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 arXiv:2512.20623v1 Announce Type: new Abstract: Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural language commands via Google Home/IFTTT integration and learns from implicit feedback through manual overrides. Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy. This work establishes a practical framework for deploying adaptive AI on resource-constrained IoT devices, enabling intelligent home automation without cloud dependencies. - Read More cs.AI updates on arXiv.org

 arXiv:2512.20624v1 Announce Type: new Abstract: This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent reinforcement learning, applied to UAVassisted 6G network deployment. We consider a cooperative scenario where ten intelligent UAVs autonomously coordinate to maximize signal coverage and support efficient network expansion under partial observability and dynamic conditions. The proposed approach integrates classical MARL algorithms with quantum-inspired optimization techniques, leveraging variational quantum circuits VQCs as the core structure and employing the Quantum Approximate Optimization Algorithm QAOA as a representative VQC based method for combinatorial optimization. Complementary probabilistic modeling is incorporated through Bayesian inference, Gaussian processes, and variational inference to capture latent environmental dynamics. A centralized training with decentralized execution CTDE paradigm is adopted, where shared memory and local view grids enhance local observability among agents. Comprehensive experiments including scalability tests, sensitivity analysis, and comparisons with PPO and DDPG baselines demonstrate that the proposed framework improves sample efficiency, accelerates convergence, and enhances coverage performance while maintaining robustness. Radar chart and convergence analyses further show that QI MARL achieves a superior balance between exploration and exploitation compared to classical methods. All implementation code and supplementary materials are publicly available on GitHub to ensure reproducibility. - Read More cs.AI updates on arXiv.org

 arXiv:2512.20626v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual understanding and holistic comprehension due to limited context windows, which constrain their ability to perform deep reasoning over long-form, domain-specific content such as full-length books. To solve this problem, knowledge graphs (KGs) have been leveraged to provide entity-centric structure and hierarchical summaries, offering more structured support for reasoning. However, existing KG-based RAG solutions remain restricted to text-only inputs and fail to leverage the complementary insights provided by other modalities such as vision. On the other hand, reasoning from visual documents requires textual, visual, and spatial cues into structured, hierarchical concepts. To address this issue, we introduce a multimodal knowledge graph-based RAG that enables cross-modal reasoning for better content understanding. Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process. Experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches on both textual and multimodal corpora. - Read More cs.AI updates on arXiv.org

 arXiv:2512.20628v1 Announce Type: new Abstract: This volume presents the proceedings of the 20th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2025), held in Nagaoka, Japan, on December 3-5, 2025. The conference, organized in cooperation with the IEICE Proceedings Series, provides a multidisciplinary forum for researchers in artificial intelligence, knowledge engineering, human-computer interaction, and creativity support systems. The proceedings include peer-reviewed papers accepted through a double-blind review process. Selected papers have been recommended for publication in IEICE Transactions on Information and Systems after an additional peer-review process. - Read More cs.AI updates on arXiv.org

 arXiv:2512.20630v1 Announce Type: new Abstract: Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliability assessment using only 100 strategically selected probe examples. Our method combines strategic prompt diversity across five key reliability dimensions with advanced uncertainty quantification and adaptive weighting to efficiently detect potential failure modes. Through extensive empirical evaluation on multiple language models (GPT-2 variants, GPT-2 Medium, GPT-2 Large) and cross-domain validation (healthcare, finance, legal), we demonstrate that microprobe achieves 23.5% higher composite reliability scores compared to random sampling baselines, with exceptional statistical significance (p < 0.001, Cohen's d = 1.21). Expert validation by three AI safety researchers confirms the effectiveness of our strategic selection, rating our approach 4.14/5.0 versus 3.14/5.0 for random selection. microprobe completes reliability assessment with 99.9% statistical power while representing a 90% reduction in assessment cost and maintaining 95% of traditional method coverage. Our approach addresses a critical gap in efficient model evaluation for responsible AI deployment. - Read More cs.AI updates on arXiv.org

 arXiv:2512.20632v1 Announce Type: new Abstract: This report provides a detailed introduction to Erkang-Diagnosis-1.1 model, our AI healthcare consulting assistant developed using Alibaba Qwen-3 model. The Erkang model integrates approximately 500GB of high-quality structured medical knowledge, employing a hybrid approach combining enhanced pre-training and retrieval-enhanced generation to create a secure, reliable, and professional AI health advisor. Through 3-5 efficient interaction rounds, Erkang Diagnosis can accurately understand user symptoms, conduct preliminary analysis, and provide valuable diagnostic suggestions and health guidance. Designed to become users intelligent health companions, it empowers primary healthcare and health management. To validate, Erkang-Diagnosis-1.1 leads GPT-4 in terms of comprehensive medical exams. - Read More cs.AI updates on arXiv.org