mlsanityData to 13 Oct 2026

When Does Latent Communication Pay? A Causal Audit of Relayed KV Caches in Multi-Agent LLMs

arXiv 2608.04893 ↗First posted 5 Aug 2026cs.CR cs.AI cs.LGcited by 3

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

also in 1 more
Abstract (arXiv)

Multi-agent LLM systems relay key-value caches instead of text and credit their gains to exchanged "latent thoughts". That credit is a claim about which example's cache is relayed, not merely that one is. We audit it causally in released systems. The cache is replaced with deranged (mismatched-example), zeroed, and moment-matched random counterparts, under two regimes defined by whether the receiver needs the sender's private information. Where it does, the battery reads ceiling: 100% against 23-25% for answer-irrelevant relays on the primary backbone, a contrast replicated across three families, five checkpoints, and a prose document-QA surface. Where it does not, a pre-registered five-seed protocol establishes equivalence within 2.8 points, a margin anchored to the audited system's reported gain, under Holm-corrected TOST on GSM8K and ARC-Challenge across three Qwen3 scales and on MedQA at 8B (one cell shows a small detected advantage inside the margin); a second family shows no detected advantage. A large cache effect need not be a pairing effect. In one natural cell, zeroing the relay costs 14.7 points; a mismatched cache, 0.4. Nor is need sufficient: under the same test, delivered channels span ceiling (LatentMAS's native relay), partial (KVComm's layer subset), and no detected example-specific transfer (C2C's released projector). Benchmark deltas do not by themselves establish latent-thought transmission; establishing it takes a mismatched-cache audit, which we release.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 48 of its 53 references that are in the map. First posted on arXiv 5 Aug 2026.

Builds on

48 of 53 references.

Closest ancestors · 12

Same direction, newest first

  1. 2026Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM29 Jul
  2. 2026Multi-Agent LLMs Fail to Explore Each Other13 Jul
  3. 2026Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems4 Jun
  4. 2026HyLaT: Efficient Multi-Agent Communication via Hybrid Latent-Text Protocol25 May
  5. 2026Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems23 Apr
  6. 2026When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration14 Apr
  7. 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs12 Feb
  8. 2026Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems3 Feb
  9. 2025Latent Collaboration in Multi-Agent Systems25 Nov
  10. 2025Enabling Agents to Communicate Entirely in Latent Space12 Nov
  11. 2025Communicating Activations Between Language Model Agents23 Jan
  12. 2024Mixture-of-Agents Enhances Large Language Model Capabilities7 Jun

Other references · 36

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,916
  2. 2025Qwen3 Technical Reportcited by 8,510
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,657
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,798
  5. 2022Locating and Editing Factual Associations in GPTcited by 2,650
  6. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,758
  7. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,338
  8. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,145
  9. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 815
  10. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 782
  11. 2024Phi-4 Technical Reportcited by 696
  12. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 652
24 more
  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 566
  2. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 553
  3. 2021Causal Abstractions of Neural Networkscited by 408
  4. 2016Learning Multiagent Communication with Backpropagationcited by 359
  5. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 345
  6. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 259
  7. 2016Multi-Agent Cooperation and the Emergence of (Natural) Languagecited by 181
  8. 2025Cache-to-Cache: Direct Semantic Communication Between Large Language Modelscited by 54
  9. 2025Thought Communication in Multiagent Collaborationcited by 28
  10. 2025Activation Space Interventions Can Be Transferred Between Large Language Modelscited by 17
  11. 2025Transferring Linear Features Across Language Models With Model Stitchingcited by 17
  12. 2025KVComm: Enabling Efficient LLM Communication through Selective KV Sharingcited by 17
  13. 2026LatentMem: Customizing Latent Memory for Multi-Agent Systemscited by 14
  14. 2026Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Evictioncited by 11
  15. 2026ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothingcited by 9
  16. 2026The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systemscited by 8
  17. 2026LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systemscited by 6
  18. 2026See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agentscited by 6
  19. 2026Representational Collapse in Multi-Agent LLM Committees: Measurement and Diversity-Aware Consensuscited by 3
  20. 2026Out of Sight, Not Out of Mind: Unveiling Latent Attack in Latent-based Multi-Agent Systemscited by 3
  21. 2026A Negative Result on Cross-Model Activation Transfer in a Pythia Multi-Hop Settingcited by 3
  22. 2026LatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosiscited by 2
  23. 2026When Latent Agents Lie: KV-Cache Integrity in Multi-Agent LLM Collaborationcited by 2
  24. 2026QKVShare: Quantized KV-Cache Handoff for Multi-Agent On-Device LLMscited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

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  3. 2026Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell
  4. 2026Faithful, Not Corrective: Model Capability Governs Message-Format Effects in Multi-Hop Agent Relays
  5. 2026Receiver-Conditioned Latent Communication gives 94% CacheBack
  6. 2026Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
  7. 2026Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?
  8. 2026Detection Without Correction: A Two-Parameter Decomposition of Multi-Stage LLM Pipelines