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KnowAct-GUIClaw achieves a groundbreaking 64.1% success rate in long-horizon task execution, outperforming all existing agent frameworks and closed-source models.
Achieving state-of-the-art reranking performance with a model as small as 0.27B parameters, KaLM-Reranker-V1 challenges the notion that bigger models are always better.
Context-aware ASR corrections can be dramatically improved by leveraging a dynamically structured ontology memory, leading to more accurate and relevant corrections in long conversations.
