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4,009 papers
The Agentic Video Auto-Encoder (AVA-Encoder), a novel auto-encoding framework driven by agentic self-evolution to learn agent-native video representations that outperforms a carefully human-tuned policy while using 74.3% fewer shot-level and keyframe-level system-prompt tokens.
This paper proposes Scaffolding Minds, a dedicated scaffolding encoder that provides an optimized target in latent space, and learns both the mean and variance of the RL sampler, and shows that these two improvements are complementary, together yielding substantial gains over strong baselines.
Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning. However, current LVLM-based IAD methods still struggle to produce precise pixel-level anomaly maps from generated language judgm...
This work presents a framework for quantizing VLMs for efficient inference on resource-constrained hardware, which combines a quantization pipeline that uses the model itself to generate training data and does not require access to the training setup, with a novel 2.7-bit-per-parameter format.
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This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.
This work fusing lower-face video with facial electromyography from the occluded upper face to classify seven emotional categories, demonstrating that upper-face EMG provides robust complementary information under HMD-induced visual occlusion and establishing a foundation for multimodal emotion recognition in naturalis...
Chehre, an emoji-prompted video dataset with a wide range of dynamic facial expressions for exploring perceptual variation, finds that persona prompting can act as a controllable way to shift model perception while helping models better capture the variation observed across human annotators.
Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot disting...
Editable Visual Design is proposed, a new paradigm driven by a Coding Agent that achieves both refined aesthetics and production-grade editability and faithfully reproduces the creative and reasoning trajectory akin to that of professional human designers.
Paired SylFishBD interventions show no significant weak-blur effect, modest losses from stronger blur/gray masking, a larger white-mask artifact, and strong species dependence.
Along the way, an implementation bug in the own AKS baseline and a 0.74 point gap between two harnesses running the same published rules at the same budget show why these comparisons need to happen inside one controlled harness rather than across papers.
VisCAD is presented, a foundation model suite designed to provide both broad generalization and strong CAD capability for realistic industrial products that includes a domain-specific harness that leverages frontier models for complex assembly generation and demonstrates advantages over general-purpose harnesses in bot...
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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