LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting

arXiv:2609.01337 · cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/layingfish/LEAP

Terminology

• Benchmark — 31× in this paper · explained in 2 episodes
• Aggregation — 26× in this paper · explained in 1 episode
• Calibration — 24× in this paper · explained in 7 episodes
• Clustering — 18× in this paper · explained in 2 episodes
• Trace — 18× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Hallucination — 6× in this paper · explained in 3 episodes
• Probabilistic Forecasting — 6× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Agentic Search — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes

Sources

• AIA Forecaster: Technical Report
• WebGPT: Browser-assisted question-answering with human feedback
• Agentic Forecasting with Structured Linguistic Beliefs
• BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

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