Multimodal

PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding
Abstract: We present PromptCD, a test-time method for controlling LLM behavior without additional training. The approach creates paired positive and negative guiding prompts for a target behavior and contrasts model responses at the token-probability level for LLMs and through visual attention patterns for VLMs.
Baolong Bi, Yuyao Ge 葛钰峣, Shenghua Liu, Yuchen He, Siqian Tong, Lizhe Chen, Lingrui Mei, Zehao Li, Yiwei Wang, Yujun Cai, Ming-Hsuan Yang, Xueqi Cheng
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PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding
Kimi K2.5: Visual Agentic Intelligence
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. The model focuses on …
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Kimi K2.5: Visual Agentic Intelligence
A Survey of Vibe Coding with Large Language Models
The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding …
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A Survey of Vibe Coding with Large Language Models
Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in …
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Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning