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.
Progressive supernet training for efficient visual autoregressive mod- eling
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
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
-
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.
-
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.
-
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.