📢 CFP: IIR 2026 — 16th Italian Information Retrieval Workshop 📍 Sapienza University of Rome, Nov 12–14, 2026 🔗 Co-located with #CIKM2026 (Nov 7–11)
 ⏰ Submission deadline: Sept 5, 2026 (AoE)
 🌐 lnkd.in/dqSagFcT

🎉 Two papers at #SIGIR2026! 🐱 CATS: Clustering + Thompson Sampling for smarter negative mining in RecSys. +20% NDCG@10. ✂️ Load-sensitive Selective Pruning: dynamic embedding dimensionality for dense retrieval under load. +400% queries served. See you in Melbourne! #IR #RecSys

Giulia Iadisernia is a first-year PhD student in Data Science at Sapienza, supervised by Fabrizio Silvestri a. Her research focuses on the #trustworthiness and #explainability of LLMs. She also collaborates with the Applied Research Team at the Bank of Italy.

Alessia Borghini is a first-year PhD student in the National Doctorate in AI supervised by Fabrizio Silvestri. Her research focuses on Mechanistic Interpretability for #AISafety, aiming to understand LLMs and improve alignment and reliability. Fell free to reach out!!

New paper from @wanifarooq848 on Vision Language Models! Read it on arxiv! #RSTLess #Sapienza

Alessio Borgi is a PhD student in Graph Neural Networks & Generative AI—bridging geometry, topology, and diffusion models for robotics, vision, and biomedical AI. Feel free to contact him for collaborations. #AI #GNN #GenerativeAI #Robotics #RSTLess #Sapienza

Gavriel Di Nepi is a first-year PhD student in Data Science at Sapienza under the supervision of prof. @fabreetseo . His research focuses on human-inspired memory for #AI to boost performance and reasoning, in collaboration with @bakerhughesco. #RSTLess #Sapienza

Meet Maria Vittoria Vestini, 1st-year PhD student in Data Science at Sapienza University of Rome, supervised by @fabreetseo. Her research explores interactions and learning dynamics between large and small language models. Learn more: rstless.it/ #RSTLess #Sapienza

- We introduce the notion of Directed Cellular Sheaves over directed hypergraphs - From this construction, we derive the Directed Sheaf Hypergraph Laplacian, a complex-valued Hermitian operator that unifies and generalizes existing graph and hypergraph convolutional operators.

4/5 Crucially, these circuits are composable. We show they can be recombined into generalist models, behaving like modular building blocks rather than isolated explanations.

3/5 We introduce a training-time objective that steers circuit formation toward specific concepts. After training, we use pruning to extract class-specific circuits: functional subnetworks specialized for a subset of classes.

2/5 Most mechanistic interpretability work finds circuits post hoc, after training a model. We ask a different question: can we control which concepts shape circuits during training?