arXiv (All)
open access
CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory
Long-term conversational agents must answer user queries by recalling information from extended dialogue histories, yet directly using the full history is costly and often unreliable, while compressed memory units may lose fine-grained evidence needed for question answering. Motivated by the reconst
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Current Landscape of Mental Health Conversational Agents From a Trauma-Informed Care Lens: Scoping Review.#148319
- EMP: Integrating Emotion Reasoning, Memory Structuring, and Persona Refinement for Long-Term Personalized Dialogue Generation#381284
- Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT#1130795
- SALT: Salience-Aware Lexical Trie for Long-Context Compression#799006
- SALT: Salience-Aware Lexical Trie for Long-Context Compression#797591
Record · ID 1000310
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive