Author

M. Kaya

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Preprint Oct 2026

BatchParScale: Sparse Batch-Mixing for Sequence Models

Recent test-time scaling methods improve model performance by increasing inference-time compute. One practical form of this is parallel scaling, where a model samples multiple independent reasoning streams and aggregates their answers using methods like voting, reranking, or verification. While effective, this approach...

Hans Peter Lyngsøe Raaschou-jensen, M. Kaya, Desmond Elliott · 0 citations

Preprint Oct 2026

TraceICL: Thread-Resolved Demonstration Curation with Consistency Calibration for Robust Multimodal In-Context Learning

Interleaved multimodal in-context learning (ICL) lets frozen large vision-language models (LVLMs) acquire new tasks from a handful of image-text-answer demonstrations, yet it remains fragile: demonstrations bind to the wrong images, redundant exemplars crowd out informative ones, and noisy answers are copied verbatim i...

Y. Yılmaz, Elena Petrova, M. Kaya et al. · 0 citations