Faithful, Not Corrective: Model Capability Governs Message-Format Effects in Multi-Hop Agent Relays

Abstract:When LLM agents hand information to one another, does the message format matter? Two literatures disagree: format-optimization work reports that structured messages cut cost without hurting accuracy, while format-restriction studies find that imposing structure degrades generation. Neither line has measured what happens when messages traverse multiple hops, where copy fidelity, rather than one-shot generation quality, dominates. We introduce a controlled relay testbed in which briefs of twelve programmatic atomic facts are re-encoded hop by hop in five formats (free natural language, precision-instructed NL, JSON, triples, key-value) over six hops, scored against programmatic ground truth by a fixed strong grader, across two relay-capability tiers, a cognitive-load condition, and a paired-fork error injection. We find that (i) a strong relay is nearly lossless for every format (hop-6 QA recall $\geq 0.973$), with residual loss concentrated at the first encoding step; (ii) per-hop cognitive load raises generation cost by 24-53% while fidelity changes stay within $\pm 1.8$ points; (iii) under a weak 1.5B relay, the across-format dispersion of hop-6 recall grows by a factor of $8.7$ (CI 5.3-15.5), driven by an encode-drift trade-off that flips the format ranking in transit; and (iv) once an injected error is present, every format propagates it faithfully (surface persistence 83-100%) and no format cascades collateral damage onto neighboring facts. Structure buys a faithful, error-localizing channel, not an error-correcting code.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2607.09678 [cs.AI]

(or arXiv:2607.09678v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.09678 Focus to learn more arXiv-issued DOI via DataCite

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