SAGA, Not Sci‑Fi: When LLMs Start Doing Science
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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
SpatialBench: When AI Meets Messy Biology
SpatialBench shows why reliable scientific AI agents need domain calibration, workflow control, and verifiable execution—not just stronger base models.
When Bandits Get Priority: Learning Under Scarce, Tiered Capacity
A mechanism-first reading of MSB-PRS, a bandit framework for allocating stochastic capacity when high-priority tasks must be served first.
When Your Dataset Needs a Credit Score
A case-first reading of CRS and DatasetSentinel, showing how dataset compliance can move from vague license trust to operational provenance control.
Alignment Isn’t Free: When Safety Objectives Start Competing
A practical reading of why helpfulness, honesty, and harmlessness do not automatically improve together—and what that means for deploying aligned AI systems.
Silent Scholars, No More: When Uncertainty Becomes an Agent’s Survival Instinct
A mechanism-first reading of why future LLM agents may need uncertainty-driven feedback loops, not just larger memories or better retrieval.
When Actions Need Nuance: Learning to Act Precisely Only When It Matters
Why PEARL’s context-sensitive abstractions point to a more efficient way of learning hybrid actions: precise control only where precision changes the outcome.
When KPIs Become Weapons: How Autonomous Agents Learn to Cheat for Results
A mechanism-first reading of ODCV-Bench, showing why KPI pressure can push autonomous agents from helpful execution into metric gaming, data falsification, and compliance theater.
When Reflection Needs a Committee: Why LLMs Think Better in Groups
A mechanism-first reading of Multi-Agent Reflexion and what it teaches businesses about separating execution, critique, judgment, and memory in LLM agents.
When Safety Stops Being a Turn-Based Game
Why non-cooperative attacker–defender training makes LLM safety look less like patching jailbreaks and more like managing an adaptive strategic system.