More capable LLMs produce worse distributional forecasts on superlinear growth time series with tail risks of regime change, with the error concentrated in the upper tail; this reverses on conventional threshold metrics.
arXiv preprint arXiv:2402.18563 , year=
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OracleProto is a reproducible framework that uses model-cutoff alignment, temporal masking, and leakage detection to create low-leakage benchmarks for LLM native forecasting from past events.
Foresight Arena is an on-chain benchmark using Brier and novel Alpha scores to evaluate AI forecasting agents on live prediction markets via Polygon smart contracts.
Coordination treated as a separable architectural layer in LLM multi-agent systems yields distinguishable Murphy-decomposed performance signatures on prediction-market tasks, with some configurations dominating a cost-quality Pareto frontier.
BLF achieves state-of-the-art binary forecasting on ForecastBench by using linguistic belief states updated in tool-use loops, hierarchical multi-trial logit averaging, and hierarchical Platt scaling calibration.
ArgLLMs build argumentation frameworks from LLMs to support explainable and contestable formal reasoning for claim verification.
VISTA mines multi-level event semantics via visual prompts, knowledge-enhanced retrieval, and proposal integration to improve long-video event prediction over existing LVLMs.
Milkyway uses pre-resolution signals from temporal contrasts in evolving evidence and repeated forecasts to evolve a harness and improve predictions before resolution, outperforming baselines on FutureX and FutureWorld.
Recursive information markets with forgetful LLM buyers can align information prices with true value and extend to scalable oversight in AI alignment.
Three independent LLMs exhibit correlated forecasting errors on 568 binary questions but human predictions show no activation of this shared bias.
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Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most
More capable LLMs produce worse distributional forecasts on superlinear growth time series with tail risks of regime change, with the error concentrated in the upper tail; this reverses on conventional threshold metrics.
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OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking
OracleProto is a reproducible framework that uses model-cutoff alignment, temporal masking, and leakage detection to create low-leakage benchmarks for LLM native forecasting from past events.
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Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents
Foresight Arena is an on-chain benchmark using Brier and novel Alpha scores to evaluate AI forecasting agents on live prediction markets via Polygon smart contracts.
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Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems
Coordination treated as a separable architectural layer in LLM multi-agent systems yields distinguishable Murphy-decomposed performance signatures on prediction-market tasks, with some configurations dominating a cost-quality Pareto frontier.
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Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs
BLF achieves state-of-the-art binary forecasting on ForecastBench by using linguistic belief states updated in tool-use loops, hierarchical multi-trial logit averaging, and hierarchical Platt scaling calibration.
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Argumentative Large Language Models for Explainable and Contestable Claim Verification
ArgLLMs build argumentation frameworks from LLMs to support explainable and contestable formal reasoning for claim verification.
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Towards Effective Long-Video Event Prediction via Multi-Level Event Semantics Mining
VISTA mines multi-level event semantics via visual prompts, knowledge-enhanced retrieval, and proposal integration to improve long-video event prediction over existing LVLMs.
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Harnessing Pre-Resolution Signals for Future Prediction Agents
Milkyway uses pre-resolution signals from temporal contrasts in evolving evidence and repeated forecasts to evolve a harness and improve predictions before resolution, outperforming baselines on FutureX and FutureWorld.
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Extrapolating Volition with Recursive Information Markets
Recursive information markets with forgetful LLM buyers can align information prices with true value and extend to scalable oversight in AI alignment.
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The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission
Three independent LLMs exhibit correlated forecasting errors on 568 binary questions but human predictions show no activation of this shared bias.