Failure Geometry as a Constructive Control Paradigm for Large Language Models Attractor-Driven Semantic Collapse Under Hard Termination Constraints

Large language model (LLM) failures are commonly treated as undesirable artifacts arising from hallucination, misalignment, or insufficient training. In this work, we demonstrate that failure can instead be deliberately constructed, stabilized, and studied as a geometric property of constrained semantic spaces. We present a controlled experimental system in which an instruction-tuned local LLM is placed within a semantic manifold containing strong topic attractors, mandatory escalation rules, and physically enforced termination boundaries. Under these conditions, the model exhibits predictable attractor-driven spiraling behavior characterized by increasing epistemic certainty, decreasing evidentiary grounding, and inevitable semantic collapse. We argue that this behavior is not pathological but geometrically necessary given the imposed constraints. The resulting system functions as a runnable instantiation of Failure Geometry, providing both a diagnostic instrument for LLM behavior and a constructive framework for analyzing obsession, recursion, and collapse in generative systems.