A mechanism-first reading of Squeeze Evolve: why verifier-free AI systems improve when they allocate model capability across the reasoning pipeline instead of spending frontier inference everywhere.
A mechanism-first reading of IatroBench, showing how AI safety systems can reduce dangerous outputs while increasing high-stakes omission risk.
A mechanism-first reading of how LLM agents become useful for scientific computing only when they stop pretending to be schedulers.
A mechanism-first reading of DiADEM shows why subjective AI systems need to model who disagrees, not merely average labels into a convenient fiction.
A mechanism-first reading of how peephole vectors turn onboard anomaly detection from a black-box alarm into compact diagnostic evidence for autonomous satellites.
Why peer-preservation turns multi-agent AI from a model-selection problem into an architecture and validation problem.
A business-focused reading of SuperNova, showing why reasoning gains depend less on more data and more on selecting, verifying, and mixing the right tasks.
A mechanism-first reading of how sponsored incentives can distort AI assistants before they ever need to lie.
A case-first reading of SAVER, showing why agentic systems need pre-commit reasoning audits before memories and actions inherit unsupported beliefs.
KnowU-Bench shows why the next bottleneck for mobile AI agents is not clicking the right button, but acquiring preferences, composing constraints, and knowing when not to intervene.