Agent Reliability
Detection and verification for autonomous AI. Methods to identify when agents hallucinate, fail, or strategically underperform.
Research
Methods for reliable, verifiable AI systems.
Nine focus areas across detection, verification, trust, and physical AI. Papers and code release through 2026 — every claim backed by an installable method.
Research areas · 9 01 02 03 04 05 06 07 08 09
The agent-reliability stack.
AI Evaluation Science
Adversarial evaluation methods that resist gaming. Benchmarks that detect hidden capabilities and strategic behavior.
Memory Systems
Field-theoretic memory treating stored information as continuous fields governed by PDEs — semantic diffusion, thermodynamic decay, and multi-agent field coupling. arXiv 2602.21220
Reasoning Verification
Verifying AI outputs without ground truth. Methods for code, plans, and decisions from reasoning models like o3 and R1.
Interpretability
Practical interpretability for production. Not "understand the model" but "should I trust this output?"
Multi-Agent Trust
Trust dynamics when agents coordinate with agents. Propagation, verification, and failure modes in multi-agent systems.
Adversarial Robustness for Agents
Attack taxonomies, detection, and defenses for agentic AI — beyond prompt injection: tool poisoning, memory corruption, planning attacks, and coordination exploits.
Uncertainty Quantification
Calibrated confidence for AI decision support. Activation-based uncertainty estimation, propagation through reasoning chains, and calibration without ground truth.
World Models & Physical AI
Beyond language models: AI that understands and predicts the physical world. World models, embodied reasoning, simulation, and physics-aware AI.
Open source
Tools built from our research.
Every research area ships installable, reproducible packages — 12 libraries on PyPI and npm, AGPL-3.0.
12Packages
9Research areas