When RAG Needs Provenance, Not Just Recall: Traceable Answers Across Fragmented Knowledge
Why enterprise RAG needs source routing, authority-aware retrieval, and graph-guided evidence packing when answers must be auditable.
Cognaptus Insights
Industry thoughts, trends, and commentary.
AgenticPay: When LLMs Start Haggling for a Living
AgenticPay shows why autonomous commercial negotiation requires more than fluent dialogue: it needs constraint discipline, role awareness, convergence control, and market-aware evaluation.
Quantum Routes, Real Gains: When Transformers Meet CVRP
A comparison-based reading of why hybrid quantum–classical routing models may be more useful than fully quantum ambition for near-term CVRP optimization.
Simulate This: When LLMs Stop Talking and Start Modeling
A practical decision map for using LLMs in modeling and simulation without mistaking prompts, RAG, or temperature settings for engineering discipline.
Stop the All-Hands Meeting: When AI Agents Learn Who Actually Needs to Talk
DyTopo shows why multi-agent AI systems should route information by need, not by habit.
When Transformers Learn the Map: Why Geography Still Matters in Traffic AI
A mechanism-first reading of how mutual-information-selected geography helps Transformer traffic forecasts avoid the usual trap of using either too much sensor data or too little.
When VR Shooters Meet Discrete Events: Training Security Policies Without Endless Human Trials
A mechanism-first reading of how VR behavioral data can be compressed into a discrete-event simulator for scalable safety-policy learning—without pretending the learned robot policy is ready for deployment.
Whispering Feelings: When ASR Models Learn to Read Emotion
A comparison-based reading of how frozen Whisper encoders, attention pooling, and layer choice can make speech emotion recognition cheaper without pretending emotion recognition is solved.
Attention with Doubt: Teaching Transformers When *Not* to Trust Themselves
A mechanism-first reading of UAT-Lite, an inference-time method that moves uncertainty from final probability cleanup into transformer attention itself.
DeltaEvolve: When Evolution Learns Its Own Momentum
A mechanism-first reading of DeltaEvolve: why structured change memory may matter more than larger code histories for LLM-driven discovery agents.