The Search That Remembers: Training AI Without Answers
How Cycle-Consistent Search turns the search trajectory itself into a reward signal for training AI agents when gold answers are unavailable.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Epistemic Infrastructure: Why Your AI Knows Less Than It Thinks
A measured reading of OIDA: why organizational AI needs memory that tracks decisions, contradictions, and open questions—not just better retrieval.
From Playbooks to Probabilities: When AI Starts Thinking Like a Football Manager
GenTac shows why tactical AI is moving from single forecasts to controllable probability spaces—and what that means for decision support beyond sports.
Meerkat or Mirage? When AI Safety Fails in Plain Sight (Across Traces)
A case-first reading of Meerkat shows why AI agent safety failures increasingly require repository-level investigation, not one-trace-at-a-time monitoring.
Playing Both Sides: How Multi-Agent Scripts Teach AI to Lie, Detect, and Decide
A mechanism-first reading of how multi-agent murder-mystery simulations can train vision-language models to reason under deception, partial evidence, and role-dependent incentives.
Thinking Fast, Remembering Slow: Why SWE-AGILE Fixes the Memory Crisis of AI Agents
A mechanism-first reading of SWE-AGILE: why the next bottleneck for AI agents is not only reasoning depth, but remembering the right layer of reasoning at the right cost.
When AI Drives, Who’s in Control? — Reclaiming Determinism in Agentic Systems
A mechanism-first reading of how reactor-based orchestration can make agentic AI safer by bounding nondeterminism instead of pretending to remove it.
When Physics Meets Pixels: Rethinking Post-Blast Damage Assessment
A mechanism-first reading of Blast-Mamba shows why post-blast damage assessment improves when satellite imagery is fused with simulated blast physics, not treated as ordinary visual change detection.
Anchors Away: Rethinking How AI Agents Learn to Use Tools
A mechanism-first reading of E³-TIR, a tool-agent training method that uses expert prefixes as exploration anchors instead of treating demonstrations and reinforcement learning as rival religions.
One Point to Rule Them All: Why AI Optimization Is Quietly Abandoning the Pareto Frontier
A closer look at why many-objective Bayesian optimization may be better served by finding one deployable trade-off point than by approximating an entire Pareto frontier.