Papers by Juntian Zhang
The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents (2025.emnlp-main)
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| Challenge: | Existing studies assume fake news is inherently existing rather than exploring its gradual formation. |
| Approach: | They propose a Large Language Model-based simulation approach explicitly focusing on fake news evolution from real news. |
| Outcome: | The proposed framework captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. |
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)
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| Challenge: | Existing platforms lack a mechanism for user actions to dynamically reshape the environment. |
| Approach: | They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism. |
| Outcome: | The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty. |
Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026.acl-long)
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| Challenge: | Recent latent reasoning methods suffer from a bandwidth bottleneck . explicit textual rationales suffer from premature semantic collapse . |
| Approach: | They propose a new paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance among latent reasoning methods surpassing the strong baseline Monet by 5.03% on average. |
Detecting AI-Generated Video: A Vision–Language Dual-View Survey (2026.findings-acl)
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| Challenge: | realism of AI-generated Videos (AIGC-V) rendering artifact-centric detection insufficient, authors argue . a vision–language dual-view taxonomy is proposed to systematize this rapidly evolving field . |
| Approach: | They propose a Vision–Language Dual-View taxonomy to systematize AIGC-V detection . they propose realism of AI-generated Videos is rendering traditional inspection insufficient . |
| Outcome: | The proposed model aims to show that the existing methods are consistent with real-world facts. |
Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains (2025.acl-long)
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| Challenge: | Existing vision-language models struggle to disentangle information scattered across complex visual inputs, leading to performance degradation. |
| Approach: | They propose a focus-centric visual chain paradigm that enhances VLMs’ perception, comprehension, and reasoning abilities in multi-image scenarios. |
| Outcome: | The proposed approach achieves average performance gains of 3.16% and 2.24% across two distinct model architectures, without compromising the general vision-language capabilities. |
DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain (2026.acl-long)
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| Challenge: | Existing vision-language models lack fine-grained classification, single-view imagery, and inaccurate metadata. |
| Approach: | They propose a hierarchical, multi-view benchmark to evaluate VLMs across three levels of cognitive complexity. |
| Outcome: | The proposed benchmark evaluates vision-language models across three levels of complexity . it systematically identifies five primary failure modes . the proposed benchmarks are available on https://github.com/meituan/DiningBench. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
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| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |