16 papers
Test-Time Agent Evolution for Long-Horizon Legal Reasoning
Haotian Chen, Shuaicheng Niu, Haocong Rao +5
Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. However, real-world legal deploym…
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
Yunbei Zhang, Zijian Jin, Yuanzhe Liu +14
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previ…
Geometry-Aware Test-Time Learning for Quantitative Spatial Reasoning
Gege Zhang, Shuaicheng Niu, Gang Dai +2
Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space from a 2D image and a natura…
Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models
Yiwen Liang, Hui Chen, Yizhe Xiong +7
Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined with vision-language models. H…
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Sarthak Kumar Maharana, Shambhavi Mishra, Yunbei Zhang +6
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data…
Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
Gang Dai, Yining Huang, Yiming Xia +2
Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solutions largely restrict their sear…
EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
Guohao Chen, Shuaicheng Niu, Geng Li +4
Test-time model evolution offers a promising way for deployed models to improve from unlabeled test-time experience, yet most existing methods depend on backpropagation (BP), which…
Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models
Yunbei Zhang, Shuaicheng Niu, Chengyi Cai +2
Test-Time Adaptation (TTA) for black-box models accessible only via APIs remains a largely unexplored challenge. Existing approaches such as post-hoc output refinement offer limite…
ZOTTA: Test-Time Adaptation with Gradient-Free Zeroth-Order Optimization
Ronghao Zhang, Shuaicheng Niu, Qi Deng +3
Test-time adaptation (TTA) aims to improve model robustness under distribution shifts by adapting to unlabeled test data, but most existing methods rely on backpropagation (BP), wh…
POEM: Explore Unexplored Reliable Samples to Enhance Test-Time Adaptation
Chang'an Yi, Xiaohui Deng, Shuaicheng Niu +1
Test-time adaptation (TTA) aims to transfer knowledge from a source model to unknown test data with potential distribution shifts in an online manner. Many existing TTA methods rel…
Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations
Yiwen Liang, Hui Chen, Yizhe Xiong +7
Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable, which has motiva…
Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Runhao Zeng, Qi Deng, Ronghao Zhang +4
Test-time adaptation (TTA) aims to boost the generalization capability of a trained model by conducting self-/unsupervised learning during the testing phase. While most existing TT…
DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
Jueqing Lu, Yuanyuan Qi, Xiaohao Yang +8
The performance of Visio-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete infor…
Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers
Qi Deng, Shuaicheng Niu, Ronghao Zhang +4
Test-time adaptation (TTA) aims to fine-tune a trained model online using unlabeled testing data to adapt to new environments or out-of-distribution data, demonstrating broad appli…
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
Jingyuan Zhang, Yiyang Duan, Shuaicheng Niu +2
Federated Domain Adaptation (FDA) is a Federated Learning (FL) scenario where models are trained across multiple clients with unique data domains but a shared category space, witho…
DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
Yunbei Zhang, Akshay Mehra, Shuaicheng Niu +1
Continual Test-Time Adaptation (CTTA) seeks to adapt source pre-trained models to continually changing, unseen target domains. While existing CTTA methods assume structured domain…