Think Fast, Think Slow: How Omni-AutoThink Rewrites Multimodal Reasoning
A mechanism-first reading of Omni-AutoThink, showing why adaptive multimodal reasoning is a training problem, not a prompting trick.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When Research Becomes a Tree: Why Static-DRA Matters in an Agentic World
A mechanism-first analysis of Static-DRA, a tree-based deep research agent that turns research depth and breadth into explicit business controls.
Agents Without Prompts: When LLMs Finally Learn to Check Their Own Homework
A mechanism-first look at how prompt-free verification-refinement agents turn existing system prompts into reusable quality-control infrastructure for paper-to-code automation.
Counterfactuals, Concepts, and Causality: XAI Finally Gets Its Act Together
A causal concept-based XAI framework shows why useful model explanations need more than heatmaps, concept labels, and wishful thinking.
Digging Deeper with Bayes: Why AI May Finally Fix Mineral Exploration
A decision-science reading of why AI’s real value in mineral exploration may be reducing false-positive drilling, not replacing geologists.
Flame Tamed: Can LLMs Put Out the Internet’s Worst Fires?
A comparison-based reading of new research on LLMs as online mediators, separating moderation, model performance, human style, and practical deployment boundaries.
Prompting on Life Support: How Invasive Context Engineering Fights Long-Context Drift
A mechanism-first reading of Invasive Context Engineering, a training-free proposal for keeping LLM control instructions alive inside long conversations and agentic reasoning loops.
Scan, Plan, Report: When Agentic AI Starts Thinking Like a Radiologist
A mechanism-first look at why Radiologist Copilot matters less as a report generator and more as a workflow engine for high-stakes medical AI.
Stuck on Repeat: Why LLMs Reinforce Their Own Bad Ideas
A mechanism-first reading of Martingale Score, a new unsupervised way to detect when LLM reasoning becomes prior-protecting rather than truth-seeking.
Blunders, Patterns, and Predictability: What n‑Gram Models Teach Us About Human Chess
A mechanism-first look at how skill-specific n-gram models turn chess move prediction from optimal play into human behavior modeling.