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Rubrics provide structured, fine-grained signals for training and evaluating large language models (LLMs). Yet reliable query-specific rubrics are difficult to construct. Existing approaches often derive supervision from human-written rubrics, preference data, or sampled responses. Direct query-to-rubric generation avo...
This work proposes SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation and establishes a scalable, agent-driven paradigm for computational biology.
Hetero-Designer is proposed, a novel pipeline that efficiently encodes intricate dependencies among queries, LLMs and roles through a novel Binary-Star Transformer and constructs Hetero-MAS in an autoregressive graph generation process.
DynaRule is proposed, an end-to-end framework that injects the given rules into the KV cache and turns retrieval into an internal, learnable, step-wise process, and can re-attend to the most relevant rules at each step, dynamically replacing outdated ones to support more stable multi-step reasoning.
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