Research note explores resource justice at the joint layer, highlighting failures in long-horizon AI operations, suggesting further evaluation is necessary.
This research note introduces Decision-OS V14 as Resource Justice at the Joint Layer. The note argues that long-horizon AI operation should not be evaluated only at the content layer, such as correctness, fluency, reasoning quality, coding ability, or task completion. Instead, many failures occur at the joint layer: the transfer point where state, ownership, judgment trajectory, Seat, Time Anchor, protected object, and responsibility must continue across chats, agents, tools, files, repositories, or execution contexts. V14 defines Resource Justice as the evaluation of whose finite judgment resources are consumed when continuation fails. It identifies joint-layer failure modes including Responsibility Transfer Failure, Memory Continuity vs. Ownership Continuity, State Handoff vs. OS Handoff, Seat Object Misidentification, Temporal Joint Drift, Summary-Induced Temporal Flattening, and the requirement that safe self-recursion preserve Temporal Ownership. This is a timestamped research note intended to preserve the structure at this stage. It does not define a full implementation architecture, scoring system, repository format, automated handoff engine, or empirical benchmark.
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Shinichi Nagata (2026) studied this question.
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