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A preliminary diagnostic is distill -- prefer activation-norm when output structure dominates, treat causal selectors as a hypothesis for content-dominated tasks -- and release masks, scores, predictions, and evaluation files for direct replay.
Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a controlled replay in low-resource African-language classification, we show that this proxy breaks. Across four languages (Amharic, Hausa, Swahili, Yoruba), two classification...
A new composition rule materially changes what the merged model preserves, suppresses, and calibrates in what the merged model preserves, suppresses, and calibrates.
This paper presents the methodologies and results of the NOWJ team's participation across all five tasks of the COLIEE 2026 competition and adopts a retrieval-augmented generation framework with dense retrieval, attention-based reranking, and few-shot-prompted LLM reasoning.
This work presents Q U DAR, a dual-perspective adaptive retrieval framework motivated by a systematic analysis of retrieval behavior across retriever type and query format, enabling effective integration of complementary retrieval signals while mitigating noise.
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