Cognaptus Insights

A sharper reading of a EUR/USD LSTM study showing why feature governance, not indicator hoarding, is the real business lesson in AI trading.

A mechanism-first look at why AI research agents fail less when they explore diverse, executable solution paths before burning compute.

IPR-1 shows why interactive agents need a shared latent action language, not just bigger vision-language models, to reason through physical consequences.

Terra Nova shows why serious agent evaluation must test coupled strategy, uncertainty, cooperation, and long-horizon trade-offs rather than another tidy task list.

A mechanism-first reading of TIM, a multi-agent LLM framework that turns opaque DeFi transactions into evidence-ranked intent labels without pretending to read private motives.

A field experiment in AI-authored and AI-reviewed science shows that research agents are useful only when wrapped in disclosure, verification, and human judgment.

Memory-R1 shows why durable AI agents need learned memory operations, not just bigger context windows or more enthusiastic vector search.

A mechanism-first reading of Octopus, a multimodal agent framework that treats reasoning as capability orchestration rather than a bigger-model contest.

A mechanism-first analysis of how rate–distortion theory and fused Gromov-Wasserstein alignment can make educational knowledge graphs more useful, not merely larger.

A mechanism-first look at how heterogeneous multi-agent reinforcement learning could turn distribution-grid restoration into faster, constraint-aware decision support.