4981 papers
TimeSliver is a novel explainable deep learning framework for time-series classification that jointly leverages raw data and symbolic abstraction to linearly encode temporal segment contributions, achieving superior attribution accuracy and competitive predictive performance compared to existing methods.
This paper proposes TGCC, a novel causal-invariance-based graph dataset condensation method that extracts domain-invariant features and injects them via spectral contrastive learning to significantly improve performance in cross-task and cross-domain scenarios while maintaining state-of-the-art results in single-task settings.
FlowSymm is a novel graph attention architecture that recovers missing network flows by combining a physics-aware, symmetry-preserving group-action framework with a lightweight Tikhonov refinement, ensuring exact adherence to local conservation laws while outperforming state-of-the-art methods across transportation, energy, and mobility benchmarks.
Mem-T introduces an autonomous memory agent trained via the MoT-GRPO framework, which densifies sparse long-horizon rewards through tree-guided backpropagation to achieve superior performance and efficiency compared to existing memory management systems.
This paper proposes a Bitcoin price prediction model using Combinatorial Fusion Analysis (CFA) to integrate diverse machine learning models via rank-score characteristics and weighted combinations, achieving a superior Mean Absolute Percentage Error (MAPE) of 0.19% that outperforms individual models and existing prediction methods.
This paper introduces Adam-Aware In-Run Data Shapley, a novel method that overcomes the limitations of SGD-based attribution in adaptive optimizers by deriving a closed-form approximation and a Linearized Ghost Approximation to achieve near-perfect fidelity in data contribution estimation while maintaining high training efficiency.
This paper investigates whether Schwartz higher-order values improve sentence-level human value detection, finding that while hierarchical gating offers limited benefits, calibration techniques and hybrid ensembles significantly boost performance, suggesting the value hierarchy is more effective as an inductive bias than a rigid routing mechanism.
This paper introduces LatentMem, a learnable multi-agent memory framework that addresses memory homogenization and information overload by using an experience bank and a memory composer to generate customized, token-efficient latent memories, further optimized via Latent Memory Policy Optimization (LMPO) to significantly enhance multi-agent system performance.
This paper introduces T2T (Thickening-to-Thinning), a dynamic reward shaping framework inspired by human learning dynamics that enhances LLM reasoning by encouraging longer, exploratory trajectories on incorrect attempts and penalizing length upon success, thereby outperforming standard baselines on mathematical benchmarks.
This paper introduces a novel inference-time backdoor attack that exploits maliciously modified chat templates to compromise open-weight language models without altering weights or training data, demonstrating high success rates in degrading factual accuracy and inducing harmful outputs while evading current security scans.
This paper introduces the Hinge Regression Tree (HRT), a novel oblique decision tree method that reframes split learning as a non-linear least-squares problem solvable via a damped Newton method, offering provable convergence, universal approximation capabilities, and superior performance with compact structures compared to existing baselines.
This paper introduces Radial Müntz-Szász Networks (RMN), a highly parameter-efficient neural architecture that utilizes learnable radial power bases and a log-primitive to accurately model multidimensional singular fields like $1/r\log r$, achieving significantly lower error rates than standard MLPs and SIREN on benchmark tasks while providing closed-form gradients for physics-informed learning.
SDFed is a heterogeneous federated prompt learning framework that addresses local-global discrepancies by combining a fixed-length global prompt with variable-length local prompts, enhanced by subspace refinement and divergence control strategies to improve performance and robustness in privacy-sensitive, resource-constrained multi-party settings.
This paper identifies and formalizes "Retrieval Pivot Attacks" in Hybrid RAG systems, demonstrating how vector-retrieved seeds can inadvertently pivot through knowledge graph links to cause cross-tenant data leakage, and proves that enforcing authorization specifically at the graph expansion boundary effectively mitigates this risk with minimal overhead.
This paper proposes a unified diffusion-guided pretraining framework for brain graph foundation models that overcomes the limitations of existing methods by using diffusion to preserve semantic connectivity patterns during augmentation and to enable topology-aware global reconstruction, thereby achieving robust and transferable representations across diverse neuroimaging datasets.
This paper investigates document page reordering in heterogeneous Dutch freedom of information releases, identifying that while specialized models achieve high accuracy on short documents, seq2seq transformers fail to generalize to longer texts due to fundamental differences in required ordering strategies, a challenge effectively addressed through model specialization rather than curriculum learning.
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.