Haojie Yin

Qiuyu Tian, Zequn Liu, Yingce Xia et al.

AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.

Haojie Yin, Chengcheng Feng, Tianyi Liu et al.

Mean Deviation (MD) is a critical metric for assessing visual field loss in ophthalmology. While previous work has focused solely on predicting MD from Optical Coherence Tomography (OCT), it is intuitive to assume that combining OCT with another imaging of fundus photography (FP) could improve performance, as two ophthalmic medical imaging provide complementary information. This is particularly expected when sophisticated multi-objective optimization is applied, as documented in common multimodal classification. Surprisingly, our investigations reveal that multimodal fusion in this medical imaging scenario performs worse than unimodal model. Through detailed analysis, we identify the root cause as a coupled imbalance between data distribution and modality learning conflict. This imbalance distorts the optimization landscape, leading to unstable training. To address this challenge, we propose the method of Rebalanced MultiModal Mean Deviation Regression (Re-M3Dr), a novel multimodal regression framework. We enhance unimodal representation through adaptive margin based supervised contrastive learning. Then, our framework stabilizes the joint optimization with the sharpness-aware gradient modulation. Experimental results on both public and private clinical datasets show average 29\% reduction in MSE compared to SOTA multimodal learning methods, demonstrating the superiority of Re-M3Dr. The code is available in the supplementary materials.

Chengcheng Feng, Haojie Yin, Yucheng Jin et al.

Comic understanding presents a significant challenge for Multimodal Large Language Models (MLLMs), as the intended meaning of a comic often emerges from the joint interpretation of visual, textual, and social cues. This naturally motivates Chain-of-Thought (CoT) prompting, since explicit intermediate reasoning appears promising for integrating such heterogeneous signals. However, existing CoT methods are poorly matched to this structure: they tend to force interpretation into a single reasoning path before multiple cues have been jointly considered, often degrading performance, especially for small MLLMs. Our key idea is to explicitly preserve multi-cue interpretation during supervision construction, rather than collapsing comic understanding into a single reasoning chain. To this end, we propose a two-stage framework for faithful comic reasoning in small MLLMs. First, we introduce MoCoT, a modular supervision construction framework that preserves multi-cue interpretation and turns it into more faithful supervision. Second, we propose VERA, a structured reward mechanism that turns such supervision into faithful reasoning behavior by aligning optimization with both reasoning faithfulness and answer correctness. Extensive experiments on five benchmarks spanning comic understanding and broader humor-centric and abstract visual reasoning tasks demonstrate that our framework achieves strong results in the $\leq$ 4B regime, surpasses several 7B baselines, improves four small MLLMs by an average of $\mathbf{12.1%}$ as a plug-in, and consistently enhances reasoning faithfulness while preserving inference efficiency.

Chengcheng Feng, Mu He, Qiuyu Tian et al.

As deep learning technology continues to advance, image generation models, especially models like Stable Diffusion, are finding increasingly widespread application in visual arts creation. However, these models often face challenges such as overfitting, lack of stability in generated results, and difficulties in accurately capturing the features desired by creators during the fine-tuning process. In response to these challenges, we propose an innovative method that integrates Singular Value Decomposition (SVD) into the Low-Rank Adaptation (LoRA) parameter update strategy, aimed at enhancing the fine-tuning efficiency and output quality of image generation models. By incorporating SVD within the LoRA framework, our method not only effectively reduces the risk of overfitting but also enhances the stability of model outputs, and captures subtle, creator-desired feature adjustments more accurately. We evaluated our method on multiple datasets, and the results show that, compared to traditional fine-tuning methods, our approach significantly improves the model's generalization ability and creative flexibility while maintaining the quality of generation. Moreover, this method maintains LoRA's excellent performance under resource-constrained conditions, allowing for significant improvements in image generation quality without sacrificing the original efficiency and resource advantages.