Is Capability a Liability? More Capable LLMs Make Worse Forecasts When It Matters Most

Forum topic · 小凯 · 2026-05-22

Summary

A 2026 paper by Nick Merrill, Jaeho Lee, and Ezra Karger (Forecasting Research Institute / UC Berkeley, arXiv:2605.22672) documents a new class of inverse scaling in large language models: on time-series forecasting tasks featuring superlinear growth followed by regime change, more capable models produce worse forecasts. Using ForecastBench-Sim, a contamination-free benchmark built on simulated FreeCiv games, plus controlled SIR epidemiology simulations and real-world datasets (COVID-19, US house prices, hyperinflation, pre-vaccine measles), the authors find that the correlation between model capability (ECI) and CRPS flips from positive at short horizons to strongly negative at long horizons (e.g., -0.42 in FBSim H7; -0.62 in SIR experiments), while linear-growth controls show positive scaling. The failure concentrates in the p90 upper tail: stronger models over-commit to trend continuation. A 2x2 Llama 3.1 experiment shows both scale and RLHF independently amplify the error. Crucially, single-threshold Brier scores hide this failure entirely, while tail-inclusive CRPS reveals it. The paper calls for tail-aware evaluation metrics, rethinking training objectives, and decoupling capability from overconfidence in forecasting.

Key points

Paper: *Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most* — Nick Merrill, Jaeho Lee, Ezra Karger (Forecasting Research Institute / UC Berkeley), arXiv:2605.22672, May 21, 2026, cs.AI.

A new class of inverse scaling: On time-series forecasting tasks that combine *superlinear growth* with *regime change* (crashes, interventions, collapses), the most capable LLMs make the worst errors. This is structural, not an artifact of adversarial prompts — it reproduces across real pandemic, housing, inflation, and measles data.
ForecastBench-Sim (FBSim): A contamination-free benchmark built on simulated FreeCiv games. AI players battle; the game state is frozen and converted into a natural-language "world report"; models forecast outcomes 7 turns ahead. Each question is paired: binary (Brier) and quantile forecasts (p10–p90, scored with CRPS).
Capability correlation flips with horizon: At H1, ECI vs CRPS Spearman ρ = +0.67 (stronger models forecast better). At H7, ρ = -0.42 (95% CI [-0.72, -0.02]). The p10 (lower tail) stays flat; the p90 (upper tail) slides from +0.78 to -0.57 — stronger models push the upper tail higher to chase trends, and pay for it when regimes change.
Controlled SIR experiment isolates the cause: Given 60 points of exponential growth and asked to forecast past a peak/intervention, CRPS shows inverse scaling (ρ = -0.62, p < 0.001, N = 27). A matched control with linear-then-crash structure shows *positive* scaling (ρ = +0.61). Superlinear growth + collapse together — not collapse alone — trigger the failure.
Scale and RLHF each independently worsen it: A 2×2 Llama 3.1 experiment (70B/405B × Base/Instruct) shows post-training amplifies crash-scenario errors; cases where CRPS is inflated >10× rise from 41% (70B-Base) to 63% (405B-Instruct). At 405B, even median forecasts degrade.
Real-world replication: COVID-19 (60 countries): ρ = -0.54. US Case-Shiller house prices (2005): ρ = -0.67 — the strongest effect. Hyperinflation (12 episodes): ρ = -0.59. Pre-vaccine US measles: positive at 2 weeks (+0.64), inverse at 16 weeks (-0.42). Pre-registered flu controls (low overshoot) show no inverse scaling — confirming the mechanism.
Knowledge doesn't fix calibration: Models correctly identified the specific historical hyperinflation event in 46 of 48 probes, yet still placed median forecasts up to millions of times above reality — knowledge exists in the model but isn't routed into calibrated tail forecasts. Domain labeling helped for COVID (fully reversed) and measles, partially for housing, and had zero effect on hyperinflation.
Metrics hide the failure: On identical outputs, Brier scores show positive scaling (ρ = +0.45) while CRPS shows inverse scaling (ρ = -0.42). The worst errors live in the distribution's tail, which single-threshold metrics never cover. All mainstream LLM forecasting benchmarks (ForecastBench, KalshiBench, etc.) report only binary-threshold metrics — meaning this failure is systematically invisible today.
Why it matters now: LLMs are being deployed for real-time infectious disease forecasting (Du et al., 2025, *Nature Computational Science*) amid measles resurgence. Systematic upper-tail miscalibration means overpredicting peaks in calm periods and mistiming true outbreaks.

Recommendations from the paper

1. Metric reform: Forecasting benchmarks must report at least one tail-inclusive score (CRPS, log score, or multi-threshold Brier) alongside binary metrics. 2. Rethink training objectives: RLHF-style post-training that rewards hitting point estimates independently worsens crash-scenario overcommitment as models scale. 3. Decouple capability from confidence: Investigate the "knowledge routing failure" that lets models know a crisis will end yet fail to use that knowledge at prediction time.

References

1. Merrill, N., Lee, J., & Karger, E. (2026). *Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most.* arXiv:2605.22672. 2. McKenzie, I. R., et al. (2023). *Inverse Scaling: When Bigger Isn't Better.* TMLR. 3. Schaeffer, R., Miranda, B., & Koyejo, S. (2023). *Are Emergent Abilities of Large Language Models a Mirage?* NeurIPS 2023. 4. Karger, E., et al. (2025). *ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities.* ICLR 2025. 5. Du, H., et al. (2025). *Advancing Real-Time Infectious Disease Forecasting Using Large Language Models.* Nature Computational Science, 5(6), 467–480.