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Teacher Alignment is proposed, which directly adapts the teacher toward the student's distribution without discarding data or degrading reasoning quality, and which significantly outperforms baselines across diverse reasoning benchmarks and distillation methods.
Existing summarizers for memory systems are typically optimized for human-facing criteria such as faithfulness, which misaligns with their true objective: preserving the evidence needed to support future queries. We show that conditioning summarization on query-answer pairs substantially improves answer quality, and th...
Repository-level issue localization aims to identify and rank the files and functions relevant to resolving a reported issue. LLM agents approach this task iteratively: they identify a set of potentially relevant locations, inspect the corresponding code, and revise their judgments about these candidates as new evidenc...
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