activity
20202026
most citedHigh-dimensional approximation spaces of artificial neural networks and applications to partial differential equations

5 citations · 10 across the 18 of their papers we have counts for

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cs.AI2026

Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization

Leonardo Torti, Emanuele Rimoldi, Tomer Galanti +2

With the growing number of LLMs available, selecting which one to use for a prompt has become a key question. A large part of the routing literature relies on semantically embeddin…

cs.AI2026

Distribution-Aware Programming: Learning Specialized Solvers from Experience

Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano +1

Many optimization problems are solved repeatedly on instances drawn from the same underlying distribution. In this setting, a system can begin with a general-purpose solver and use…

cs.AI2026

Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure

Federico Bottino, Carlo Ferrero, Nicholas Dosio +1

Organizational knowledge used by AI agents typically lacks epistemic structure: retrieval systems surface semantically relevant content without distinguishing binding decisions fro…

cs.AI2026

Inference-Time Amplification of Weak Reasoning Models

Varun Sunkaraneni, Pierfrancesco Beneventano, Riccardo Neumarker +2

How much of the capability of a reasoning model exposed by repeated sampling can an imperfect selector recover? We study this problem as {\em inference-time amplification}, separat…

cs.AI2026

pAI/MSc: ML Theory Research with Humans on the Loop

Mahmoud Abdelmoneum, Pierfrancesco Beneventano, Tomaso Poggio

We present pAI/MSc, an open-source, customizable, modular multi-agent system for academic research workflows. Our goal is not autonomous scientific ideation, nor fully automated re…