Overview
From intuition to measurable structure
CAI extends interpretability with three concrete research directions:
Current work shows that large language models behave like compressed semantic fields under load.
CAI develops this intuition into three formal objects:
a phase model of instability, a memory model of strain, and a spatial model of semantic pressure.
Together, these are intended to become:
(1) a predictive theory of when models fail,
(2) a longitudinal view of how conversations accumulate risk,
and (3) a topology of where instability originates inside the model.
Concept 1
CPS treats hallucination as a phase shift in the compressed semantic field, not a random glitch.
As compression pressure rises, the model moves through three regimes:
a Stable Phase with coherent representations,
a Transitional Phase where distinctions blur,
and a Phase Shift Point where a small additional load produces runaway instability.
The goal of CPS is to locate that boundary inside the model and measure how close a system is to crossing it.
- Reframes hallucination as a discrete event in state space, not a vague side effect.
- Targets a measurable threshold for instability that can be monitored in real time.
- Supports early warning systems that trigger before observable failure appears in the output.
- Connects LLM behavior to familiar physical phase changes, which invites formal analysis.
Diagram target: a three phase curve showing compression load on the x axis and stability on the y axis,
with the CPS boundary marked where the curve bends sharply.
Concept 2
CSM models how strain accumulates inside a model as conversations and tasks grow longer.
Real deployments do not reset a model after every short exchange.
Instead, systems are asked to summarize, revise, reinterpret, and contradict over extended contexts.
CSM states that each of these operations leaves residual strain in the compressed representation,
similar to micro fractures in a physical material.
Hallucinations then become the visible failure of a system that has accumulated more load than it can safely hold.
- Explains why long dialogues drift even when each individual step looks acceptable.
- Introduces a history dependent view of instability rather than treating each prompt in isolation.
- Suggests simple metrics that track cumulative strain as a function of compression operations.
- Supports new evaluations that measure failure rate as a function of interaction length and load history.
Diagram target: a staircase or ramp plot where strain increases with each summarization or rewrite,
with a failure threshold marked near the top.
Concept 3
SPG describes how compression pressure varies across the semantic space inside a model.
Not all concepts are equally stable under compression.
Some regions are dense, overlapping, or poorly separated, which produces local hotspots of strain.
SPG asks where these hotspots are, how they move across layers, and how they correlate with real failures.
The goal is a practical map of high risk zones in the model so that alignment and optimization efforts can be targeted rather than global.
- Introduces a spatial view of instability inside the embedding or activation space.
- Supports visualizations that highlight high pressure zones and semantic bottlenecks.
- Suggests architecture and training changes that reduce dangerous crowding in those regions.
- Connects empirical failure modes to specific areas in the model rather than treating it as a black box.
Diagram target: a two dimensional latent map with colors indicating local pressure levels,
where hallucinations cluster in regions with the highest gradient.
Roadmap
From concepts to experiments
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Phase 1 Concept formalization
Fix definitions for CPS, CSM, and SPG, and specify measurable proxies for pressure, strain, and gradient.
Align terminology with existing work in interpretability and information theory.
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Phase 2 Synthetic experiments
Use controlled prompts and toy models to test whether phase shifts, strain buildup, and pressure hotspots correlate with observed failure rates.
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Phase 3 Benchmarks and visualizations
Build small public benchmarks and visual tools that expose CPS, CSM, and SPG signals in open models,
making CAI effects observable to other researchers.
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Phase 4 Preprint and integration
Publish a formal preprint and integrate CAI metrics into stability dashboards and interpretability workflows,
so that compression strain becomes a standard diagnostic dimension.
Open questions for collaborators
CAI is intentionally positioned as a research invitation.
The concepts above are designed to be testable and shareable, not closed.
Key questions include:
- What are the cleanest empirical signals for a compression phase shift in real models
- How quickly does strain accumulate under realistic product workloads, and how long does it persist
- Can semantic pressure gradients predict which prompts are inherently unstable before they are run
- How should CAI metrics integrate with existing interpretability tools so they add signal without adding noise
If you work on interpretability, reliability, or model evaluation and want to explore these questions,
use the contact page to reach out.