arXiv subjects

Haojie Yin

Publications and source records attributed to Haojie Yin.

4 recordsLinked to original sources

arXiv2026

Rethinking Gradient Modulation in Multimodal Regression

Even balanced multimodal learning methods do not consistently translate additional modalities into better regression performance. To understand this limitation, we revisit the optimization mechanism of balanced multimodal learning, using MMPareto as a representative case. We reveal a previously overlooked issue: MMPareto uses a fixed gradient modulation strength throughout training, while different training stages favor different strengths; an inappropriate modulation strength can instead hinder subsequent optimization. To address this issue, we propose SGM, which adapts the modulation strength according to the current training behavior. We provide theoretical and empirical analyses to characterize and validate the modulation decisions made by SGM. We further build a two-stage multimodal regression framework that integrates AM for unimodal regression representation learning and SGM for adaptive multimodal optimization. Extensive experiments under the same total training budget demonstrate consistent improvements over strong unimodal and multimodal baselines. Our code is provided in the supplementary material.

cs.CV↗

arXiv2026

ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment

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.

cs.AI↗

arXiv2026

Towards Faithful Reasoning in Comics for Small MLLMs

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.

cs.CV↗

arXiv2024

TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation

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.

cs.CV↗