Publications (11)
Zero-Overhead Introspection for Adaptive Test-Time Compute
Rohin Manvi, Joey Hong, Tim Seyde +3
Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real…
Predicting Bandwidth Utilization on Network Links Using Machine Learning
Maxime Labonne, Charalampos Chatzinakis, Alexis Olivereau
Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur. In this paper, we present a solut…
In-Place Tokenizer Expansion for Pre-trained LLMs
Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera +7
The paper proposes an in‑place tokenizer expansion method that continues a pre‑trained model’s BPE merges on multilingual data, reuses existing token embeddings, and initializes ne…
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
Short-Term Flow-Based Bandwidth Forecasting using Machine Learning
Maxime Labonne, Jorge López, Claude Poletti +1
This paper proposes a novel framework to predict traffic flows' bandwidth ahead of time. Modern network management systems share a common issue: the network situation evolves betwe…
On Iterative Evaluation and Enhancement of Code Quality Using GPT-4o
Rundong Liu, Andre Frade, Amal Vaidya +5
This paper introduces CodeQUEST, a novel framework leveraging Large Language Models (LLMs) to iteratively evaluate and enhance code quality across multiple dimensions, including re…