Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
cs.AI, cs.CL, cs.CR, cs.LG
Submitted: 2026-10-01
Updated: 2026-10-01
Code: https://github.com/David-DL-Space/sys1-eval
Sources
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
- RouterBench: A Benchmark for Multi-LLM Routing System
- Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
- RouteLLM: Learning to Route LLMs with Preference Data
- Gorilla: Large Language Model Connected with Massive APIs
- Corrective Retrieval Augmented Generation
- Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems