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Selected work

Representative Papers

EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval

Sep 07, 2026

We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users'emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: https://github.com/NasonovIvan/EmoMed-Agent

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MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

Aug 06, 2026

This work addresses the cold-start problem in biomedical knowledge graphs, where out-of-graph molecules—provided only as SMILES strings—cannot be linked or reasoned over. To tackle this, the authors propose MolBioKG, a two-tier system that first constructs a static index of 2.74 million molecules based on scaffolds, fragments, functional groups, and molecular fingerprints, enabling multi-resolution structural anchoring and static multi-anchor retrieval via Reciprocal Rank Fusion. Building upon this, an adaptive, interpretable knowledge graph traversal is performed using a tool-augmented large language model (Adapt-KG). Requiring no task-specific training, the approach significantly improves multi-hop reasoning performance, raising Hits@10 from 0.585 to 0.876, and boosts target recall for out-of-graph molecules from 0.145 to 0.269, while ensuring predictions are grounded in structural anchors and traceable evidence.

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Latest Papers

EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval

Sep 07, 2026

We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users'emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: https://github.com/NasonovIvan/EmoMed-Agent

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MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

Aug 06, 2026

This work addresses the cold-start problem in biomedical knowledge graphs, where out-of-graph molecules—provided only as SMILES strings—cannot be linked or reasoned over. To tackle this, the authors propose MolBioKG, a two-tier system that first constructs a static index of 2.74 million molecules based on scaffolds, fragments, functional groups, and molecular fingerprints, enabling multi-resolution structural anchoring and static multi-anchor retrieval via Reciprocal Rank Fusion. Building upon this, an adaptive, interpretable knowledge graph traversal is performed using a tool-augmented large language model (Adapt-KG). Requiring no task-specific training, the approach significantly improves multi-hop reasoning performance, raising Hits@10 from 0.585 to 0.876, and boosts target recall for out-of-graph molecules from 0.145 to 0.269, while ensuring predictions are grounded in structural anchors and traceable evidence.

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