Xudong Han

Existing patent examination approaches face fundamental limitations: they struggle with comprehensive prior art coverage due to maximum similarity scoring without considering all claim elements, provide limited ranked retrieval through binary classification without confidence scoring, and incur substantial computational overhead while generating generic outputs that miss claim-specific details. To address these challenges, we introduce \textbf{Integrated Patent Prior Art Search with Claim-Aware Retrieval and Novelty Assessment} (IPAS-CARNA), a novel three-stage pipeline combining enhanced claim-document matching, continuous novelty assessment, and claim-aware summarization. Our approach models element-wise claim coverage through adaptive chunking and weighted aggregation, integrates continuous novelty scoring with confidence assessment, and introduces claim-aware summarization with dynamic length control. Extensive experiments on CLEF-IP 2013, USPTO examination records, and HUPD validation sets demonstrate significant improvements: MAP@100 of 0.342 with 14.8\% improvement in retrieval recall, 18.2\% improvement in NDCG@10 for novelty ranking, technical accuracy above 0.85, and ROUGE-L scores of 0.456 for summarization. Our work establishes an effective integrated solution for automated patent prior art analysis.

Neural network interpretability methods have produced powerful approaches for gradient-based feature attribution, energy landscape analysis, and uncertainty quantification. State-of-the-art methods rely on structural weight analysis, Monte Carlo dropout uncertainty estimation, and attention mechanisms for interpretable predictions with quantified confidence. However, existing methods face fundamental challenges: unreliable explanations with poor uncertainty quantification on complex medical imaging tasks, difficulty identifying important network weights due to fixed thresholds, and computational overhead from attention mechanisms operating without uncertainty guidance. We introduce Adaptive Uncertainty-Guided Interpretable Networks (AUGIN), a framework combining adaptive structural weight analysis, uncertainty-aware prediction intervals, and uncertainty-guided attention through three interconnected modules. Our approach models adaptive threshold computation evolving with layer depth and architecture, integrates feature-level uncertainty into prediction interval generation, and introduces uncertainty-guided attention focusing on uncertain regions. Experiments on ISLES stroke prediction, BraTS brain tumor segmentation, and CT-CTA thrombectomy outcome prediction demonstrate superior performance: 0.7891 AUC-ROC on ISLES (1.59 percentage point improvement), 0.8567 Dice score on BraTS, and 0.7456 attention IoU, while maintaining excellent uncertainty calibration (0.8123) and explanation fidelity (0.8234). Our work advances neural network interpretability by providing accurate predictions with reliable, uncertainty-quantified explanations for safety-critical medical imaging applications.

Recent advances in reinforcement learning for large language models have produced powerful agent frameworks that achieve strong performance on multi-turn tool use, interactive search, and complex reasoning. However, existing reinforcement learning frameworks for large language model agents face three critical limitations: difficulty in handling dynamic user interactions owing to reliance on pre-scripted queries, limited scalability across varying interaction horizons with fixed scaling schedules, and substantial reward engineering overhead requiring domain-specific manual tuning. We introduce Progressive Multi-Turn Reinforcement Learning for Dynamic User-Interactive Tool Agents, a novel framework that integrates progressive user-interactive training to overcome sparse reward signals, adaptive horizon management that monitors performance metrics and adjusts training complexity accordingly, and domain-adaptive tool orchestration that learns optimal tool selection patterns across domains. Extensive experiments on WebArena, TAU-Bench, Berkeley Function-Calling Leaderboard Version 3, BabyAI, and SciWorld demonstrate that our method achieves 28.4% success rate on WebArena and 76.3\% on TAU-Bench, substantially outperforming the baselines, such as ReAct (16.2%) and MUA-RL (24.6%), while maintaining 94.7% performance on embodied reasoning tasks and 78.9\% cross-domain performance retention. Our work establishes a unified framework for realistic user interaction training, performance-adaptive complexity scaling, and domain-flexible tool orchestration.

Multidisciplinary team (MDT) consultations are the gold standard for cancer care decision-making, yet current practice lacks structured mechanisms for quantifying consensus and ensuring decision traceability. We introduce a Multi-Agent Medical Decision Consensus Matrix System that deploys seven specialized large language model agents, including an oncologist, a radiologist, a nurse, a psychologist, a patient advocate, a nutritionist and a rehabilitation therapist, to simulate realistic MDT workflows. The framework incorporates a mathematically grounded consensus matrix that uses Kendall's coefficient of concordance to objectively assess agreement. To further enhance treatment recommendation quality and consensus efficiency, the system integrates reinforcement learning methods, including Q-Learning, PPO and DQN. Evaluation across five medical benchmarks (MedQA, PubMedQA, DDXPlus, MedBullets and SymCat) shows substantial gains over existing approaches, achieving an average accuracy of 87.5% compared with 83.8% for the strongest baseline, a consensus achievement rate of 89.3% and a mean Kendall's W of 0.823. Expert reviewers rated the clinical appropriateness of system outputs at 8.9/10. The system guarantees full evidence traceability through mandatory citations of clinical guidelines and peer-reviewed literature, following GRADE principles. This work advances medical AI by providing structured consensus measurement, role-specialized multi-agent collaboration and evidence-based explainability to improve the quality and efficiency of clinical decision-making.

Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy. It has demonstrated substantial advancements in various applications including image classification, object detection, language modeling, text classification, and sentiment analysis. Recent innovations in KD methods, such as attention-based approaches, block-wise logit distillation, and decoupling distillation, have notably improved student model performance. These techniques focus on stimulus complexity, attention mechanisms, and global information capture to optimize knowledge transfer. In addition, KD has proven effective in compressing large language models while preserving accuracy, reducing computational overhead, and improving inference speed. This survey synthesizes the latest literature, highlighting key findings, contributions, and future directions in knowledge distillation to provide insights for researchers and practitioners on its evolving role in artificial intelligence and machine learning.

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