mlsanityData to 7 Oct 2026

LatticeMind: A Conflict-Aware Memory Primitive for Multi-Agent Systems

arXiv 2608.08236 ↗First posted 8 Aug 2026cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding which incompatible claim should currently be trusted. Majority vote, debate, and judge-based selection choose an output without recording which claim wins, which is contested, or why a later update supersedes it. We present termLatticeMind, a conflict-aware structured memory that handles contradiction at write time. It maintains explicit item status, applies cheap symbolic conflict checks, and invokes LLM reconciliation only for unresolved semantic cases. On a label-blind ConflictBank evaluation that removes source-name hints, LatticeMind reaches 0.97 accuracy versus 0.61 for the strongest aggregation baseline, with the gap significant at p<10-6 by paired McNemar test. Ablations show that removing the checker or the reconciler costs 12 to 14 points. On four secondary planning benchmarks the picture is mixed: LatticeMind beats naive merge on three of four, but does not replace deliberation methods on tasks rewarding iterative search.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 31 of its 45 references that are in the map. First posted on arXiv 8 Aug 2026.

Builds on

31 of 45 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Memory in the Age of AI Agents15 Dec
  2. 2025MIRIX: Multi-Agent Memory System for LLM-Based Agents10 Jul
  3. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions7 Jul
  4. 2025A-MEM: Agentic Memory for LLM Agents17 Feb
  5. 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memory20 Jan
  6. 2024Evaluating Very Long-Term Conversational Memory of LLM Agents27 Feb

Other references · 25

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  6. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  7. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  8. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  9. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  10. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  11. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  12. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
13 more
  1. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
  2. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  3. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 385
  4. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 372
  5. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  6. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 195
  7. 2024NATURAL PLAN: Benchmarking LLMs on Natural Language Planningcited by 125
  8. 2024ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMcited by 73
  9. 2025ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Taskscited by 30
  10. 2026LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfacescited by 29
  11. 2025REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Taskscited by 16
  12. 2026Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agentscited by 5
  13. 2025LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledgecited by 3

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