Probe, Then Commit: Why Solver Tuning Finally Grew Up
A practical reading of the Probe and Solve Algorithm, a two-phase method for tuning constraint programming solvers under real time budgets.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Punching Above Baselines: When Boxing Strategy Learns to Differentiate
BoxMind shows that applied AI becomes useful when perception, prediction, and intervention are joined into a closed operational loop.
Think-with-Me: When LLMs Learn to Stop Thinking
A mechanism-first reading of Think-with-Me, a test-time intervention framework that turns LLM reasoning from uncontrolled token generation into a feedback-guided control loop.
When LLMs Read the Room: Predictive Process Monitoring Without the Data Buffet
A mechanism-first reading of why LLMs can predict process outcomes from tiny event logs, and why the advantage depends on semantics rather than spreadsheet magic.
Fish in the Ocean, Not Needles in the Haystack
A mechanism-first reading of SIN-Bench, and why enterprise AI evaluation must move from answer accuracy to auditable evidence chains.
One-Shot Brains, Fewer Mouths: When Multi-Agent Systems Learn to Stop Talking
A mechanism-first reading of TOPODIM, a multi-agent framework that replaces chatty coordination with sparse, task-specific topology generation.
Redundancy Overload Is Optional: Finding the FDs That Actually Matter
Why redundancy-driven top-k functional dependency discovery is not just faster FD mining, but a cleaner way to decide which database constraints deserve attention.
Seeing Is Not Thinking: Teaching Multimodal Models Where to Look
LaViT shows why multimodal models can copy answers without inheriting visual grounding, and why enterprise AI teams should audit where models look, not only what they say.
When AI Stops Pretending: The Rise of Role-Playing Agents
A mechanism-first reading of role-playing agents: why the future of digital humans depends less on charming prompts and more on personality models, memory, behavior control, data rights, and evaluation.
When Models Read Too Much: Context Windows, Capacity, and the Illusion of Infinite Attention
A grounded analysis of why long-context models can still fail after finding the right evidence—and what that means for AI system design.