4 citations · 7 across the 24 of their papers we have counts for
26 papers
Experimental Experience Modeling for Autonomous Research
Wenda Wei, Yingchen Zhang, Ruqing Zhang +3
Autonomous research agents can generate hypotheses and conduct experiments, but experimentation remains a major source of computational cost. A fundamental challenge is deciding wh…
From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search
Yunfei Zhong, Yinqiong Cai, Lixin Su +7
Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents instead serv…
Token-Level Credit Assignment Optimization for Generative Document Retrieval
Xinpeng Zhao, Yang Liu, Ran Chen +6
Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, i.e…
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
A deployable multilingual reranker must not only generalize across languages, domains, and ranking tasks, but also remain efficient to serve as a second-stage reranker in practical…
Thinking as Compression: Your Reasoning Model is Secretly a Context Compressor
Guoxin Ma, Yibing Liu, Chengzhengxu Li +7
Context compression aims to shorten long context inputs with minimal information loss for LLM inference acceleration. While existing methods have shown promise, they typically rely…
Beyond Imitation: Reflective On-Policy Self-Distillation for LLM Reasoning
Ziqi Zhao, Xinyu Ma, Liu Yang +6
On-policy self-distillation (OPSD) improves the reasoning capabilities of large language models (LLMs) by providing dense token-level supervision for on-policy rollouts. However, e…
Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search
Mingyue Wang, Xingyu Xie, Hang Yang +5
Understanding how events evolve over time is essential for search engines handling queries about trending news. We present QDET (Query-Driven Event Timeline Summarization), a produ…
RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search
Tingyu Chen, Wenkai Zhang, Li Gao +4
In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely…
ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training
Yu Liang, Liangxin Liu, Longzheng Wang +5
Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering greater representational capacity…
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework
Kai Qin, Liangxin Liu, Yu Liang +7
Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment quality of Large Language Models (…
UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning
Xiaolong Wei, Zerun Zhu, Simin Niu +9
A fundamental challenge in creative writing lies in reconciling the inherent tension between maintaining global coherence in long-form narratives and preserving local expressivenes…
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
Jiahan Chen, Da Li, Hengran Zhang +6
Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classi…
Bagging-Based Model Merging for Robust General Text Embeddings
Hengran Zhang, Keping Bi, Jiafeng Guo +4
General-purpose text embedding models underpin a wide range of NLP and information retrieval applications, and are typically trained on large-scale multi-task corpora to encourage…
Agentic-R: Learning to Retrieve for Agentic Search
Wenhan Liu, Xinyu Ma, Yutao Zhu +4
Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success…
Reinforced Efficient Reasoning via Semantically Diverse Exploration
Ziqi Zhao, Zhaochun Ren, Jiahong Zou +9
Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)-based extensio…
COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence
Zefeng Zhang, Xiangzhao Hao, Hengzhu Tang +8
Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still strugg…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…
Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation
Hengran Zhang, Minghao Tang, Keping Bi +5
This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems, aiming to reduce…
Bacon-Shor Board Games
M. Sohaib Alam, Jun Zen, Thomas R. Scruby
We identify a period-4 measurement schedule for the checks of the Bacon-Shor code that fully covers spacetime with constant-weight detectors, and is numerically observed to provide…
GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval
Wonduk Seo, Haojie Zhang, Yueyang Zhang +6
Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query.…
TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
Yiqun Chen, Qi Liu, Yi Zhang +6
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs f…
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
Yiqun Chen, Jiaxin Mao, Yi Zhang +7
Search result diversification (SRD), which aims to ensure that documents in a ranking list cover a broad range of subtopics, is a significant and widely studied problem in Informat…
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
Lianshang Cai, Linhao Zhang, Dehong Ma +6
Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient comput…
CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval
Wenbiao Li, Pan Tang, Zhengfan Wu +7
Embedding-based retrieval (EBR) is a technique to use embeddings to represent query and document, and then convert the retrieval problem into a nearest neighbor search problem in t…