Research tools for AI trust and reliability

Context intelligence for AI agents. Ingest, search, and subscribe to shared context across agent systems with keyword, semantic, and hybrid modes.

Detection tools for strategic underperformance in AI systems. Activation probes, behavioral analysis, and sandbagging detection with 90-96% accuracy.

Runtime behavior control via activation steering. Guide model outputs toward desired behaviors using contrastive activation addition.

Neuro-symbolic synthesis of verified code using Z3 and LLMs with CE2P (Counterexample-to-Prompt) feedback loop.

Configuration-driven orchestration engine for multi-stage decision routing with adaptive pattern learning and 21 condition operators.

Evolutionary adversarial testing. Red-teaming with quality-diversity optimization, MAP-Elites archive, and multi-provider LLM targets.

GNN-based trust propagation. Model trust relationships with PageRank, EigenTrust, and learned propagation algorithms.

Field-theoretic memory for AI agents. Continuous dynamics for context preservation in multi-agent systems.

Comprehensive LLM evaluation with statistical rigor. Lexical, semantic, LLM-judge, and RAG metrics with confidence intervals and significance testing.

Inference acceleration with Triton kernels and speculative decoding. RMSNorm, SwiGLU, RoPE, INT8 quantization, and KV-cache compression.

Compliance evaluations for AI systems. EU AI Act, SOC2 Type II, and HIPAA frameworks with encrypted audit trails and report generation.

Reasoning chain capture for auditing. Extract and analyze CoT, ReAct, and Tree-of-Thought reasoning with anomaly detection.