Below you can see a list of past talks at the Logic Seminar in this season. For forthcoming talks, see the main page.
Thursday, April 2, 2026
Radu Negulescu (The Informational Buildup Foundation)
The Informational Buildup Framework: A Computable Theory of Coherence-Preserving Dynamics
Abstract:
Mainstream AI treats information as something to store, transmit, and optimize. This talk starts from a different premise: information is not data, but the achievement of structural alignment between a system’s internal configuration and the structure of its environment.
From this premise, we introduce the Informational Buildup Framework (IBF), and here develop a computable formulation of its core dynamics through two coupled equations. The first is a Law of Motion that drives the system toward greater coherence. The second is the Modification Dynamics, which continuously reshape the coherence landscape in response to local discrepancy signals. In this framework, memory, agency, intelligence, and self-correction are not separate modules, but they emerge from the dynamics themselves.
To test IBF, we instantiate it in a controlled non-stationary environment and in chess, evaluated independently with Stockfish. Despite storing no raw training data, the framework retains knowledge better than replay-based baselines and yields significant behavioral gains from reward-free dynamics alone.
The framework predicts an emergent agency channel, confirmed empirically, that determines what the system learns. Ablation studies reveal a cascade of capacities: memory forms first, agency guides exploration, agency enables self-correction, and curated memory supports intelligence. When agency is removed, the cascade fails and the system forgets.
Controlled ablations confirm the predicted dependencies between mechanisms and capacities: when a mechanism is removed, the corresponding capability degrades in the expected way.
Thursday, March 26, 2026
Ciprian Păduraru (University of Bucharest)
Reinforcement Learning: Foundations, Algorithms, and LLM Applications I
Abstract:
This two-part seminar introduces the main ideas and methods of reinforcement learning, from foundational concepts to modern applications. The first part covers the basic framework of reinforcement learning, with emphasis on Deep Q-Networks (DQN) and an introduction to policy gradients. The second part focuses on more advanced algorithms such as Proximal Policy Optimization (PPO) and explores the growing role of reinforcement learning in the training and alignment of large language models.
Thursday, March 19, 2026
Gheorghe Ștefănescu (University of Bucharest)
Adaptive virtual organisms: A compositional model for complex hardware-software binding
Abstract:
The relation between a structure and the function it runs is of interest in many fields, including computer science, biology (organ vs. function) and psychology (body vs. mind). Our paper addresses this question with reference to computer science recent hardware and software advances, particularly in areas as Robotics, Self-Adaptive Systems, IoT, CPS, AI-Hardware, etc.
At the modelling, conceptual level our main contribution is the introduction of the concept of “virtual organism” (VO), to populate the intermediary level between reconfigurable hardware agents and intelligent, adaptive software agents. A virtual organism has a structure, resembling the hardware capabilities, and it runs low-level functions, implementing the software requirements. The model is compositional in space (allowing the virtual organisms to aggregate into larger organisms) and in time (allowing the virtual organisms to get composed functionalities).
The virtual organisms studied here are in 2D (two dimensions) and their structures are described by 2D patterns (adding time, we get a 3D model). By reconfiguration an organism may change its structure to another structure in the same 2D pattern. We illustrate the VO concept with a few increasingly more complex VO’s dealing with flow management or a publisher-subscriber mechanism for handling services. We implemented a simulator for a VO, collecting flow over a tree-structure (TC-VO), and the quantitative results show reconfigurable structures are better suited than fixed structures in dynamically changing environments. Finally, we briefly show how Agapia – a structured parallel, interactive programming language where dataflow and control flow structures can be freely mixed – may be used for getting quick implementations for VO’s simulation.
Bibliography:
C. I. Păduraru. G. Ștefănescu, Adaptive Virtual Organisms: A Compositional Model for Complex Hardware-software Binding, Fundamenta Informaticae 173(2-3) (2020), 139-176, doi:10.3233/FI-2020-1919.
Thursday, March 12, 2026
Gheorghe Ștefănescu (University of Bucharest)
Mixed control-flow and data-flow computing models
Abstract:
We start with a brief presentation of the evolution of formalisms for understating brain neuronal activity and the connections with computing machines, particularly with an emphasis on the works of Boole (1854), McCulloch and Pitts (1943), Kleene (1951) et al. Then we go to the technical part of the talk.
Symmetric monoidal category with feedback (a.k.a. traced monoidal category), introduced by Ștefănescu in 1986, is a versatile algebraic structure. Initially, an additive interpretation of the monoidal operation was considered, aiming to model control structures like flowchart schemes or finite automata. Later on, with a multiplicative interpretation of the monoidal operation, the structure was successfully used to capture parallel computing models, in particular data-flow networks.
Classical sequential programs have limited parallelism, while parallel data-flow networks have limited control. Hence, it is desirable to have a setting freely mixing control and parallelism. We propose symmetric semiringal categories (having two monoidal operations: addition and multiplication) with feedback as an algebraic candidate to study this complicated setting. Finally, we present a few partial results (the construction of the free mixalgebra and the related free distributed category; an example showing the formal extraction of parallel components of a cyclic sequential program).
Thursday, March 5, 2026
Marius Popescu (University of Bucharest)
NeurASP, a differentiable extension of the Answer Set Programming
Abstract:
We present NeurASP, a differentiable extension of the Answer Set Programming. We describe the basic concepts and syntax of ASP, the associated differentiable extension including semantics, inference, and learning procedures.