Sixun Dong (Ironieser)

Investigates the capabilities of Large Reasoning Models (like OpenAI o1/o3) on Theory of Mind tasks, revealing that continuous reasoning does not always guarantee better socially-aware outcomes.

Proposes an LLM-agent-based post-ASR correction framework for dysarthric speech recognition that focuses on semantic accuracy rather than just minimizing Word Error Rate (WER).

Proposes MMTok to efficiently select informative vision tokens via a multimodal coverage maximization strategy, significantly accelerating VLM inference while maintaining model performance.

Proposes TimesCLIP, innovatively aligning time series data with visual and textual multi-modal perspectives to effectively enhance both short-term and long-term forecasting performance.

Proposes a reward-guided hierarchical diffusion model that "sculpts" data representations from noise to generate optimal feature transformations for specific downstream tasks.

Introduces the MLLM-Tool framework and contributes a specialized dataset to empower Multimodal Large Language Models (MLLMs) with the ability to understand, learn, and invoke external tools.

Proposes a weakly supervised video representation learning approach that utilizes unaligned text for sequential videos, significantly reducing the reliance on fine-grained video-text alignment annotations.

Introduces the RepCount dataset and pioneers the use of regression density maps alongside a Transformer-based architecture to encode multi-scale temporal correlations, significantly improving repetitive action counting.