HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
Progressive supernet training for efficient visual autoregressive mod- eling
4 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal LLMs process code as images to achieve up to 8x token compression, with visual cues like syntax highlighting aiding tasks and clone detection remaining resilient or even improving under compression.
Sol2Vy transfers vulnerability detection from Solidity to Vyper in zero-shot fashion, outperforming prior methods on reentrancy, weak randomness, and unchecked transfers.
OmniVLA-RL uses a mix-of-transformers architecture and flow-matching reformulated as SDE with group segmented policy optimization to surpass prior VLA models on LIBERO benchmarks.
citing papers explorer
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HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling
HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
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CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding
Multimodal LLMs process code as images to achieve up to 8x token compression, with visual cues like syntax highlighting aiding tasks and clone detection remaining resilient or even improving under compression.
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Zero-Shot Vulnerability Detection in Low-Resource Smart Contracts Through Solidity-Only Training
Sol2Vy transfers vulnerability detection from Solidity to Vyper in zero-shot fashion, outperforming prior methods on reentrancy, weak randomness, and unchecked transfers.
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OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL
OmniVLA-RL uses a mix-of-transformers architecture and flow-matching reformulated as SDE with group segmented policy optimization to surpass prior VLA models on LIBERO benchmarks.