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3,367 papers
This work presents a constrained two-view learning framework for robust graph learning, which aligns structure-aware GNN embeddings with a structure-free feature prior and designs a channel-split adaptive gated layer within DSAL to effectively integrate this prior.
This review surveys how Large Language Models are adding semantic interfaces, code generation, and tool orchestration to established numerical nanophotonic workflows, and looks ahead to the next generation of multimodal foundation models with physical perception capabilities.
This work deconstructs the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface, and uses the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty.
A deployment-oriented environmental-AI pipeline for day-ahead hourly PV forecasting that corrects timestamp conventions, constructs leakage-safe solar-geometry and clearness-index features, adds short-term atmospheric context, and combines complementary predictors through validation-learned stacking is developed.
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The main result is an adversarial resilience theorem for the Spiteful Greedy Swap Poisson Process (SGS-Poisson): without modifying its Poisson intensity, single-element exchange rule, or spiteful drop step, the algorithm retains limiting approximation factors for non-monotone objectives and $1-1/e for monotone objectives.
A new FAL framework is proposed that utilizes federated representation learning to align client data in a shared embedding space that achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under privacy constraints.
A supervised ensembling framework that trains a classifier over heterogeneous UQ-based scorer outputs on a small, domain-specific dataset of labeled LLM responses, then applies it to out-of-sample hallucination classification without retrieval, tools, or reference documents is studied.
This work introduces a general steering technique called Semantic Overlays: small learned adapters applied at chosen prefill positions to a frozen model's residual stream that defends against the broad class of prompt injections that add instructions in untrusted context.
Text Prompt Boosting (TPB), an AdaBoost-inspired framework that treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble by explicitly targeting hard, misclassified examples, is proposed.
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.
ClosureBench is introduced, a constructive benchmark for compositional graph-relational reasoning with programmatically verified ground truth with programmatically verified ground truth: each task's reference answer is computed by executing a program in the Ein tensor-logic language, ensuring machine-verified correctness.
A practical decision framework for four foundational measures - Entropy, KL divergence/cross-entropy, Mutual Information, and Transfer Entropy is provided, organized around three prescriptive questions for each: what question does the measure answer and in which AI context; which estimator is appropriate for the data type and dimensionality; and what is the most dangerous misuse.
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
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