arXiv AI
Jul 14
Faithful, Not Corrective: Message-Format Effects in Multi-Hop Agent Relays Are Tier-Dependent
arXiv:2607. 09678v1 Announce Type: new Abstract: When LLM agents hand off information to one another, does the message format matter?
Faithful, Not Corrective: Model Capability Governs Message-Format Effects in Multi-Hop Agent Relays
The study investigates how different message formats affect the fidelity of information as it passes through multiple LLM agent relays. Using a controlled testbed, the authors encode twelve atomic facts in five formats (free natural language, precision‑instructed NL, JSON, triples, key‑value) across six hops and evaluate recall against programmatic ground truth. Results show that strong relays maintain near‑lossless recall for all formats, while weaker relays exhibit significant format‑dependent recall loss, and that any injected error is faithfully propagated across all formats without causing collateral damage.
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arXiv AI
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arXiv AI
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arXiv Machine Learning
Jul 31
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arXiv AI
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arXiv AI
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ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control
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