5308 papers
This paper introduces Open-Vocabulary Domain Generalization in Semantic Segmentation (OVDG-SS), a new setting and benchmark for autonomous driving that addresses both unseen domains and categories, and proposes S2-Corr, a state-space-driven mechanism to refine text-image correlations in Vision-Language Models to achieve robust performance across diverse urban environments.
This paper introduces INDUCTION, a benchmark designed to evaluate the ability of AI models to synthesize compact, generalizable first-order logic formulas that explain target predicates across small finite relational worlds, revealing distinct performance patterns and generalization strategies among recent elite models.
This paper presents SKYLIGHT, a scalable 3D photonic in-memory tensor core architecture that leverages co-designed innovations in topology, wavelength routing, and non-volatile weights to achieve energy-efficient, real-time AI inference and local learning, outperforming state-of-the-art GPUs in throughput and power efficiency while maintaining robustness against hardware non-idealities.
UniMatch is a novel coarse-to-fine framework that establishes dense semantic correspondences between strongly non-isometric, cross-category 3D shapes by leveraging class-agnostic segmentation, multimodal language models for part identification, and a rank-based contrastive learning scheme to overcome the limitations of prior isometry-dependent methods.
This paper argues that iCloud's fundamental failure in supporting complex workflows stems from a "Category Mistake" where its POSIX-like filesystem interface falsely projects a linear temporal chain onto a distributed causal graph, a structural error that causes data divergence and corruption but could be resolved by adopting Open Atomic Ethernet's transactional semantics to align protocol behavior with physical reality.
InfScene-VF proposes a diffusion-based framework for arbitrary-size image super-resolution that eliminates boundary artifacts and enables memory-efficient, parallelized inference on gigapixel imagery by introducing Variance-Corrected Fusion and Spatially-Decoupled Variance Correction to achieve spatially continuous joint-denoising.
This paper proposes an online data manipulation scheme that decomposes training images into independent object, scene, and camera components and recomposes them with perturbed poses to generate diverse training data, thereby improving the data efficiency and performance of monocular 3D object detection models across both fully and sparsely supervised settings.
This paper presents a cycle-consistent tuning framework that leverages lightweight LoRA adaptation of pretrained diffusion models to achieve robust, high-fidelity layered image decomposition, specifically for challenging logo-object separation, by enforcing bidirectional reconstruction consistency and iteratively refining performance through a progressive self-improving process.
This paper proposes "See It, Say It, Sorted," a lightweight, training-free, and plug-and-play framework that mitigates visual hallucination in large vision-language models by iteratively supervising each reasoning step with dynamically extracted visual evidence, thereby significantly improving reasoning accuracy without requiring additional model training.
This paper introduces ARLArena, a unified framework that systematically analyzes training instability in agentic reinforcement learning to derive SAMPO, a stable optimization method that ensures consistent performance across diverse agentic tasks.
This paper introduces a unified language modeling approach for semantic and panoptic segmentation in images and videos that discretizes masks into run-length encoded tokens, employing novel compression strategies to enable autoregressive generation despite computational constraints.
This paper introduces EmoOmni, a unified framework that leverages an emotional Chain-of-Thought (E-CoT) to bridge the gap between fine-grained multimodal perception and accurate emotional expression in Omni-LLMs, accompanied by a new dataset and benchmark for systematic evaluation.
CryoNet.Refine is a novel one-step diffusion model that automates and accelerates the refinement of atomic structures against cryo-EM density maps, outperforming traditional tools like Phenix in both model-map correlation and geometric quality while supporting diverse protein and nucleic acid complexes.
This paper argues that AI agents equipped with specialized skills can augment, but not fully replace, social scientists by executing codifiable research tasks autonomously through "vibe researching," while highlighting the enduring necessity of human theoretical originality and tacit knowledge alongside the profession's emerging risks of stratification and pedagogical crisis.
This paper analyzes physician disagreement in the HealthBench dataset, revealing that while the majority of variance is structural and irreducible, a small but actionable portion stems from reducible uncertainties like missing context, suggesting that improving evaluation design to close information gaps could meaningfully reduce disagreement on borderline medical AI cases.
WISER is a training-free framework for Zero-Shot Composed Image Retrieval that unifies Text-to-Image and Image-to-Image paradigms through a "retrieve-verify-refine" pipeline, leveraging wider search, adaptive fusion, and self-reflection to significantly outperform existing methods across diverse benchmarks.
The paper introduces PackUV, a novel 4D Gaussian representation and fitting method that maps volumetric video attributes into structured UV atlases for efficient, codec-compatible storage and streaming, while demonstrating superior temporal consistency and rendering fidelity on the newly proposed large-scale PackUV-2B dataset.
This paper proposes a sample-efficient approach to generalized planning that learns explicit neural transition models to predict intermediate world states, demonstrating superior out-of-distribution performance and data efficiency compared to direct action-sequence prediction methods.
This paper proposes HART, an annotation-free framework that leverages a novel Advantage Preference Group Relative Policy Optimization (AP-GRPO) algorithm to enable Large Multimodal Models to autonomously identify and verify key high-resolution image regions, thereby improving reasoning performance without requiring costly human grounding labels.
This paper introduces PEPA, a three-layer cognitive architecture that leverages personality traits to enable embodied agents to autonomously generate goals and sustain long-term operation in dynamic environments without relying on external task specifications.