Blink and You Miss It: The Two-Stage Reality Check for Multimodal AI
A practical framework for evaluating multimodal AI across both evidence capture and final output quality.
Cognaptus Insights
Industry thoughts, trends, and commentary.
OCR and the City: Why Document AI Still Needs Eyes
A comparison-based reading of arXiv 2606.02162, showing when OCR text, document images, fine-tuned Transformers, and prompt-based LLMs actually help enterprise document classification.
Pixels to Purchase Orders: A Business Map for Choosing Vision-Language Models
A category-based guide to reading Vision-Language Models as deployment patterns, not leaderboard theater.
Roll the Tape, Call the Tools: ReTool-Video and the Evidence-Routing Problem
A mechanism-first reading of ReTool-Video, showing why business video AI needs evidence orchestration more than longer context windows.
Search, Critique, Repeat: Critic-R Turns RAG Complaints into Retriever Training
A mechanism-first reading of Critic-R, a framework that uses agent introspection to repair retrieval at inference time and train better retrievers without gold passage labels.
The Policy Has to Work Somewhere: RL for Scale, Trust, and Other Inconveniences
A business-focused reading of how reinforcement learning can address the two deployment problems that benchmarks politely ignore: distributed scale and trustworthy agent behavior.
Wrong on Purpose: FalsifyBench and the Agent Skill We Keep Forgetting
A mechanism-first reading of FalsifyBench, showing why business AI agents need active negative testing rather than prettier confidence.
LoRA, Less Luggage: Choosing the Right Shortcut for Instance Segmentation
A comparison-based reading of when LoRA and adapters actually help large segmentation models, and when cheap fine-tuning quietly becomes cheap overconfidence.
MoA Than One Curve: Teaching FFNs to Choose Their Nonlinearity
A mechanism-first reading of Mixture of Activations, and why token-adaptive nonlinearities may matter more than another round of parameter routing.
MoE Than a Cost Trick: How Sparse Experts Became an Architecture Stack
A business-focused synthesis of three new MoE papers showing why sparse experts are becoming a design language for conversion, composition, and iterative computation—not merely a cheaper inference trick.