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

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TL;DR - An arXiv preprint that causally audits whether multi-agent LLM systems relaying KV caches actually transmit example-specific "latent thoughts," by swapping in deranged (mismatched-example), zeroed, and moment-matched random caches. It matters because benchmark gains alone are shown to be insufficient evidence for latent-thought transfer.

  • Two regimes are tested: when the receiver needs the sender's private information, the audit reads ceiling (100% vs 23–25% for answer-irrelevant relays on the primary backbone), replicated across three model families, five checkpoints, and a prose document-QA surface.
  • When private information isn't needed, a pre-registered five-seed protocol with Holm-corrected TOST establishes equivalence within 2.8 points on GSM8K and ARC-Challenge across three Qwen3 scales and MedQA at 8B; one cell shows a small detected advantage inside the margin, and a second family shows none.
  • Cache magnitude ≠ pairing: in one natural cell, zeroing the relay costs 14.7 points while a mismatched cache costs only 0.4 — so a large cache effect need not be example-specific.
  • Delivered channels differ under the same test: LatentMAS's native relay hits ceiling, KVComm's layer subset is partial, and C2C's released projector shows no detected example-specific transfer; the authors release the mismatched-cache audit.

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

arXiv cs.CR Jiaming Cheng, Subhransu Das, Rajiv Ramnath 2026-08-05 arXiv:2608.04893
Public signals Semantic Scholar citations 2 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 2 · Influential citations 0 X · N/A Fetched 2026-08-24 14:26:17.553396 UTC

TL;DR - An arXiv preprint that causally audits whether multi-agent LLM systems relaying KV caches actually transmit example-specific "latent thoughts," by swapping in deranged (mismatched-example), zeroed, and moment-matched random caches. It matters because benchmark gains alone are shown to be insufficient evidence for latent-thought transfer.

  • Two regimes are tested: when the receiver needs the sender's private information, the audit reads ceiling (100% vs 23–25% for answer-irrelevant relays on the primary backbone), replicated across three model families, five checkpoints, and a prose document-QA surface.
  • When private information isn't needed, a pre-registered five-seed protocol with Holm-corrected TOST establishes equivalence within 2.8 points on GSM8K and ARC-Challenge across three Qwen3 scales and MedQA at 8B; one cell shows a small detected advantage inside the margin, and a second family shows none.
  • Cache magnitude ≠ pairing: in one natural cell, zeroing the relay costs 14.7 points while a mismatched cache costs only 0.4 — so a large cache effect need not be example-specific.
  • Delivered channels differ under the same test: LatentMAS's native relay hits ceiling, KVComm's layer subset is partial, and C2C's released projector shows no detected example-specific transfer; the authors release the mismatched-cache audit.
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