Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering

• URL: http://arxiv.org/abs/2608.27925v1
• Date: Fri, 28 Aug 2026 05:03:33 GMT
• Title: Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering
• Abstract summary: On 1,986 questions from ten LoCoMo conversations, graph retrieval raises official evidence recall at top-k 25 from 79.7468% to 84.4842%.<n>Four paper-eligible requested configurations support empirical robustness across the tested GPT-3.5 and DeepSeek extractors on both outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity-Memory graph retrieval keeps dialogue turns as verbatim Memory nodes, links repeated mentions through shared Entities, and connects adjacent Memories with directed chronological edges. At query time the retriever moves from Entity gating through semantic fusion and one-hop chronological recovery to dense backfill. The path can keep a neighboring Memory that dense cosine ranking would otherwise omit. A matched dense control shares the Memory and query vectors, context budget, requested answer protocol, and evaluator, isolating graph structure from changes to the reader. On 1,986 questions from ten LoCoMo conversations, graph retrieval raises official evidence recall at top-k 25 from 79.7468% to 84.4842%. The recall advantage is supported from top-k 5 to 50, while no matched cutoff supports an overall final-answer F1 difference. Four paper-eligible requested configurations support empirical robustness across the tested GPT-3.5 and DeepSeek extractors on both outcomes. Embedding robustness is mixed: F1 has no supported contrast, but recall is sensitive to the embedding artifact. The comparison isolates a retrieval-coverage gain from graph structure. It does not establish a final-answer F1 gain, model or embedding equivalence, or cross-dataset generalization.

Related papers

• When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents [21.60115070942764]
  We introduce LOCOMO-CONV, a memory benchmark derived from Lo- CoMo with four query styles: dialog, implicit, counterfactual, and composed.<n>Across five rep- resentative memory systems, we evaluate both retrieval recall and end-to-end response qual- ity.
  arXiv  Detail & Related papers  (2026-09-03T07:24:33Z)
• AWM: Answerable Working Memory for Long-Document VQA Agents [57.741922050360074]
  Working memory should carry answer-supporting evidence across page inspections for later grounded answering.<n>We introduce emphmemory-only answerability, a diagnostic that asks whether a reader can answer from the question and terminal working memory alone.<n>AWM-GRPO improves final-answer accuracy over the RAG baseline by 8.1 and 11.9 points on textscMMLongBench-Doc and textscLongDocURL and reduces the memory-missing-correct rate by 2.7 points over answer-only GRPO.
  arXiv  Detail & Related papers  (2026-08-26T10:35:25Z)
• CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion [21.692254412768936]
  Link construction should instead prioritize a sparse set of retriever-complementary associations.<n>We present CABLE, a plug-in augmentation that constructs links to extend the host retriever's direct semantic reach.<n>CABLE yields higher mean LLM-judge scores in every evaluated system-level setting.
  arXiv  Detail & Related papers  (2026-08-18T15:40:29Z)
• MemoryLake on MemoryArena: A Matched Study of Agent Memory Backends [8.130461324371604]
  We compare MemoryLake with Mem0, text-embedding-3-small vector RAG, and a long-context control across all five MemoryArena domains.<n>Because each backend bundles write, retrieval, consolidation, budgeting, and prompt-assembly choices, the study is a matched system-level comparison.
  arXiv  Detail & Related papers  (2026-08-14T02:19:26Z)
• RippleMem: From Isolated Retrieval to Associative Recollection for Long-Term Agent Memory [13.519130680921565]
  We introduce RippleMem, a long-term memory system that replaces one-shot retrieval with adaptive associative recollection.<n>LoCoMo and LongMemEval-S show that RippleMem achieves the best overall performance across evaluated settings.
  arXiv  Detail & Related papers  (2026-08-13T15:05:01Z)
• MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations [50.24315431387575]
  We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations.<n>MemOps represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence.<n>Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals.
  arXiv  Detail & Related papers  (2026-07-14T15:33:44Z)
• Echo-Memory: A Controlled Study of Memory in Action World Models [58.77172260403133]
  We present a controlled study of memory mechanisms in action-conditioned world models.<n>Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences.<n>We use Echo-Memory to fix the action-to-video interface and varies only how history is stored and read by the generator.
  arXiv  Detail & Related papers  (2026-06-08T17:54:10Z)
• Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents [51.30250860677378]
  We propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.<n>Running on a Cue-Tag-Content graph, our active reconstruction mechanism integrates reasoning directly into memory access.<n>Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:29:46Z)
• Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding [55.7992006853979]
  SAVEMem is a framework that brings semantic awareness into memory generation and lets the retrieval scope adapt per query.<n>We present SAVEMem, a training-free dual-stage framework that brings semantic awareness into memory generation and lets the retrieval scope adapt per query.
  arXiv  Detail & Related papers  (2026-05-08T15:40:40Z)
• HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents [9.454943641700392]
  HiGMem is a two-level event-turn memory system that uses event summaries as semantic anchors to predict which related turns are worth reading.<n>On the LoCoMo10 benchmark, HiGMem achieves the best F1 on four of five question categories and improves adversarial F1 from 0.54 to 0.78 over A-Mem.
  arXiv  Detail & Related papers  (2026-04-20T14:44:12Z)
• GAAMA: Graph Augmented Associative Memory for Agents [0.0]
  GAAMA is a graph-augmented associative memory system that constructs a concept-mediated hierarchical knowledge graph.<n>On the LoCoMo-10 benchmark (1,540 questions across 10 multi-session conversations), GAAMA achieves 78.9% mean reward, outperforming a tuned RAG baseline.
  arXiv  Detail & Related papers  (2026-03-29T23:33:38Z)
• Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval [59.295767860331004]
  RF-Mem is a familiarity uncertainty-guided dual-path memory retriever.<n>It embeds human-like dual-process recognition into the retriever.<n>It consistently outperforms one-shot retrieval and full-context reasoning under fixed budget and latency constraints.
  arXiv  Detail & Related papers  (2026-03-10T06:31:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.