Greedy Enough to Win: When Loss Starts Driving the Learning Rate
A close reading of GreedyLR shows why loss-driven learning-rate scheduling is less a clever trick than a practical way to reduce wasted training motion.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Model First, Think Later: Why LLMs Fail Before They Reason
A practical reading of Model-First Reasoning: why agent failures often begin with unstable problem representation, not weak reasoning.
Picking Less to Know More: When RAG Stops Ranking and Starts Thinking
A mechanism-first reading of Context-Picker, a RAG framework that treats evidence selection as minimal sufficient subset choice rather than fixed Top-K retrieval.
Ports, But Make Them Agentic: When LLMs Start Running the Yard
PortAgent shows how LLM agents can compress vehicle-dispatch deployment by combining retrieval, modeling, code generation, and execution-based correction.
Reasoning Loops, Not Bigger Brains
A mechanism-first reading of URM: why recurrent refinement and strong nonlinearity, not architectural ornamentation or raw scale, drive its ARC-style reasoning gains.
Shaking the Stack: Teaching Seismology to Talk Back
A mechanism-first look at how MCP turns legacy seismic simulation software into an agent-controlled workflow without pretending that case studies equal autonomous discovery.
When Attention Learns to Breathe: Sparse Transformers for Sustainable Medical AI
A mechanism-first reading of SMMT, a sparse multi-modal Transformer that links Alzheimer’s classification accuracy, missing-modality robustness, and training-energy reduction.
NeuralFOMO: When LLMs Care About Being Second
A mechanism-first reading of NeuralFOMO, showing how peer comparison can turn LLM behavior from cooperative optimization into status-sensitive rivalry.
When LLMs Stop Talking and Start Choosing Algorithms
A closer look at how LLM hidden states can support combinatorial-optimization algorithm selection—without pretending the model has become a reliable optimizer.
When Medical AI Stops Guessing and Starts Asking
A mechanism-first reading of MedInsightBench, showing why medical AI needs structured questioning, evidence extraction, and evaluation beyond ordinary answer accuracy.