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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
When Precedent Gets Nuanced: Why Legal AI Needs Dimensions, Not Just Factors
A formal debate about legal precedent becomes a practical design lesson for legal AI: abstraction is useful, but strength still has to be represented.
When Reasoning Needs Receipts: Graphs Over Guesswork in Medical AI
MedCEG shows how evidence graphs can turn medical LLM reasoning from persuasive prose into auditable process supervision.
When Rewards Learn Back: Evolution, but With Gradients
A mechanism-first reading of DERL: how reward design becomes a learnable outer-loop problem, and why that matters for enterprise agents.
When Small Models Learn From Their Mistakes: Arithmetic Reasoning Without Fine-Tuning
A mechanism-first reading of how error clustering, code generation, and selective prompt rules can make small on-premise models more reliable for tabular arithmetic.
Benchmarks on Quicksand: Why Static Scores Fail Living Models
A practical map for turning AI benchmarks from static leaderboard scores into reproducible, cost-aware, application-relevant evaluation systems.