134 citations · 330 across the 35 of their papers we have counts for
38 papers
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents
Shoufa Chen, Luyuan Wang, Xuan Yang +7
As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use ta…
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
Zifan Lyu, Chahine Nejma, Tobias Wegel +2
Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this is often wasteful: if a mode…
Fast Unlearning at Scale via Margin Self-Correction
Federico Di Gennaro, Alexander Shevchenko, Fanny Yang
Language-model unlearning updates a trained model to behave as if it had not seen selected training examples, while preserving utility and avoiding costly retraining. Existing appr…
Hedging on the Frontier: Learning New Tasks with Few Samples
Tobias Wegel, Federico Di Gennaro, Geelon So +1
When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks i…