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Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration. However, these systems are prone to deviating from the overall goal as execution unfolds, which wastes execution resources and leads to undesired outcomes, a fai...
SKILLGRAPH is a framework that represents reusable skills as nodes in a directed graph, with typed edges encoding prerequisite, enhancement, and co-occurrence relations, that achieves state-of-the-art performance against memory-augmented RL methods, with especially large gains on complex tasks that require composing mu...
Large language model (LLM) agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating runtime defenses that prevent unsafe action in execution across diverse risks while preserving benign-task utility. Existing system-level defenses either focus on risk detection...
A novel set identifier paradigm is introduced, representing each item as a set of order-agnostic tokens, which proves SETRec’s superior efficiency and scalability on cold-start items as model sizes increase, and SETRec++’s potential in the scalability of order-agnostic identifier.
It is demonstrated that the hidden states of probed answers more effectively differentiate distinct solution paths than semantic embeddings, and the perplexity of probed answers serves as a practical proxy for reasoning correctness.
A novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world data distributions is presented, and a subset-replace strategy that efficiently provides reward signals by updating only a small subset of a generated reference set is introduced.
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