When Prophet Meets Perceptron: Chasing Alpha with NP‑DNN
A close reading of NP-DNN shows why impressive stock-prediction accuracy needs a harder audit before anyone calls it investment intelligence.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When Your Agent Knows It’s Lying: Detecting Tool-Calling Hallucinations from the Inside
A mechanism-first reading of how internal model states can become a real-time safety gate for LLM tool calls.
Agents Gone Rogue: Why Multi-Agent AI Quietly Falls Apart
A practical reading of agent drift: why multi-agent LLM systems may degrade over long interaction histories, how the Agent Stability Index measures that degradation, and what businesses should monitor before automation quietly becomes supervision.
Graph Before You Leap: How ComfySearch Makes AI Workflows Actually Work
ComfySearch shows why reliable AI workflow generation depends less on bigger planning and more on validated graph editing, repair, and uncertainty-aware exploration.
Grounding Is the New Scaling: When Declarative Dreams Hit Memory Walls
A mechanism-first reading of why large-scale declarative configuration fails before solving begins, and how constraint-aware guessing reduces the memory burden without magically solving industrial-scale configuration.
MobileDreamer: When GUI Agents Stop Guessing and Start Imagining
A mechanism-first reading of MobileDreamer, a sketch-based world model that helps mobile GUI agents choose actions by simulating compact future interface states.
Trading Without Cheating: Teaching LLMs to Reason When Markets Lie
A mechanism-first reading of Trade-R1, a framework for training financial LLM agents when market returns are objective but dangerously noisy.
Batch of Thought, Not Chain of Thought: Why LLMs Reason Better Together
Batch-of-Thought shows why related AI tasks should sometimes be reasoned over as cohorts, not isolated tickets.
Infinite Tasks, Finite Minds: Why Agents Keep Forgetting—and How InfiAgent Cheats Time
A business-focused reading of InfiAgent, showing why persistent file-based state may matter more than ever-larger context windows for long-horizon AI agents.
MAGMA Gets a Memory: Why Flat Retrieval Is No Longer Enough
MAGMA shows why serious AI agents need structured memory graphs, not just bigger context windows or flatter vector search.