Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
Raw feed or Signals?
Raw feed is chronological evidence. Signals ranks and interprets material change.
BearingNAS introduces a hardware-aware neural architecture search framework to deploy intelligent fault diagnosis directly on sensor microcontrollers.
Research paper proposes a deep reinforcement learning approach to optimize service placement, computational delegation, and power in edge-cloud networks.
Research introduces a systematic framework and benchmark scenarios for continual anomaly detection (CAD) in tabular data, addressing limitations of current benchmarks.
Research proposes a Dual-Domain Fused LSTM (DDF-LSTM) model to improve time-dependent reliability analysis for engineering systems under uncertainty.
Research compares Monte Carlo Dropout and Deep Ensemble for uncertainty quantification in AI-driven crash simulation surrogates, using an open-source bumper beam.
Research indicates that current LLM alignment schemes and safety filters may not reduce harmful outputs to zero, posing challenges for robust safety.
Research introduces domain-conditional position offsets to reduce cold-start inaccuracy in autoregressive language models by adding a learned vector.
Research introduces the 'information shadow,' structural limits on what language models can learn from text alone, beyond data coverage gaps.
Research explores using LLMs for agentic calibration of grey-box simulation models, addressing high-dimensional parameter spaces and expensive evaluations.
Interactive Training 2 introduces an open-source control plane for auditable steering of live model training, abstracting trainer-specific code.
Research proposes a federated fine-tuning method that reduces communication bottleneck by averaging small trainable latents instead of full model weights.
Research introduces geometric analysis of transformer residual streams, measuring representation movement and structural changes across model layers.
Research explores Wireless Physical Neural Networks (WPNNs) that embed neural computation in analog hardware using MIMO relays and power amplifiers.
Researchers propose physical self-supervised learning for IMU-based sensing, reducing reliance on labeled data and improving robustness to heterogeneity.
Research introduces a scalable spiking embedding predictive architecture for dynamic graphs, improving fraud detection and recommender systems.
New research addresses quantifying the risk of rare failures in language models, proposing improved methods for low probability estimation to avoid bias.
New research introduces AHEAD, a multi-class label aggregation method using interpretable cross-annotator modeling to infer true labels from noisy crowdsourced data.
Research introduces Hybrid Latent-Structural Fusion (HLSF), combining tensor decomposition and normalizing flows for cyber anomaly detection.
A research paper systematically compares two geospatial foundation models, TerraMind and THOR, developed under the European Space Agency's $\Phi$-lab.
Researchers introduced Signed Rectified Flow, a generative model generalizing Rectified Flow to promote desired distributions while suppressing undesired ones.
Research paper proposes a two-stage online learning framework for detecting service-affecting failures in mobile core networks using traffic data.
Research proposes a censoring-aware in-context learning method for generalized supplier lead time estimation, addressing right-censored data.
Researchers demonstrated a looped transformer, Ouro-RLTT, can predict computational success during an ongoing task, improving accuracy on GSM8K.
Research proposes a multi-view classification method using global anchor consensus to improve robustness under noisy data supervision.
Research models transformer dynamics as interacting particle systems, reformulating deep linear encoder-only transformers as a generalized Kuramoto-type model.
Research explores self-evolving default actions for cooperative multi-agent reinforcement learning (MARL) in continuous action spaces, addressing bias.
Research introduces GNNAS-TSP, a Graph Neural Network (GNN)-based framework for automated algorithm selection (AS) in the Traveling Salesman Problem.
New research proposes an exposure-based reinforcement learning method for ranking, aiming to simplify complex gradient computations.
ConceptCF introduces a method for generating counterfactual explanations for time series models based on human-interpretable concepts.
Research introduces Decafs, a flow-based generative model using Lie groups to disentangle latent spaces, improving interpretability and controlled generation.
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