Trending Research
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Through these operations based on in-context learning our framework enables the creation and evaluation of explainable computational graphs.
Do Large Language Models Need a Content Delivery Network?
lmcache/lmcache
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As the use of large language models (LLMs) expands rapidly, so does the range of knowledge needed to supplement various LLM queries.
OmniGen2: Exploration to Advanced Multimodal Generation
To facilitate the training of OmniGen2, we developed comprehensive data construction pipelines, encompassing image editing and in-context generation data.
Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation
Audio-driven human animation methods, such as talking head and talking body generation, have made remarkable progress in generating synchronized facial movements and appealing visual quality videos.
Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
hcplab-sysu/causal-vlreasoning
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Drawing inspiration from the orchestration of diverse specialized agents collaborating to tackle intricate tasks, we propose a framework named Causal-Consistency Chain-of-Thought (CaCo-CoT) that harnesses multi-agent collaboration to bolster the faithfulness and causality of foundation models, involving a set of reasoners and evaluators.
MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments
To overcome this limitation, we introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions.
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
hkust-knowcomp/autoschemakg
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We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
Eyeline-Research/FlashDepth
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A versatile video depth estimation model should (1) be accurate and consistent across frames, (2) produce high-resolution depth maps, and (3) support real-time streaming.
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.
Ranked #1 on
Image Retrieval
on AIC-ICC
Adapting Precomputed Features for Efficient Graph Condensation
Xtra-Computing/GCPA
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ICML 2025
To address this, Graph Condensation (GC) methods aim to compress large graphs into smaller, synthetic ones that are more manageable for GNN training.
