Full-Stack Platform for Conversational Search
Shaojie Jiang, Svitlana Vakulenko, Maarten de Rijke
Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two…
The Unfairness of Multifactorial Bias in Recommendation
Masoud Mansoury, Jin Huang, Mykola Pechenizkiy +2
Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfai…
Re-Rankers as Relevance Judges
Chuan Meng, Jiqun Liu, Mohammad Aliannejadi +3
Using large language models (LLMs) to predict relevance judgments has shown promising results. Most studies treat this task as a distinct research line, e.g., focusing on prompt de…
Can Small Agents Collaborate to Beat a Single Large Language Model?
Agata Żywot, Xinyi Chen, Yifei Yuan +2
Recent progress in language modeling has largely relied on scaling model size, yet larger models do not reliably improve performance on tasks requiring multi-step reasoning and too…