3 papers
cs.CR2026
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…
cs.AI2025
app.build: A Production Framework for Scaling Agentic Prompt-to-App Generation with Environment Scaffolding
Evgenii Kniazev, Arseny Kravchenko, Igor Rekun +5
We present app.build (https://github.com/neondatabase/appdotbuild-agent), an open-source framework that improves LLM-based application generation through systematic validation and…
cs.CV2019
Safe Augmentation: Learning Task-Specific Transformations from Data
Irynei Baran, Orest Kupyn, Arseny Kravchenko
Data augmentation is widely used as a part of the training process applied to deep learning models, especially in the computer vision domain. Currently, common data augmentation te…