Object Commitment as a Diagnostic Pressure Point in Grounded Planning

Contemporary grounded planning agents commonly delegate object selection to external resolvers—APIs, heuristics, and privileged interfaces—a pattern that masks representational failures. We isolate object commitment as a diagnostic variable: two agents with identical perception, world models, and training differ only in whether object arguments are resolved externally (Variant A) or internally (Variant B). This exposes when mean-pooled representations, standard in model-based RL systems like Dreamer and PlaNet, fail at categorical object grounding. We demonstrate four non-intuitive dissociations. First, planning and grounding are orthogonal: the same model achieves 100% multi-step planning success while failing completely (0%) at object selection on identical tasks when entropy increases from 4 to 30+ objects (Archive Dichotomy). Second, the Data Scale Paradox: homogeneous training data causes performance collapse from 100% (300 trajectories) to 0% (500+ trajectories)—more data actively harms performance through statistical gravity that reinforces dominant patterns over task-conditional reasoning. Third, width-only capacity scaling destroys intelligence: 8M parameters (23× increase) underperform the 343K base model, while balanced width+depth scaling (75M, 220× increase) recovers planning but not grounding, revealing a width-to-depth ratio requirement for capability emergence. Fourth, grounding bottlenecks are architectural, not parametric: even 220× scaling cannot overcome categorical failures under high entropy, confirming mean pooling imposes a representational ceiling. These failure modes—semantic drift under statistical gravity, topology-dependent collapse, and entropy-sensitive grounding—parallel pathologies in large language models (hallucinations, entity confusion), suggesting mechanistic connections between filesystem agents and frontier AI systems. Delegation conceals these failures; internal commitment exposes them. The gap is diagnostic.