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9 storiesInnovation
Systematic review maps the growing use of large language models in mental health
A systematic review surveys how large language models are being studied for mental-health analysis, risk assessment, therapy support and multimodal monitoring, while stressing unresolved ethical and regulatory challenges.arxiv.orgPolicy
Model Cards Alone May Not Govern Open-Weight Foundation Models, Position Paper Argues
An ICML 2026 position paper analyzing 500 Hugging Face model cards argues that open-weight foundation models need coordinated model cards, acceptable-use policies, and licenses to address safety and governance gaps.arxiv.orgInnovation
A proposed metric would measure how difficult game worlds are to predict
A position paper proposes the Transition Complexity Profile, a standardized way to describe how unpredictable and long-range the dynamics of game environments are for game-world modeling and reinforcement learning.arxiv.orgInnovation
FM-Bench tests whether AI agents can manage a football club for 20 years
A new arXiv benchmark places 15 language-model agents in a 20-year football-management simulation, testing whether they can make consistent decisions when short-term choices affect long-term outcomes.arxiv.orgInnovation
FinRCA-Bench finds financial AI diagnosis depends heavily on evidence retrieval
A new arXiv benchmark reports that changing only the retrieval method raised a fixed model’s exact accuracy on financial reconciliation cases from 2.05% to 72.44%. The study also finds that a correct root-cause label often does not mean the system returned sufficient evidence for an auditable diagnosis.arxiv.orgInnovation
Abra paper maps compute and data tradeoffs in diffusion image training
A new arXiv study presents scaling-law experiments for text-to-image diffusion models across compute budgets from 10^19 to 10^22 FLOPs. Its authors report that image models need substantially more data per parameter than language models to train efficiently.arxiv.orgInnovation
Delta2Gamma reports 92.4% accuracy using EEG to detect Alzheimer’s disease
A new arXiv paper describes a self-supervised EEG method that reports 92.4% accuracy distinguishing Alzheimer’s disease from cognitively normal controls on the ADFTD cohort.arxiv.orgInnovation
Agentic ESOpt proposes lower-memory fine-tuning for long-horizon AI agents
An arXiv paper introduces Agentic ESOpt, a proposed evolution-strategy framework for fine-tuning long-horizon language-model agents with inference-level GPU memory. The authors report gains for Qwen-3.5-27B on WebArena-Lite and improvements in 28 of 36 prompt-optimization settings.arxiv.orgInnovation
Paper proposes CSE to evaluate irregular time-series forecasts beyond MSE
An arXiv paper argues that mean squared error can misjudge irregular time-series forecasts and proposes a continuous-time metric tested across synthetic, semi-synthetic and eight real-world datasets.arxiv.org
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