SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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Investigating the Effectiveness of BPE: The Power of Shorter Sequences
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2026 11representative citing papers
Introduces route-specialized dual adapters that route prompts to either an edit adapter or a locality adapter, achieving highest accuracy on CF, ZSRE, and MQuAKE benchmarks for 7B/8B models.
Byte-level simulations show subword tokenization improves LLM training mainly via increased throughput and boundary priors.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
Proposes treating Pāṇini's Astādhyāyī as a unifying computational architecture and benchmark foundation for Indic language NLP to improve accuracy, data efficiency, and transfer.
The paper presents a threat model, taxonomy, and six-dimension measurement framework for AI sandboxes to clarify valid testing claims for safety, security, and regulatory assurance.
CUCI-Net abstracts context-utterance dependency into an interpretation cue that combines local modality signals with global context and feeds it into the final multimodal interaction for context-conditioned predictions.
A multi-agent framework decomposes multimodal empathetic response generation into structured reasoning steps and uses global reflection to reduce emotional biases, outperforming prior methods on IEMOCAP and MELD benchmarks.
The paper introduces a measure of semantic entanglement in embeddings and a pipeline that improves Top-K retrieval precision from 32% to 82% on a healthcare knowledge base.
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.