🏢 Tsinghua University

ComplexFuncBench, a new benchmark, rigorously evaluates LLMs’ complex function-calling abilities across real-world scenarios involving multi-step processes, constraints, and long contexts.

This survey paper explores the exciting new frontier of Large Reasoning Models (LRMs), focusing on how reinforcement learning and clever prompting techniques are boosting LLMs’ reasoning capabilities.

Parameter-Inverted Image Pyramid Networks (PIIP) drastically cut visual model computing costs without sacrificing accuracy by using smaller models for higher-resolution images and larger models for lo…

URSA-7B: A new multimodal model significantly improves chain-of-thought reasoning in mathematics!

This paper unveils critical thresholds for efficient visual autoregressive model computation, proving sub-quartic time is impossible beyond a certain input matrix norm while establishing efficient app…

EpiCoder revolutionizes code generation by using feature trees to create diverse and complex training data, resulting in state-of-the-art performance on various benchmarks.

MotionBench, a new benchmark, reveals that existing video models struggle with fine-grained motion understanding. To address this, the authors propose TE Fusion, a novel architecture that improves mo…

Boosting code generation accuracy with more unit tests! This research shows that increasing the number of unit tests used to evaluate code generated by LLMs significantly improves accuracy, especially…

VisionReward, a novel reward model, surpasses existing methods by precisely capturing multi-dimensional human preferences for image and video generation, enabling more accurate and stable model optimi…

New benchmarks, HumanEval Pro and MBPP Pro, reveal LLMs struggle with self-invoking code generation, highlighting a critical gap in current code reasoning capabilities.

FoPE enhances attention’s periodic extension for better length generalization in language models by addressing spectral damage in RoPE using Fourier Series and zeroing out destructive frequencies.

Distilled Decoding (DD) drastically speeds up image generation from autoregressive models by using flow matching to enable one-step sampling, achieving significant speedups while maintaining acceptabl…

ReMoE: Revolutionizing Mixture-of-Experts with fully differentiable ReLU routing, achieving superior scalability and performance.

Token-level editing prevents language model collapse from synthetic data by theoretically bounding test error and empirically improving model performance.

LLaVA-UHD v2 enhances MLLMs by integrating high-resolution visual details using a hierarchical window transformer.

Self-play method SPAR enhances LLMs instruction following abilities, beating GPT-4 on IFEval

ColorFlow, a new AI model, accurately colorizes black-and-white image sequences while preserving character identity.

SynerGen-VL: A simpler, more powerful unified MLLM for image understanding and generation.

InternVL 2.5, a new open-source multimodal LLM, surpasses 70% on the MMMU benchmark, rivaling top commercial models through model, data, and test-time scaling strategies.

GENMAC: Multi-agent collaboration revolutionizes compositional text-to-video generation, achieving state-of-the-art results by iteratively refining videos via specialized agents.