DISARM, but Make It Agentic: When Frameworks Start Doing the Work
A mechanism-first reading of how an agentic DISARM pipeline turns disinformation investigation from expert taxonomy work into auditable, semi-automated evidence production.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Many Minds, One Solution: Why Multi‑Agent AI Finds What Single Models Miss
A mechanism-first reading of why multi-agent LLM systems can improve results without adding information: they factor constraints across agents and stabilize solutions that single update dynamics may not reach.
Noise Without Regret: How Error Feedback Fixes Differentially Private Image Generation
A mechanism-first reading of how error feedback, reconstruction loss, and noise injection improve differentially private image generation without pretending the privacy-utility tradeoff has disappeared.
Pay to Think: Incentive Design Is the Hidden Variable in Human–AI Research
A mechanism-first reading of why participant incentives are not administrative trivia, but part of the experimental machinery behind human–AI decision-making evidence.
When Data Can’t Travel, Models Must: Federated Transformers Meet Brain Tumor Reality
A practical reading of how federated Transformer-GNN training can help medical-AI teams overcome local data scarcity without pretending privacy is solved by architecture alone.
Your Agent Remembers—But Can It Forget?
Why memory rewriting, not just memory retention, is becoming a hard diagnostic problem for reinforcement learning agents.
From Talking to Living: Why AI Needs Human Simulation Computation
A mechanism-first reading of Human Simulation Computation, showing why adaptive AI needs closed-loop action, reflection, learning, and scheduling—not just better language generation.
Lost Without a Map: Why Intelligence Is Really About Navigation
A mechanism-first reading of why adaptive intelligence may depend less on bigger models and more on systems that can remap, navigate, and correct themselves across changing problem spaces.
Rebuttal Agents, Not Rebuttal Text: Why ‘Verify‑Then‑Write’ Is the Only Scalable Future
How RebuttalAgent turns author responses from fluent text generation into auditable concern tracking, evidence construction, and strategic planning.
When Benchmarks Break: Why Bigger Models Keep Winning (and What That Costs You)
A business reading of benchmark scaling research: why larger models can remain predictably stronger on average while still becoming harder to justify in production.