QED-Nano: Small Models, Big Proof Energy
A mechanism-first reading of QED-Nano shows why small theorem-proving models need more than long thinking: they need curated proof data, rubric rewards, scaffold-aware RL, and disciplined test-time compute.
Cognaptus Insights
Industry thoughts, trends, and commentary.
The Cost of Convenience: When AI Help Becomes Cognitive Debt
A research-backed look at why AI assistance can improve immediate task performance while weakening later independent performance, persistence, and capability formation.
The Proof Is in the Instance: Why AI Safety Can’t Be Fully Verified
A mechanism-first reading of why formal AI safety verification hits an information-theoretic ceiling, and why serious assurance must move toward instance-level certificates.
Trust Issues? When AI Governance Stops Trusting Humans
A mechanism-first reading of AI Trust OS, showing why enterprise AI governance is moving from human attestation to telemetry-backed control evidence.
When Models Learn… or Just Get Easier: Decoding Adaptive AI Evaluation
A practical diagnostic framework for separating real adaptive-model learning from dataset shifts, forgotten knowledge, and convenient evaluation luck.
AgentHazard: Death by a Thousand ‘Harmless’ Steps
A mechanism-first reading of AgentHazard, and why enterprise AI safety has to move from prompt refusal to trajectory-level execution governance.
From Seeing to Doing: Why Agentic AI Still Trips Over Reality
Agentic-MME shows why multimodal agents fail less from lack of tools than from weak coordination between visual evidence, web retrieval, execution discipline, and process verification.
Proofs at Scale: When 30,000 Agents Replace the Referee
A mechanism-first reading of automatic textbook formalization: why the breakthrough is not just stronger theorem proving, but disciplined agent orchestration at repository scale.
Seeing Charts Like a Quant: When RL Teaches Vision Models to Actually Reason
A business-oriented reading of Chart-RL, showing why small reinforcement-tuned vision-language models may beat larger untuned models on chart reasoning when accuracy, latency, and customization all matter.
When Squirrels Outsmart Your AI: Why Control, Memory, and Verification Refuse to Stay Separate
A squirrel-inspired agentic AI framework shows why reliable enterprise agents need control, memory, and verification designed as one operational loop, not three polite departments.