43 citations · 51 across the 8 of their papers we have counts for
8 papers
APPA: Recoverable Information-Flow Control for Real-World LLM Agents
Arseny Kravchenko, Vadim Liventsev, Innokentii Konstantinov +2
LLM agents deployed in practical workflows routinely mix private context, untrusted tool and web outputs, and external side effects. While information-flow control (IFC) provides s…
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models
Anastasiia Grishina, Vadim Liventsev, Aki Härmä +1
Program synthesis with Large Language Models (LLMs) suffers from a "near-miss syndrome": the generated code closely resembles a correct solution but fails unit tests due to minor e…
PhilHumans: Benchmarking Machine Learning for Personal Health
Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah +14
The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other applicati…
Intensive Care as One Big Sequence Modeling Problem
Vadim Liventsev, Tobias Fritz
Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control. However, previous research has demonst…
Fully Autonomous Programming with Large Language Models
Vadim Liventsev, Anastasiia Grishina, Aki Härmä +1
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answ…
Autoencoders as Tools for Program Synthesis
Sander de Bruin, Vadim Liventsev, Milan Petković
Recently there have been many advances in research on language modeling of source code. Applications range from code suggestion and completion to code summarization. However, compl…
Neurogenetic Programming Framework for Explainable Reinforcement Learning
Vadim Liventsev, Aki Härmä, Milan Petković
Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods…
BF++: a language for general-purpose program synthesis
Vadim Liventsev, Aki Härmä, Milan Petković
Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge int…