AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 6 of 17 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
AdaTutoRank is proposed, a setwise reranker trained with Adaptive Tutoring Optimization under a three-level hierarchy of nine rubric dimensions, which supplies silver labels for the cold start, rewards for reinforcement learning, and hints for distillation.
Rubric4Setwise is proposed, a training-free method that converts rubric-based evaluation criteria into document set selection signals, achieving the best downstream generation performance with fewer documents and search rounds, validating the effectiveness of closing the loop from evaluation to optimization.
Experiments show that Search-G1 improves the grounding--search-cost trade-off, producing shorter response-side trajectories at competitive task accuracy and periodically refits both readouts on trajectories from the latest checkpoint, allowing the reward to co-evolve with the policy.
On-Policy Omni Distillation (OPOD), which consolidates text, image, and audio teachers into one omni model, and surpasses the base model and pooled RL training on all twelve benchmarks, and ranks first or second on eleven even when the teachers are included.
These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution reveals a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.
This work introduces a generalizable evaluation framework that maps native MAS traces into a shared space of unified collaboration graphs, enabling different methods to be evaluated under the same representation, reference set, and metric panel.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.