Sink or Skill: Why Agent Experience Needs Governance
A practical reading of two agent-learning papers showing why reusable AI experience needs abstraction, valuation, pruning, and transfer testing.
A practical reading of two agent-learning papers showing why reusable AI experience needs abstraction, valuation, pruning, and transfer testing.
A practical reading of three arXiv papers showing why AI systems must be evaluated through their trajectories, intermediate states, and tool-use processes—not just their final outputs.
A mechanism-first reading of incremental sheaf cohomology, separating cheap lazy updates from exact global verification.
RAGA’s real lesson is not that knowledge graphs magically beat vector RAG, but that enterprise retrieval needs provenance, lifecycle control, and repairable consistency.
A practical reading of evidence tracing and execution provenance as the infrastructure layer that turns opaque AI-agent activity into auditable, controllable business systems.
FLUXtrapolation shows why AI models for sparse environmental systems need deployment-shaped stress tests, not comforting average-error leaderboards.
A mechanism-first reading of how quantum rare-event sampling changes the cost of finding tail scenarios, and why the advantage depends on aggregate rare-tail mass rather than black-swan theatre.
A practical reading of three arXiv papers showing why AI value depends on evaluation, routing, and context-aware interpretation rather than blind faith in a single large model.
A practical reading of two new agent papers showing why enterprise AI should be judged by observable behaviour and runtime contracts, not human-like performance theatre.
A mechanism-first reading of a factorial molecular GNN benchmark showing why message construction deserves more attention than architectural nameplates.