ExtraVAR enables resolution extrapolation in visual autoregressive models by stage-aware RoPE remapping and entropy-driven attention scaling, suppressing repetition and detail loss.
Extending llms’ context window with 100 samples
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Continued pre-training with balanced long-document VQA data extends a 7B LVLM to 128K context, improving long-document VQA by 7.1% and generalizing to 512K without further training.
AdaSplash-2 introduces a histogram-based initialization for the α-entmax normalizer that cuts iterations to 1-2 and, with a sparsity-aware GPU kernel, matches or beats FlashAttention-2 training speed at moderate-to-high sparsity while delivering long-context gains.
CORE is a lightweight two-stage prompt compression method for edge-device RAG QA that builds answer and clue sets via NER and semantic matching then refines them to deliver higher accuracy and lower resource costs than baselines.
citing papers explorer
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ExtraVAR: Stage-Aware RoPE Remapping for Resolution Extrapolation in Visual Autoregressive Models
ExtraVAR enables resolution extrapolation in visual autoregressive models by stage-aware RoPE remapping and entropy-driven attention scaling, suppressing repetition and detail loss.
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Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context
Continued pre-training with balanced long-document VQA data extends a 7B LVLM to 128K context, improving long-document VQA by 7.1% and generalizing to 512K without further training.
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AdaSplash-2: Faster Differentiable Sparse Attention
AdaSplash-2 introduces a histogram-based initialization for the α-entmax normalizer that cuts iterations to 1-2 and, with a sparsity-aware GPU kernel, matches or beats FlashAttention-2 training speed at moderate-to-high sparsity while delivering long-context gains.
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Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices
CORE is a lightweight two-stage prompt compression method for edge-device RAG QA that builds answer and clue sets via NER and semantic matching then refines them to deliver higher accuracy and lower resource costs than baselines.