Mikhail Menschikov

Mikhail Menschikov, Matvey Iskornev, Alexander Kharitonov et al.

We introduce PersonalAI 2.0 (PAI-2), a novel framework, designed to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG). The proposed approach addresses key limitations of existing Graph Retrieval-Augmented Generation (GraphRAG) methods by incorporating a dynamic, multistage query processing pipeline. The central point of PAI-2 design is its ability to perform adaptive, iterative information search, guided by extracted entities, matched graph vertices and generated clue-queries. Conducted evaluation over six benchmarks (Natural Questions, TriviaQA, HotpotQA, 2WikiMultihopQA, MuSiQue and DiaASQ) demonstrates improvement in factual correctness of generating answers compared to analogues methods (LightRAG, RAPTOR, and HippoRAG 2). PAI-2 achieves 4% average gain by LLM-as-a-Judge across four benchmarks, reflecting its effectiveness in reducing hallucination rates and increasing precision. We show that use of graph traversal algorithms (e.g. BeamSearch, WaterCircles) gain superior results compared to standard flatten retriever on average 6%, while enabled search plan enhancement mechanism gain 18% boost compared to disabled one by LLM-as-a-Judge across six datasets. In addition, ablation study reveals that PAI-2 achieves the SOTA result on MINE-1 benchmark, achieving 89% information-retention score, using LLMs from 7-14B tiers. Collectively, these findings underscore the potential of PAI-2 to serve as a foundational model for next-generation personalized AI applications, requiring scalable, context-aware knowledge representation and reasoning capabilities.

Mikhail Menschikov, Alexander Kharitonov, Maiia Kotyga et al.

Large Language Models (LLMs) exhibit position bias - a systematic tendency to neglect information at specific context positions. However, the patterns of position bias behavior, depending on the language or model, remain unexplored. We present a multilingual study across five typologically distinct languages (English, Russian, German, Hindi, and Vietnamese) and five model architectures, examining how position bias interacts with prompt strategies and affects output entropy. Our key findings are: (1) Position bias is primarily model-driven, yet exhibits language-specific variations. For instance, Qwen2.5-7B-Instruct and DeepSeek 7B Chat consistently favors late positions, challenging established assumptions of a universal early-token bias in LLMs. (2) Explicitly instructing the model that "the context is relevant to the query" unexpectedly reduces accuracy across languages, undermining common prompt-engineering practices. (3) While the largest accuracy drop occurs when relevant information is placed in the middle of the context, this is not explicitly reflected by a corresponding peak in output entropy.

Mikhail Menschikov, Dmitry Evseev, Victoria Dochkina et al.

Personalizing language models by effectively incorporating user interaction history remains a central challenge in the development of adaptive AI systems. While large language models (LLMs) combined with Retrieval-Augmented Generation (RAG) have improved factual accuracy, they often lack structured memory and fail to scale in complex, long-term interactions. To address this, we propose a flexible external memory framework based on knowledge graphs, automatically constructed and updated by the LLM itself, and capable of encoding information in multiple formats-including nodes, triplets, higher-order propositions, and episodic traces. Building upon the AriGraph architecture, we introduce a novel hybrid graph design that supports both standard edges and two types of hyperedges, enabling rich and dynamic semantic and temporal representations. Our framework also supports diverse retrieval mechanisms, including A*, water-circle propagation, beam search, and hybrid methods, making it adaptable to different datasets and LLM capacities. We evaluate our system on three benchmarks-TriviaQA, HotpotQA, and DiaASQ-demonstrating that different memory and retrieval configurations yield optimal performance depending on the task. Additionally, we extend the DiaASQ benchmark with temporal annotations and internally contradictory statements, showing that our system remains robust and effective in managing temporal dependencies and context-aware reasoning.