mlsanityData to 14 Oct 2026

ConvMemory v3: A Validity Context Layer for Conversational Memory via Target-Conditioned Relation Verification

arXiv 2606.26753 ↗First posted 25 Jun 2026cs.CL cs.IRcited by 0

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Abstract (arXiv)

Conversational memory retrieval optimizes relevance, yet a retrieved memory can be relevant and simultaneously outdated: a later turn updates, corrects, or supersedes it. ConvMemory v3 adds a validity context layer that detects and surfaces this update evidence through target-conditioned relation verification, sitting after the v1/v2 retrieval path. The core mechanism is a dual-evidence gate that conditions a relation judgment on the specific target proposition, scoring a (target, source) pair through the product of a MiniLM slot head and a DeBERTa-v3 slot head and gating it by conservative event/operation evidence. On a synthetic multi-hop validity benchmark the gate reaches 90.12% +/- 1.73 accuracy; through a real-data feedback loop that mines failure patterns but trains on synthetic pairs only, the verifier transfers to Memora role binding with zero target-side labels, reaching 98.8% +/- 0.9 group-all-correct. The deployed layer preserves retrieval by default: a context mode attaches structured validity metadata while keeping the candidate set and rank order fixed, and a query-conditioned demote mode is an explicit opt-in for dense current-state workloads, where it raises current-active H@1 from a never-demote baseline of 45.1% to 95.7% +/- 1.2 while protecting non-superseded memories at 99.4% recall. Six machine-verifiable safety contracts pin the layer's behavior. Multi-hop graph propagation is validated as a mechanism; fully automatic construction of strict prerequisite edges is characterized as a boundary, since strict necessity requires counterfactual world knowledge. This report extends ConvMemory v1 (arXiv:2605.28062) and v2 (arXiv:2606.10842).

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References · 16

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  4. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  5. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
  6. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  7. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  8. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  9. 2022Mass-Editing Memory in a Transformercited by 1,001
  10. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  11. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
  12. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 779
4 more
  1. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
  2. 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agentscited by 36
  3. 2026ConvMemory: A Lightweight Learned Memory Reranker, a Negative Attribution Result, and a Research-Preview Conflict Editorcited by 2
  4. 2026ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrievalcited by 2

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