Molecular property prediction is fundamental to drug discovery and materials science, yet existing models suffer from limited interpretability and poor cross-task generalization. Without explainable chemical rationales, accurate predictions alone are insufficient for guiding costly downstream experiments. We propose MppReasoner, a multimodal large language model that incorporates verifiable chemical reasoning for molecular property prediction. Built on Qwen2.5-VL-7B-Instruct, MppReasoner integrates 2D molecular images with SMILES strings through a two-stage training strategy: supervised fine-tuning (SFT) on 16,000 expert-guided reasoning trajectories, followed by Reinforcement Learning from Principle-Guided Rewards (RLPGR). RLPGR applies rule-based rewards verified by RDKit to evaluate chemical principle usage, structural analysis, and logical consistency, providing objective feedback without human preference labels. Experiments across 8 benchmark datasets show that MppReasoner outperforms the strongest baseline by 2.95% and 5.49% on in-distribution and out-of-distribution tasks, respectively. Beyond predictive accuracy, MppReasoner generates chemically grounded reasoning traces, with expert evaluation confirming high reasoning quality across three assessed dimensions. Code is available at https://github.com/Jesse-zjx/MPPReasoner.

Multi-objective alignment for text-to-image generation is commonly implemented via static linear scalarization, but fixed weights often fail under heterogeneous rewards, leading to optimization imbalance where models overfit high-variance, high-responsiveness objectives (e.g., OCR) while under-optimizing perceptual goals. We identify two mechanistic causes: variance hijacking, where reward dispersion induces implicit reweighting that dominates the normalized training signal, and gradient conflicts, where competing objectives produce opposing update directions and trigger seesaw-like oscillations. We propose APEX (Adaptive Priority-based Efficient X-objective Alignment), which stabilizes heterogeneous rewards with Dual-Stage Adaptive Normalization and dynamically schedules objectives via 𝒫³ Adaptive Priorities that combine learning potential, conflict penalty, and progress need. On Stable Diffusion 3.5, APEX achieves improved Pareto trade-offs across four heterogeneous objectives, with balanced gains of +1.31 PickScore, +0.35 DeQA, and +0.53 Aesthetics while maintaining competitive OCR accuracy, mitigating the instability of multi-objective alignment.

Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data.In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis & Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including biology, chemistry, material, and medicine.To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement. We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis.SciAssess and its resources are available at https://github.com/sci-assess/SciAssess.