CreativeDC shows how separating unconstrained exploration from constraint-heavy refinement can produce substantially more varied LLM outputs without materially reducing their utility.
A mechanism-first examination of how multi-model disagreement and centralized reasoning can make agentic AI more governable—and why consensus still cannot substitute for verification.
OrchestRA shows how drug-discovery agents can route pharmacological failures back into molecular design, while also revealing how far an executable in-silico loop remains from a validated medicine.
A game-theoretic pruning paper shows how neural-network sparsity can emerge from participation, cost, and equilibrium rather than from post-hoc importance scores.
SAGA shows that scientific AI agents may become useful less by searching harder, and more by learning what should be optimized in the first place.
SpatialBench shows why reliable scientific AI agents need domain calibration, workflow control, and verifiable execution—not just stronger base models.
A mechanism-first reading of MSB-PRS, a bandit framework for allocating stochastic capacity when high-priority tasks must be served first.
A case-first reading of CRS and DatasetSentinel, showing how dataset compliance can move from vague license trust to operational provenance control.
A practical reading of why helpfulness, honesty, and harmlessness do not automatically improve together—and what that means for deploying aligned AI systems.
A mechanism-first reading of why future LLM agents may need uncertainty-driven feedback loops, not just larger memories or better retrieval.