01 · ABOUT AVIKRAT ARCHITECTURE
Building a different way to carry context.
AVIKRAT is exploring compact persistent state as a foundational architectural approach to constant-memory long-context LLM inference.
- Process long history once.
- Preserve useful information compactly.
- Decode locally.
Why AVIKRAT Exists
Longer context should not mean unlimited cost.
As context grows, conventional decoding continues to carry an expanding KV cache and repeated attention cost. That increases memory footprint, latency, serving cost and GPU pressure.
AVIKRAT is exploring whether useful long-term context can instead be represented through a compact persistent state.
What We Are Building
From growing history to compact state.
AVIKRAT is developing a Hidden State Simulator for compact-state long-context inference.
Encode long history once into a compact hidden state, then decode using only a short local window instead of the full growing context.
Global Prefill
Read the full history once.
Compact State
Persist a small learned memory.
Local Decode
Use a short recent window only.
Next Token
Generate efficiently.
Stage labels describe the intended pipeline of the Hidden State Simulator, not measured results.
Our Approach
Four ideas shape the architecture.
Each idea is a single, well-defined step. Together they describe how the simulator intends to turn unbounded conversations into bounded compute.
01
Global Prefill
Read the full history once.
02
Compact State
Persist a small learned memory.
03
Local Decode
Use a short recent window only.
04
Next Token
Generate efficiently.
Stage labels describe the intended pipeline of the Hidden State Simulator, not measured results. Images are conceptual.
Where We Are Today
A working simulator is already in place.
A working simulator already predicts hidden-state structure and tracks validation behavior.
These are the evaluation dimensions the current system exercises. They are capabilities the simulator provides — not validated benchmark scores.
Current validation signals
True vs Predicted Context Slices
Compares the simulated hidden state against the observed structure of the context.
Error Metrics
Tracks how predicted state diverges from the reference across runs.
Cosine Similarity
Measures structural alignment between prediction and reference.
Checkpoint Selection
Determines which slices of context are worth persisting.
A hidden-state simulation visualizes how a predicted persistent state can be checked against a reference — the basis for the validation signals above, pending larger-scale benchmarking.
From POC to Infrastructure
From simulator to production infrastructure.
The simulator is the foundation for the next stage of validation and development.
- 01Next
Benchmark
Validate perplexity, latency and GPU memory at larger context lengths.
Future direction — not yet built or measured.
- 02Validation
Pilot
Explore long-context assistants, enterprise workflows and edge inference.
Future direction — not yet built or measured.
- 03Opportunity
Infrastructure
Turn the simulator into production infrastructure.
Future direction — not yet built or measured.
These are directions under exploration, not completed programs.
Potential Applications
Designed for systems that need to remember.
Where the architecture could matter — potential applications of compact persistent state.
AVIKRAT does not currently serve these markets. This illustrates where the direction could be useful.
Enterprise Copilots
Persistent conversations and large document sessions.
Edge AI
Smaller persistent memory for constrained devices.
Agent Infrastructure
Long-running workflows without exploding state cost.
Real-Time Systems
Continuous context becomes more practical when memory stays bounded.
The Vision
Make long-context AI more practical.
AVIKRAT is exploring a new way to serve long-context AI without paying the full context cost at every step.
Long History
The full conversation is read once.
Compact Persistent State
What matters is preserved compactly.
Local Decoding
Only a short window stays active.
Long-Running AI
Systems that keep going, bounded.
What We Believe
Context should be useful, not endlessly expensive.
Memory should scale differently.
Long-running AI needs better state management.
Efficiency and useful context must coexist.
These are design principles guiding the architecture — not claims of measured results.
What We Are Looking For
Help us prove the next step.
AVIKRAT is looking for collaborators to validate the architecture at scale and partners to take it toward real workloads.
08 · MISSION DIRECTIVE
Constant-memory
long-context inference.
A new computational paradigm to serve LLMs without paying the linear context tax on every step.
AVIKRAT ARCHITECTURAL DIRECTION · RESEARCH PARADIGM
COLLABORATION & RESEARCH INITIATIVES
Let's build the next generation
of AI infrastructure.
Interested in benchmarking, piloting, collaborating or evaluating AVIKRAT's architecture?