most citedPORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CY2026

Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?

Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi +3

In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent, defined by jurisdiction, temp…

cs.IR20261 cited

PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models

Lorenzo Molfetta, Giacomo Frisoni, Nicolò Monaldini +1

Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage la…

cs.LG2026

Mixture of Masters: Sparse Chess Language Models with Player Routing

Giacomo Frisoni, Lorenzo Molfetta, Davide Freddi +1

Modern chess language models are dense transformers trained on millions of games played by thousands of high-rated individuals. However, these monolithic networks tend to collapse…

cs.CV2026

Graph-of-Mark: Promote Spatial Reasoning in Multimodal Language Models with Graph-Based Visual Prompting

Giacomo Frisoni, Lorenzo Molfetta, Mattia Buzzoni +1

Recent advances in training-free visual prompting, such as Set-of-Mark, have emerged as a promising direction for enhancing the grounding capabilities of multimodal language models…

cs.AI2026

Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era

Giovanni Pio Delvecchio, Lorenzo Molfetta, Gianluca Moro

The integration of symbolic computing with neural networks has intrigued researchers since the first theorizations of Artificial intelligence (AI). The ability of Neuro-Symbolic (N…

cs.AI2026

FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System

Lorenzo Molfetta, Alessio Cocchieri, Stefano Fantazzini +3

Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extrem…