7179 papers
This paper proposes Local Classifier Alignment (LCA), a novel loss function that resolves the mismatch between task-specific classifiers and adapted backbones in continual learning, thereby enhancing generalization and robustness while achieving state-of-the-art performance on standard benchmarks.
This paper proposes a lightweight, training-free framework that parameterizes prompts as actions to dynamically influence LLM multi-agent dialogue behaviors, demonstrating through experiments that this policy-based approach effectively controls conversational dynamics across various indicators.
The paper proposes MSSR, a memory-aware adaptive replay framework that estimates sample-level memory strength to dynamically schedule rehearsal intervals, effectively mitigating catastrophic forgetting while maintaining fast adaptation in continual LLM fine-tuning.
MedMASLab is a unified framework and benchmarking platform that addresses architectural fragmentation in medical multi-agent systems by introducing a standardized communication protocol, an automated zero-shot clinical reasoning evaluator, and an extensive multimodal benchmark spanning 473 diseases to reveal critical performance gaps in cross-specialty transitions.
This paper presents an AI-driven, data-driven framework that utilizes validated proxies from site license and crowdsourced data to accurately forecast spectrum demand across five major Canadian cities, thereby enabling more efficient dynamic spectrum planning and resource allocation for regulators and network operators.
The paper introduces ACADiff, an adaptive clinical-aware latent diffusion framework that synthesizes missing multimodal brain imaging data (sMRI, FDG-PET, and AV45-PET) by integrating imaging observations with GPT-4o-encoded clinical metadata, achieving superior generation quality and robust diagnostic performance even when up to 80% of modalities are missing.
This paper presents a data-driven methodology using geospatial analytics and machine learning to accurately estimate and identify the drivers of spectrum demand variations across urban regions, achieving 70% predictive accuracy to support flexible spectrum access policies for future 6G networks.
PathMem is a memory-centric multimodal framework that enhances pathology large language models by organizing structured domain knowledge into long-term memory and utilizing a Memory Transformer to dynamically activate and ground this knowledge for improved diagnostic reasoning and report generation.
The paper proposes k-MTR, a novel framework that bypasses the traditional image reconstruction step by directly learning multi-task cardiac diagnostic features from undersampled k-space data through a shared semantic manifold, thereby eliminating reconstruction artifacts and achieving competitive performance across regression, classification, and segmentation tasks.
This paper establishes that confidence-based abstention in ranked decision systems improves quality only under specific structural conditions, demonstrating that while structural uncertainty allows for monotonic gains, contextual uncertainty fundamentally undermines standard confidence signals and exception-based interventions, necessitating domain-specific diagnostic checks before deployment.
This paper introduces the Overfitting-Underfitting Indicator (OUI) as an efficient, early-stage metric based on hidden neuron activation patterns to distinguish optimal learning rates in PPO actor-critic training, demonstrating its superior ability to prune unpromising runs compared to traditional criteria by revealing distinct structural signatures in actor and critic networks.
This paper introduces "neural debuggers," a new class of language models that emulate traditional debugging interactions like setting breakpoints and stepping through code to enable both forward and inverse execution prediction, thereby laying the foundation for more powerful agentic coding systems and automated debugging.
This paper demonstrates that reasoning consistently enhances honesty in large language models by guiding them through a representational space where deceptive states are metastable and easily disrupted, ultimately nudging the models toward more stable, honest defaults.
This paper introduces BEACON, a language-conditioned navigation system that overcomes the limitations of existing 2D image-space methods by predicting an occlusion-aware Bird's-Eye View affordance heatmap from surround-view RGB-D observations, thereby significantly improving the accuracy of inferring traversable targets in occluded regions.
This paper extends the classical "bee equation" for swarm decision-making into an agent-based model where emotional valence and arousal modulate interaction rates, revealing how emotional contagion and non-linear amplification mechanisms bias consensus outcomes and accelerate convergence in collective choices.
This paper presents a study of a large language model-powered "sighted guide" for blind and low vision users in social virtual reality, revealing that participants adapt their interaction from a tool-based approach when alone to a companionable relationship in the presence of others, thereby offering key design recommendations for future accessible VR guides.
This paper challenges the standard view of superposition in neural networks by demonstrating that, unlike in idealized uncorrelated settings where interference is merely noise, realistic feature correlations allow models to arrange features so that interference becomes constructive, thereby naturally forming the semantic clusters and cyclical structures observed in real language models.
This paper introduces two online neural network-based algorithms for change-point detection in large time series that demonstrate linear computational complexity, outperform existing methods on various datasets, and are proven to converge to optimal solutions under specific conditions.
This paper provides a comprehensive overview of Automated Reinforcement Learning (AutoRL), surveying existing literature including recent LLM-based techniques, discussing promising non-tailored methods for future integration, and outlining current challenges and research directions in automating MDP modeling, algorithm selection, and hyper-parameter optimization.
This paper proposes an uncertainty-aware, explainable-by-design subsequence-based model that achieves state-of-the-art classification performance for astronomical uncertain time series by incorporating data uncertainty as an input, thereby enabling domain experts to inspect predictions and potentially inspire new theoretical astrophysics developments.