IntentVLA is introduced, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation and improves rollout stability and outperforms strong VLA baselines.
Shijie Lian, Bin Yu, Xiaopeng Lin et al.· arXiv.org· 6 citations
The authors' layer-resolved SR analysis reveals an Alignment-Aggregation Divergence: visual structure is preserved as a stable ``Structural Plateau''within the backbone, while the final layers reshape it into a sparse, query-aligned form unsuitable for pruning.
Zhu Liu, Ziyu Hu, Yao Zhang et al.· 7 citations· ⚡2
EA-SLT is proposed, a gloss-free end-to-end framework that uses facial dynamics to provide complementary cues to manual signals and achieves state-of-the-art BLEU performance among gloss-free methods, while targeted analyses support improved translation of facial-sensitive utterances.
This work introduces DEER-3D, an error-driven framework that diagnoses grounding failures and generates targeted counterfactual training supervision via a structured "Decompose, Diagnose, Edit, and Retrain"loop, and underscores the effectiveness of targeted, error-driven scene editing in bridging linguistic reasoning w...
Yue Zhang, Zun Wang, Han Lin et al.· arXiv.org· 0 citations
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This work proposes CaRGo-T (Causal Reasoning Graph-of-Thought), a reasoning framework that represents the causal and contextual relationships underlying multimodal humor as a lightweight graph-based reasoning structure.
Abhilash Nandy, Rahul Seetharaman, Aman Bansal et al.· 0 citations
It is shown that a lightweight LLM-only DPO update on tiny single-object-pair synthetic data mitigates the bias, lifting four-way robust accuracy by up to 100 points on synthetic data, and by 68.1 points on broader evaluation datasets WhatsUp, SpatialMQA-Direct, and VSR.
Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding rhetorical hate mechanisms with target community features and preventing causal evaluation of model vulnerabilities. To address this, we introduce FBHM, a systematically...
UReason is introduced, a benchmark for evaluating reasoning-to-generation alignment in this paradigm, consisting of 2,000 human-curated and human-verified instances spanning five reasoning-intensive tasks: Code, Arithmetic, Spatial, Attribute, and Text, and it is found that decontextualized generation consistently outp...
Cheng Yang, Chufan Shi, Bo Shui et al.· 5 citations
This work introduces EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games, and evaluates memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines.
Xinze Li, Zi-Yue Zhu, Siyuan Liu et al.· arXiv.org· 7 citations· ⚡1
The introduction of MM-BrowseComp, a novel benchmark comprising 400 challenging, hand-crafted questions designed to evaluate multimodal retrieval and reasoning capabilities, is introduced, establishing MM-BrowseComp as a rigorous new standard for the field.
Shilong Li, Xingyuan Bu, Wenjie Wang et al.· arXiv.org· 37 citations· ⚡7
This work demonstrates that a VLM-based pipeline can successfully unlock historical auction catalogs for large-scale automated analysis, and benchmark the methods across different deployment modes ranging from commercial providers to locally hosted, quantized models.
Mathias Zinnen, Alisha Mund, Sabine Lang et al.· 0 citations
Experimental results show that the proposed model can not only recognize characters in document images but also locate word boundaries, removing the need for an extra word segmentation step in a conventional sequential pipeline.
Marry Kong, Rina Buoy, Sovisal Chenda et al.· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
