arXiv:2607. 00248v1 Announce Type: new Abstract: We present Seed2.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
We present Seed2. 0, a model series that takes a meaningful step toward solving complex, real-world tasks.
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
arXiv:2606. 29929v1 Announce Type: new Abstract: Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research.
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.
arXiv:2605. 28566v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token prediction -- is inherently myopic and prone to cascading errors.