v1.14.8a2 adds a single agent action to Flow definitions and validates CEL expressions at load time. It includes a new Datadog integration guide with an importable operations dashboard and updated snapshot and changelog for v1.14.8a1.
FedMGS addresses client- and node-level modality imbalance in federated graph learning by synthesizing latent semantic representations. It integrates an availability-aware graph encoder, prototype-guided semantic synthesizer, and reliability-calibrated fusion mechanism to recover missing modalities while preserving semantic alignment. Experiments show FedMGS achieves up to 17.41% performance gains over baselines across four tasks.
A new framework generates synthetic dialogue without human-annotated data, using only intent definitions. It incorporates topic and style attributes, with post-hoc stylization models Univ and Exam, and an LLM-as-a-judge filtering process. Results show up to 93.3% of human-annotated data performance, confirming that style diversity is more critical than topic diversity for data utility.
Direct Advantage Estimation (DAE) is extended to partially observable domains with minimal modifications. A discrete latent dynamics model reduces computational overhead by efficiently approximating transition probabilities, enabling scalable and sample-efficient deep reinforcement learning in high-dimensional observation spaces.
A new defense framework enhances deep neural networks' resilience to false data injection attacks in power grids by adding a padding layer with pseudofeatures derived from input statistical distributions. This lightweight, model-agnostic approach increases input dimensionality in a randomized, data-aware way, making adversarial perturbations non-transferable and unpredictable, thus effectively countering attacks without performance degradation.
A study shows diffusion models can achieve global minimizers without explicit timestep embeddings. Ablation studies on CelebA and CIFAR-10 reveal time-agnostic models maintain high fidelity and outperform conditioned ones in FID, precision, and recall.
DeepGaLA is a neural-network surrogate that provides uncertainty-aware predictions for inverse problems in partial differential equations. It achieves accuracy comparable to Gaussian-process surrogates while maintaining efficiency in high-dimensional parameter spaces and incorporating differential-equation constraints.
A synthetic framework reveals that superposition increases over time with transient dips at task boundaries, indicating boundary-specific interference. Higher feature sparsity promotes superposition without inevitable forgetting, provided representation strength is maintained. Task-level effective rank grows with sparsity, showing broader capacity usage under sparse conditions.
HEPTv2 achieves 98.6% tracking efficiency with 0.8% fake rate on TrackML, using only 15 ms inference time and 0.4 GB memory per event. It outperforms prior transformer and graph-based methods in efficiency and reduces latency by factors of 7 and 38–52, respectively, enabling real-time particle reconstruction at the HL-LHC.
A two-stage evolutionary strategy improves Physics-Informed Neural Network performance by first screening hyperparameter candidates via low-fidelity training, then refining top candidates with gradient-based optimization. The approach reduces mean error significantly across Advection, Klein-Gordon, and Helmholtz equation problems under fixed computational budgets.
A new method combines topological data analysis and machine learning to monitor high-dimensional dynamic processes. It represents time-series data as manifolds, uses topological descriptors to capture structure, and employs neural ordinary differential equations to model dynamic evolution. The approach effectively detects diverse events in industrial process data and outperforms reconstruction-based and trajectory-based alternatives.
SSH-Net is a structured deep neural network designed to predict failure time distribution functions under competing risks. It uses separate sub-networks for different covariate groups, improving accuracy by aligning neural structure with data hierarchy. The model is validated through simulation studies and applied to Titan GPU failure data.
A pretrained speech classifier is repurposed as a backbone for guided diffusion-based speech generation. By attaching a lightweight subnetwork and training it under denoising score matching, the approach achieves high speech quality with reduced memory and computational cost, using a single model instead of two separately trained components.
A new framework addresses data bias in machine learning by incorporating coverage constraints to ensure sufficient representation of intersectional subgroups. It trades small bias errors for greater data efficiency and formulates bias mitigation as an integer linear program, characterizing the price of fairness as a function of fairness tolerance to guide data governance and legal compliance.
ASYS proposes a prior-guided framework that uses mathematical theory and evolutionary search to generate interpretable symbolic forms of PDE solutions. It produces analytical representations for complex problems like Allen-Cahn dynamics and Keller-Segel blow-up, offering new pathways for mathematical analysis beyond traditional methods.
This study introduces Riemannian sharpness, a reparametrization-invariant measure of flatness grounded in Fisher Information Matrix geometry. It proves SGD's stationary distribution concentrates at Riemannian-flat minima and links this geometric bias to generalization via a PAC-Bayes bound. Experiments on MNIST and CIFAR-10 show Riemannian sharpness better tracks generalization than Euclidean sharpness, with scaling consistent with theory.
UltraQuant introduces a 4-bit KV caching method tailored for context-heavy agent workloads. It achieves 3.47x reduction in P50 time-to-first-token in late rounds and 1.63x higher output throughput compared to FP8 KV caching, using FP8 queries, FP4 KV tensors, and native AMD CDNA4 scaled-MFMA support.
This paper introduces Marginal Advantage Accumulation (MAA), a post-processing architecture that addresses cross-batch inconsistency in memory-driven agent self-evolution. MAA formalizes alignment and comparability as structural conditions, uses differential signals and exponential moving average to accumulate signed evidence per operation, and ensures traceability via semantic identity merging. It outperforms batch-level baselines in 14 out of 16 settings and reduces token consumption by about 75%.
RefRad2D is a large-scale bilingual dataset of 1.2M CT and MR image-text pairs from clinical practice. Trained on this data, RadGrounder achieves competitive results in VQA and report generation while maintaining language quality through spatial grounding supervision without performance degradation.
A new dataset, IFLLM, collects mouse trajectories and eye gazing data from users interacting with LLMs. It shows that implicit feedback significantly improves LLM alignment, boosting text-based reward model accuracy from 55% to 64% and nearly tripling response quality improvements after DPO training on eight LLMs.