Select a research vertical to inspect the core algorithms and deployment proofs.
Geometry-first inference control and world-model drift detection.
Two views of one result. 0.40-nat loss saving at constant compute — a regime the canonical scaling laws say requires roughly 10× more training.
Our proprietary LLM controller that guides token distribution channels mathematically. Bypasses statistical entropy-steering and massive parameters, achieving precise output alignment and high-diversity sampling.
Enables Joint Embedding Predictive Architecture (JEPA) world-model coordinate mapping. Captures sequence dynamics inside latent space steps, actively combating model drift with real-time vector alignment.
Replaces opaque statistical weights with structural high-dimensional pattern geometry. Provides built-in confidence parameters, zero-shot verification, and on-device secure-enclave handoffs.
Saves ≈ 0.40 nats on sequence entropy, delivering a 10× compute-equivalence offset over standard scaling laws.
Dramatically reduces Joint-Embedding sequence MSE by −80%, locking the world model into calibrated structural fields.
Detects structural drift in agentic memory tracks and aligns vectors instantly without retraining overhead.
High-density token matrices are correlated positionally inside the CPU, ensuring perfect evidence-to-answer fidelity.
Three protocols. One substrate. Two active IETF drafts. A live registry running at reason.astrognosy.com.
The protocol stack for the agentic web. Xchange is Astrognosy's open-source offering, arbitrating competing agent answers by structural convergence and creating verifiable communication pathways across networks.
PACIFIC is our commercial product line built on Positional Correlation Fields. Deterministic & verifiable signal intelligence with zero training data and sub-millisecond overhead.
Deterministic routing for LLMs. Sub-millisecond latent categorization without embedding models or vector databases.
Training-free network anomaly detection. F1 0.969 BruteForce, F1 0.949 DDoS on CICIDS2017. Beats GPU-trained ML on novel attacks.
Industrial signal detection and sensor-fusion fault classification. Bearing F1 0.832 on CWRU. Edge-deployable, zero training.
Exchanging data securely without exposing underlying information.
Advertisers publish centroid packets. The device matches locally. Non-invertible signatures, designed for native silicon and OEM integration.
Institutional transfer framework enabling mathematical construction and safe exchange for on-device matching. Xfer allows verifiable communication channels among agents without compromising source truth.
Our proprietary advertising protocol — the exact Xact implementation on the edge. Non-invertible signatures, complete privacy for local matching and targeting. Designed for native silicon integration; targeting widespread OEM adoption.
Our verifiable trust layer powers specific verticals — delivering fast, secure, and predictable autonomous systems across the agentic web.
Mathematical privacy through non-invertible signatures and on-device matching for security firms and enterprises.
Deterministic signal intelligence and verifiable agent arbitration for AI architects and platform developers.
CPU-native, zero-training edge inference with sub-millisecond latency for hardware manufacturers and silicon integration.
Fills the Privacy Sandbox gap with completely private local matching for advertising networks and brands.
Our technology stack enables zero-training, CPU-native high-dimensional pattern geometry and stream processing across the agentic network.