Newport Resonance research
Eliminating Context Rot in Frozen LLMs: A Three-Mode Structural State-Coupling Architecture
A technical paper on context degradation in frozen LLM workflows and the design implications for stateful, governed AI systems.
- Status
- Published technical paper
- Publication date
- Author
- Ken Morkaya
Paper summary
Abstract
This paper examines context degradation in frozen language models and evaluates a structured state-coupling approach for preserving continuity over long-horizon tasks. The public abstract focuses on the failure modes, evaluation posture, and product implications for systems that need disciplined memory and auditability without exposing protected implementation mechanics.
Result in context
What was measured—and what the comparison does not establish
Can important information survive a long-running model interaction?
Measure
Verified recalls at approximately 64K tokens
Comparison · 20 cases pooled across two independently authored pools
- Baseline
- 17/20 · 85%
- Full condition
- 20/20 · 100%
- Scope
- This result pools 20 verified-recall cases from two independently authored test pools at approximately 64K tokens. It is not a claim about every long-context task.
- Study context
- The study used frozen Gemma 4 26B 4-bit MLX at 64K tokens. The Wilson 95% interval for the full condition’s 20/20 result was 84–100%.
- Principal limitation
- One quantised model configuration, one context size, one benchmark class and a mechanically densified prompt shape. This is preliminary research evidence, not universal context retention.
Citation
Ken Morkaya. (2026). Eliminating Context Rot in Frozen LLMs: A Three-Mode Structural State-Coupling Architecture. Newport Resonance. /research/eliminating-context-rot-in-frozen-llms-a-three-mode-structural-state-coupling-architecture.pdf