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arXiv:2604.05400v1 Announce Type: new Abstract: Machine data is central to observability and diagnosis in modern computing systems, appearing in logs, metrics, telemetry traces, and configuration …
arXiv:2604.05643v1 Announce Type: new Abstract: Extending CoT through RL has been widely used to enhance the reasoning capabilities of LLMs. However, due to the sparsity …
arXiv:2604.05273v1 Announce Type: new Abstract: Human communication is fundamentally creative, and often makes use of subtext -- implied meaning that goes beyond the literal content …
arXiv:2604.05445v1 Announce Type: new Abstract: Vision-language reward modeling faces a dilemma: generative approaches are interpretable but slow, while discriminative ones are efficient but act as …
arXiv:2604.05613v1 Announce Type: new Abstract: Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent …
arXiv:2604.04996v1 Announce Type: new Abstract: Strategically locating a sawmill is vital for enhancing the efficiency, profitability, and sustainability of timber supply chains. Our study proposes …
arXiv:2604.05306v1 Announce Type: new Abstract: Large language models are increasingly used in settings where uncertainty must drive decisions such as abstention, retrieval, and verification. Most …
arXiv:2604.05700v1 Announce Type: new Abstract: High-fidelity modeling of turbulent flows requires capturing complex spatiotemporal dynamics and multi-scale intermittency, posing a fundamental challenge for traditional knowledge-based …
arXiv:2604.05318v1 Announce Type: new Abstract: Harmful content detectors-particularly disinformation classifiers-are predominantly developed and evaluated on Standard American English (SAE), leaving their robustness to dialectal variation …
arXiv:2604.05551v1 Announce Type: new Abstract: Self-conditioning has been central to the success of continuous diffusion language models, as it allows models to correct previous errors. …
arXiv:2604.05396v1 Announce Type: new Abstract: Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. …
arXiv:2604.05116v1 Announce Type: new Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed …