# Reliable Post-Retrieval Assembly for Agent Memory: Separating Evidence Extraction from Policy Execution arXiv: https://arxiv.org/abs/2606.01435 ยท HTML: https://mlsanity.com/paper/2606.01435/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. - Builds on 24 of its 26 references that are in the map. - First posted on arXiv 31 May 2026. ## Placed in - LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/) ## Builds on (most cited first, 24 of 24) - [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023) - [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022) - [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022) - [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025) - [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024) - [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022) - [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025) - [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://mlsanity.com/paper/2305.15294/) (2023) - [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022) - [Faithful Chain-of-Thought Reasoning](https://mlsanity.com/paper/2301.13379/) (2023) - [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024) - [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023) - [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025) - [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://mlsanity.com/paper/2502.14802/) (2025) - [Uncovering Overfitting in Large Language Model Editing](https://mlsanity.com/paper/2410.07819/) (2024) - [Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities](https://mlsanity.com/paper/2406.12468/) (2024) - [TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs](https://mlsanity.com/paper/2511.10375/) (2025) - [MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.21544/) (2025) - [Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents](https://mlsanity.com/paper/2604.22085/) (2026) - [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024) - [Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs](https://mlsanity.com/paper/2506.07270/) (2025) - [Micro-Act: Mitigating Knowledge Conflict in LLM-based RAG via Actionable Self-Reasoning](https://mlsanity.com/paper/2506.05278/) (2025) - [Temporal Fact Conflicts in LLMs: Reproducibility Insights from Unifying DYNAMICQA and MULAN](https://mlsanity.com/paper/2603.15892/) (2026) - [Freshness and the Limits of Heuristic Trend Detection in Temporal RAG](https://mlsanity.com/paper/2509.19376/) (2025) ## Built on by (most cited first, 8 of 8) - [Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings](https://mlsanity.com/paper/2607.21962/) (2026) - [When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory](https://mlsanity.com/paper/2608.25553/) (2026) - [The Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory Agents](https://mlsanity.com/paper/2609.01852/) (2026) - [Retrieved but Not Delivered: Multimodal Memory Delivery for Long-Term Agents](https://mlsanity.com/paper/2609.32590/) (2026) - [Control-Plane Placement Shapes Forgetting: An Architectural Study of Agent Memory Across Thirteen System Configurations](https://mlsanity.com/paper/2606.15903/) (2026) - [Contract Memory Compiler: Resolve, Then Traverse](https://mlsanity.com/paper/2609.32658/) (2026) - [RoutePrism: Tracing Construction Order Effects in Agent Memory](https://mlsanity.com/paper/2609.34160/) (2026) - [The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State](https://mlsanity.com/paper/2610.00041/) (2026) ## Abstract (arXiv) LLM-based memory systems can retrieve relevant evidence yet still fail when answer generation entangles semantic filtering, conflict resolution, prior suppression, and output generation in one step. We study this failure as a problem of post-retrieval assembly. In the MemoryAgentBench (MAB) release used here, FactConsolidation explicitly states that newer facts have larger serial numbers, yet the best reported retrieval/memory result is 54% single-hop and all 22 reported systems score at most 7% multi-hop. We evaluate a structured assembly interface in which an LLM first extracts semantically matching evidence into a candidate representation and a separate stage executes the required answer policy. At 262K, this pipeline reaches 82%/93% single-hop and 27%/41% multi-hop with gpt-4o-mini/gpt-4o, exceeding every result reported in the MAB v3 FactConsolidation comparison. This is a task-level result, not a claim that the evaluated memory architectures are broadly inferior. A controlled whole-pipeline comparison, with identical backbone, retrieved top-10 evidence, chunking, and n=100 per cell, improves single-hop accuracy by 10.8 percentage points (pp) on average and 21 pp at 262K. A targeted comparison using the same extraction setup shows that changing only the final policy executor contributes 2.0 pp on average and 0 pp at 262K. Most of the gain therefore comes from separating evidence identification from final policy execution rather than from the freshness operator itself. A LongMemEval check finds no significant overall advantage (26/45 versus 29/45; paired exact McNemar p=0.45), bounding the result to current-value questions with explicit version metadata. The evidence identifies post-retrieval assembly as a distinct reliability boundary between retrieval and answer generation.