Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction

arXiv:2608.24001 · cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.AI

Submitted: 2026-08-25

Updated: 2026-08-26

Comments: 13 pages, 4 figures, 5 tables. Submitted to IEEE BigData 2026

Code: https://github.com/google-deepmind/natural-plan

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 71× in this paper · explained in 2 episodes
• Clustering — 37× in this paper · explained in 2 episodes
• Aggregation — 25× in this paper · explained in 1 episode
• Trace — 14× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Reasoning Traces — 10× in this paper · explained in 3 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Abstention — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Self-consistency — 2× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Bench to the Future: A Pastcasting Benchmark for Forecasting Agents
• Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information

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