Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
arXiv:2608. 11327v1 Announce Type: new Abstract: Specialist training beats generalist scale when forecasting financial statements.
arXiv:2608. 12027v1 Announce Type: cross Abstract: Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intrinsic ambiguity, expert judgment, and stochastic corruption.
arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.
arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.
arXiv:2509. 16749v1 Announce Type: cross Abstract: LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners.
arXiv:2608. 12192v1 Announce Type: new Abstract: Foundation models for protein structure prediction remain unreliable on certain targets.
arXiv:2608. 11727v1 Announce Type: new Abstract: When a coding agent obeys a rule, it may simply have been going to do that anyway.
arXiv:2608. 11847v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) integrate visual perception with language generation, enabling responses that span image understanding and complex reasoning.
arXiv:2608. 11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems.
arXiv:2608. 11291v1 Announce Type: cross Abstract: Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods.
arXiv:2608. 11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, which makes them unreliable measures of model knowledge.
arXiv:2608. 11749v1 Announce Type: cross Abstract: Multi-objective optimization (MOO) has demonstrated significant success in multi-task learning by mitigating task conflicts through gradient manipulation.
arXiv:2608. 11815v1 Announce Type: new Abstract: Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models.
arXiv:2608. 11713v1 Announce Type: cross Abstract: Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems.
arXiv:2608. 11499v1 Announce Type: cross Abstract: Task vectors enable model merging without joint retraining.
arXiv:2608. 12194v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions.
arXiv:2608. 11375v1 Announce Type: new Abstract: In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling's T-squared test.
arXiv:2608. 12086v1 Announce Type: cross Abstract: Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical artificial intelligence.