When AI Argues Back: The Promise and Peril of Evidence-Based Multi-Agent Debate
ED2D shows that evidence-grounded AI debate can make misinformation correction more persuasive, but also more dangerous when the system is wrong.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When AI Discovers Physics: Inside the Multi-Agent Renaissance of Scientific Machine Learning
A mechanism-first reading of AgenticSciML, a multi-agent framework that turns scientific machine learning design into an evaluated evolutionary search rather than a one-shot prompting exercise.
Better Wrong Than Certain: How AI Learns to Know When It Doesn’t Know
A new reject-option framework separates noisy data from insufficient data, giving AI systems a sharper way to decide when prediction should become deferral.
Cities That Think: Reasoning AI for the Urban Century
A mechanism-first reading of why urban-planning AI may need verifiable reasoning agents, not just better prediction models.
Dirty Data, Clean Machines: How LLM Agents Rewire Predictive Maintenance
A new benchmark shows where LLM agents can clean predictive-maintenance logs today, and where industrial deployment still needs rules, temporal logic, and human discipline.
Memory With a Pulse: Real-Time Feedback Loops for RAG Systems
A mechanism-first look at Dynamic Memory Alignment, a RAG framework that turns live human feedback into retrieval updates without retraining the generator.
Thinking Fast and Flowing Slow: Real-Time Reasoning for Autonomous Agents
A sharp look at why real-time AI agents need latency-aware architectures, not merely bigger models or longer reasoning traces.
When Algorithms Command: AI's Quiet Revolution in Battlefield Strategy
A mechanism-first reading of a battlefield decision-support prototype, and what it teaches business leaders about simulation, option generation, and human control.
When Compliance Blooms: ORCHID and the Rise of Agentic Legal AI
ORCHID shows that legal AI becomes useful when it is engineered as an auditable workflow, not a confident oracle.
Aligning the Unalignable: How CORE Redefines Multistain Image Registration
CORE shows how digital pathology can align multi-stain whole-slide images by separating fast tissue-level registration from slower nuclei-level precision.