Challenge: Existing personalized dialogue systems struggle to reconcile unbounded interactions with finite context constraints.
Approach: They propose a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling.
Outcome: The proposed framework outperforms existing systems in suppressing contextual noise and persona inconsistency.

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Challenge: Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality.
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Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning (2025.emnlp-main)

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Challenge: Existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning.
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PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

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Challenge: Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
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Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues (2026.findings-acl)

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Challenge: Existing alignment methods focus on universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem.
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In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)

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Challenge: Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations.
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“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)

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Challenge: Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create.
Approach: They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles.
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Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)

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Challenge: Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions.
Approach: They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning .
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Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)

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Challenge: Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments .
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A Persona-Aware LLM-Enhanced Framework for Multi-Session Personalized Dialogue Generation (2025.findings-acl)

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Challenge: Existing personalized dialogue models focus on dialogue history and personality information, reducing the responses’ consistency.
Approach: They propose a Persona-Aware LLM-enAnCEd(PALACE) framework that generates responses consistent with dialogue history and personality information across multiple sessions to engage users’ interest in the dialogue.
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U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents (2026.findings-acl)

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Challenge: Existing context-folding methods are designed for single-query or single-intent scenarios.
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