7 papers
Planning to Explore: Curiosity-Driven Planning for LLM Test Generation
Alfonso Amayuelas, Firas Laakom, Piotr PiÄkos +5
The use of LLMs for code generation has naturally extended to code testing and evaluation. As codebases grow in size and complexity, so does the need for automated test generation.…
PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection
Jinhan Liu, Yibo Yang, Ruiying Lu +4
Detecting pre-training data in Large Language Models (LLMs) is crucial for auditing data privacy and copyright compliance, yet it remains challenging in black-box, zero-shot settin…
Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Wenyi Wang, Piotr PiÄkos, Li Nanbo +5
Recent studies operationalize self-improvement through coding agents that edit their own codebases. They grow a tree of self-modifications through expansion strategies that favor h…
PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors
Yimeng Chen, Piotr Piȩkos, Mateusz Ostaszewski +2
Evaluating the scientific discovery capabilities of large language model based agents, particularly how they cope with varying environmental complexity and utilize prior knowledge,…
Hyperbolic Residual Quantization: Discrete Representations for Data with Latent Hierarchies
Piotr PiÄkos, Subhradeep Kayal, Alexandros Karatzoglou
Hierarchical data arise in countless domains, from biological taxonomies and organizational charts to legal codes and knowledge graphs. Residual Quantization (RQ) is widely used to…
Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing
Piotr PiÄkos, Róbert Csordás, Jürgen Schmidhuber
Recent advances in large language models highlighted the excessive quadratic cost of self-attention. Despite the significant research efforts, subquadratic attention methods still…