Cognaptus Insights

A simulated college benchmark shows why enterprise agents need structured memory, salience, and initiative—not merely larger models with longer prompts.

StepWiser shows that judging reasoning steps works better when the judge is trained to reason about the reasoning, not merely classify it.

A behavioural-economics reading of how simple self-labels can shift LLM cooperation, and what that means for multi-agent AI operations.

A mechanism-first look at MUA-RL, a reinforcement learning framework that trains tool-using agents inside dynamic multi-turn user interactions rather than static function-calling scripts.

GitTaskBench shows that code-agent value depends less on writing fresh code and more on surviving the messy chain from repository comprehension to execution, quality, and cost.

A mechanism-first reading of agentic reasoning frameworks as operational control loops, not just model upgrades.

A mechanism-first reading of an AI Data Scientist that turns hypothesis testing into the organising layer for automated analytics.

TradingGroup shows that the useful lesson from finance agents is not autonomous trading theatre, but disciplined feedback loops that turn decisions, outcomes, and risk signals into trainable operating data.

Hermes 4 shows that reasoning models need operational controls around data, stopping behaviour, evaluation, and refusal policy—not just larger thinking budgets.

A mechanism-first reading of THEME, a CIKM 2025 framework that turns thematic investing from static ETF mimicry into semantic-temporal stock retrieval.