186 citations · 187 across the 11 of their papers we have counts for
14 papers
Evidence-First Agentic Verification
Reza Sanayei, Razvan-Gabriel Dumitru, Sunjie Hou +7
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the…
NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI
Darius Peteleaza, Razvan-Gabriel Dumitru, Bogdan Neamtu +3
Pediatric brain tumors are a leading cause of cancer-related mortality in children, and their small, rare, and often low-contrast subregions make accurate manual delineation challe…
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
Harsh Raj, David Lee, Anas Mahmoud +7
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 ou…
Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-Based RL
Minglai Yang, Xinyu Guo, Zhengliang Shi +8
Rubric-based reinforcement learning, in which an LLM judge scores responses against prompt-specific criteria, has become a standard way to post-train language models on tasks witho…
Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures
Harsh Raj, Vipul Gupta, Anas Mahmoud +4
Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates…
CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data
Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru +3
Evaluations should do more than measure a models current performance. They should tell us what to fix for the next model iteration and provide a way to generate targeted post train…
Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers
MohammadHossein Rezaei, Anas Mahmoud, Zihao Wang +6
Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based training methods rely on an LL…
ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models
Razvan-Gabriel Dumitru, Darius Peteleaza, Vikas Yadav +1
Large language models excel at complex tasks by breaking down problems into structured reasoning steps. However, reasoning traces often extend beyond reaching a correct answer, cau…
CopySpec: Accelerating LLMs with Speculative Copy-and-Paste Without Compromising Quality
Razvan-Gabriel Dumitru, Minglai Yang, Vikas Yadav +1
We introduce CopySpec, a simple yet effective technique to tackle the inefficiencies LLMs face when generating responses that closely resemble previous outputs or responses that ca…
Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy
Razvan-Gabriel Dumitru, Paul-Ioan Clotan, Vikas Yadav +2
This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established b…
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
Razvan-Gabriel Dumitru, Vikas Yadav, Rishabh Maheshwary +3
We present a simple meta quantization approach that quantizes different layers of a large language model (LLM) at different bit levels, and is independent of the underlying quantiz…
ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition
Haris Riaz, Razvan-Gabriel Dumitru, Mihai Surdeanu
In this work, we revisit the problem of semi-supervised named entity recognition (NER) focusing on extremely light supervision, consisting of a lexicon containing only 10 examples…
Enhancing Transformer RNNs with Multiple Temporal Perspectives
Razvan-Gabriel Dumitru, Darius Peteleaza, Mihai Surdeanu
We introduce the concept of multiple temporal perspectives, a novel approach applicable to Recurrent Neural Network (RNN) architectures for enhancing their understanding of sequent…
Using DUCK-Net for Polyp Image Segmentation
Razvan-Gabriel Dumitru, Darius Peteleaza, Catalin Craciun
This paper presents a novel supervised convolutional neural network architecture, "DUCK-Net", capable of effectively learning and generalizing from small amounts of medical images…