Author name cluster

Bin Guo 0001

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4 papers

1 author row

ECAI Conference 2025 Conference Paper

MemoCue: Empowering LLM-Based Agents for Human Memory Recall via Strategy-Guided Querying

Qian Zhao
Zhuo Sun 0002
Bin Guo 0001
Zhiwen Yu 0001

Agent-assisted memory recall is one critical research problem in the field of human-computer interaction. In conventional methods, the agent can retrieve information from its equipped memory module to help the person recall incomplete or vague memories. The limited size of memory module hinders the acquisition of complete memories and impacts the memory recall performance in practice. Memory theories suggest that the person’s relevant memory can be proactively activated through some effective cues. Inspired by this, we propose a novel strategy-guided agent-assisted memory recall method, allowing the agent to transform an original query into a cue-rich one via the judiciously designed strategy to help the person recall memories. To this end, there are two key challenges. (1) How to choose the appropriate recall strategy for diverse forgetting scenarios with distinct memory-recall characteristics? (2) How to obtain the high-quality responses leveraging recall strategies, given only abstract and sparsely annotated strategy patterns? To address the challenges, we propose a Recall Router framework. Specifically, we design a 5W Recall Map to classify memory queries into five typical scenarios and define fifteen recall strategy patterns across the corresponding scenarios. We then propose a hierarchical recall tree combined with the Monte Carlo Tree Search algorithm to optimize the selection of strategy and the generation of strategy responses. We construct an instruction tuning dataset and fine-tune multiple open-source large language models (LLMs) to develop MemoCue, an agent that excels in providing memory-inspired responses. Experiments on three representative datasets show that MemoCue surpasses LLM-based methods by 17. 74% in recall inspiration. Further human evaluation highlights its advantages in memory-recall applications.

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ECAI Conference 2024 Conference Paper

CAMAOT: Channel-Aware Multi-Camera Active Object Tracking System

Maolong Yin
Bin Guo 0001
Zhuo Sun 0002
Lei Wu
Zhaotie Hao
Zhiwen Yu 0001

Multi-Camera Active Object Tracking is an attractive technique in the area of intelligent surveillance, where cameras share their observations via the wireless communication to collaboratively track the target. Due to the variability in wireless channel, the dynamic transmission delay between cameras significantly affects the collaboration performance, especially when the tracking is time-sensitive. In this paper, we propose a channel-aware multi-camera active object tracking (CAMAOT) system, to achieve the stable and improved tracking performance. Specifically, a communication decision module is designed in CAMAOT, where the cameras’ communication graph and communication resource allocation adapt to the channels. Our experiments demonstrate that for time-varying channels, CAMAOT has a stable performance improvement over other systems, particularly when the communication resources are limited.

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ECAI Conference 2024 Conference Paper

HAIformer: Human-AI Collaboration Framework for Disease Diagnosis via Doctor-Enhanced Transformer

Xuehan Zhao
Jiaqi Liu 0002
Yao Zhang 0005
Zhiwen Yu 0001
Bin Guo 0001

Online disease diagnosis, gathering the patients’ symptoms and making diagnoses through online dialogue, grows rapidly worldwide. Manual-based approach, e. g. , Haodaifu, employs real-world doctors, providing high-quality but high-cost medical services. In contrast, machine-based approach, e. g. , 01bot, that utilizes machine learning models can make automatic diagnosis but lacks reliable accuracy. While some work has enabled human-AI collaboration in disease diagnosis, their collaboration pattern is simple and needs to be further improved. Therefore, we aim to introduce a doctor-enhanced and low-cost human-AI collaboration pattern. There are two key challenges. 1) How to utilize expert knowledge in doctor feedback to enhance AI’s capability? 2) How to design a collaboration workflow to achieve a low-cost doctor workload while ensuring accuracy? To address the above challenges, we propose the Human-AI collaboration framework for disease diagnosis via doctor-enhanced transformer, called HAIformer. Specifically, to enhance AI’s capability, we propose a machine module that leverages doctors’ medical knowledge through doctor-enhanced attention, using a graph attention-based matrix; to reduce doctor workload, we propose an activation module that uses two units in a cascading manner for human-AI allocation. Experiments on four real-world datasets show that HAIformer can achieve up to 91. 2% accuracy with only 18. 9% human effort and one-third of dialogue turns. Further real-world clinic study highlights its advantages in practical applications.

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ECAI Conference 2023 Conference Paper

PiercingEye: Identifying Both Faint and Distinct Clues for Explainable Fake News Detection with Progressive Dynamic Graph Mining

Yasan Ding
Bin Guo 0001
Yan Liu 0045
Hao Wang 0182
Haocheng Shen
Zhiwen Yu 0001

Explainability is crucial for the successful use of AI for fake news detection (FND). Researchers aim to improve the explainability of FND by highlighting important descriptions in crowd-contributed comments as clues. From the perspective of law and sociology, there are distinct clues that are easy to discover and understand, and faint clues that require careful observation and analysis. For example, in fake news related to COVID-Omicron showing increased pathogenicity and transmissibility, distinct clues might involve virologists’ opinions regarding the inverse correlation between pathogenicity and transmissibility. Meanwhile, faint clues might be reflected in an infected person’s claim that the symptoms are milder than a cold (indirectly indicating reduced pathogenicity). Occasionally, the statements of some ordinary eyewitnesses can decisively reveal the truth of the news, leading to the judgment of fake news. Existing methods generally use static networks to model the entire news life-cycle, which makes it fail to capture the subtle dynamic interactions between individual clues and news. Thereby faint clues, whose relations to the truth of news are challenging to be characterized and extracted directly, are more likely to be overshadowed by distinct clues. To address this issue, we propose an explainable FND method, dubbed as PiercingEye, which leverages dynamic interaction information to progressively mine valuable clues. PiercingEye models the news propagation topology as a dynamic graph, with interactive comments serving as nodes, and employs the time-semantic encoding mechanism to refine the modeling of temporal interaction information between comments and news to preserve faint clues. Subsequently, it utilizes the self-attention mechanism to aggregate distinct and faint clues for FND. Experimental results demonstrate that PiercingEye outperforms state-of-the-art methods and is capable of identifying both faint and distinct clues for humans to debunk fake news.

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