Test-Time Scaling for VLMs

VLM: Vision-Language Model

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Architectural Sampling: Test-Time Scaling via Computational Diversity in Frozen Vision-Language Models
Oct 1, 2026Akshit Singh, Shyam Marjit, Wei Lin +2Test-Time Scaling for VLMs Large Vision-Language Models
Dartmouth College · IISc Bangalore · Xero
Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning
Sep 28, 2026Hao-Xuan Ma, Yihao Liu, Yutao Sun +8Test-Time Scaling for VLMs Test-Time Scaling
Qwen Business Unit of Alibaba · School of Artificial Intelligence, Nanjing University · National Key Laboratory for Novel Software Technology, Nanjing University +5
It's the Decoding Format, Not the Perturbation: Auditing Consistency-Based Selection for Vision-Language Test-Time Scaling
Aug 2, 2026Puzhuo Zheng, Hasan KurbanVLM Evaluation Self-Consistency Decoding
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Jul 21, 2026Feinan Cheng, Dongliang Xu, Wenli Nong +4Vision-Language Models UAV Navigation
Shandong University Weihai, China · China University of Petroleum (East China) Qingdao, China · MBZUAI Abu Dhabi, United Arab Emirates
Test-Time Scaling for Small VLMs on Multilingual Visual MCQ
Jul 10, 2026Spiros Baxevanakis, Peng-Jian YangVisual Question Answering Test-Time Scaling for VLMs
University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
On Test-Time Scaling for Vision-Language Models
Jun 27, 2026Fawaz Sammani, Tzoulio Chamiti, Nikos DeligiannisVLM Evaluation Test-Time Scaling for VLMs
ETRO Department, Vrije Universiteit Brussel, Belgium · imec, Kapeldreef 75, B-3001 Leuven, Belgium
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jun 10, 2026Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7Test-Time Scaling for VLMs Inference-Time Scaling
Stanford University · University of Waterloo · NVIDIA
AVIS: Adaptive Test-Time Scaling for Vision-Language Models
Jun 10, 2026Ahmadreza Jeddi, Minh Ngoc Le, Amirhossein Kazerouni +8Test-Time Adaptation of VLMs Efficient VLM Inference
AI Center-Toronto, Samsung Electronics · University of Toronto · Vector Institute +1
Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
May 29, 2026Yijie Tong, Yifan Hou, Shaobo Cui +2VLM Evaluation Vision-Language Models
ETH Zürich · Shanghai Jiao Tong University · EPFL
Test-Time Hinting for Black-Box Vision-Language Models
May 13, 2026Kaihua Hou, Abhijith Varma Mudunuri, Jiaxing Qiu +3Visual Question Answering VLM Adaptation
UC Berkeley · UCSF · University of Virginia +1
SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
Feb 6, 2026Niccolo Avogaro, Nayanika Debnath, Li Mi +6Visual Question Answering Efficient VLM Inference
ETH Zürich · IBM Research · MIT-IBM Watson AI Lab.
CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation
Jan 12, 2026Chaoyu Li, Fei Tao, Pooyan FazliTest-Time Scaling for VLMs Test-Time Scaling
Arizona State University · NewsBreak
Understanding the Effects of Distractors on Reasoning Vision-Language Models
Nov 26, 2025Jiyun Bae, Hyunjong Ok, Sangwoo Mo +1VLM Evaluation Visual Question Answering
Pohang University of Science and Technology (POSTECH)

Oct 1, 2026, cs.CVJ/K move · Enter open · S save

Architectural Sampling: Test-Time Scaling via Computational Diversity in Frozen Vision-Language Models

Akshit Singh, Shyam Marjit, Wei Lin, Leonid Karlinsky+1

Dartmouth College · IISc Bangalore · Xero

Test-Time Scaling for VLMs Large Vision-Language Models Diverse Text Generation

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Test-time scaling often seeks better answers by sampling multiple responses from a frozen model, yet conventional temperature sampling generates every candidate along the same fixed computation path. We introduce architectural sampling, a training-free method that generates candidates through distinct forward computations by reusing selected blocks of decoder layers. Varying the block location and repetition count introduces computational diversity without updating model weights or adding auxiliary parameters. Across five Qwen checkpoints and twelve multimodal benchmarks, architectural sampling improves pass@9 over standard-path temperature sampling by 6.58 percentage points on average at the same nine-candidate budget. Reusing early layers yields the strongest gains, and the improvement in candidate coverage persists even under greedy decoding. The resulting candidates show lower lexical overlap and improve accuracy when used as rollouts for label-free test-time reinforcement learning. These findings extend the benefits of our architectural sampling beyond candidate coverage, demonstrating more effective learning from a model's own outputs.

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