RESEARCH & SIMULATOR PROOF-OF-CONCEPT
A working simulator that predicts hidden state.
AVIKRAT's Hidden State Simulator models the structure of persistent hidden states and evaluates validation convergence across key mathematical dimensions.
01 · True vs Predicted Context Slices
Structure that can be predicted before it exists.
The simulator compares the hidden state the model would form from the true context against a compressed reconstruction. Comparing true and predicted slices is how the compact representation is validated.
A conceptual research capability — no numeric results are claimed.
02 · Error Metrics
Measured as distance from the true state.
The discrepancy between true and predicted structures is tracked through error metrics, providing a quantitative signal for how well the compact representation preserves the behavior that matters for generation.
The metrics substantiate where the compact representation is close — and where it still deviates.
03 · Cosine Similarity
Directional agreement between states.
Cosine similarity measures how aligned the reconstructed representation is with the true hidden state. It is one signal among several for deciding whether the compact form retains the information generation depends on.
Part of the validation toolkit, not a published performance claim.
04 · Checkpoint Selection
Choosing when a persistent state is reliable.
Across longer histories, the simulator explores where and how the persistent state should be updated. Checkpoint selection — deciding when the compact representation is trustworthy enough to build on — is an open area of investigation.
This area is under active exploration rather than settled results.
RESEARCH FRAMEWORK INTEGRATION
These capabilities feed the validation framework.
The next phase executes rigorous empirical comparisons against matched conventional decoding baselines across perplexity, latency, GPU memory footprint and sequence scaling.
Explore Benchmarks