A mechanism-first look at how coefficient-aware propagation heuristics improved RoundingSAT on large pseudo-Boolean benchmarks, and what that means for business optimization systems.
A mechanism-first reading of pessimistic verification: why proof checking improves when models stop voting and start hunting for disqualifying errors.
A sharp reading of responsible computational foresight: why AI’s strategic value lies less in predicting one future than in helping organizations rehearse many.
A comparison-based reading of how system dynamics and structural equation modeling can share a mathematical language without pretending they solve the same causal problem.
ViLoMem shows why multimodal AI agents need separate memories for visual traps and logical mistakes, and what that means for enterprise AI systems that must learn from repeated failures.
A behavioral audit shows why professional personas can suppress AI self-disclosure, why bigger models do not solve it, and how enterprises should test trust before deploying expert-like agents.
SpatialBench shows why multimodal models that recognize scenes can still fail at the harder work of spatial abstraction, causality, and planning.
A close reading of an 8-puzzle study showing why fluent reasoning traces are still a poor substitute for explicit state management, validators, and real planning systems.
How FRAGMENTA reframes small-data drug lead optimization as a feedback-loop problem, not merely a bigger-model problem.
A mechanism-first reading of how GPT-style Transformers can be adapted from text tokens to continuous mobility trajectories without pretending the tutorial is a benchmark race.