RAG in the Wild: When More Knowledge Hurts
A practical reading of why retrieval-augmented generation can degrade when enterprise knowledge sources become heterogeneous, noisy, and poorly routed.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Seeing is Believing? Not Quite — How CoCoT Makes Vision-Language Models Think Before They Judge
A mechanism-first reading of CoCoT, a structured reasoning scaffold that helps vision-language models separate what they see from what they infer and what they judge.
When Your AI Disagrees with Your Portfolio
A practical reading of how LLMs reveal, reinforce, and operationally weaponise hidden investment biases under conflicting evidence.
Graft and Go: How Knowledge Grafting Shrinks AI Without Shrinking Its Brain
Knowledge grafting offers a compact-model strategy for edge AI by transplanting selected intermediate features from a large model into a smaller deployable architecture.
Mind the Earnings Gap: Why LLMs Still Flunk Financial Decision-Making
FinanceBench shows why financial AI fails less from lack of fluency than from brittle retrieval, fragile numerical reasoning, and weak evidence discipline.
Rollout Renaissance: How Pareto-NRPA Revives Monte Carlo for Multi-Objective Optimization
Pareto-NRPA shows that policy-adaptive Monte Carlo search can be unusually strong when multi-objective decisions are sequential, discrete, and constrained.
The Sims Get Smart? Why LLM-Driven Social Simulations Need a Reality Check
LLM agents can make simulations feel alive, but the paper’s real lesson is that fluency must be constrained by theory, validation, and classical agent-based modelling.
Tool Up or Tap Out: How Multi-TAG Elevates Math Reasoning with Smarter LLM Workflows
Multi-TAG shows that better LLM reasoning comes from structured tool arbitration, not merely from adding another calculator to the prompt.
All Eggs, One Basket: When Diversification Backfires in Risk Modeling
A mechanism-first reading of when extremely heavy-tailed risks can make diversification more dangerous than concentrated exposure.
Boxed In, Cashed Out: Deep Gradient Flows for Fast American Option Pricing
A mechanism-first look at how Time Deep Gradient Flow adapts neural PDE solvers to American-option pricing, and where the speed claim is genuinely useful.