Supplementary Results for Bayesian Forecasting Paper
sirbayes · x · 2026-07-14
The author elaborates that the core of this method involves using an LLM to estimate the log-likelihood ratio \(\lambdat\) at each step to recursively update log-odds, while introducing a tempering parameter \(\alpha\) to curb overconfidence.
They also mention receiving a runner-up best paper award at ICML for this Bayesian forecasting work. During the presentation, they showcased stricter Bayesian approaches, and these new findings have now been integrated into the arxiv paper.
Related event: Bayesian Prediction Updating Method Using LLMs(3 posts)→
More from Research
- OpenAI says long-horizon models need safety and alignment checks across full action sequences — rhiever · 2026-07-22
- A Reddit user proposes a consistency LoRA to keep anime and game scenes visually stable — ThirdWorldBoy21 · 2026-07-22
- Graph workload 854.graph500 enters SPEC CPU 2026 as a new CPU benchmark — Prof_DavidBader · 2026-07-22
- BlackboxNLP 2026 is recruiting extra reviewers after a high submission volume — hanjie_chen · 2026-07-22
- AWS shows self-distilled reasoning can preserve math and coding skills during SFT — AWS ML Blog · 2026-07-22
- UI2App shows screenshot fidelity still lags real interaction recovery — Grace Man Chen · 2026-07-22