Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2609.37509v1 Announce Type: new Abstract: The boundary element method (BEM) provides an efficient numerical framework for solving multiple scattering problems in unbounded homogeneous domains....
arXiv:2609.37515v1 Announce Type: cross Abstract: Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities...
arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
arXiv:2609.37664v1 Announce Type: new Abstract: Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training dat...
arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pai...
arXiv:2609.37825v1 Announce Type: cross Abstract: Assigning credit to intermediate steps remains a central challenge in training Large Language Models (LLMs) on multi-step reasoning tasks with sparse...
arXiv:2609.37989v1 Announce Type: new Abstract: Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, ta...
arXiv:2609.38004v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these...
arXiv:2609.38049v1 Announce Type: new Abstract: Generative models for function-valued data, such as time series and solutions of partial differential equations, must learn distributions over infinite...
arXiv:2609.38065v1 Announce Type: cross Abstract: Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides...
arXiv:2609.38090v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-...
arXiv:2609.38095v1 Announce Type: new Abstract: Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires $\approx$ 600GB of GPU...
arXiv:2609.35898v1 Announce Type: cross Abstract: This paper proposes Wasserstein Causal Forests (WCF) for settings in which each unit's outcome is itself a probability distribution. This study also...
arXiv:2609.35914v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sens...
arXiv:2609.35932v1 Announce Type: cross Abstract: Prompt injection against LLM agents becomes much stronger when the injected instruction is wrapped in the model's own chat template. A forged templat...
arXiv:2609.35966v1 Announce Type: cross Abstract: Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, compl...
Mirror-Score is a calibrated, inference‑only scoring framework that evaluates heterochiral D‑peptide/L‑protein complexes, addressing the inadequacies of raw ProteinMPNN negative log‑likelihood (NLL) ranking. The authors benchmarked 31 crystal complexes across four target families, showing that NLL has poor affinity correlation (Spearman 0.19) while Boltz‑2 mirror‑space co‑folding confidence and pLDDT metrics can correctly rank peptides within a viral‑entry family. The study also proposes a prospective design protocol for antimicrobial‑resistance targets and releases all code, data, and structures publicly.
arXiv:2609.36134v1 Announce Type: cross Abstract: Functional Kolmogorov-Arnold Networks (FunKAN) achieve state-of-the-art accuracy on MRI Gibbs artifact removal and anatomical segmentation, but their...
PreviewDiff is a test‑time search method that uses multimodal critics to guide diffusion model sampling. By decoding partial previews at selected denoising checkpoints, scoring them with a multimodal judge, and branching over semantic prompt edits, it allows the generation process to be edited and rerouted before completion. The approach consistently outperforms budget‑matched Best‑of‑N sampling and scalar‑search baselines on image and video benchmarks, with early interventions and wider search yielding the biggest gains.
arXiv:2609.36303v1 Announce Type: cross Abstract: Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problem...