2325 papers
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.
The paper introduces the Kolmogorov-Arnold Energy Model (KAEM), a generative framework that leverages the Kolmogorov-Arnold Representation Theorem to impose a univariate latent structure, thereby achieving a unique balance of fast, exact inference, high interpretability, and competitive sample quality compared to traditional VAEs and diffusion models.
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.
This paper introduces TableEG, a framework that leverages fine-tuned large language models to generate authentic, distribution-aligned synthetic errors in tabular data, thereby addressing the scarcity of real-world error datasets and establishing a robust benchmark for evaluating data cleaning techniques.
This paper proposes MCULoRA, a novel parameter-efficient framework featuring modality combination aware low-rank adaptation and dynamic parameter fine-tuning to resolve gradient conflicts and improve performance in incomplete multimodal emotion recognition.
This paper identifies a pervasive "agreement bias" in Multimodal LLM verifiers that causes them to over-validate agent behavior, and proposes a lightweight Self-Grounded Verification (SGV) method that significantly improves failure detection and task completion across web navigation, computer use, and robotics by decoupling prior generation from trajectory evaluation.
This paper presents the first comprehensive survey of flow matching applications in biology and life sciences, systematically reviewing its theoretical foundations and categorizing its recent advancements in biological sequence modeling, molecule design, and protein generation.
This paper proposes a tree-based Weak-to-Strong Generalization framework that leverages Monte Carlo Tree Search to organize both successful and failure trajectories from weak models, thereby significantly enhancing the reasoning and decision-making capabilities of strong models in complex interactive environments.
This paper investigates how malicious auditees can construct fairness-compliant yet representative-looking samples from non-compliant distributions to deceive auditors, formalizes these manipulation strategies using optimal transport and entropic projections, and proposes statistical tests to detect such distributional manipulation attacks.
This paper introduces a Dynamic, Automatic, and Systematic (DAS) red-teaming framework that exposes a critical "Benchmarking Gap" in medical large language models, revealing that despite high static benchmark scores, most models exhibit profound brittleness, privacy leaks, bias, and hallucinations when subjected to continuous, adversarial stress-testing.