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Works138 from public data
- MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework2,626
MetaGPT is introduced, an innovative framework that incorporates efficient human workflows as a meta programming approach into LLM-based multi-agent collaboration and leverages the assembly line paradigm to assign diverse roles to various agents, thereby establishing a framework that can effectively and cohesively deconstruct complex multi- agent collaborative problems.
- Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis1,687
Video-MME is introduced, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis, and it is revealed that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models with an average accuracy of 75%, compared to 71.9% for GPT-4o.
- VITA: Towards Open-Source Interactive Omni Multimodal LLM204
VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience is introduced.
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- Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models120
This work proposes a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA), which adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters).
- Meta Architecture for Point Cloud Analysis115
This paper takes the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit, and shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta.
- Multinomial Distribution Learning for Effective Neural Architecture Search115
A Multinomial Distribution Learning for extremely effective NAS is proposed, which considers the search space as a joint multinomial distribution, and the optimal network structure is obtained by the operations with the most likely probability in this distribution.
- FastVGGT: Training-Free Acceleration of Visual Geometry Transformer108
This work proposes FastVGGT, which, for the first time, leverages token merging in the 3D domain through a training-free mechanism for accelerating VGGT, and devise a unique token partitioning strategy tailored to 3D architectures and tasks.
- PAMS: Quantized Super-Resolution via Parameterized Max Scale104
A new quantization scheme termed PArameterized Max Scale (PAMS), which applies the trainable truncated parameter to explore the upper bound of the quantization range adaptively and a structured knowledge transfer (SKT) loss is introduced to fine-tune the quantized network.
- AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration94
This work designs a unified search space that consists of all possible time steps and various architectures and introduces a two stage evolutionary algorithm to find the optimal solution in the designed search space.
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- Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale Layer77
A novel metric learning scheme, termed Normalize-Scale Layer and Decorrelated Global Centralized Ranking Loss, which achieves extremely efficient and discriminative learning, i.e., 5× speedup over triplet loss and 12% recall boost on CARS196.
- Cantor: Inspiring Multimodal Chain-of-Thought of MLLM74
It is argued that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks and an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture is proposed, demonstrating the efficacy of the proposed framework.
- AffineQuant: Affine Transformation Quantization for Large Language Models72
This paper advocates for the direct optimization using equivalent Affine transformations in PTQ (AffineQuant) using the corresponding inverse matrix to ensure equivalence between the pre- and post-quantization outputs of PTQ, thereby maintaining its efficiency and generalization capabilities.
- OMPQ: Orthogonal Mixed Precision Quantization72
This work proposes to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width and achieves high accuracy without compromise on quantization accuracy.
- Training-free Transformer Architecture Search68
Experimental results demonstrate that the TF- TAS achieves a competitive performance against the state-of-the-art manually or automatically design ViT architectures, and it promotes the searching efficiency in ViT search space greatly.
- Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval63
A novel centralized ranking loss (CRL) is introduced, which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ?
- Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding53
This work introduces Video-MME-v2, a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding, and designs a progressive tri-level hierarchy that incrementally increases the complexity of video comprehension.
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- Knowledge Prompt-tuning for Sequential Recommendation46
This work introduces an external knowledge base and proposes Knowledge Prompt-tuning for Sequential Recommendation (KP4SR), which improves NDCG@5 and HR@5 by 40.65% and 36.65%, and proposes a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem.
- Solving the Catastrophic Forgetting Problem in Generalized Category Discovery43
This work designs a novel learning approach, LegoGCD, which is seamlessly integrated into previous methods to enhance the discrimination of novel classes while maintaining performance on previously encountered known classes and effectively addresses the known category forgetting issue across all datasets.
- Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy40
An effective multi-modal training schema that starts with large language models and proceeds through vision-language alignment, general knowledge learning, and two sequential stages of long-sequence fine-tuning to serve as a competitive baseline and offer valuable insights for the open-source community in advancing long-context multi-modal understanding.
- An Information Theory-Inspired Strategy for Automated Network Pruning36
This paper introduces a new theorem to illustrate that the hidden representation should compress information with each other to achieve a better generalization, and provides rigorous proof to reveal that optimizing the normalized HSIC simultaneously minimizes the mutual information between different layers.
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- UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding34
UI-AGILE is introduced, a suite of improvements to the Reinforcement Fine-Tuning process to balance planning with speed and grounding accuracy, and a cropping-based resampling strategy to mitigate the sparse reward problem and improve learning on complex tasks.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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