Recall
Bit-perfect memory over published benchmark runs.
Every final answer has to match the recorded telemetry exactly.
TeleMemetry™ · How it works
An autonomous robot accumulates telemetry across millions of turns. Legacy AI replays the entire history to answer one question - context window exhausted, operation stopped.
TeleMemetry returns one exact telemetry packet from an addressed local store. No replay. No inference. Bit-perfect. The archive can hold millions of turns - the token cost stays flat.
1,000,000 turns · 106 tokens · 0 failures
TeleMemetry™ - A short walkthrough
Instant Bit-Perfect Memory for AI
Sustained recall for long-running AI systems.
Drop-in middleware · 100% verified recall · Indefinite turns · massive token and power reduction
Within scope, TeleMemetry verifies operational recall with absolute certainty: each returned value either matches the stored reference exactly or fails verification.
Try it locally · Ask ChatGPT · NVIDIA Launchable
- 100,000 Turns100% 1:1 verified recallEvery final answer matched the stored reference value after field-level verification.
- 3,548x Fewer Tokens~98.6B replay tokens avoidedBounded telemetry packets replaced full-history replay for each probe.
- 278 Tokens / TurnEven at maximum throughputThe active context stayed tiny while the benchmark ran continuously.
- Zero Response Failures2 first-pass deviations rejectedThe verification gate rejected two imperfect first-pass renderings before final output.
- Perfect Isaac Lab Runs23 fields · 50 episodesThe run queried complex Isaac Lab robotics telemetry fields across 50 episodes.
- Proof & Try LocallySHA256 receipts · local runnerThe public files are checksum-backed, with a local reproduction path for review.
- The BenchmarksOpen the verified benchmark runs
- Benchmark FAQRead the scope and methodology
100,000 1:1
Bit-Perfect AI Turns
Every final Isaac Lab answer passed exact output verification.
278 Tokens
Average per turn
The model saw a small bounded telemetry packet, not the full archive.
3,548x
Replay Reduction
Estimated reduction versus replaying full history into every turn.
Low GPU Load
Recall runs near idle
Measured 135.21W net recall power in the flagship Isaac 100k run.
What This Proves
TeleMemetry keeps recorded telemetry outside the model, verifies the requested data, and returns only what the model needs for the current turn.
Flagship Run Isaac Lab 100k
Verified Turns 100,000 / 100,000
First-Pass Exactness 99.998%
Final Failures 0
SHA256 Receipt e85d25a1...
Scale
Flat model input as history grows.
The model gets the current packet instead of carrying the archive.
Reproduce
Launch it and review the output files.
The public path includes the Launchable, benchmark summary, SHA256 manifest, and local runner.
