The paper introduces BdSLIG, the first Bengali Sign Language Instruction Generation dataset, aimed at evaluating Vision Language Models on under-resourced SLIG tasks and long-tail visual concepts. It proposes Sign Parameter-Infused (SPI) prompting, which embeds standard sign parameters such as hand shape, motion, and orientation into textual prompts to improve zero-shot performance and produce more structured, reproducible instructions. The work seeks to promote inclusivity and advance sign language learning systems for under-resourced communities.
By Md Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna, Md Kamrul Hasan, Hasan Mahmud
Vision-language-action (VLA) models are expected to follow instructions like their language backbones, yet prior work has focused on What-level instructions that specify the task rather than How-level...
arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.
By Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy
The paper investigates whether vision‑language‑action (VLA) models truly follow How‑level instructions that detail how to perform a task. The authors find that current VLAs do not benefit from such instructions and that task success actually decreases. They propose training with segment‑level enriched instructions and a new benchmark, SIMPLER‑IN, to better assess How‑level instruction following, but conclude that this remains an open challenge.
By Sangwu Park, Wonjoong Kim, Yeonjun In, Chanyoung Park
The paper introduces V‑GIFT, a lightweight method that enhances visual instruction tuning for multimodal large language models by incorporating a small set of visually grounded self‑supervised tasks expressed as natural language instructions. These tasks—such as rotation prediction, color matching, and cross‑view correspondence—are reformulated into image‑instruction‑response triplets, providing supervision that cannot be solved without visual evidence. The approach requires no human annotations, architectural changes, or extra training stages, and consistently improves vision‑centric performance across various models, regimes, and benchmarks when only 3‑10% of the training data is augmented with these instructions.
By Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris
arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.
By Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen
Vision-language-action (VLA) models commonly adopt an LLM-centric $V \to L \to A$ pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.
arXiv:2512. 20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects.
By Sangoh Lee, Sangwoo Mo, Wook-Shin Han
arXiv:2610.09718v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models acquire broad manipulation capabilities via large-scale pretraining, yet eliciting them through language requires...
By Masatoshi Tateno, Takehiko Ohkawa, Yueh-Hua Wu, Hanlong Li, Tatsuya Matsushima, Yoichi Sato, Kei Ota
The paper explores soft prompting for few‑shot object detection with vision‑language models, showing that optimizing a small number of continuous prompt tokens—especially when placed at the cross‑modal boundary and initialized from an empty space token—can match LoRA performance while training far fewer parameters. Soft prompting also avoids catastrophic forgetting, transfers to newer models, and can be verbalized into readable prompts. The study extends these findings to manipulation tasks, indicating that VLMs already contain much of the necessary knowledge for specialized domains, and the main challenge is learning how to ask for it.
By Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang, Cesar Daniel Hernandez, Wei Zhao, Wolfgang M. Pauli, John Galeotti, Deva Ramanan
The paper introduces MIME, a task‑agnostic visual in‑context learning (vICL) dataset that removes textual shortcuts by omitting explicit rules linking demonstration images to answers, forcing models to infer the rule visually. Fine‑tuning vision‑language models on MIME boosts vICL accuracy by 8.1–18.0 points across three VLMs while preserving single‑image performance and improving multi‑image and text‑only in‑context learning. The authors also propose DTAD, a technique that randomly drops demonstration labels during training to encourage visual inference, achieving comparable accuracy with more consistent attention to demonstration images and outperforming frontier models on the TrueMICL benchmark.
By Nimrod Shabtay, Asaf Yehudai, Avihu Dekel, Eli Schwartz, Raja Giryes
The paper "ActionCodec: What Makes for Good Action Tokenizers" addresses the lack of design guidance for action tokenization in Vision‑Language‑Action (VLA) models. By deriving information‑theoretic principles—maximizing temporal token overlap, reducing vocabulary redundancy, boosting multimodal mutual information, and ensuring token independence—the authors propose ActionCodec, a tokenizer that improves training efficiency and VLA performance. Experiments show that a SmolVLM2‑2.2B fine‑tuned with ActionCodec reaches a 95.5 % success rate on LIBERO, rising to 97.4 % with architectural tweaks, setting a new state‑of‑the‑art for VLA models without robotics pre‑training.
By Zibin Dong, Yicheng Liu, Shiduo Zhang, Baijun Ye, Yifu Yuan, Fei Ni, Jingjing Gong, Xipeng Qiu, Hang Zhao, Yinchuan Li, Jianye Hao