The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary

In three linesDecoder-only models hit an information-theoretic limit in deterministic state-tracking tasks beyond ~25 steps. An Attention Bottleneck Theorem bounds capacity to O(H·log(L/H)·√dh). Across 12 models and 8 domains (SWE-Bench, WebArena, SQL), tool delegation achieves 86-94% vs 24-42% for pure neural reasoning. Fine-tuning improves <5%, confirming an architectural ceiling.

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