Navigating biopharmaceutical intellectual property necessitates precisely associating visual chemical structures with their textual referents across lengthy documents. Despite its critical role in drug discovery, this multimodal coreference task remains underexplored. It presents unique challenges, including handling Markush structures and distinguishing the atom-level differences between adjacent structures. To bridge this gap, we define the multimodal Chemical Structure-Text coreference and introduce CheST, the first dataset explicitly designed for the task. Furthermore, to satisfy the strict logical consistency in the task, we propose RULER, a RULE-guided multimodal Reinforcement learning framework built upon an SFT cold start. RULER utilizes rule-driven reward functions operationalizing multidimensional consistencies, acting as a domain-specific “verifier” to obtain the correct domain knowledge. Experimental results demonstrate that RULER achieves a 40% improvement over the strongest baseline–Gemini-2.5-Pro, demonstrating the superior efficacy.

Aligning Large Language Models (LLMs) for specialized domains presents a fundamental optimization challenge: the “alignment tax.” In biomedicine, this manifests as a conflict between the need for rigorous, encyclopedic factual accuracy and the requirement for flexible, user-friendly instruction following. Existing methods, primarily relying on Supervised Fine-Tuning (SFT) or standard Reinforcement Learning from Human Feedback (RLHF), often fail to navigate this Pareto frontier, resulting in models that are either knowledgeable but rigid, or chatty but hallucination-prone. In this paper, we propose BalancedBio, a novel alignment framework that explicitly models and optimizes conflicting objectives. While utilizing standard high-quality instruction data, our contribution lies in the algorithmic innovation: (1) We introduce Capability-Aware Group Relative Policy Optimization (GRPO), which eliminates the need for a value network and reduces gradient variance in high-entropy reasoning tasks; (2) We propose a Dynamic Hybrid Reward Mechanism that adaptively balances domain correctness, reasoning validity, and format compliance during training; (3) We provide a theoretical analysis demonstrating how our method enforces gradient orthogonality to mitigate catastrophic forgetting. Extensive experiments on BIOMED-MMLU, MedQA, and IFEval show that BalancedBio-7B achieves state-of-the-art performance, surpassing Med-PaLM-8B in domain tasks while maintaining robust general instruction-following capabilities. We will release our model and partial data.

Recent advancements in Multimodal Large Language Models (MLLMs) have largely been driven by aligning visual encoders with pre-trained Large Language Models (LLMs). While effective, the geometric nature of this alignment remains under-explored. Existing methods often assume a symmetric interaction between visual and textual modalities, implying that both spaces adapt to each other. In this paper, we challenge this assumption and propose the “Text Space as Anchor” hypothesis. We argue that the semantic space of LLMs is rigid, anisotropic, and dominant; thus, effective cross-modal alignment may be an asymmetric projection of visual features onto this pre-existing text manifold without distorting it. We identify a potential issue in current parameter-efficient tuning paradigms where task-specific visual adjustments inadvertently disrupt the projector’s geometry, leading to “catastrophic forgetting” of the alignment mechanism itself. To address this, we introduce Anchor-Preserving Projection (APP), a novel method that regularizes the projector to maintain the geometric structure of the text embedding space via spectral filtering. Extensive experiments on 8 diverse cross-modal tasks and 3 pure language benchmarks demonstrate that APP preserves the LLM’s inherent linguistic capabilities (e.g., MMLU, GSM8K) and reduces object hallucination significantly better than standard fine-tuning methods. We release our code.

Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. However, a critical gap persists in reliably evaluating their curation ability—the process by which models select and integrate relevant references while filtering out noise. To address this, we introduce the benchmark for Curation of Retrieval-Augmented LLMs in Biomedicine (CRAB), the first multilingual benchmark tailored for evaluating the biomedical curation of retrieval-augmented LLMs, available in English, French, German and Chinese. By incorporating a novel citation-based evaluation metric, CRAB quantifies the curation performance of retrieval-augmented LLMs in biomedicine. Experimental results reveal significant discrepancies in the curation performance of mainstream LLMs, underscoring the urgent need to improve it in the domain of biomedicine.