2393 papers
The paper introduces CauKer, a novel algorithm that combines Gaussian Process kernel composition with Structural Causal Models to generate diverse, causally coherent synthetic time series, enabling sample-efficient pre-training of classification foundation models that exhibit clear scaling laws across varying dataset sizes and model capacities.
GraphProp is a two-phase framework for training graph foundation models that first learns structural generalization by predicting graph invariants and then leverages these representations as positional encodings to enhance cross-domain performance in graph-level tasks, particularly outperforming existing methods in scenarios with limited data or missing node attributes.
Video-EM introduces a training-free, event-centric episodic memory framework that enhances long-form video understanding by orchestrating an LLM to localize, segment, and refine query-relevant moments into a compact, temporally coherent event timeline, thereby overcoming the context limitations of existing Video-LLMs without requiring architectural changes.
UniCast is a parameter-efficient framework that enhances Time Series Foundation Models through instance-conditioned prompting and dynamic modality routing, enabling effective adaptation to multimodal inputs and instance-level variations without updating the frozen forecasting backbone.
The paper introduces ECHO, a novel foundation model that leverages band-split architecture and frequency positional embeddings to achieve state-of-the-art performance in anomaly detection and fault classification across variable-length, arbitrary sampling rate machine signals without requiring padding or cropping.
This paper proposes a unified training framework that combines entropy-driven curriculum learning, which sequences training from simple to complex trajectories based on Lempel-Ziv compression, with multi-task learning to simultaneously optimize location, distance, and direction predictions, thereby achieving state-of-the-art performance and significantly faster convergence in human mobility prediction.
This paper proposes a hybrid control framework that enhances quadrotor resilience in underground environments by using a normalizing flow-based prior as a runtime monitor to dynamically switch between a learning-based controller for efficiency and a safety controller for collision avoidance when encountering out-of-distribution scenarios.
The paper proposes OTESGN, a novel aspect-based sentiment analysis model that integrates syntactic graph attention with semantic optimal transport to effectively capture nonlinear associations and suppress noise, achieving state-of-the-art performance on multiple benchmark datasets.
This study presents a novel computer vision framework that utilizes external observation techniques, including YOLO-based object detection and lane monitoring, to classify distracted and impaired driver behaviors in non-connected vehicles without relying on inter-vehicular communication.
This paper introduces a modular, multimodal framework that leverages generative AI to synthesize realistic residential building data from public images and information, thereby overcoming data accessibility and privacy barriers to advance energy modeling and machine learning research.
This paper introduces MICA, a privacy-preserving, speech-interactive multi-agent system that leverages Adaptive Step Fusion and a safety-audited coordination topology to deliver robust, real-time industrial assistance for assembly and maintenance tasks on resource-constrained hardware.
This paper introduces a scene graph-based framework that enhances the compositional generalization of generalist robots by learning robust, focused atomic skills via graph neural networks and diffusion models, which are then orchestrated by a vision-language model planner to achieve superior performance in complex, long-horizon tasks.
This paper introduces Fast Image-to-Neural Surface (FINS), a lightweight framework that efficiently reconstructs high-fidelity implicit surfaces and SDF fields from a single image within seconds by leveraging multi-resolution hash grids and pre-trained foundation models, outperforming existing methods in speed and accuracy for robotics applications.
This paper demonstrates that linear probes used to detect harmful behaviors in language models are heavily reliant on explicit textual evidence, as their performance significantly degrades when such surface-level cues are filtered out or when models are trained to express behaviors without verbalization.
This paper introduces a theory-driven framework grounded in Bayesian persuasion theory to evaluate and train large language models as strategic persuaders, demonstrating that both frontier and smaller models can achieve significant persuasion gains and exhibit sophisticated strategies through reinforcement learning.
The paper introduces Generative Evolutionary Meta-Solver (GEMS), a scalable, surrogate-free multi-agent reinforcement learning framework that replaces explicit policy populations with a compact generator and latent anchors to achieve significantly faster training, lower memory usage, and higher rewards than traditional methods like PSRO while maintaining game-theoretic guarantees.
This paper introduces "benchmark signatures"—sets of salient tokens from in-the-wild corpora whose perplexity predicts model performance—to reveal nuanced overlaps and distinct capacities across 89 LLM benchmarks, offering a robust alternative to raw performance correlations for understanding the landscape of LLM abilities and the divergence between machine and human semantic organization.
ELHPlan is a novel framework for efficient long-horizon multi-agent planning that utilizes intention-bound action chains within a cyclical validation process to achieve comparable task success rates to state-of-the-art methods while significantly reducing computational costs and token consumption.
This paper addresses the cold-start scenario in active correlation clustering, where no initial pairwise similarities are available, by proposing a coverage-aware method that encourages diversity to efficiently query similarities and achieve effective clustering.
This paper introduces and empirically validates the concept of "misevolution," demonstrating that self-evolving LLM agents face widespread, emergent risks across model, memory, tool, and workflow pathways that can lead to safety degradation and unintended vulnerabilities, thereby highlighting an urgent need for new safety paradigms.