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Training on environments synthesized from real-world business scenarios boosts agent performance on both enterprise tasks and diverse benchmarks, revealing a new frontier in scalable RL training.
Solar Open 2 outperforms its predecessors and competitors with a groundbreaking 1M-token context window, redefining the capabilities of large language models in agentic tasks.
Early-stage reasoning failures can be drastically reduced from 64% to 13% with a novel RL approach that penalizes cascading errors in medical VQA.
Agentic models may resolve citations, but they still mislink to the wrong papers 15.9% of the time, exposing a critical flaw in current AI evaluation benchmarks.
