CCRV-Bench is a constraint‑driven benchmark designed to evaluate visual causal reasoning in vision‑language models on single‑image physical scenarios. It assesses four causal task dimensions—causal relation discovery, state prediction, causal diagnosis, and intervention—while applying constraints such as entity symbolization, spatial grounding, factual adversarial constraints, and minimalist output constraints to reduce shortcut learning. Experiments on 15 multimodal models reveal that constraint sensitivity varies by task and model, with intervention and spatial grounding having the largest impact and factual adversarial constraints improving causal diagnosis across models.
By Linyuan Gao, Yuan Wu, Yi Chang
arXiv:2608.23330v1 Announce Type: new
Abstract: Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human action...
By Jiapeng Li, Ping Wei, Wenjuan Han, Song-Chun Zhu, Lifeng Fan
arXiv:2608. 19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored.
By Yinfeng Wang, Zhiyuan Yao, Zheren Fu, Lei Zhang, Zhendong Mao
arXiv:2510. 01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples.
By Wa\"iss Azizian, Ali Hasan
arXiv:2605. 31272v2 Announce Type: replace Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected individuals.
By Wenshuo Dong, Jiaming Zhang, Shaopeng Fu, Hongbin Lin, Di Wang, Lijie Hu
arXiv:2602. 23197v2 Announce Type: replace-cross Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations.
By Chungpa Lee, Jy-yong Sohn, Kangwook Lee
arXiv:2610.01054v1 Announce Type: cross
Abstract: In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requi...
By Guangzhi Xiong, Zhenghao He, Bohan Liu, Sanchit Sinha, Wenqian Ye, Aidong Zhang
arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.
By Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren
arXiv:2607. 25294v1 Announce Type: cross Abstract: Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge.
By Lai Wei, Chengqi Li, Jiapeng Li, Ruina Hu, Yue Wang, Weiran Huang
The paper introduces Dual-Stream Semantic Guidance (DSSG), a framework for Source‑Fully‑Free Domain Adaptation of Vision‑Language Models that mitigates dual semantic drift through a caption stream and a class‑anchor stream. It adds a Dynamic Cross‑Modal Knowledge Distillation module and a Prototype Anchor Calibration extension (DSSG‑PAC) to reduce computation while maintaining performance. Experiments show DSSG outperforms state‑of‑the‑art methods and DSSG‑PAC cuts adaptation time by 18.9% with minimal loss in accuracy.
By Weiwei Xiang, Shun Peng, Guangyi Xiao, Hao Chen, Lei Yang
arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.
By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong
arXiv:2610.01625v1 Announce Type: new
Abstract: Federated parameter-efficient fine-tuning enables distributed clients to adapt pretrained vision-language models without sharing raw data or updating t...
By Wentao Yue, Qingyu Mao, Tianyou Lai, Ahmed M. Abdelmoniem, Qilei Li