Author

Razvan-Gabriel Dumitru

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#machine learning Preprint Sep 2026

NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI

NeuroTS-Net is introduced, a three-dimensional encoder-decoder convolutional neural network architecture for multi-class semantic segmentation that incorporates a dual-scale raw-detail stream, adaptive low-resolution context selection, and detail-preserving multipath downsampling that efficiently modeling broader tumor...

Darius Peteleaza, Razvan-Gabriel Dumitru, Bogdan Neamtu et al. · 0 citations

#artificial intelligence Review Sep 2026

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the need for automa...

Harsh Raj, David Lee, Anas Mahmoud et al. · 0 citations

Preprint Jul 2026

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data

CRAFT is introduced, a method that converts any rubric based evaluation dataset into a model specific diagnosis of weak capabilities, and yields both a sharper picture of what a model cannot do and measurably better models after finetuning on that diagnosis.

Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru et al. · 0 citations

Preprint Aug 2026

Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL

This work proposes Rubric Dropout, a one-line fix borrowed from neuron dropout that randomly drops a subset of the rubric's criteria before computing the reward, so the policy never optimizes the same rubric twice.

Minglai Yang, Xinyu Guo, Utkarsh Tyagi et al. · 0 citations

Preprint Jul 2026

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

This work introduces an interaction-centric taxonomy that localizes failures to the interactions in which they originate and identifies the responsible component, and organizes 41 failure modes by assigning each to an edge between two components and a fault side indicating where the repair belongs.

Harsh Raj, Vipul Gupta, Anas Mahmoud et al. · 0 citations