Author

Xiaoqian Liu

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#artificial intelligence Feb 2026

When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning

Leveraging reinforcement learning, MPAR$^2$ improves perception performance on CAFE from 31.74% to 63.51% and effectively mitigates perception decay, concurrently enhancing reasoning capabilities to achieve a significant 74.59% accuracy on the MMAU benchmark.

Rui Mao, Xiangnan Ma, Dan-Qi Chen et al. · arXiv.org · 1 citation

Jul 2026

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

Flow Continuous Trajectory Supervision (FlowCTS), which matches subsequent student and reference trajectories initialized from the same student-visited state to derive a temporally weighted velocity-matching upper bound and discretize it into practical objectives parameterized by the number of supervision steps.

Kaiyang Ye, Yuan Ge, Junxia Zhang et al. · arXiv.org · 0 citations

Preprint Aug 2026

Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision

A comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts is established and a dense supervision training strategy that synthesizes multiple non-interfering concepts into single image pairs is proposed that significantly enhances both training efficiency and overall model performance.

Long Cui, Xiao-Qian Liu, Qi Qin et al. · 0 citations