This research highlights the heightened threats to data integrity and stakeholder trust in these evolving ecosystems through an intensive examination of the literature, initiating a pioneering discourse emphasizing fostering a foundation for developing secure and trustworthy Liquid AI environments.
M. Agbese, Niko Mäkitalo, Muhammad Waseem et al.· IoT· 6 citations· ⚡1
Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers, is proposed.
M. Abbasi, T. Mikkonen, Petri Ihantola et al.· 2026 IEEE 23rd International...· 0 citations
SemanticSlider3D is presented, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training and supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
Ru Wang, Rahul Jain, Koichiro Niinuma et al.· 0 citations
FISICA is a body-assessment and recommendation service running in production that measures the avatar with the same function used on the subject and solves until the two agree, on a sampling-invariant spinal metric.
Juhwan Song, Heejung Kim, Jun Hee Noh et al.· 0 citations
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The best machines now out-see young adults here, without matching the human balance between suspicion and trust, without matching the human balance between suspicion and trust.
Sunwhi Kim, Sunyul Kim, Meounggun Jo et al.· 0 citations
GapSight is proposed, a framework for learning visual re-reading: a VLM first takes a global glance, then selectively returns to a free-form region when the question calls for local evidence, and Mechanism analyses show that the router rescues concrete wrong answers, adapts its action rate by task, and forms a favorabl...
Jin-Chang Zhu, Rong Fu, Yi-Cheng Ding et al.· 1 citation
Experiments show that PlanCraft achieves a 61.1\% lower FID than the best existing 2D method and surpasses existing 3D systems by 15 points in expert-rated spatial rationality, with a sketch at only 25% completion already outperforming all fully specified baselines.
Peng-Yu Zeng, Yu-Qin Dai, Jun Yin et al.· arXiv.org· 1 citation
Inspired by dual-coding theory, this work proposes a memory architecture that uses parallel visual and verbal codes, which it calls DualMem, and views this as a step towards memory systems that preserve a richer record of agents'observations.
This work introduces PuMVR (Punjabi Multimodal Visual Reasoning), a benchmark of 1,000 strictly parallel image-text instances across Punjabi's three active scripts and proposes the Script Consistency Rate (SCR), which falls as low as 24.8% on this benchmark, as a mandatory metric for script-agnostic evaluation to ensur...
Prabhjot Singh, Bhushan Pawar, M. Reddiboina et al.· arXiv.org· 0 citations
This work constructs two human-verified benchmarks, VRQABench for controllable spatial lookahead and OpenWorldQA for open-domain physical prediction, and proposes Privileged-Future On-Policy Self-Distillation (PF-OPSD), a model that outperforms baseline by 10.6% and 10.9% on VRQABench and OpenWorldQA, respectively, whi...
It is found that current models often generate visually coherent videos, but struggle with constraint reasoning, counterfactual grounding, physical interaction, and unsafe-instruction suppression, and results show that visual quality and surface-level instruction following are insufficient for trustworthy robotic video...
Huiqiong Li, Jia-Yu Wang, Zhiting Mei et al.· arXiv.org· 4 citations
LALS (Latent Association Leaning Score) is introduced, a zero-shot metric that projects visual-token activations into the model's text-embedding space to measure concept associations per token and layer and shows that culturally loaded visual cues further modulate these internal associations.
Arnau Marin-Llobet, Simon Henniger, M. Banaji· arXiv.org· 1 citation
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.
