Large language models are increasingly applied to various development scenarios. However, in on-chain transaction scenarios, even a minor error can cause irreversible loss for users. Existing evaluations often overlook execution accuracy and safety. We introduce EVM-QuestBench, an execution-grounded benchmark for natural-language transaction-script generation on EVM-compatible chains. The benchmark employs dynamic evaluation: instructions are sampled from template pools, numeric parameters are drawn from predefined intervals, and validators verify outcomes against these instantiated values. EVM-QuestBench contains 107 tasks (62 atomic, 45 composite). Its modular architecture enables rapid task development. The runner executes scripts on a forked EVM chain with snapshot isolation; composite tasks apply step-efficiency decay. We evaluate 20 models with 5 independent rounds each and find large performance gaps, with split scores revealing persistent asymmetry between single-action precision and multi-step workflow completion. Code: https://github.com/OpenEdgeHQ/EVM-quest-bench.

Generating logically coherent video from text (T2V) for reasoning-intensive tasks like mathematical problem-solving presents a significant challenge for Vision-Language Models (VLMs). Therefore, we introduce VisualEDU, a benchmark based on Manim package to rigorously evaluate VLM capabilities in producing coherent, step-by-step video solutions for educational purposes, with a framework that integrates meta-prompt learning, visual and code feedback, and a modular drawing toolkit to enhance output quality. Novel metrics for temporal consistency, logical correctness, and visual clarity are proposed, and extensive experiments across nine VLMs reveal that while advanced proprietary models show promise, all struggle significantly with increasing task complexity (e.g., the performances of Claude-3.7-Sonnet and GPT-4o are below 56% on difficult tasks ), highlighting limitations in code generation, visual feedback correction and precise tool invocation. VisualEDU offers a robust platform for systematic T2V assessment in reasoning-intensive domains and guides future VLM improvements in this area.

This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of lacking personalized interactions in conversational AI, and offers a solution with detailed and emotionally nuanced character portrayals. We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas, thus enhancing the realism and complexity of language modeling interactions. Additionally, our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant. The effectiveness of RoleCraft-GLM is validated through various case studies, highlighting its versatility and skill in different scenarios. Our framework excels in generating dialogues that accurately reflect characters’ personality traits and emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks a significant leap in personalized AI interactions, and paves the way for more authentic and immersive AI-assisted role-playing experiences by enabling more nuanced and emotionally rich dialogues.