9948 papers
This paper presents a neuro-symbolic AI system that autonomously solved an open problem in theoretical physics by deriving novel, exact analytical solutions for the gravitational radiation power spectrum of cosmic strings, thereby surpassing previous partial results and demonstrating AI's potential to accelerate mathematical discovery.
This paper proposes "Interactive Benchmarks," a unified evaluation paradigm that assesses model intelligence through active information acquisition and reasoning under budget constraints in interactive proofs and games, demonstrating that current models still have significant room for improvement in these dynamic scenarios.
This paper challenges the prevailing view of AI memory as a mere functional tool by proposing the "Memory-as-Ontology" paradigm and its associated "Animesis" system, which prioritizes identity continuity and governance over retrieval performance to enable persistent digital citizens that survive model transitions.
This paper introduces CONE, a hybrid transformer encoder that utilizes a novel composite embedding algorithm to preserve the semantics of units and variables for complex numerical data, achieving state-of-the-art performance in numerical reasoning tasks across diverse domains.
The paper introduces DARE, a lightweight retrieval model that integrates data distribution information with function metadata to significantly improve R package retrieval and LLM agent performance in statistical analysis tasks, supported by a new knowledge base and evaluation framework.
This paper argues that the rise of agentic AI necessitates a reconceptualization of Team Situation Awareness to address the structural uncertainty of open-ended agency, distinguishing between continuous alignment mechanisms and emerging tensions to guide future human-AI teaming research.
HiMAP-Travel is a hierarchical multi-agent framework that addresses long-horizon travel planning with hard constraints by employing a Coordinator and parallel Day Executors linked through transactional monitoring and bargaining protocols, achieving state-of-the-art performance and reduced latency on TravelPlanner and FlexTravelBench benchmarks.
To overcome the limitations of existing benchmarks for evaluating Search Agents—such as high construction costs, dynamic obsolescence, attribution ambiguity, and reproducibility issues—this paper introduces Mind-ParaWorld, a novel framework that simulates a parallel world with synthetic, time-shifted scenarios and atomic facts to rigorously assess agents' evidence collection, synthesis, and stopping decisions.
MOOSEnger is a domain-specific AI agent that combines retrieval-augmented generation with deterministic, MOOSE-aware parsing and execution tools to automatically convert natural language into validated simulation inputs, achieving a 93% execution success rate on a diverse benchmark compared to just 8% for an LLM-only baseline.
The paper proposes SharedLLM, a novel framework that extends the context window of large language models to over 128K tokens using a multi-scale self-injection architecture with stacked short-context models and a tree-based retrieval structure, achieving superior performance and efficiency without requiring costly long-context pre-training.
This landscape commentary evaluates the GPT-5 family against GPT-4o, revealing substantial improvements in expert-level textual reasoning and multimodal synthesis that approach state-of-the-art performance in tasks like mammography, while highlighting that generalist models still lag behind specialized systems in perception-critical domains such as neuroradiology.
This paper proposes DSA-SRGS, the first super-resolution Gaussian splatting framework for dynamic sparse-view DSA reconstruction, which integrates a Multi-Fidelity Texture Learning Module with confidence-aware supervision and Radiative Sub-Pixel Densification to recover fine-grained vascular details while avoiding blurring and hallucination artifacts.
The paper proposes MADCrowner, a margin-aware framework that combines a template deformation network (CrownDeformR) with a novel margin segmentation network (CrownSegger) to automatically generate high-precision, clinically feasible dental crowns by addressing limitations in spatial resolution and surface overextension found in existing learning-based methods.
TSEmbed is a universal multimodal embedding framework that addresses task conflict by synergizing Mixture-of-Experts with Low-Rank Adaptation and introducing Expert-Aware Negative Sampling to achieve state-of-the-art performance on both benchmark and industrial datasets.
This paper introduces RLSTA, a reinforcement learning framework that leverages single-turn capabilities as stable anchors to overcome "Contextual Inertia," thereby enabling large language models to effectively integrate new information and maintain robust reasoning in multi-turn interactions.
The paper introduces Timer-S1, an 8.3B-parameter Mixture-of-Experts time series foundation model that achieves state-of-the-art forecasting performance through a novel "Serial Scaling" strategy across architecture, dataset, and training pipeline, featuring a specialized Serial-Token Prediction objective and a curated trillion-point dataset called TimeBench.
This paper introduces "LAW & ORDER," a dual-adapter framework that employs Learnable Adaptive Weighting to stabilize diffusion-based medical image synthesis and Optimal Region Detection to enhance efficient segmentation, collectively addressing spatial imbalance to significantly improve generative quality and segmentation accuracy while maintaining a lightweight model architecture.
This review paper evaluates traditional and deep learning methods for brain glioma segmentation and classification, concluding that convolutional neural network architectures outperform traditional techniques in post-MRI analysis to enhance treatment planning and patient outcomes.
This paper proposes Clustering-Sampling-Voting (CSV), a novel framework that significantly reduces the linear latency and token costs of semantic filtering in large language models by embedding tuples into semantic clusters, sampling subsets for evaluation, and inferring cluster-level labels through voting strategies, thereby achieving sublinear complexity with strong error guarantees.
This paper proposes Diffusion Contrastive Reconstruction (DCR), a method that injects contrastive signals derived from reconstructed images into the diffusion process to resolve gradient conflicts and jointly optimize both discriminative and detail-perceptive abilities, thereby overcoming the limitations of CLIP's visual encoder for balanced visual representation.