Typed Decision Models: An Early Evidence Audit and Evaluation Checklist

arXiv:2609.32160 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/youzizzz1028/Awesome-Jev

Terminology

• Calibration — 31× in this paper · explained in 7 episodes
• Accuracy — 28× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 7× in this paper
• AUROC — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• LLM judge — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Deferral — 3× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 3× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Reinforcement Learning for Calibrated Decisions — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Structured Prompting — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Harness Tokenomics: A Router for the Enterprise Agentic Control Plane
• Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
• this-that-model-1.0: A typed decision model that decides in 30 ms, for a millionth of a cent
• Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains
• Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequences
• Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya as System One Decision Layers for LLM-Driven Pentest Agents
• Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures
• Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality
• Evaluating Decision Models for Text Annotation in Computational Social Science
• Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
• Language Models (Mostly) Know What They Know
• Building Efficient Universal Classifiers with Natural Language Inference
• JEV-as-a-Judge: Accept When Confident, Escalate When Unsure
• Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)
• JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places

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