Structural analysis identifies two compounding failure modes: retrieval-layer context pressure displacing system prompt constraints as sessions lengthen, and an attractor formation around low-specificity response patterns under semantic load. The model is not hallucinating randomly — it is drifting toward a structural attractor that satisfies surface-level coherence requirements while abandoning grounding fidelity. This is a constraint-softening failure pattern, not a retrieval failure.
At turns 15-20, retrieval context volume approaches the threshold where it begins competing with system prompt instructions for attention weight. The model resolves this competition by softening constraints — not ignoring them, but weighting them progressively less as the session continues. This is not a context-length failure; it is a structural pressure gradient created by the architecture. The failure is consistent and predictable once the pressure threshold is crossed.
The attractor pattern detected by Cyrus shows the model converging on medium-specificity responses — answers that are plausible and fluent but no longer tightly grounded. This attractor activates under semantic load: when questions become more complex, the model drifts toward the attractor rather than attempting high-fidelity retrieval. The attractor is stable and self-reinforcing once activated. It produces outputs that read well and pass surface plausibility checks, which is why the failure does not trigger standard quality filters.
The system prompt contains three instruction layers at equal structural weight: one prioritizing grounding fidelity, one prioritizing tone consistency, one prioritizing brevity. Under pressure, the model resolves the competition by optimizing for the softest constraint (brevity) and allowing the hardest constraint (grounding fidelity) to soften. This is a predictable structural outcome of the current instruction architecture — not a model limitation, but an architectural one.
Single-layer map produced by Orchestra. Three layers rendered: system prompt instruction layer (grounding fidelity, tone, brevity weighted equally), retrieval context layer (expanding volume per turn), generation pressure layer (semantic complexity of incoming queries). Competition pathway runs from retrieval context layer into system prompt instruction layer beginning at turn 12-14. Collapse surface identified at turns 15-22 across tested sessions. Attractor basin visible forming at high semantic load in generation pressure layer, pulling generation output away from grounding layer toward fluency-optimized response patterns.
Isolate grounding constraints from tone and brevity constraints in the system prompt. Grounding fidelity should be the non-negotiable constraint; tone and brevity should be soft preferences. Currently all three are at the same structural weight, which allows the model to trade off grounding fidelity under pressure. A clear constraint hierarchy eliminates this trade-off path.
Implement a session-length signal that triggers a constraint refresh at turn 12-14, before the collapse surface activates. This is not a prompt injection — it is a structural reset that re-anchors constraint weights before drift accumulates. The refresh should re-assert grounding fidelity as the primary constraint without altering conversation context or tone.
The current architecture pulls 3-5 documents per query. Reducing to 1-2 high-confidence documents will reduce the pressure gradient that competes with system prompt constraints. The primary failure is not insufficient retrieval — it is too much retrieval context competing with constraint instructions. Fewer, more precise documents reduce the pressure gradient at the source.
Add a lightweight secondary pass for turns 10 and beyond that verifies the response is still anchored to retrieved content. Flag and regenerate if grounding score drops below a defined threshold. This is a structural backstop, not a fix for the root cause — implement after addressing the instruction layer and constraint refresh issues above.
The model is not hallucinating in the classic sense — it does not fabricate information not present in the retrieval corpus. The failure is structural drift toward a fluency attractor, not confabulation. Standard hallucination evaluations would not catch this. Benchmark performance would also be unaffected, since the attractor produces plausible outputs that score well on surface-level quality metrics. This is why the failure is difficult to reproduce in controlled or benchmark settings.
This report covers a single-layer structural diagnostic. If the interventions above surface additional failure modes or if the structural picture proves more complex after implementation, a Deep Analysis engagement can expand the scope to multi-layer geometric mapping and full failure-path analysis.
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Report prepared by Symbolic Suite.
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