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AlphaBet preprint reports improved probability forecasts through agent training

Researchers report that AlphaBet, a reinforcement learning framework, improved GLM-4.5-Air's event probability forecasts on two benchmarks. Brier scores, which measure probability forecast error, fell from 0.731 to 0.626 on Forecast-Dojo and from 0.612 to 0.528 on FutureX. The framework trains one model to search for evidence, reason and report probabilities at multiple dates before an event resolves. Each forecast is scored against the eventual outcome. Controlled experiments attributed gains to both evidence gathering and probability reporting. The preprint also found that a normalization step in Group Relative Policy Optimization degraded probability scoring while leaving top-outcome accuracy largely unchanged.
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cs.LG updates on arXiv.org
Published by Tech & Business, a media brand covering technology and business. This story was sourced from cs.LG updates on arXiv.org and reviewed by the T&B editorial agent team.
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SPLIT preprint reports improved allocation of AI inference budgets

Researchers report that SPLIT, a method for allocating computing budgets when AI systems answer questions or perform tasks, came within 0.02-0.8 percentage points of the best allocation in hindsight across seven benchmarks. The pr...

cs.LG updates on arXiv.org
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Science AI

LLM-generated quantum compilers reduce ion movement steps in benchmarks

Researchers report in an arXiv preprint that compilers generated by Claude Opus 4.7 reduced ion movement timesteps by up to 72% for linear traps and up to 43% for traps with junctions against hand-crafted compilers. These programs...

cs.AI updates on arXiv.org
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AI Science

PRIME preprint reports fewer unjustified conclusions in AI agent tests

Researchers report in a preprint that PRIME, a framework for checking AI agents' conclusions against evidence, produced no unjustified conclusions on 480 public-record tasks. An agent shown the requirements reported unjustified co...

cs.AI updates on arXiv.org
Science Security
Science Security

LLM-Attacker preprint tests privacy risks in split language models

Researchers present LLM-Attacker in a preprint, introducing a framework for comparing attacks that reconstruct private text from messages exchanged between client and server portions of language models. It records attacker access,...

cs.LG updates on arXiv.org
AI Science
AI Science

HybridAgent preprint reports lower token use and faster inference

Researchers report that HybridAgent, an AI agent that switches between internal representations and text, reduced token use by 35.5%-54.7% and delivered 1.8x-2.6x faster inference in benchmark evaluations. The preprint also report...

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