10070 papers
This paper proposes FreST Loss, a model-agnostic training objective that leverages the Joint Fourier Transform to align predictions with ground truth in the joint spatio-temporal frequency domain, thereby effectively decorrelating complex dependencies and outperforming state-of-the-art baselines on real-world datasets.
Through a large-scale study of Qwen-VL and LLaVA-1.5, this paper demonstrates that vision-language models exhibit significant context-dependent affordance drift, where both lexical and semantic outputs vary substantially based on agentic personas, suggesting a need for dynamic, query-dependent ontological projection in robotics rather than static world modeling.
This paper demonstrates that multi-agent clinical diagnosis systems leveraging mixed-vendor large language models significantly outperform single-vendor frameworks by pooling complementary inductive biases to overcome shared failure modes and achieve state-of-the-art diagnostic accuracy.
FedEMA-Distill is a robust and communication-efficient federated learning framework that leverages server-side exponential moving average smoothing and ensemble knowledge distillation from compressed client logits to achieve superior accuracy, faster convergence, and Byzantine resilience under non-IID data conditions without requiring client-side software modifications.
This paper addresses the scarcity of high-quality maritime radio data by introducing a compliance-aware Self-Instruct framework enhanced with LoRA fine-tuning and a 26-filter verification pipeline to generate realistic, SMCP-compliant VHF dialogues for AI-assisted safety systems.
The paper introduces Delta-Crosscoder, a robust method that combines BatchTopK sparsity with a delta-based loss to effectively identify and mitigate localized behavioral changes in narrowly fine-tuned models, outperforming existing SAE-based baselines across diverse model architectures and tasks.
This paper proposes reducing KV cache memory usage in transformers by employing low-dimensional keys for attention selection while maintaining full-dimensional values for semantic transfer, a strategy validated across multiple models and datasets to achieve up to 75% cache savings with minimal performance degradation.
This paper introduces a system for multi-agent LLM inference on edge devices that persists 4-bit quantized KV caches to disk, enabling direct cache restoration to eliminate redundant prefill computations and achieve up to 136x faster time-to-first-token while fitting four times more agent contexts into limited RAM.
This paper introduces the "What Is Missing" (WIM) rating system, which converts natural language feedback describing output deficiencies into interpretable scalar ratings via sentence embedding similarity, thereby improving preference learning signals and enabling qualitative debugging compared to traditional discrete numerical ratings.
The paper introduces SOLID, a mask-conditioned diffusion framework that learns spatiotemporal dynamics directly from sparse observations without requiring dense ground truth, achieving superior probabilistic forecasting and calibrated uncertainty through a novel dual-masking objective.
This paper proposes ZorBA, a zeroth-order federated fine-tuning framework for large language models that reduces VRAM usage and communication overhead through heterogeneous block activation and shared random seeds, while optimizing convergence via a novel lexicographic algorithm.
This paper proposes ASFL, an adaptive split federated learning framework that jointly optimizes model splitting and resource allocation via an online block coordinate descent algorithm to significantly reduce training delay and energy consumption while accelerating convergence in wireless networks.
The paper proposes CogGen, a fully unsupervised deep generative modeling framework for compressively sampled MRI reconstruction that enhances fidelity and convergence by regulating cognitive load through a self-paced curriculum learning strategy that progressively schedules k-space data fitting from low-frequency, high-SNR samples to more complex, noise-dominated measurements.
This paper proposes a systematic approach to Chalmers' "easy problems" of consciousness by demonstrating how an executable cognitive system, grounded in Kantian conceptual knowledge, can computationally derive abilities such as discrimination, attention, and reportability through its learning, emotional, and information-manipulation mechanisms.
This paper introduces AMV-L, a value-driven memory lifecycle framework for long-running LLM agents that replaces age-based retention with utility-based tiering to bound retrieval workloads, thereby achieving significantly improved tail-latency control and throughput compared to traditional TTL and LRU policies.
The paper introduces vLLM Semantic Router, a signal-driven decision framework that composes heterogeneous request features and neural classifiers into configurable policies to intelligently route queries across diverse Mixture-of-Modality models while enforcing privacy, safety, and cost constraints in production environments.
SkillNet is an open infrastructure that addresses the lack of systematic skill accumulation in AI agents by providing a unified ontology, a repository of over 200,000 skills, and evaluation tools to create, connect, and assess skills, thereby significantly enhancing agent performance and efficiency across diverse tasks.
This research proposes an explainable ensemble learning framework that integrates structured clinical and cognitive data with advanced preprocessing and hybrid class balancing techniques to achieve accurate and transparent Alzheimer's disease prediction, demonstrating that optimized ensemble models outperform deep learning while providing actionable clinical insights through SHAP analysis.
This paper proposes MPBMC, a hybrid approach that leverages Graph Neural Network embeddings and runtime design statistics to functionally cluster properties, thereby significantly accelerating multi-property Bounded Model Checking verification on HWMCC benchmarks compared to state-of-the-art methods.
This paper introduces the Non-Classical Network (NCnet), a classical neural architecture that exhibits quantum-like non-classical statistical behaviors through gradient competitions and implicit inter-task correlations, revealing that the resulting CHSH statistic serves as a novel indicator for understanding internal network dynamics and generalization performance across different resource regimes.