6378 papers
This paper introduces a response-free framework that leverages natural language processing and topic modeling to automatically simplify psychological scales by identifying semantic latent structures, achieving an average 60.5% reduction in item count while preserving psychometric validity and construct alignment.
TrasMuon is a novel optimizer that enhances the stability and convergence of Muon-style methods by integrating global RMS calibration and energy-based trust-region clipping to preserve near-isometric geometry while mitigating sensitivity to step-size hyperparameters and high-energy outliers.
This paper identifies and validates the critical issue of benchmark data leakage in LLM-based recommendation systems, demonstrating that exposure to evaluation data during training can artificially inflate performance metrics for domain-relevant leaks while degrading accuracy for irrelevant ones, thereby undermining the reliability of current evaluation practices.
This paper introduces the Mean Velocity Policy (MVP), a novel one-step generative policy that employs an Instantaneous Velocity Constraint (IVC) to theoretically guarantee high expressiveness while achieving state-of-the-art performance and significantly faster training and inference speeds on challenging robotic manipulation tasks compared to existing flow-based baselines.
The paper introduces Pawsterior, a variational flow-matching framework that enhances simulation-based inference by incorporating geometric confinement for structured domains and enabling the handling of discrete latent structures, thereby improving posterior fidelity and expanding applicability to complex physical systems.
This paper argues that the superior progress of code generation over reinforcement learning stems from code's dense, verifiable feedback structure, proposing a five-level hierarchy of learnability to demonstrate that the fundamental ceiling of machine learning progress depends more on a task's inherent learnability and information structure than on model scaling alone.
LongAudio-RAG is a hybrid edge-cloud framework that enables precise, low-hallucination question answering over multi-hour audio streams by converting recordings into timestamped event records for SQL-based retrieval, which then grounds Large Language Model responses in structured evidence rather than raw audio.
This paper introduces Direct Kolen-Pollack Predictive Coding (DKP-PC), a novel algorithm that enhances the efficiency and scalability of biologically inspired predictive coding by establishing direct learnable feedback connections from the output to all hidden layers, thereby reducing error propagation time complexity from O(L) to O(1) while mitigating vanishing updates and maintaining local learning.
This paper demonstrates that strategically screening and training on a carefully selected subset of high-quality, relevant data sources is sufficient to achieve statistically optimal shared feature extraction, even when discarding a substantial portion of available data.
The paper introduces Emotion Collider (EC-Net), a hyperbolic hypergraph framework that leverages Poincaré-ball embeddings, bidirectional message passing, and contrastive learning to achieve robust and noise-resilient multimodal sentiment analysis by preserving high-order semantic relations and enhancing class separation.
ModalImmune is a training framework that enhances the robustness of multimodal systems against input channel loss by intentionally collapsing selected modality information during training through a combination of adaptive regularization, targeted intervention, and certified meta-parameter adaptation.
This paper introduces FlyGM, a whole-brain connectomic graph model that leverages the exact static neural architecture of an adult fruit fly to achieve stable, sample-efficient whole-body locomotion control in embodied reinforcement learning without task-specific tuning.
This paper introduces a framework for operational certification of split conformal predictors that moves beyond marginal coverage by providing finite-sample guarantees for critical deployment metrics like commitment frequency and error exposure through Small-Sample Beta Correction, an independent audit-based auditing protocol, and a geometric analysis of Pareto trade-offs.
This paper demonstrates that neural networks learning equivariant operators in a latent space can effectively generalize to out-of-distribution symmetric transformations on simple datasets like rotated MNIST, while also highlighting the significant challenges involved in scaling this approach to more complex data.
This paper establishes a comprehensive multi-KPI benchmark for Multi-Agent Reinforcement Learning in urban energy management using the CityLearn environment, demonstrating that Decentralized Training with Decentralized Execution (DTDE) consistently outperforms Centralized Training with Decentralized Execution (CTDE) in both average and worst-case performance while offering greater resilience and sustainability.
This paper introduces RAmmStein, a deep reinforcement learning framework that optimizes liquidity provision in concentrated Automated Market Makers by solving an impulse control problem via a Hamilton-Jacobi-Bellman quasi-variational inequality, thereby significantly reducing rebalancing frequency and gas costs while maximizing net returns through regime-aware, mean-reversion-informed decision-making.
This paper benchmarks four GNN architectures on molecular regression tasks, demonstrating that a hierarchical fusion framework combining GNNs with molecular fingerprints outperforms standalone models by over 7% in RMSE, while CKA analysis reveals that GNN and fingerprint embeddings occupy highly independent latent spaces despite high convergence among isotopic GNN architectures.
The paper introduces MrBERT, a family of efficient, open-source multilingual encoders built on the ModernBERT architecture that achieves state-of-the-art performance in specific languages and specialized domains while leveraging Matryoshka Representation Learning to reduce inference and storage costs.
The paper introduces AVDE, a lightweight and efficient autoregressive framework that leverages contrastive learning and multi-scale token prediction to decode EEG signals into coherent images, outperforming state-of-the-art methods with significantly fewer parameters while mimicking the hierarchical nature of human visual perception.
CeRA overcomes the linear performance ceiling of Low-Rank Adaptation (LoRA) in complex reasoning tasks by introducing a weight-level parallel adapter with SiLU gating and structural dropout to induce manifold expansion, thereby achieving superior spectral efficiency and preventing rank collapse.