The EDR
Context
Engine is like a thermodynamic engine, but for information.
It actively breaks down history into a
high-entropy,
non-ciphertext space, and then structurally reassembles it into low-entropy, high-precision context when
needed - all while keeping the model's active working memory flatlined.
Turn history no longer competes with
inference.The EDR Context Engine™ is a programmable operational continuity layerProgrammable
Memory Modes
Robotics, Autonomous Driving, Industrial
Telemetry, Scientific Computing, Customer Support,
Enterprise Agents, Healthcare, Finance,
Legal, Custom
Different workloads require different memory
behavior. EDR Context Engine's programmable memory modes allow the same engine to optimize retrieval for
each domain while keeping the model's active working context bounded. that records,
verifies, and retrieves
evidence while maintaining a bounded active working set.
Active Context Scaling Chart
Active tokens 18k | / 💥 Legacy replay exceeds configured context budget | / 15k | / | / 10k | / | / 5k | / | / 1.9k| / BOUNDED ACTIVE CONTEXT ───────────────────────────────────────────── 0k |───────/──────────────────────────────────────────────────────────────────────── 0 1k 2k 3k 4k 5k turns
[+] Developer Tools: Export Raw Benchmark Data (Python)
# download_rtx2070_data.py
# TODO: Fix the path to the repository prefix inside this script if referencing local db files
import json
data = {
"profile": "rtx2070",
"total_turns": 5000,
"edr_token_budget": 1900,
"turns_data": [
{
"turn": t,
"legacy_replay_tokens": int(t * 12.0) if t <= 1500 else None,
"edr_tokens": 1900
} for t in range(0, 5001, 100)
]
}
filename = "rtx2070_5k_turns_benchmark.json"
with open(filename, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2)
print(f"Saved benchmark data to {filename}")
The Problem: Traditional architectures force a trade-off between memory depth and context limits, causing linear token expansion and out-of-memory errors at scale.
The Solution: EDR Context Engine processes and offloads streaming telemetry directly to an indexed operational state archive under the model.
The Result: The background storage layer is designed to scale independently of active working context into gigabytes of data while active working context remains strictly bounded at ~372 tokens (mean), regardless of archive depth.
Benchmark duration demonstrating long-session stability under continuous telemetry.
Evidence is deterministically addressed rather than searched.
Verifies sparse model invocation frequency relative to total ingestion rate.
Demonstrates local execution footprint on standard workstation hardware.
Establishes system durability and throughput consistency over long runs.
Validates recall accuracy of historical states at random intervals.
36.3 average FPS throughout the benchmark.
Shows total storage footprint for 1M turns of raw telemetry.
EDR Context Engine™ completes the run on a single workstation-class GPU (A4000).
Robotics, Autonomous Driving, Industrial Telemetry, Scientific Computing, Customer Support, Enterprise Agents, Healthcare, Finance, Legal, Custom that records, verifies, and retrieves evidence while maintaining a bounded active working set.
Full replay baselines scale active working context length linearly with operational history (yielding quadratic cumulative attention/compute scaling over the session). EDR Context Engine maintains a bounded active working context.
EDR Context Engine is a programmable memory/session layer that keeps active working context bounded while archive size grows.
Scope: real nuPlan telemetry replay · bounded-memory evidence run · reconstruction verification
· grounding metrics.
Production certification, regulated compliance, and adversarial safety validation are separate review lanes.