arXiv:2602.19458v1 Announce Type: new Abstract: Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM using complementary information as reward to output signals that complement existing agent decisions. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information and produces plausible explanations of complementary signals to support downstream decision-makers.

arXiv:2602.20130v1 Announce Type: cross Abstract: Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed. Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA. Metrics included accuracy, total generated tokens, and inference time. Results: Selective CoT reduced inference time by 13-45% and token usage by 8-47% with minimal accuracy loss ($\leq$4\%). In some model-task pairs, it achieved both higher accuracy and greater efficiency than standard CoT. Compared with fixed-length CoT, Selective CoT reached similar or superior accuracy at substantially lower computational cost. Discussion: Selective CoT dynamically balances reasoning depth and efficiency by invoking explicit reasoning only when beneficial, reducing redundancy on recall-type questions while preserving interpretability. Conclusion: Selective CoT provides a simple, model-agnostic, and cost-effective approach for medical QA, aligning reasoning effort with question complexity to enhance real-world deployability of LLM-based clinical systems.

arXiv:2509.03830v3 Announce Type: replace Abstract: Historic urban quarters are increasingly shaped by tourism and lifestyle consumption, yet planners often lack scalable evidence on what visitors notice, prefer, and criticize in these environments. This study proposes an AI-based, multimodal framework to decode tourist perception by combining visual attention, color-based aesthetic representation, and multidimensional satisfaction. We collect geotagged photos and review texts from a major Chinese platform and assemble a street view image set as a baseline for comparison across 12 historic urban quarters in Shanghai. We train a semantic segmentation model to quantify foregrounded visual elements in tourist-shared imagery, extract and compare color palettes between social media photos and street views, and apply a multi-task sentiment classifier to assess satisfaction across four experience dimensions that correspond to activity, physical setting, supporting services, and commercial offerings. Results show that tourist photos systematically foreground key streetscape elements and that the color composition represented on social media can differ from on-site street views, indicating a perception-reality gap that varies by quarter. The framework offers an interpretable and transferable approach to diagnose such gaps and to inform heritage management and visitor-oriented urban design.