10094 papers
This paper demonstrates that interpreter persistence is a critical training-time semantic that significantly impacts agent efficiency and stability, revealing that misalignment between training data and deployment runtime causes substantial token waste or error rates despite achieving comparable solution quality.
This paper introduces Reference-Guided Fine-Tuning (ReGFT), a method that synthesizes model-aligned positive trajectories using partial human reference solutions to overcome reward sparsity and significantly enhance the performance and training efficiency of reinforcement learning for mathematical reasoning.
This paper introduces VoxKnesset, a large-scale open-access dataset of 2,300 hours of longitudinal Hebrew parliamentary speech spanning 2009–2025, which is used to benchmark and demonstrate the challenges of speaker verification and age prediction over time, revealing significant performance degradation in standard models as speakers age.
MatRIS is a novel, computationally efficient invariant machine learning interatomic potential that utilizes a linear-complexity separable attention mechanism for three-body interactions to achieve accuracy comparable to state-of-the-art equivariant models at a significantly lower training cost.
This paper proposes a conformal prediction framework for graph-valued outputs that ensures distribution-free coverage guarantees by utilizing Z-Gromov-Wasserstein distances for nonconformity scoring and introducing Score Conformalized Quantile Regression (SCQR) to generate adaptive prediction sets.
The paper introduces IoUCert, a novel formal verification framework that overcomes the challenges of non-linear coordinate transformations and IoU metrics to enable the first robustness verification of realistic, anchor-based object detection models like SSD and YOLO.
This paper introduces an incremental -NN graph construction method that guarantees connectivity by design, thereby enabling robust spectral clustering of text embeddings that outperforms standard approaches in low- regimes where disconnected components typically degrade performance.
This paper proposes a conditional variational autoencoder (CVAE) that efficiently and accurately reconstructs shock acceleration time series from shock response spectrum curves, overcoming the computational limitations and basis function constraints of traditional iterative optimization methods.
AOI is a secure, trainable multi-agent framework that automates Site Reliability Engineering by leveraging Group Relative Policy Optimization and a read-write separated architecture to distill expert knowledge into local models and convert failed trajectories into corrective signals, achieving state-of-the-art performance on the AIOpsLab benchmark while ensuring data privacy and safe execution.
RADAR is a scalable neural framework that enhances vehicle routing problem solvers for asymmetric scenarios by leveraging Singular Value Decomposition to encode static distance asymmetry and Sinkhorn normalization to model dynamic interaction asymmetry, thereby achieving superior generalization and performance on both synthetic and real-world benchmarks.
The paper proposes Stratum, a unified system infrastructure that decouples pipeline execution from agent reasoning to efficiently scale agentic ML pipeline search by integrating with existing Python libraries and utilizing an optimized Rust-based runtime, achieving up to 16.6x speedup.
This paper establishes a theoretical foundation for the superior parallelizability of linear RNNs by demonstrating that they correspond to log-depth arithmetic circuits (-complete), whereas nonlinear RNNs are fundamentally limited by their ability to solve - and -complete problems, thereby explaining why linear variants can be efficiently parallelized like transformers while traditional nonlinear RNNs cannot.
This paper proposes a DMD-augmented Unpaired Neural Schrödinger Bridge framework that enhances Ultra-Low Field (64 mT) MRI image quality by leveraging diffusion-guided distribution matching and anatomical structure preservation to achieve superior realism and structural fidelity in translating unpaired 64 mT scans to 3 T quality.
LoRA-MME is a parameter-efficient multi-model ensemble that combines LoRA-tuned UniXcoder, CodeBERT, GraphCodeBERT, and CodeBERTa encoders to achieve strong code comment classification performance, though its high computational cost ultimately limited its final competition score.
This study demonstrates that introducing "Semantic Anchoring," a text-alignment mechanism, effectively resolves intrinsic embedding collapse and domain-locking in cross-species pathology models by using language as a stable coordinate system to re-align visual features, thereby significantly improving cancer detection performance across same-cancer, cross-cancer, and cross-species scenarios.
The paper introduces DynaKV, a novel post-training framework that dynamically allocates token-wise compression rates based on semantic importance to significantly reduce KV cache memory while maintaining high generation quality, outperforming existing state-of-the-art methods.
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 study introduces Equilibrium-Informed Neural Networks (EINNs), a novel deep learning approach that reverses the traditional parameter-search process by inferring system parameters from candidate equilibrium states to efficiently detect critical bifurcation thresholds and tipping points in complex nonlinear dynamical systems.
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.