ExpertFingerprinting: Behavioral Pattern Analysis and Specialization Mapping of Experts in GPT-OSS-20B's Mixture-of-Experts Architecture

A Java client to interact with Arize API

Model audit software

Capability Schema Spec defines a shared semantic language for world model evaluation. Standardize capability definition, observation, and verification across models and benchmarks. Not a benchmark—a shared language. Define • Observe • Verify

Architecture and training decisions determine how observable an LLM is. Transformer activations carry decision-quality signals that output confidence misses; training can preserve or erase them during convergence, even as predictive performance improves.

How to observe LLMs using Traceloop

"An end-to-end Medical Imaging pipeline built on AWS SageMaker utilizing Transfer Learning (ResNet18). The project implements Hyperparameter Optimization (HPO) to minimize loss, leverages SageMaker Debugger & Profiler for resource optimization, and concludes with a Production-ready real-time inference endpoint

Reference implementation of the Capability Schema Specification. Proves that world model capabilities can be defined, observed, and verified in practice — with real checkpoints, real simulators, and real scores. Define • Observe • Verify • Deliver

ML Monitoring System – Production-Ready Model Observability Platform A Flask-based ML observability system designed to track model performance, detect drift, and monitor real-time prediction behavior.

Token-level expert routing capture for Nemotron-Cascade-2-30B-A3B MoE layers during vLLM inference. Parquet output.

Model observability and incident response platform with PSI drift checks, SLO monitoring, incident dedupe, likely root cause, and runbook-driven response.