mlsanityData to 14 Oct 2026

TRACE-Memory: Public-Conditioned Retrieval and Utility-Aware Evidence Admission for Personalized Generation

arXiv 2608.08446 ↗First posted 9 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concern the wrong preference aspect, duplicate public information, or provide insufficient support. We argue that personal memory should be used only when it adds utility beyond a public-only response. We propose TRACE-Memory, a two-stage framework for selective personalization. Stage 1 queries for user-specific information missing from the request and public context, then retrieves a coverage-oriented candidate pool. Stage 2 admits a compact subset of source-traceable evidence units, or the empty set, according to response-level incremental utility. We progressively train the query-generation and evidence-admission policies through structured SFT initialization, reduced-space stage-wise GRPO warm-up, and nested multi-sample Joint GRPO. Across 4,500 Controlled and Natural tasks from Goodreads, Amazon Reviews, and Reddit, TRACE-Memory consistently outperforms random and lexical memory use, improves over semantic retrieval, remains competitive with frontier-LLM memory pipelines as local generator capacity increases, and conditions evidence admission on public-context sufficiency, supporting selective rather than default personalization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 9 Aug 2026.

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33 of 43 references.

References · 33

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  9. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  10. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  11. 2017Selective Classification for Deep Neural Networkscited by 852
  12. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
21 more
  1. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
  2. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  3. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  4. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 578
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  6. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  7. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  8. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  9. 2020The Pushshift Reddit Datasetcited by 292
  10. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  11. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
  12. 2024Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoderscited by 224
  13. 2025Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scalecited by 178
  14. 2025Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMscited by 148
  15. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 99
  16. 2024LongLaMP: A Benchmark for Personalized Long-form Text Generationcited by 97
  17. 2023Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrieverscited by 92
  18. 2019Personalized Dialogue Generation with Diversified Traitscited by 74
  19. 2024Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Effortscited by 74
  20. 2026OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agentscited by 22
  21. 2026Response-Aware User Memory Selection for LLM Personalizationcited by 1

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