by ODEFTO AI Labs
arXiv:2610.27597v1 Announce Type: cross Abstract: Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently…
arXiv:2603.17372v3 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual…
arXiv:2610.27610v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at…
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01319-4 A neural network approach bridging Lagrangian and Eulerian perspectives transforms sparse…
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01318-5 Sharp et al. investigate the implications of unequal access to AI…
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01316-7 Wan et al. introduce a physics-informed learning model that simultaneously predicts…
arXiv:2610.21337v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) effectively transfers capabilities from a teacher to a student through feedback on…
arXiv:2610.23716v1 Announce Type: cross Abstract: Deep neural networks demonstrate remarkable generalization despite being highly over-parameterized, challenging traditional statistical learning theories.…
arXiv:2610.23847v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) can induce behaviors beyond those explicitly targeted. For example, training…