AI and science frontiers · 2026-10-12

A language model learns behavioral reasoning from prediction-market price movements, reaching 0.674 directional accuracy on Polymarket and transferring zero-shot to four user-simulation benchmarks without any individual-level annotations.

A language model learns behavioral reasoning from prediction-market price movements, reaching 0.674 directional accuracy on Polymarket and transferring zero-shot to four user-simulation benchmarks without any individual-level annotations.

Multi-agent reinforcement learning no longer needs peers' private rewards: each agent evaluates others' transitions with its own learned reward model, learns cooperation in Escape Room, Clean Up, and Commons Harvest, and often divides returns more evenly than agents that see the true rewards.

Multi-agent reinforcement learning no longer needs peers' private rewards: each agent evaluates others' transitions with its own learned reward model, learns cooperation in Escape Room, Clean Up, and Commons Harvest, and often divides returns more evenly than agents that see the true rewards.

Constraining supervised fine-tuning updates to the direction identified by on-policy training lets SFT match GRPO's generalization while cutting training time by more than half.

Constraining supervised fine-tuning updates to the direction identified by on-policy training lets SFT match GRPO's generalization while cutting training time by more than half.

Across 5,601,890 Texas crashes from 2017 to 2025, a model reading the coded table and a model reading officer narratives were joined under one sampling design to produce population counts with intervals: narratives document 15,074 phone-use injury crashes against 7,340 in the coded field.

Across 5,601,890 Texas crashes from 2017 to 2025, a model reading the coded table and a model reading officer narratives were joined under one sampling design to produce population counts with intervals: narratives document 15,074 phone-use injury crashes against 7,340 in the coded field.

Deciding whether a change can ship on offline evals alone requires that eval gains predict online gains across comparable changes, not that the eval agrees with human judgment; on 183 independent interventions from the Upworthy archive, both sides were measured precisely yet the pooled relationship stayed weak, and the illustrative gate sent every change to an A/B test.

Deciding whether a change can ship on offline evals alone requires that eval gains predict online gains across comparable changes, not that the eval agrees with human judgment; on 183 independent interventions from the Upworthy archive, both sides were measured precisely yet the pooled relationship stayed weak, and the illustrative gate sent every change to an A/B test.