Hugging Face Trending Papers
TRIPROBE: Probing Task Separability Beyond Classification for XAI
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arXiv AI
Sep 17
TRIPROBE: Probing Task Separability Beyond Classification for XAI
TRIPROBE is a multi-level probing framework designed to diagnose task separability in machine learning pipelines. It evaluates separability across input spaces, learned feature representations, and classifier outputs by decomposing multi-task problems into binary subtasks and applying Foundational, Latent, and Final probes. Using Maximum Fisher's Discriminant Ratio, TRIPROBE identifies bottlenecks and task pairs that affect performance, as demonstrated on the Roshambo sEMG benchmark.
arXiv Machine Learning
Aug 13
LLM Router: Rethinking Routing with Prefill Activations
arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.
arXiv Machine Learning
Jul 21
Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation
arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.
arXiv AI
4d ago
MLToolBench: Learning Tool-Augmented Agents for Machine Learning Development
arXiv:2609.36679v1 Announce Type: new Abstract: Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computatio...
arXiv AI
Jul 16
Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation
arXiv:2603. 00546v2 Announce Type: replace Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm across various domains.
arXiv Machine Learning
Jun 10
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
arXiv:2606. 09853v1 Announce Type: new Abstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from any single modality alone.