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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.
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.
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.
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.
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.
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.
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.
An arXiv paper introduces Relative Transfer, a measure of how training examples at different difficulty levels affect one another, and proposes Transfer-aware Dynamic Curriculum Sampling for adapting training schedules during post-training.
An arXiv preprint describes Co-RL, a multi-agent reinforcement-learning framework in which separate models reward one another. The authors report gains across text-only and multimodal benchmarks without ground-truth labels, while acknowledging the risks of self-reinforcing errors and training collapse.
A new arXiv paper proposes Task Specialization Fine-Tuning to allocate limited fine-tuning across related task regions. It reports improved task coverage in optimization, control, and LLM experiments, but gives no numerical results or independent validation.
Accepted to the 2026 Asilomar Conference, a paper proposes SURE-Ridge, a closed-form method for causal-graph estimation in equal-variance linear Gaussian models. Authors report lower small-sample structural error and fastest tested-baseline runtime; the source does not establish performance beyond these assumptions.
Researchers describe J-Miner, a method that converts internal signals from fine-tuned language-model classifiers into inspectable rules, reporting up to 98.3% reproduction of source decisions and nearly the same average task accuracy in much smaller student models.
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