Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology
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arXiv:2603.22492v2 Announce Type: replace-cross Abstract: Inference-time scaling has emerged as an effective way to improve generative models at test time…
arXiv:2603.24284v1 Announce Type: cross Abstract: When multiple LLM-based code agents independently implement parts of the same class, they must agree…
arXiv:2603.24291v1 Announce Type: cross Abstract: Recent work distinguishes two heterophily regimes: adversarial, where cross-class edges dilute class signal and harm…
arXiv:2603.23064v2 Announce Type: replace-cross Abstract: We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered…
arXiv:2603.23539v1 Announce Type: new Abstract: We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics…
arXiv:2603.23558v1 Announce Type: cross Abstract: Uncertainty quantification is a key aspect in many tasks such as model selection/regularization, or quantifying…
arXiv:2603.24124v1 Announce Type: cross Abstract: RLHF-aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40-79% of questions produce a single…
arXiv:2603.22153v2 Announce Type: replace-cross Abstract: Recent advances in cross-view geo-localization (CVGL) methods have shown strong potential for supporting unmanned aerial…