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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
Green Is the New Gray: When ESG Claims Meet Evidence
A mechanism-first look at EmeraldMind, a knowledge-graph and RAG framework that turns greenwashing detection from label prediction into evidence-grounded claim review.
Kill the Correlation, Save the Grid: Why Energy Forecasting Needs Causality
A mechanism-first reading of causal energy-demand forecasting, showing why confounders—not missing features alone—can distort load attribution and operational forecasts.
When LLMs Get Fatty Liver: Diagnosing AI-MASLD in Clinical AI
A case-first reading of AI-MASLD, showing why medical LLMs that look competent on clean cases can fail when patients speak like actual patients.
When the AI Becomes the Agronomist: Can Chatbots Really Replace the Literature Review?
A comparison of DeepSeek and ChatGPT in agroecological crop-protection synthesis shows why web-grounded AI improves coverage but still needs expert verification.
When Tools Think Before Tokens: What TxAgent Teaches Us About Safe Agentic AI
A mechanism-first reading of TxAgent shows why safe medical AI depends on tool selection, source governance, and retrieval evaluation before the model begins to reason.