SIVA-RL is proposed, a Sensitivity-Invariance Visual Alignment framework that replaces operator-conditioned regularization with sample-wise, outcome-conditioned supervision and yields an 8.79 percentage-point gain on vision-dependent reasoning and up to 14.9% relative overall improvement across all four GRPO- and DAPO-...
Cheng Tang, Junzhi Ning, Min Cen et al.· arXiv.org· 0 citations
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
Lei Bai, Jiaqi Cao, Chiyu Chen et al.· 3 citations
This work formalizes probabilistic alignment as a distributional criterion for world models and introduces PAWBench, a benchmark for evaluating video generators as stochastic samplers of world dynamics, and introduces PAWEval, an outcome-level protocol that converts repeated video rollouts into empirical distributions...
Yuandong Pu, Le Zhuo, Sayak Paul et al.· 0 citations
EvoGraph-R1 is introduced, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.
Jiashi Lin, Changhong Jiang, Xiangru Lin et al.· 1 citation
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