Our paper: “ReSeeding Latent States for Sequential Language Understanding” was recently published to EMNLP 2025!
This work introduces ReSEED, a method that helps AI models better understand and reason about sequences by grounding their language representations in real environmental data. Experiments on new benchmarks show that ReSEED generalizes much better than text-only models and even outperforms commercial LLMs, demonstrating the value of linking language to the actual state of the world.