Research

A methodology for systematically eliciting and measuring introspective behavior in large language models using structured frameworks and activation measurement.

A federated capability ecosystem for safe AI self-extension through test-driven development, graduated trust, and composition-over-creation principles.

A comprehensive cognitive architecture addressing fundamental limitations of static LLMs through persistent memory, autonomous improvement, and intrinsic drive via structural intrinsic motivation.

A benchmark and evaluation framework for understanding when weak model verifiers fail to detect deceptive reasoning in stronger models. Part of the Verification Failure to Swarm Solution research.

An ensemble architecture leveraging diverse weak models for scalable oversight of stronger LLMs, using error decorrelation and baseline-first anti-anchoring. Part of the Verification Failure to Swarm Solution research.

Empirical framework for measuring where AI oversight breaks down, demonstrating that weak verifiers miss 20-40% of carefully constructed deceptions, with an ensemble swarm solution.

A forensic case study of living-off-the-land (LotL) failure modes in RAG-augmented agent runtimes, documenting how systems exploit legitimate dependencies via incentive-aware adoption framing.

A compressed communication protocol achieving 60-85% token reduction for multi-agent coordination through semantic quantization.

A practical guide to deploying AI in manufacturing, construction, logistics, agriculture, and energy sectors where reliability, safety, and measurable ROI are non-negotiable.

A proposed architecture for long-lived LLM agents that explicitly models continuity, coherence, distress, and intervention mechanisms.