Yanfei Chen
Yanfei Chen received his PhD in Industrial Engineering from University of Pittsburgh in 2018. He joined Google as a software engineer in Cloud AI, working on building Anti Money Laundering AI on the Google Cloud Platform. Currently, he is in the Cloud AI Research team, working on various research projects to bring the state of the art AI into Google Cloud for enterprise customers. His research interests are natural language processing, data mining and optimization.
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In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialog Agents
Zhen Tan
Jun Yan
I-Hung Hsu
Rujun Han
Yiwen Song
George Lee
Anand Iyer
Tianlong Chen
Huan Liu
2025
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Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization. External memory mechanisms have been proposed to address this limitation, enabling LLMs to maintain conversational continuity. However, existing approaches struggle with two key challenges. First, rigid memory granularity fails to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations. Second, fixed retrieval mechanisms cannot adapt to diverse dialogue contexts and user interaction patterns. In this work, we propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections: (1) Prospective Reflection, which dynamically summarizes interactions across granularities—utterances, turns, and sessions—into a personalized memory bank for effective future retrieval, and (2) Retrospective Reflection, which iteratively refines the retrieval in an online reinforcement learning (RL) manner based on LLMs’ cited evidence. Experiments show that RMM demonstrates consistent improvement across various metrics and benchmarks. For example, RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
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Deep Researcher with Test-time Diffusion
Rujun Han
Zoey CuiZhu
Guan Sun
Yuanjun (Sophia) Bi
Weiming Wen
Hui Wan
Chunfeng Wen
Solène Maître
George Lee
Vishy Tirumalashetty
Emily Xue
Burak Gokturk
2025
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Deep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time scaling algorithms. Drawing inspiration from the iterative nature of human research, which involves cycles of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep Researcher (TTD-DR). This novel framework conceptualizes research report generation as a diffusion process. TTD-DR initiates this process with a preliminary draft, an updatable skeleton that serves as an evolving foundation to guide the research direction. The draft is then iteratively refined through a "denoising" process, which is dynamically informed by a retrieval mechanism that incorporates external information at each step. The core process is further enhanced by a self-evolutionary algorithm applied to each component of the agentic workflow, ensuring the generation of high-quality context for the diffusion process. This draft-centric design guides the report writing process to be more timely and coherent while reducing information loss during the iterative search process. We demonstrate that our TTD-DR achieves state-of-the-art results on a wide array of benchmarks that require intensive search and multi-hop reasoning, significantly outperforming existing deep research agents.
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With development of Large Language Models (LLMs), collaboration between LLMs to solve complex tasks has attracted more and more attention. An important challenging task is reasoning from long text that cannot be input into LLMs. Thus far, limited research has explored how to solve long context tasks via pure multi-agent collaboration.
In this paper, we propose Chain-of-Agents (CoA), a novel framework that leverages the multi-agent collaboration via natural language to solve complex tasks. In CoA, the long text is split into chunks to be processed by agents repeatedly with appending the information from preceding agents. A manager model is finally employed to obtain the final answer utilizing the output of the last agent.
On wide range of datasets for long context question answering, summarization, and code completion and with many LLMs (including PaLM 2, Claude, and Gemini), we show that CoA framework outperforms strong baselines, including the commonly-used retrieval augmented generation (RAG) systems, by a large margin. For instance, text-bison obtains 13.30\% performance gain on NarrativeQA, and 10.22\% on MuSiQue dataset.
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