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Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they often fail to recover once their reasoning enters an erroneous path. Existing test-time fusion methods rely on local fusion signals to determine when to trigger fusion, w...
It is demonstrated that LLMs struggle to handle negation effectively, applying affirmative knowledge inappropriately, leading to hallucinations that contradict the negated input, compromising the logical consistency and factual accuracy of LLM outputs.
Directional verification mitigates the transfer of errors from teacher-generated answers to students by providing more accurate training targets, and introduces directional label distillation, in which frozen teachers score candidate answers in that known direction and the best-scoring candidate becomes the student's t...
Experimental results show that GLIME significantly improves knowledge generalization in lifelong editing settings while maintaining both editing performance and general capabilities.
Redline is a finite-sample procedure that selects operating points, hand-picked or learned, from the correctness of their answers on calibration prompts, and keeps the reference-relative risk, the joint probability that the reference answers correctly and a candidate configuration does not, within a user-chosen budget...
GLANCE is presented, the first one-pass block drafter that is lossless on an unmodified VLM target, and it breaks the cycle at both ends of a self-defeating cycle.
Because the signal spans a contiguous layer band, LayerMix aggregates it to match oracle-layer performance without oracle access, and characterize the geometry within the controlled paired-example paradigm.
This work shows that training a strong instruction-tuned reasoning model on its own answer-conditioned chains sharply lowers its verifiable-reasoning accuracy, and generates answer-blind data, because no correctness filter can see this damage in the data.
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