The paper introduces TriMem, a memory system for lifelong LLM agents that stores raw dialogue segments, extracted atomic facts, and synthesized profiles to preserve detail, enable efficient retrieval, and support deep reasoning. It uses TextGrad-based prompt optimization to iteratively refine extraction and profiling prompts without updating model parameters. Experiments on LoCoMo and PerLTQA show TriMem outperforms strong memory baselines across multiple LLM backbones.
By Jingwei Sun, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han
The article surveys multi‑turn conversational AI, highlighting its shift from isolated text prompts to sustained, multimodal interactions that involve clarifying goals, revising requests, and switching topics. It reviews literature across text‑only dialogue, AudioLLMs, multimodal and omni‑modal systems, and tool‑augmented agents, organizing findings around datasets, models, training, evaluation, and cross‑cutting challenges. The analysis reveals that while multimodal perception and action have progressed rapidly, systems still struggle with persistent memory, cross‑turn grounding, full‑duplex interaction, robust evaluation, and cultural alignment.
By Syeda Faiza Ahmed, Zien Sheikh Ali, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury
The paper introduces the Explainability Assistant, an open‑source conversational XAI system designed to interpret complex energy consumption forecasting models. By leveraging large language model function‑calling, it achieves 94% intent‑parsing accuracy and supports flexible natural‑language interaction across different ML problem types without task‑specific fine‑tuning. A comparative evaluation with energy domain specialists shows improved usability and consistent task accuracy, with all experts preferring the conversational interface over a traditional XAI dashboard.
By Rodion Krjut\v{s}kov, Eduard Barbu, Nikos Sakkas, Sofia Yfanti
Hy‑MultiTurn is a Chinese benchmark designed to evaluate deep multi‑turn dialogue understanding over long interactions. It introduces six controlled evaluation modes—constraint memory, precise execution, constraint synthesis, object localization, action suppression, and reference resolution—across 209 tasks ranging from 12 to 76 turns, incorporating dialogue length, irrelevant distractions, and colloquial phrasing. Testing 22 state‑of‑the‑art models shows the benchmark is highly challenging, with even the best model meeting all criteria only 41.1% of the time and no model excelling in every mode.
By Eileen Ye, Jiawen Tao, Yaoming Li, Chenxu Liu, Wenhan Yu, Yaxin Fan, Xiaokun Yuan, Mengzhou Wu, Yanbing Jiang, Maxm Pan
arXiv:2602.14035v2 Announce Type: replace
Abstract: Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-spe...
By Jinzi Zou, Bolin Wang, Shuo Zhang, Nuo Xu, Junzhou Zhao
The paper introduces a Symbolic-RAG-Generative architecture called GRACE for goal‑oriented conversational systems. GRACE transforms business intent into a fixed objective set and uses a constrained policy to update the conversation state based on visitor‑authored evidence while ensuring visitor utility. Evaluation on real‑estate and professional‑cleaning dialogues shows high accuracy in state transitions, evidence precision/recall, and monotonicity.
By Ramon Gonzalez (Mentomy AI), Antonio Diaz (Mentomy AI)
arXiv:2602. 15707v2 Announce Type: replace-cross Abstract: Real-time conversational assistants for procedural manual tasks often depend on video input, which can be computationally expensive and compromise user privacy.
By Rehana Mahfuz, Yinyi Guo, Erik Visser, Phanidhar Chinchili
MADS (Multi-Agent Dialog Simulation) is a synthetic data generation pipeline that creates structured, diverse dialogue trajectories, including adversarial conversations that reflect realistic failure scenarios. The resulting MADS-Bench benchmark covers six domains, 55 personas, and 1095 conversations, with about 66% featuring failure-prone situations. Models trained with MADS are evaluated using a two-stage approach combining supervised fine-tuning and reinforcement learning with verifiable rewards to assess robustness.
By Arkajyoti Chakraborty, Adam Zernik, Ishika Kulkarni, Mudit Arora, Hugo Lin, Justin Chiu, Alessandro Di Bari, Neha Gupta, Andreas Stolcke
arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.
By Junfeng Liu, Christopher T. Symons, Ranga Raju Vatsavai
The paper introduces mutable transcripts, an interaction paradigm that lets users edit prior turns in a chat, turning the conversation history into an editable state rather than a fixed record. A prototype was built and tested with 17 participants, who preferred mutable transcripts over standard chat for clarity, confidence, and ease of use, and reported less need to restart conversations. Analysis of user study transcripts shows that mutable transcripts can shorten conversations and remove outdated context, suggesting that user-driven revisions improve interaction quality.
By Dan Barry, Andrew Hines
arXiv:2607. 22635v1 Announce Type: new Abstract: Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations.
By Xuzhao Geng, Haozhao Wang, Xuelian Li, Zhenyu Yang, Haonan Lu, Rui Zhang, Ruixuan Li
arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.
By Nayoung Choi, Jonathan Zhang, Jinho D. Choi