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This work introduces JevSpawn, a compositional policy that connects natural language task specifications to finite probabilistic exploration, and establishes JevSpawn as a promising approach to structured agentic inference, with improved task performance and faster navigation.
Results characterize current agent capabilities across game-development activities and support combining runtime evidence with visual assessment, as well as identifying requirement omissions and gameplay logic errors as predominant implementation problems.
This work introduces CLBench-V, a benchmark for multimodal context learning that addresses the difficulty of localizing where context use breaks down by organizing tasks around three dimensions: context grounding, new information application, and new knowledge learning.
This work presents SeqLLM, a framework that adds behavioral-sequence modeling to a pretrained LLM while preserving its language ability, and achieves state-of-the-art results on public recommendation benchmarks.
It is observed that OPD-trained models maintain superior avg@K performance across sampling budgets, while the advantage in pass@K gradually shifts to the pre-OPD base models as K increases, suggesting that OPD primarily improves sampling efficiency rather than consistently expanding the student's reasoning capability b...
OrthoPilot is a clinical artificial intelligence system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care and autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patie...
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