The LLM Proposes, the Executive Disposes: A Self-Verifying Agent Instrument that Dissociates Commitment Drift from Binding Drift in Long-Horizon Agents
Mohsen Arjmandi
Why It Matters
What makes this one worth your time
Understanding and verifying the behavior of long-horizon agents is crucial for developing reliable AI systems, especially in applications requiring sustained autonomy.
A novel verification method for long-horizon agents dissociates commitment drift from binding drift.
Summary
The paper introduces a verification methodology for long-horizon agents that separates commitment drift from binding drift by using a deterministic executive to own beliefs and a language model to file proposals. The methodology includes a self-invalidating mechanism and a shadow reference for drift metrics, reporting a clean result on goal-abandonment and binding error.
Key contributions
- A verification methodology that structurally dissociates commitment drift from binding drift.
- A self-invalidating mechanism for identifying defects in agent architecture.
- A shadow reference system for defining drift metrics even in ablation scenarios.
Notable insights
- The use of a deterministic executive to own all beliefs while a language model only files proposals.
- A self-invalidating mechanism that identifies defects by breaching specific error floors.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2608.04066v1 Announce Type: new Abstract: How do you verify a long-horizon agent when its own state and self-reports are exactly what you cannot trust? We present an agent instrument built so that verification is structural rather than post-hoc. A deterministic Executive owns all belief; a language model may only file typed proposals, and a claim is admitted only when a prediction pre-registered before acting is matched against observation by code. Two properties make the instrument a verifier of its own science, not just of the agent: every run invalidates itself when per-organ write-error, render-size, or salted-canary-echo floors are breached (four of the first eight architecture runs were invalidated, each localizing a real defect); and a render-invisible shadow reference compiles the plan the full system would have committed in every ablation cell, so drift metrics are defined even where the mechanism under test has been removed. Using this instrument we report a clean, single-variable result on a failure every long-horizon agent suffers: ablating the commitment mechanism flips goal-abandonment from 0.00 to 1.00 while binding error stays flat at 0.00 (three seeds per cell, up to 394 reference beats per run, every run gated valid). The binding channel, by contrast, does not reappear as per-beat drift when its repair is ablated -- because binding is code-owned, the failure class is structurally absorbed, its only residue appearing one layer upstream as a collapse in hypothesis formation. We report these under full disclosure that task efficacy is null (zero level completions across 52 gated runs on ARC-AGI-3), pre-registered as a structural defeater. The contribution is a verification methodology for agent development and the drift decomposition it makes measurable.