Trending Research
BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
Building on our innovative model design, training recipe, and datasets, we develop BLIP3-o, a suite of state-of-the-art unified multimodal models.
Aligning Anime Video Generation with Human Feedback
Existing reward models, designed primarily for real-world videos, fail to capture the unique appearance and consistency requirements of anime.
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes.
Parallel Scaling Law for Language Models
qwenlm/parscale
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We apply $P$ diverse and learnable transformations to the input, execute forward passes of the model in parallel, and dynamically aggregate the $P$ outputs.
Fully Open Source Moxin-7B Technical Report
Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities.
FastVLM: Efficient Vision Encoding for Vision Language Models
apple/ml-fastvlm
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At different operational resolutions, the vision encoder of a VLM can be optimized along two axes: reducing encoding latency and minimizing the number of visual tokens passed to the LLM, thereby lowering overall latency.
Thinkless: LLM Learns When to Think
vainf/thinkless
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Reasoning Language Models, capable of extended chain-of-thought reasoning, have demonstrated remarkable performance on tasks requiring complex logical inference.
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
We hope OpenThinkIMG can serve as a foundational framework for advancing dynamic, tool-augmented visual reasoning, helping the community develop AI agents that can genuinely "think with images".
MASS: Multi-Agent Simulation Scaling for Portfolio Construction
LLM-based multi-agent has gained significant attention for their potential in simulation and enhancing performance.
WorldPM: Scaling Human Preference Modeling
Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference modeling.
