Agents Without Time: When Reinforcement Learning Meets Higher-Order Causality
Wilson’s formal bridge between deterministic POMDP agents and process functions shows why causal order can become an architectural constraint in multi-agent AI.
Cognaptus Insights
Industry thoughts, trends, and commentary.
HAROOD: When Benchmarks Grow Up and Models Stop Cheating
A business-oriented reading of HAROOD, the benchmark that turns human-activity recognition from a leaderboard game into four concrete deployment failure tests.
Replace, Don’t Expand: When RAG Learns to Throw Things Away
SEAL-RAG shows why multi-hop retrieval systems often need better evidence replacement, not larger context windows.
Safety Without Exploration: Teaching Robots Where Not to Die
A mechanism-first reading of V-OCBF: how offline robot logs can become deployable safety filters, and where the guarantees still depend on approximation.
When AI Becomes the Reviewer: Pairwise Judgment at Scale
A mechanism-first look at how LLMs can turn expensive proposal review into pairwise ranking, audit signals, and similarity checks without pretending the committee has disappeared.
When Circuits Go Atomic: Pruning Transformers One Neuron at a Time
A mechanism-first reading of multi-granular node pruning, and why the practical value is cheaper model diagnosis rather than magical model compression.
You Know It When You See It—But Can the Model?
A business-focused reading of Agile Deliberation, a framework for turning vague subjective visual concepts into working VLM classifiers through structured human reflection.
Crowds, Codes, and Consensus: When AI Learns the Language of Science
A mechanism-first reading of MatSci-YAMZ, showing why AI-assisted vocabulary work is less about automated definitions and more about governed semantic negotiation.
Fault, Interrupted: How RIFT Reinvents Reliability for the LLM Hardware Era
RIFT shows how LLM accelerator reliability can move from broad random fault campaigns to targeted, workflow-ready diagnosis of the few faults that actually matter.
Graph Theory in Stereo: When Causality Meets Correlation in Categorical Space
A mechanism-first reading of how categorical semantics separates graph syntax from probabilistic semantics in Bayesian and Markov networks.