Metric Time Without the Clock: Making ASP Scale Again
A mechanism-first reading of how metric temporal ASP can avoid the grounding explosion by moving time from Boolean atoms into difference constraints.
Cognaptus Insights
Industry thoughts, trends, and commentary.
REASON About Reasoning: Why Neuro‑Symbolic AI Finally Needs Its Own Hardware
A systems-level reading of REASON shows why neuro-symbolic AI may bottleneck not on neural inference, but on the messy symbolic and probabilistic reasoning that makes it useful.
Sequential Beats Parallel: When Deep Research Agents Learn to Reflect
A practical reading of Deep Researcher Reflect–Evolve, and why enterprise research agents may need shared memory and plan reflection more than larger swarms.
SokoBench: When Reasoning Models Lose the Plot
A mechanism-first reading of SokoBench, showing why long-horizon planning failures in reasoning models begin with fragile counting, state tracking, and world representation.
When ERP Meets Attention: Teaching Transformers to Pack, Schedule, and Save Real Money
A case-first reading of how multi-type transformers turn furnace loading and ERP optimization into structured, neural combinatorial decision support.
When LLMs Invent Languages: Efficiency, Secrecy, and the Limits of Natural Speech
A business-focused reading of how vision-language agents can invent compact or covert task protocols, and why efficiency in multi-agent AI can quietly collide with auditability.
CAR-bench: When Agents Don’t Know What They Don’t Know
CAR-bench shows why reliable AI agents need more than tool-calling ability: they must know when to act, when to ask, and when to admit the system cannot comply.
Optimizing Agentic Workflows: When Agents Learn to Stop Thinking So Much
A mechanism-first reading of Agent Workflow Optimization, showing how repeated agent traces can be compiled into deterministic meta-tools that reduce cost, latency, and avoidable reasoning errors.
Routing the Lottery: When Pruning Learns to Choose
A mechanism-first reading of Routing the Lottery, where pruning becomes a way to route compact specialized subnetworks instead of merely shrinking one universal model.
Safety by Design, Rewritten: When Data Defines the Boundary
A mechanism-first reading of how kernel-based ODD construction turns safety-critical AI data into conservative operational boundaries for certification and runtime monitoring.