Papers by Jingru Li

3 papers
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

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Challenge: Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach: They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome: The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.
TeachMaster: Generative Teaching via Code (2026.acl-industry)

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Challenge: Existing methods for creating video content are limited by high costs and slow update cycles.
Approach: They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome: The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.
Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking (2026.acl-long)

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Challenge: Existing attacks optimize image perturbations to maximize harmful output likelihood, but suffer from slow convergence due to gradient conflict between adversarial objectives and the model’s safety-retrieval mechanism.
Approach: They propose a push-pull approach which suppresses attention to system-prompt tokens and anchors generation on adversarial image features to avoid collisions.
Outcome: The proposed approach reduces gradient conflict by 45% and achieves 94.4% attack success rate on Qwen-VL (vs. 68.8% baseline) with 40% fewer iterations.

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