Many existing financial math reasoning benchmarks suffer from data contamination and high manual construction costs. To address this, we propose a novel formula-driven approach to dynamically construct math reasoning benchmarks in finance. Our two-stage approach: (1) generates single-formula questions by LLMs using a "Mask-for-Solve" paradigm for ground truth answers, and (2) synthesizes multi-formula questions through hierarchical tree-based DAGs. Our approach ensures novelty (via LLMs' creativity) and controllability of difficulty (via DAG structure). Based on a self-constructed financial formula bank, we utilize the proposed method to build FinMathBench, the first formula-driven and fully LLM-generated benchmark aimed at assessing LLMs' math reasoning abilities in finance, containing 946 questions across 4 complexity levels. Evaluation results on 40 LLMs demonstrate significant accuracy drops in multi-formula questions, e.g., 72.9% (1-Formula) to 14.0% (4-Formula) for GPT-4o under Chain-of-Thought prompting. Three critical flaws of LLMs are also observed: poor direct calculation performance, bias toward frequently solved variables in formulas, and erroneous "correction" of valid but extreme financial values. These findings highlight gaps in current LLMs' domain-specific reasoning and underscore FinMathBench's value for advancing robust financial LLMs.

Speculative decoding has emerged as a promising paradigm for accelerating large language model inference by leveraging a lightweight draft model to generate multiple candidate tokens.However, existing methods often incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding.To address this challenge, we propose EDSD, an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design.EDSD drives the draft model to progressively align with the target model in an easy-to-hard manner while establishing tokenlevel alignment as a dominant design principle.Extensive experiments on seven LLMs demonstrate that EDSD improves training efficiency by 24.8%, increases the average acceptance length by 4.0%, and achieves a 4.1% speedup compared to state-of-the-art methods.Furthermore, EDSD improves robustness to system prompt variations by more than 5×.Our findings establish entropy-driven alignment as an effective and principled foundation for efficient speculative decoding.We make our draft model weights available at https://github.com/KerwinKai/EDSD.

Visual Language Tracking (VLT) enables machines to perform tracking in real world through human-like language descriptions. However, existing VLT methods are limited to 2D spatial tracking or single-object 3D tracking and do not support multi-object 3D tracking within monocular video. This limitation arises because advancements in 3D multi-object tracking have predominantly relied on sensor-based data (e.g., point clouds, depth sensors) that lacks corresponding language descriptions. Moreover, natural language descriptions in existing VLT literature often suffer from redundancy, impeding the efficient and precise localization of multiple objects. We present the first technique to extend VLT to multi-object 3D tracking using monocular video. We introduce a comprehensive framework that includes (i) a Monocular Multi-object 3D Visual Language Tracking (MoMo-3DVLT) task, (ii) a large-scale dataset, MoMo-3DRoVLT, tailored for this task, and (iii) a custom neural model. Our dataset, generated with the aid of Large Language Models (LLMs) and manual verification, contains 8,216 video sequences annotated with both 2D and 3D bounding boxes, with each sequence accompanied by three freely generated, human-level textual descriptions. We propose MoMo-3DVLTracker, the first neural model specifically designed for MoMo-3DVLT. This model integrates a multimodal feature extractor, a visual language encoder-decoder, and modules for detection and tracking, setting a strong baseline for MoMo-3DVLT. Beyond existing paradigms, it introduces a task-specific structural coupling that integrates a differentiable linked-memory mechanism with depth-guided and language-conditioned reasoning for robust monocular 3D multi-object tracking. Experimental results demonstrate that our approach outperforms existing methods on the MoMo-3DRoVLT dataset. Our dataset and code are available at https://github.com/hongkai-wei/MoMo-3DVLT.