2 citations · 6 across the 11 of their papers we have counts for
14 papers
ProV-Mem: Proactive Memory for Streaming Video Question Answering
Bingbing Wang, Jing Li, Han Gao +3
Proactive streaming video question answering requires a model to continuously monitor an incoming stream and autonomously decide when to speak, what to say, and whether to emit a r…
All-in-One Memory: Process-Rewarded Reinforcement Learning for Memory Agents
Bingbing Wang, Jingjie Lin, Weiming Qiao +2
Large language models increasingly rely on external memory to answer queries grounded in long-horizon histories such as multi-session dialogues and lengthy documents. However, exis…
Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents
Bin Wu, Guanyun Zou, Bingbing Wang +2
A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep w…
Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation
Zhengda Jin, Bingbing Wang, Jing Li +2
Long-term memory is essential for persistent LLM agents, yet prevailing architectures store historical interactions as unstructured, flat text. This unconstrained storage induces p…
Proactive Memory for Ad-Hoc Recall over Streaming Dialogues
Bingbing Wang, Jing Li, Ruifeng Xu
Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think…
CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation
Jingqian Zhao, Bingbing Wang, Geng Tu +5
Data contamination poses a significant challenge to the fairness of LLM evaluations in natural language processing tasks by inadvertently exposing models to test data during traini…
PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection
Bingbing Wang, Zhixin Bai, Zhengda Jin +6
The rapid proliferation of multimodal social media content has driven research in Multimodal Conversational Stance Detection (MCSD), which aims to interpret users' attitudes toward…
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection
Bingbing Wang, Zhengda Jin, Bin Liang +4
Multimodal Stance Detection (MSD) is a crucial task for understanding public opinion on social media. Existing methods predominantly operate by learning to fuse modalities. They la…
Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models
Guangyu Xie, Yice Zhang, Jianzhu Bao +4
Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions a…
MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
Yiming Du, Bingbing Wang, Yang He +7
Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities fo…
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
Zican Dong, Junyi Li, Xin Men +5
Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of t…
Towards Real-World Stickers Use: A New Dataset for Multi-Tag Sticker Recognition
Bingbing Wang, Bin Liang, Chun-Mei Feng +6
In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively un…
Reply with Sticker: New Dataset and Model for Sticker Retrieval
Bin Liang, Bingbing Wang, Zhixin Bai +6
Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a viv…
SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection
Shijue Huang, Libo Qin, Bingbing Wang +2
Multi-modal intent detection aims to utilize various modalities to understand the user's intentions, which is essential for the deployment of dialogue systems in real-world scenari…