BENCHMARKS & SYSTEM VALIDATION
Performance claims need a baseline.
The Hidden State Simulator is engineered toward empirical long-context inference improvements. The core target is rigorous measurement against matched conventional transformer decoding baselines.
02 / Current Proof of Concept
What exists today.
A working simulator already predicts hidden-state structure and tracks validation behavior.
These are capabilities the simulator provides — not validated scores. No numeric results are claimed for any of them.
Conceptual visualization — not measured output.
03 / The Quality Gate
Efficiency is only useful if quality survives.
The benchmark must answer three questions before any efficiency claim is considered. AVIKRAT has not passed this gate.
QUALITY
Does perplexity remain competitive?
LATENCY
Does decoding latency improve?
MEMORY
Does memory remain bounded as context grows?
Validation Gate
Required validation — a pass requires competitive quality, meaningful latency improvement and bounded memory under matched conditions.
Continue
Only on evidence — conditional, not granted.
Perplexity
Preserve model quality.
Any efficiency gain must be evaluated against a matched baseline while maintaining competitive next-token prediction quality.
Conceptual image — no measured perplexity is shown.
No benchmark result is claimed here.
Latency
Make decoding faster.
Measure token generation performance under equivalent model and workload conditions.
Conceptual image — no latency measurements are shown.
GPU memory
Bound the memory footprint.
The key architectural question is whether persistent compact state can prevent memory requirements from growing in the same way as full-context decoding.
Conceptual image — no memory measurements are shown.
Context-length scaling
Test what happens as history gets longer.
Move through conceptual context stages and watch the architecture respond. Whether resource pressure stays bounded as context grows is an open question awaiting measurement.
Conceptual image — no scaling measurements are shown.
08 / Experiment Design
Make the comparison fair.
A credible benchmark controls every variable that could distort a result. Exact experimental parameters are still to be specified; the point is the discipline.
Matched model
Compare equivalent model configurations.
Matched context
Evaluate equivalent context lengths.
Matched workload
Use comparable generation conditions.
Matched hardware
Measure under controlled hardware conditions.
Quality check
Verify perplexity remains competitive.
Resource measurement
Track latency, token/sec and GPU memory.
The unchecked boxes are commitments before measurement, not completed steps.
09 / Benchmark Matrix
Where each measurement stands.
Precise status language. Nothing is marked as proven unless the evidence exists.
Perplexity
Why it matters
Model quality
Current state
POC / validation framework
Next step
Matched baseline run
Latency
Why it matters
Generation efficiency
Current state
Not yet established
Next step
Token/sec + latency measurement
GPU Memory
Why it matters
Memory scaling
Current state
Not yet established
Next step
Memory curve measurement
Context Scaling
Why it matters
Behavior at increasing context
Current state
Not yet established
Next step
Scaling benchmark
10 / The Target
Target: up to 90% lower long-context inference cost
The current pitch positions this as a target for compact-state decoding. That target requires validation against matched baselines and competitive model quality.
Target ≠ result. Nothing is claimed until the gate clears.
TARGET
the pitch asks for a bounded-cost ceiling
VALIDATE
measure against matched baselines
COMPARE
check quality, latency, memory
PROVE
evidence before any result claim
11 / What Success Looks Like
Three curves.
One question.
Quality curve
Perplexity remains competitive.
Validation criterion
Latency curve
Generation becomes more efficient.
Validation criterion
Memory curve
Resource pressure grows more slowly with context.
Validation criterion
If all three hold under matched evaluation, the architecture has a path toward meaningful long-context serving advantages.
This is a validation criterion, not a current result.
12 / Collaborators
Help us measure the next step.
AVIKRAT is looking for benchmark collaborators to validate perplexity, latency and GPU memory at larger context lengths.