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cs.AI2026
The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
Nikolai Rozanov, Subhabrata Dutta, Preslav Nakov +1
The Bitter Lesson, which posits that general-purpose methods that scale with computation and data ultimately outperform those with built-in human knowledge, has become a dominant p…
cs.AI2025
Fine-tuning with RAG for Improving LLM Learning of New Skills
Humaid Ibrahim, Nikolai Rozanov, Marek Rei
Large language model (LLM) agents deployed for multi-step tasks frequently fail in predictable ways: attempting actions with unmet preconditions, issuing redundant commands, or mis…
cs.AI2025
StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
Nikolai Rozanov, Marek Rei
Large language models (LLMs) are increasingly used as autonomous agents, tackling tasks from robotics to web navigation. Their performance depends on the underlying base agent. Exi…