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9 storiesInnovation
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.orgInnovation
Study proposes transfer-aware curriculum sampling for language-model training
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.arxiv.orgInnovation
Co-RL paper reports label-free reasoning gains from diverse model cohorts
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.arxiv.orgInnovation
A proposed fine-tuning method allocates training effort across related tasks
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.arxiv.orgInnovation
SURE-Ridge proposes a faster way to infer causal graphs from limited data
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.arxiv.orgInnovation
J-Miner paper reports executable decision rules extracted from language-model classifiers
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.arxiv.orgInnovation
Paper tests letting AI agents switch specialized LoRA adapters mid-task
A single-author arXiv paper reports that giving an agent a tool to switch between specialized LoRA adapters helped it solve two synthetic coding tasks and reduced the reported capability tax by up to 18 times.arxiv.orgInnovation
MultiSigBERT combines medical text and patient timelines for cancer survival prediction
An ECML PKDD 2026 paper presents MultiSigBERT, combining narrative medical reports, structured records and temporal patient data to estimate individualized oncology risk scores. It reports a 0.743 concordance index on an independent test set; the source has no comparator results or clinical-deployment evidence.arxiv.org
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