While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric problems. This discrepancy motivates us to investigate the root cause: is the bottleneck of multimodal geometric reasoning rooted in reasoning itself, or in the perception of geometric information from diagrams? To answer this question, we conduct an exploratory experiment and find that providing high-quality captions consistently and substantially boosts performance of MLLMs, empirically validating the visual perception bottleneck in geometric reasoning. However, MLLMs’ capabilities in visual geometric perception remain insufficiently evaluated. To fill the gap, we propose CapGeo-Bench, a benchmark of 4,641 high-quality figure–caption pairs equipped with a fine-grained keypoint-based evaluation metric. Specifically, CapGeo-Bench encompasses three geometric classes and is devided into four difficulty levels according to the complexity of geometric primitives. Captions are scored at a fine-grained level by extracting keypoints along three dimensions-geometric elements, spatial relationships, and numerical attributes. CapGeo-Bench provides a reliable instrument and guideline for evaluating and advancing MLLMs’ geometric perception capabilities. Our code and data are publicly available at: https://github.com/YuYingLi0/CapGeo.

Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the expert solutions already present in training corpora or require expensive offline annotation. We propose Gradient-Aligned Reward (GAR), which operates in the policy’s own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9% wall-clock overhead. We prove that this cosine admits a multiplicative decomposition into prediction-error and activation-pattern factors, providing a concrete characterization of what the alignment signal measures. On Qwen3-4B and Qwen3-8B, GAR consistently improves over GRPO and other baselines on competition-level math benchmarks and transfers to GPQA Diamond and MMLU-Pro without domain-specific data. Code and data are available at https://github.com/LQgdwind/GAR .

The application of autonomous agents to scientific literature review is often constrained by the reliability of online search infrastructures and linear text generation. To address these limitations, we introduce SciLENS (Scientific Localized Evidence Navigation and Synthesis), an autonomous agent framework operating entirely upon a localized dual-tier data infrastructure for deterministic access to massive academic corpora. To mitigate online dependency, SciLENS utilizes a comprehensive offline toolbox featuring topological graph traversal and structural visualization. This empowers the agent to navigate deep citation networks and organize macro-level trends into structured visual representations, mitigating context exhaustion. To train this system without human annotation, we develop an automated multi-hop data synthesis pipeline validated by cross-model consensus, followed by rubric-based reinforcement learning to align the logical planning of the agent. Evaluations across standard tasks and newly constructed complex synthesis benchmarks demonstrate that SciLENS achieves robust factual precision, outperforming existing open-source baselines and achieving performance comparable to frontier proprietary models. Our source code and data are released at https://anonymous.4open.science/r/SciLENS-48DB .

The dominant Fill-in-the-Middle (FIM) paradigm for code completion is constrained by its rigid inability to correct contextual errors and reliance on unaligned, insecure Base models. While Chat LLMs offer safety and Agentic workflows provide flexibility, they suffer from performance degradation and prohibitive latency, respectively. To resolve this dilemma, we propose Search-and-Replace Infilling (SRI), a framework that internalizes the agentic verification-and-editing mechanism into a unified, single-pass inference process. By structurally grounding edits via an explicit search phase, SRI harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing. We synthesize a high-quality dataset, SRI-200K, and fine-tune the SRI-Coder series. Extensive evaluations demonstrate that with minimal data (20k samples), SRI-Coder enables Chat models to surpass the completion performance of their Base counterparts. Crucially, unlike FIM-style tuning, SRI preserves general coding competencies and maintains inference latency comparable to standard FIM. We release our dataset and models, establishing SRI as a robust, secure, and efficient alignment recipe for next-generation interactive development.

Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce RealChart2Code, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on RealChart2Code reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions.

A core barrier preventing recommender systems from reaching their full potential lies in the inherent limitations of user-item interaction data: (1) Sparse user-item interactions, making it difficult to learn reliable user preferences; (2) Traditional contrastive learning methods often treat negative samples as equally hard or easy, ignoring the informative semantic difficulty during training. (3) Modern LLM-based recommender systems, on the other hand, discard all negative feedback, leading to unbalanced preference modeling. To address these issues, we propose LAGCL4Rec, a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. Our approach operates through three stages: (i) Data-Level: augmenting sparse interactions with balanced positive and negative samples using LLM-enriched profiles; (ii) Rank-Level: assessing semantic difficulty of negative samples through LLM-based grouping for fine-grained contrastive learning; and (iii) Rerank-Level: reasoning over augmented historical interactions for personalized recommendations. Theoretical analysis proves that LAGCL4Rec achieves effective information utilization with minimal computational overhead. Experiments across multiple benchmarks confirm our method consistently outperforms state-of-the-art baselines.