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Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verification and persistent generation loops. Such uncontrolled reasoning increases inference cost and creates risks of resource exhaustion and service degradation. However,...
Inspired by long-term memory in human listening, Long-Term Memory-Guided Audio Enhancement (LTM-AE) is proposed to improve selective target perception by refining the audio representations of ALLMs without training to improve selective target perception.
X-R OUTER is presented, a dual-axis routing framework that separates retrieval necessity from reasoning necessity under a user-defined cost–quality trade-off and reduces QA benchmarks across six QA benchmarks.
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