2294 papers
The paper introduces VISTA, a novel training-free framework that leverages Vision-Language Models to predict stock prices by jointly analyzing textual data and line charts through zero-shot prompting, achieving significant performance improvements over traditional statistical and text-only baselines.
This paper proposes a novel defense against prompt injection attacks in large language models by augmenting intermediate token representations with layer-specific trainable embeddings to enforce instruction hierarchy, achieving a 1.6x to 9.2x reduction in attack success rates compared to state-of-the-art methods without compromising model utility.
This paper proposes Orthogonal Common Neighbor (OCN), a novel link prediction method that addresses redundancy and over-smoothing in higher-order common neighbors through orthogonalization and normalization, achieving significant performance improvements over state-of-the-art baselines.
The paper introduces ViTaPEs, a transformer-based architecture that employs a novel two-stage positional encoding strategy to effectively fuse visual and tactile modalities, achieving state-of-the-art performance and zero-shot generalization across diverse recognition and robotic grasping tasks without relying on pre-trained vision-language models.
The paper introduces LoFT, a novel parameter-efficient fine-tuning method that aligns optimizer dynamics (momentum and variance) with full fine-tuning within a low-rank subspace, thereby eliminating the need for hyperparameter tuning and achieving performance comparable to full fine-tuning without increasing inference costs.
This paper introduces Progressive Neural Collapse (ProNC), a novel continual learning framework that overcomes the limitations of fixed global ETF targets by progressively expanding the simplex equiangular tight frame with new class prototypes, thereby effectively mitigating catastrophic forgetting while maintaining flexibility and efficiency.
This paper proposes a novel, lightweight, and plug-and-play adaptive correction method that utilizes a learnable operator to enforce strict conservation laws in neural operators, thereby significantly improving their accuracy, stability, and flexibility compared to existing constraint-based approaches.
ActivePusher is a novel framework that enhances data efficiency and planning reliability in nonprehensile manipulation by combining residual physics modeling with uncertainty-based active learning to prioritize informative data collection and guide control sampling toward more reliable actions.
This paper introduces MMTU, a large-scale benchmark comprising over 28,000 questions across 25 real-world expert-level table tasks, designed to comprehensively evaluate and reveal the significant limitations of current frontier models in understanding, reasoning, and manipulating structured tabular data.
This paper demonstrates that utilizing unconventionally large learning rates to induce transient chaotic dynamics during neural network training creates an optimal balance between exploration and exploitation, thereby accelerating convergence to high accuracy across various architectures and tasks.
This paper presents EROICA, the first online troubleshooting system deployed on production-scale GPU clusters (~100,000 GPUs) that effectively diagnoses complex hardware and software performance issues in large-scale model training through fine-grained profiling and differential observability with minimal impact.
BemaGANv2 is an advanced GAN-based vocoder that enhances long-term audio generation for Text-to-Music and Text-to-Audio applications by integrating Anti-aliased Multi-Periodicity composition modules in the generator and systematically evaluating novel discriminator combination strategies, including the Multi-Envelope Discriminator, to achieve high-fidelity and temporally coherent results.
This paper introduces Co-LoRA, a collaborative personalization framework that addresses both data and model heterogeneity through a task-relevance-aware aggregation strategy and a dimension-invariant module, validated by a new multi-modal benchmark and superior performance over state-of-the-art methods.
This paper introduces two efficient algorithms, Slate-GLM-OFU and Slate-GLM-TS, for the Logistic Contextual Slate Bandit problem that achieve regret and per-round computational complexity by combining local planning with global learning, demonstrating superior performance in both synthetic benchmarks and practical language model applications.
This paper characterizes how Sharpness-Aware Minimization (SAM) alters generalization during machine unlearning by abandoning its denoising properties when fitting forget signals, leading to the proposal of "Sharp MinMax"—a novel method that splits the model to simultaneously learn retain signals via SAM and unlearn forget signals via sharpness maximization, thereby achieving superior unlearning performance, reduced feature entanglement, and enhanced privacy.
This paper proposes a semi-self-supervised learning approach featuring a novel GLMask representation and a semantic-to-instance pipeline that achieves state-of-the-art instance segmentation performance with minimal manual annotation, demonstrating superior results on both dense agricultural wheat head images and the general-purpose COCO dataset.
This paper introduces SamS, an efficient algorithm that adaptively schedules training samples in Direct Preference Optimization based on the model's evolving batch-wise states, significantly improving LLM alignment performance without modifying the core DPO algorithm or incurring substantial computational overhead.
DemoDiffusion is a one-shot imitation learning method that enables robots to perform diverse manipulation tasks by leveraging kinematic retargeting to derive a rough trajectory from a single human demonstration and refining it with a pre-trained diffusion policy to ensure alignment with plausible robot actions, achieving significantly higher success rates than baseline approaches without requiring task-specific training or paired data.
By implementing a novel "developmental visual diet" inspired by human visual maturation, this study demonstrates that guiding AI learning processes rather than simply scaling data yields models with superior shape-based recognition, robustness to distortions, and alignment with human vision.
This paper establishes that Physics-Informed Neural Networks (PINNs) require a network size scaling with the number of noisy samples to achieve empirical risk below the noise variance, demonstrating that simply increasing data quantity cannot compensate for insufficient model capacity in noisy PDE training.