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3,740 papers
This work introduces REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates that achieves a higher median code-smell reduction with smaller edits and fewer public-method removals.
This work introduces FakeIDet3-DB, the first comprehensive database of digital manipulations on real, government-issued IDs, and proposes PACE, a Pseudo-Anonymized Contextual patch Extraction algorithm, which leverages Integral Image mapping and distance-driven Non-Maximum Suppression (NMS).
This work shows that camera motion, object trajectories, and depth can be unified into a single 3D point-track representation, from which one model performs joint camera and object control, depth editing, and motion transfer in a single forward pass, enabling interactive 4D-controllable streaming generation for the fir...
NaviDC-OCR introduces deformation-aware learning to incorporate geometric perception into VLMs and proposes an adaptive sampling mechanism for complex layout representation, and a content-structure decoupled learning strategy is developed to explicitly model formula grammars and table structures, enabling more effectiv...
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ClusterAttention is the first training-free method that can be successfully applied in the setting of unstructured input and a single forward pass, and is the first training-free method that can be successfully applied in the setting of unstructured input and a single forward pass without offline calibration.
ScaleResfusion is presented, a scalable diffusion framework for real-world image restoration built on pre-trained text-to-image rectified-flow models that achieves state-of-the-art performance with much higher efficiency and a knowledge-distillation pipeline to reduce sampling cost while maintaining restoration quality...
This paper argues that the key lies in designing an appropriate search-state representation that connects the internal knowledge of LLMs with the structural requirements and constraints of visual-token pruning, and proposes AutoPrune, a training-free framework for LLM-driven visual-token pruning policy design.
This work forms an episodic multi-key representation that enables precise retrieval of fine-grained memories through a simple key-matching mechanism, and introduces a neighbor filtering mechanism to capture broader semantic context without the massive computational overhead of global memory construction.
This work proposes ParallelWorld, a multi-horizon test-time scaling framework for embodied reasoning that empowers agents to simulate and evaluate multi-step future trajectories in parallel before committing to an action.
GlanceWAM is introduced, which decouples imagination from control within a single video DiT: an asynchronous proposer glances ahead on a slow clock to imagine a single lookahead frame seconds into the future in the background, while an action head decodes action chunks at control rate purely in latent space without blo...
The proposed hierarchical long-horizon VLA architecture with an explicit language-memory module improves the success rate and robustness of VLA models on complex long-horizon tasks while providing an interpretable semantic account of the decision process.
Qantara is presented, an end-to-end JEPA whose joint training objective pairs a Brownian-bridge interpolant between consecutive clean latents on the state axis with noise-to-data flow matching on the action axis, and these results move JEPA world models from single-paradigm planners to multi-paradigm controllers.
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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