Berkeley AI Research
May 8
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
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Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
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What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems
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Google AI Blog
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Cappy: Outperforming and boosting large multi-task language models with a small scorer
Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0 , FLAN , and OPT-IML .
arXiv Machine Learning
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Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting
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arXiv AI
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arXiv AI
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GLARE: A Natural Language Interface for Querying Global Explanations
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Hugging Face Trending Papers
Aug 5
CoCo-IR: Contextual Composed Image Retrieval
Current instruction-based image retrieval systems are powerful but limited to single-turn interactions, failing to capture the iterative nature of complex, real-world visual searches. To overcome this limitation, we introduce Contextual Composed Image Retrieval (CoCo-IR), a novel task that enables users to progressively refine search results through interactions.
Berkeley AI Research
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RL without TD learning
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer . Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges ), and scales well to long-horizon tasks.
Hugging Face Trending Papers
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Hierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning
Traditional image captioning methods often struggle to generate comprehensive, context-rich descriptions, especially for details not directly observable from visual cues. To overcome this, we propose a novel retrieval-augmented image captioning framework that generates captions with deeper insights, such as object attributes, event context, and underlying significance, by leveraging external knowledge.
arXiv AI
Jul 7
Correlation-Weighted Multi-Reward Optimization for Compositional Generation
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arXiv AI
Jun 9
IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment
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