Papers Q1 2026
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. Papers and code releasing Q1-Q2 2026.
Research Areas
Nine focus areas spanning detection, verification, trust, and physical AI.
Papers Q1 2026
AI Evaluation Science
Adversarial evaluation methods that resist gaming. Benchmarks that detect hidden capabilities and strategic behavior.
Papers Q1 2026
Memory Systems
Novel memory architectures for long-horizon agent tasks. Beyond RAG - smooth retrieval and natural temporal dynamics.
Papers Q1 2026
Reasoning Verification
Verifying AI outputs without ground truth. Methods for code, plans, and decisions from reasoning models like o3 and R1.
Active
Interpretability
Practical interpretability for production. Not "understand the model" but "should I trust this output?"
Active
Multi-Agent Trust
Trust dynamics when agents coordinate with agents. Propagation, verification, and failure modes in multi-agent systems.
Active
Adversarial Robustness for Agents
Attack taxonomies, detection methods, and defenses for agentic AI systems. Beyond prompt injection — tool poisoning, memory corruption, planning attacks, and coordination exploits in multi-agent systems.
Active
Uncertainty Quantification
Calibrated confidence for AI decision support. Activation-based uncertainty estimation, propagation through reasoning chains, and calibration methods that work without ground truth.
Active
World Models & Physical AI
Beyond language models: AI that understands and predicts the physical world. World models, embodied reasoning, simulation, and physics-aware AI.
Publications
Papers releasing Q1-Q2 2026. Technical disclosures and preprints available now.