The Neuro-symbolic AI Seminar is jointly organized by IMAR, FMI-CS, ILDS, with the support of BRD and IBF.
All seminars, except where otherwise indicated, will be on Thursdays between 10:00 and 12:00, Bucharest time. The seminars are held locally at Hall 203 of the Faculty of Mathematics and Computer Science, University of Bucharest (in the new PBTower location), but can also be occasionally held remotely.
To receive announcements about the seminar, please send an email to nsai-seminar@ilds.ro.
Organizers: Laurențiu Leuștean, Marius Popescu
Thursday, May 21, 2026
Radu Negulescu (The Informational Buildup Foundation)
Durable Alignment via Orthogonal Memory in Large Language Models
Abstract:
Introducing an architecture for update-stable alignment in LLMs, based on the Informational Buildup Framework (IBF), that encodes behavioral corrections in a gradient-isolated orthogonal memory space with no update path from the base model.
Alignment here denotes persistence of a specified target behavior on a delimited input region, not its specification.
The design avoids parameter entanglement and retrieval inconsistency by placing constraints outside the model’s weights, governed by dynamics that reinforce internally consistent corrections and dissolve contradicted ones.
On a frozen Mistral-7B, the system installs novel facts absent from the base model, achieves accuracy on counterfactual override while preserving unchanged facts, enables targeted geometric unlearning with near-zero collateral drift on matched controls, and maintains bounded amplitudes under continuous adversarial injection. Corrections remain effective after LoRA and instruction tuning, demonstrating durable alignment under model evolution.
Thursday, May 28, 2026
Bogdan Ichim (University of Bucharest & IMAR) and Ștefania-Alexandra Cristea (University of Bucharest)
An Introduction to Voting Theory
Abstract:
We make a brief introduction to Voting Theory, including the Condorcet and Borda paradoxes, as well as the Condorcet efficiency of a voting system. We also present some computational results obtained under the assumption of the Impartial Anonymous Culture.
Finally, we examine the potential of natural language processing techniques for estimating the electorate’s intentions and forecasting final election outcomes.