arXiv AI September 28, 2026 By Dan Barry, Andrew Hines

Mutable Transcripts: Mitigating Context Pollution through Editable Conversation State

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 Analysis of user study transcripts shows that mutable transcripts can shorten conversations and remove outdated context, suggesting that user-driven revisions improve interaction quality.

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llms

arXiv AI

Aug 12

Do LLMs Benefit From Their Own Words?

arXiv:2602. 24287v2 Announce Type: replace-cross Abstract: In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant responses.

By Jenny Y. Huang, Leshem Choshen, Wei Sun, Omar Khattab, Ram\'on Fernandez Astudillo, Mehul Damani, Tamara Broderick, Jacob Andreas

llms benchmarks safety

arXiv Machine Learning

Jul 24

LLMs Get Lost in Evolving User Intent

arXiv:2607. 20734v1 Announce Type: new Abstract: As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction.

By Jihoon Tack, Philippe Laban, Jennifer Neville

llms agents benchmarks

arXiv AI

Sep 11

PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations

PRAGMA is a benchmark designed to evaluate personalized guidance in long‑term conversations. It includes curated longitudinal conversation histories, evidence annotations, and guidance scenarios that reflect evolving user contexts and incorrect assumptions. Experiments show that current retrieval, memory, and long‑context models struggle to recover relevant conversational evidence and to use it effectively for personalized guidance.

By Hyojeong Yu, Hyukhun Koh, Minsung Kim, Yunah Jang, Kyomin Jung

llms benchmarks safety

arXiv Computation and Language

Sep 18

Towards Proactive Detection of User-Side Implicit Conflicts in Human-LLM Dialogue

The paper introduces UC-Bench, a human‑annotated benchmark for detecting user‑side implicit conflicts in Human‑LLM dialogue, a problem largely overlooked compared to LLM‑side conflicts. Experiments show current LLMs struggle with these conflicts, especially when they stem from implicit incompatibilities in dialogue history. To address this, the authors propose SynUC, a constraint‑guided data synthesis method that generates a new training set, UC‑Data, which improves performance of lightweight LLMs on UC‑Bench compared to larger general‑purpose models and existing synthesis approaches.

By Jinqiang Wang, Tao Zhu, Huansheng Ning

llms benchmarks

arXiv Computation and Language

4d ago

AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control

arXiv:2609.36976v1 Announce Type: new Abstract: Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interaction...

By Tao Hwang, Yishi Diao

llms

arXiv Machine Learning

Jun 11

Context-Driven Incremental Compression for Multi-Turn Dialogue Generation

arXiv:2606. 12411v1 Announce Type: cross Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length.

By Yeongseo Jung, Jaehyeok Kim, Eunseo Jung, Jiachuan Wang, Yongqi Zhang, Ka Chun Cheung, Simon See, Lei Chen

agents nlp efficiency benchmarks