10089 papers
The paper proposes GRAND, a hybrid hierarchical algorithm that combines reinforcement learning-based graph guidance with minimum-cost flow rebalancing and local assignment to achieve up to 10% higher throughput than state-of-the-art schedulers for large-scale, lifelong multi-agent pickup-and-delivery tasks while maintaining real-time execution.
This paper introduces fairness-aware Low-Rank Adaptation methods, specifically FR-LoRA, GR-LoRA, and Hybrid-LoRA, which utilize a differentiable MaxAccGap loss and inverse frequency weighting to significantly reduce diagnostic accuracy disparities in glaucoma detection across demographic groups while maintaining high overall accuracy with minimal trainable parameters.
The paper introduces Guided Flow Policy (GFP), a novel offline reinforcement learning method that couples a multi-step flow-matching policy with a distilled one-step actor to selectively focus on high-value actions, achieving state-of-the-art performance across diverse benchmarks by overcoming the limitations of indiscriminate behavior regularization.
This paper demonstrates that injecting a canonical action ordering signal into the reward function during RL post-training significantly improves Transformer performance on Zebra puzzles compared to optimizing for task success alone, even when the model is fine-tuned on randomized solution sequences.
NeuralRemaster introduces Phase-Preserving Diffusion (Ï-PD), a model-agnostic method that replaces standard Gaussian noise with phase-preserving, magnitude-randomized noise to enable structure-aligned image and video generation without architectural changes or inference-time costs.
This paper introduces a post-training method that induces extreme sparsity in transformer attention (reducing connectivity to ~0.4%) without sacrificing performance, thereby revealing simplified, interpretable task-specific circuits and unifying feature-based and circuit-based perspectives on model behavior.
The paper introduces SACP, a novel method that aggregates nonconformity scores from multiple predictive models into e-values using symmetric functions to generate valid and more efficient uncertainty sets within the conformal prediction framework.
This paper proposes TAP, a novel training-free framework that leverages Large Language Models and evolutionary search to automatically discover superior mixed-precision quantization proxies, eliminating the need for human expertise or costly optimization while achieving state-of-the-art performance.
ReFusion introduces a novel masked diffusion model that integrates sequence reorganization with a hybrid parallel-autoregressive decoding strategy to simultaneously achieve full KV cache efficiency, reduce learning complexity, and significantly outperform existing diffusion models while narrowing the performance gap with autoregressive models.
This paper presents non-asymptotic, distribution-free methods for estimating the calibration error of binary classifiers, including a theoretical upper bound for bounded-variation functions and a practical modification technique that ensures efficient error bounding without significantly compromising performance.
This paper introduces RePo, a novel mechanism that leverages a differentiable module to dynamically re-position tokens based on contextual dependencies rather than fixed linear indices, thereby reducing extraneous cognitive load and enhancing LLM performance on tasks involving noisy contexts, structured data, and long-range dependencies.
The paper introduces AMPEND-LS, an agentic multi-persona framework that synergizes Large Language Models and Small Language Models with evidence-grounded reasoning to achieve state-of-the-art, explainable, and robust multimodal fake news detection.
This paper introduces Parallel Token Prediction (PTP), a framework that accelerates language model inference by predicting multiple tokens in a single forward pass through deterministic functions of random input variables, achieving a 2.4x speedup over autoregressive decoding.
This paper introduces an SVGP-KAN framework that reconstructs time-resolved flow fields from sparse velocimetry data with accuracy comparable to classical methods while providing well-calibrated epistemic uncertainty estimates to guide experimental design.
This study demonstrates that integrating cellular bioelectrical impedance signatures with supervised machine learning, particularly Random Forest, enables highly accurate (~90%) prediction of cellular malignancy, offering a promising foundation for future real-time diagnostic tools.
The paper introduces the Spectral Sphere Optimizer (SSO), a novel parallel training algorithm that enforces strict module-wise spectral constraints on both weights and updates to achieve full Maximal Update Parametrization alignment, resulting in superior convergence, stability, and performance across diverse large-scale architectures compared to AdamW and Muon.
The paper proposes the Behavioral Profiling Ensemble (BPE) framework, a model-centric approach that constructs intrinsic behavioral profiles for base learners to dynamically derive aggregation weights, demonstrating superior predictive accuracy and efficiency over state-of-the-art Dynamic Ensemble Selection methods across 42 real-world datasets.
EmboTeam is a novel framework that enhances embodied multi-robot collaboration by cascading LLM-based instruction parsing into formal PDDL planning and reactive behavior tree execution, achieving significantly higher task success rates on the new MACE-THOR benchmark compared to existing baselines.
ButterflyMoE achieves sub-linear memory scaling for Mixture-of-Experts models on edge devices by representing diverse experts as geometric rotations of a shared ternary substrate, enabling a 150 memory reduction with negligible accuracy loss.
This paper introduces Yuan3.0 Ultra, an open-source, trillion-parameter Mixture-of-Experts large language model that utilizes a novel Layer-Adaptive Expert Pruning algorithm to significantly improve pre-training efficiency and reduce model size while achieving state-of-the-art performance on enterprise-oriented benchmarks.