This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Language models are widely used for probabilistic judgment (market forecasts, risk estimates, event likelihoods) and most users assume the probabilities are internally consistent. They're not. The incoherence grows under certain conditions. If you're using LLM outputs for quantitative decision-making, you need to know this and either add calibration or treat the numbers as rough signals only.
This is a realistic picture of how forecasting actually works at scale: you need domain models, constraints, uncertainty communication, and humans in the loop. CastClaw's harness engineering (data versioning, constraint checking, interactive debugging) is thoughtful. If you're shipping forecasting products, the system design is worth studying, though the novelty is more integration than breakthrough.
Chaotic systems are hard to predict from short windows, and this paper attempts to give LLMs a phase-space structure that better represents nonlinear dynamics. The idea is reasonable but the execution is complex and the results (in an excerpt) are unshown. Skip this unless you're specifically working on forecasting problems where you have chaotic dynamics and limited data. It's domain-specific and preliminary.
This is specialized research on the mechanics of forecast model training. The practical insight is real but narrow: picking your loss function shapes error structure, not just aggregate performance. For teams building forecast systems at scale, this matters enough to run experiments on your own data, but it's not a signal that existing forecasting systems are broken.