Open specification · multi-agent LLM systems
A notation for transferring reasoning between AI systems that leaves an auditable record of the transfer.
Multi-agent systems are easy to run and hard to inspect. When a planner hands work to an executor, and that output feeds a third agent, the reasoning behind the final answer is scattered across opaque messages. Plain prose loses structure at each hop; plain JSON carries data but no record of what was checked versus assumed.
Reasonledger encodes model-to-model messages as named blocks, each carrying an explicit instruction for how the receiver should process the enclosed content. Across a chain of agents, those blocks and a three-way epistemic status accumulate into an inspectable record of what each model claimed, what it verified, and what it accepted on trust. That record is the ledger.
It is a payload format. It rides inside a transport such as MCP or A2A; it does not replace one. It is not a blockchain and involves no tokens, chain, or consensus — “ledger” refers to the audit trail the format produces, nothing more.
Every claim carries one of three states, kept distinct across model boundaries rather than collapsed into a single answer.
@FORMAL [kepler_third] T² = (4π² / GM) · a³ @DERIVE [circular_velocity] (1) For a circular orbit, gravity supplies the centripetal force (2) G·M·m / r² = m·v² / r (3) From (2): v² = G·M / r →→ v = sqrt(G·M / r) @TAXONOMY [orbit_properties] bound_orbit: confirmed exact_eccentricity: open parabolic_escape: denied
After a transfer hop, the receiver's record carries the audit trail: which agent asserted what, which steps were verified, which were trusted, and where a claim's status changed along the way.
An LLM auditor's false-positive rate on identical clean reasoning traces moves from 60/60 to 0/60 on prompt wording alone.
Under a decision rule fixed before data collection, the notation gives no failure-detection advantage over information-matched JSON; its value is standardization. The stronger, separate finding is the prompt sensitivity above, reproduced on held-out traces under a design anchored before any live auditor call. These are author-run results; independent replication is invited and has not yet been performed. Reasonledger makes no claim about model cognition.
When the Auditor Cries Wolf: Prompt-Dependent False Positives in LLM Reasoning Auditors — specification, reference checker, evaluation harness, study protocols, and raw results are released.