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Yueting Zhuang

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Preprint Oct 2026

WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models

As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requires both preserving previously observed content and ensuring coherent world evolution over time. World memory is a critical capability to evaluate alongside visual fidel...

Nan-Yi Zhou, Jianxiong Gao, Qiuyu Wang et al. · 0 citations
#computer vision Preprint Oct 2026

ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills

Skill-augmented agents improve sample efficiency by distilling successful trajectories into reusable strategies. Yet most existing approaches remain text-centric, linearizing spatial layouts and action-state correspondences into language that loses critical geometric structure. Recent efforts have begun incorporating v...

Hong-Xing Li, Ding-Ming Li, Yi-Xin Li et al. · 0 citations
#computer vision Preprint Oct 2026

SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models

Existing spatial reasoning benchmarks mainly test spatial perception: reading off relations already visible in the input. Yet real-world spatial intelligence demands predictive spatial reasoning: constructing a scene from observations, anticipating how an intervention changes it, and reasoning about the unseen outcome....

Hong-Xing Li, Jin-Yue Su, Ding-Ming Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Seeing Parts, Reasoning about Worlds: Visual Inference under Partial Observation

This work introduces WorldScope, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice, and proposes WorldFlow, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice.

Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Reflect, Revise, Reuse: Training-Free Skill Evolution for GUI Agents

This work proposes EvoSkill-GUI, a training-free framework in which each skill is a structured multi-file package containing retrieval metadata, executable plans, backup localization, failure-recovery rules, accessibility utilities, and failure cases.

Bo-Fan Chen, Bo-Xuan Zhang, Fei Tang et al. · 2 citations
Review Aug 2026

BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes

BrowserForge is presented, a framework that generates web interaction data at scale by driving many browser sandboxes in parallel over the open web, and fine-tuning a compact multimodal model on this corpus raises its success rate on the live Online-Mind2Web from 25.66% to 33.33% and consistently improves step accuracy...

Fei Tang, Hua-Wen Shen, Zhiqiong Lu et al. · 0 citations

GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts

GSM8K-V is introduced, a benchmark transforming GSM8K into multi-image sequences with semantic equivalence preserved and error analysis shows that the primary bottleneck lies in Implicit Visual Inference Error (IVIE), where models fail to recover visual semantics that are implied rather than explicitly stated.

Fan Yuan, Yuchen Yan, Yi-Fan Jiang et al. · 8 citations
Preprint Aug 2026

TTPO: Test-Time Policy Optimization

Test-Time Policy Optimization is proposed, an asymmetric objective that distills agreeing rollouts via OPSD and penalizes disagreeing rollouts with Grouped RL and Token-level selection further refines both branches: distillation down-weights already-converged positions, while RL penalizes only confident errors.

Ao-Han Wang, Zhengxi Lu, Jian-Ze Wang et al. · 2 citations
#natural language process... Preprint Aug 2026

PaperGym: Rubric-Centered Evolution for Research-Plan Generation

This work introduces PaperGym, a unified framework that turns each research paper into a complete training environment, and releases the pipeline, the 20,000-instance corpus PaperGym-20k, and the benchmarks PaperGym-Innov and PaperGym-Design.

Yu-Han Wang, Zhengxi Lu, Yuchen Yan et al. · 2 citations
Conference Open access 2026

Experience-driven Multi-turn Reinforcement Learning for GUI Agents

EMPO achieves substantial gains over the base model and achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o, demonstrating better generalization than prior single-turn RL approaches.

Zhengxi Lu, Jiabo Ye, Fei Tang et al. · 0 citations

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