From the 1 of 10 linked papers with an AI index.
10 papers
XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling
Jinxiu Liu, Xuanming Liu, Kangfu Mei +2
High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling. Existing efficient method…
SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
Jinxiu Liu, Jianru Li, Tanqing Kuang +4
The paper introduces SymbOmni, an agentic omni-model for visual generation that uses a symbolic concept memory to continuously learn and compose reusable instructions, enabling con…
Beyond the Sampled Token: Preserving Candidate Support in RLVR
Ruotian Peng, Yi Ren, Zhouliang Yu +2
We revisit exploration collapse in reinforcement learning with verifiable rewards (RLVR), from the perspective of the \emph{candidate distribution} for next-token prediction. We fo…
Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
Yuhuan Yuan, Zhouliang Yu, Minghao Liu +2
LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility. We identify a data-induced failure mode, PhysHack, in which the assemblies satisfy phy…
PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective
Yangyi Huang, Ruotian Peng, Zeju Qiu +4
Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking t…
Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation
Kexuan Shi, Hanxuan Li, Zeju Qiu +3
We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam a…