Dec 2025
A member of our team was recognized among the best reviewers at NeurIPS, reflecting sustained, high-quality peer review for the machine learning …
Teaser for C-ing Clearly: Enhanced Binary Code Explanations using C code
Teodor Poncu, Ioana Pintilie, Marius Dragoi, Dragos Tantaru, Florin Brad
Under review Dec 2025
Links: arXiv Abstract Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to …
Teaser for Beyond Pass@k: Breadth-Depth Metrics for Reasoning Boundaries
Marius Dragoi, Ioana Pintilie, Florin Gogianu, Florin Brad
Accepted at NeurIPS 2025 Workshop FoRLM (Poster) Dec 2025
Links: arXiv Abstract Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm to improve Large Language Models on …
Teaser for ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Adrian Catalin Lutu, Ioana Pintilie, Elena Burceanu, Andrei Manolache
Accepted at NeurIPS 2025 Workshop BERT2S (Oral) Dec 2025
Links: arXiv GitHub Abstract We present ChronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production …
Teaser for Not All Splits Are Equal: Rethinking Attribute Generalization Across Unrelated Categories
Liviu Nicolae Fircă, Antonio Bărbălau, Dan Oneata, Elena Burceanu
Accepted at NeurIPS 2025 Workshop CauScien (Poster) Dec 2025
Links: arXiv GitHub Abstract Can models generalize attribute knowledge across semantically and perceptually dissimilar categories? While prior work …
Teaser for Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone
Antonio Bărbălau, Cristian Daniel Păduraru, Teodor Poncu, Alexandru Tifrea, Elena Burceanu
Accepted at NeurIPS 2025 Workshops: Mechanistic Interpretability; Reliable ML from Unreliable Data (Poster) Dec 2025
Links: arXiv Abstract Sparse Autoencoders (SAEs) have proven valuable due to their ability to provide interpretable and steerable representations. …
Teaser for Learning (Approximately) Equivariant Networks via Constrained Optimization
Andrei Manolache, Luiz F.O. Chamon, Mathias Niepert
Accepted at NeurIPS 2025 (Oral, top 0.4%) Dec 2025
Links: arXiv · GitHub Abstract Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and …
rfilipescu, ext-abarbalau, eburceanu
Nov 2025
Docket: BTD-2414-C1 · US application: 19/378,395 · PTO filed: Nov 4, 2025 · Status: Filed · Technologies: Machine learning Continuation patent …
rfilipescu, ext-abarbalau, eburceanu
Nov 2025
Docket: BTD-2414 · US application: 19/378,391 · PTO filed: Nov 4, 2025 · Status: Filed · Technologies: Machine learning Patent application on …
eoneata, ssmeu
Sep 2025
Docket: BTD-2511 · PTO filed: Sep 30, 2025 · Status: Filed · Technologies: Machine learning US patent application on training deepfake detectors.