Abstract
Patent drafting presents significant challenges due to its reliance on the extensive experience and specialized expertise of patent attorneys, who must possess both legal acumen and technical understanding of an invention to craft patent applications in a formal legal writing style. This paper presents a demonstration of Patentformer, an AI-powered automated patent drafting platform designed to support patent attorneys by rapidly producing high-quality patent applications adhering to legal writing standards.

Abstract
Generative AI is reshaping how software is designed, written, and maintained. Advances in large language models (LLMs) are enabling new development styles - from chat-oriented programming and 'vibe coding' to agentic programming - that can accelerate productivity and broaden access. This paper examines how AI-assisted techniques are changing software engineering practice, and the related issues of trust, accountability, and shifting skills. We survey iterative chat-based development, multi-agent systems, dynamic prompt orchestration, and integration via the Model Context Protocol (MCP). Using case studies and industry data, we outline both the opportunities (faster cycles, democratized coding) and the challenges (model reliability and cost) of applying generative AI to coding. We describe new roles, skills, and best practices for using AI in a responsible and effective way.

Abstract
Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation, yet existing research has mostly evaluated their adherence to equilibrium play or their exhibited depth of reasoning. Whether they display genuine strategic thinking, understood as the coherent formation of beliefs about other agents, evaluation of possible actions, and choice based on those beliefs, remains unexplored. We develop a framework to identify this ability by disentangling beliefs, evaluation, and choice in static, complete-information games, and apply it across a series of non-cooperative environments. By jointly analyzing models' revealed choices and reasoning traces, and introducing a new context-free game to rule out imitation from memorization, we show that current frontier models exhibit belief-coherent best-response behavior at targeted reasoning depths. When unconstrained, they self-limit their depth of reasoning and form differentiated conjectures about human and synthetic opponents, revealing an emergent form of meta-reasoning. Under increasing complexity, explicit recursion gives way to internally generated heuristic rules of choice that are stable, model-specific, and distinct from known human biases. These findings indicate that belief coherence, meta-reasoning, and novel heuristic formation can emerge jointly from language modeling objectives, providing a structured basis for the study of strategic cognition in artificial agents.

Abstract
Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges in setting prices for their second-hand products efficiently. Therefore, numerous studies have been proposed for automating price prediction. However, most of them are based on static regression models, which suffer from poor generalization performance and fail to capture market dynamics (e.g., the price of a used iPhone decreases over time). Inspired by recent breakthroughs in Large Language Models (LLMs), we introduce LLP, the first LLM-based generative framework for second-hand product pricing. LLP first retrieves similar products to better align with the dynamic market change. Afterwards, it leverages the LLMs' nuanced understanding of key pricing information in free-form text to generate accurate price suggestions. To strengthen the LLMs' domain reasoning over retrieved products, we apply a two-stage optimization, supervised fine-tuning (SFT) followed by group relative policy optimization (GRPO), on a dataset built via bidirectional reasoning. Moreover, LLP employs a confidence-based filtering mechanism to reject unreliable price suggestions. Extensive experiments demonstrate that LLP substantially surpasses existing methods while generalizing well to unseen categories. We have successfully deployed LLP on Xianyu\footnote\{Xianyu is China's largest second-hand e-commerce platform.\}, significantly outperforming the previous pricing method. Under the same 30\% product coverage, it raises the static adoption rate (SAR) from 40\% to 72\%, and maintains a strong SAR of 47\% even at 90\% recall.

Abstract
Surface wettability is a critical design parameter for biomedical devices, coatings, and textiles. Contact angle measurements quantify liquid-surface interactions, which depend strongly on liquid formulation. Herein, we present the Robotic Autonomous Imaging Surface Evaluator (RAISE), a closed-loop, self-driving laboratory that is capable of linking liquid formulation optimization with surface wettability assessment. RAISE comprises a full experimental orchestrator with the ability of mixing liquid ingredients to create varying formulation cocktails, transferring droplets of prepared formulations to a high-throughput stage, and using a pick-and-place camera tool for automated droplet image capture. The system also includes an automated image processing pipeline to measure contact angles. This closed loop experiment orchestrator is integrated with a Bayesian Optimization (BO) client, which enables iterative exploration of new formulations based on previous contact angle measurements to meet user-defined objectives. The system operates in a high-throughput manner and can achieve a measurement rate of approximately 1 contact angle measurement per minute. Here we demonstrate RAISE can be used to explore surfactant wettability and how surfactant combinations create tunable formulations that compensate for purity-related variations. Furthermore, multi-objective BO demonstrates how precise and optimal formulations can be reached based on application-specific goals. The optimization is guided by a desirability score, which prioritizes formulations that are within target contact angle ranges, minimize surfactant usage and reduce cost. This work demonstrates the capabilities of RAISE to autonomously link liquid formulations to contact angle measurements in a closed-loop system, using multi-objective BO to efficiently identify optimal formulations aligned with researcher-defined criteria.

Abstract
Vision-based Proximal Policy Optimization (PPO) struggles with visual observation-based robotic laparoscopic surgical tasks due to the high-dimensional nature of visual input, the sparsity of rewards in surgical environments, and the difficulty of extracting task-relevant features from raw visual data. We introduce a simple approach integrating MedFlamingo, a medical domain-specific Vision-Language Model, with PPO. Our method is evaluated on five diverse laparoscopic surgery task environments in LapGym, using only endoscopic visual observations. MedFlamingo PPO outperforms and converges faster compared to both standard vision-based PPO and OpenFlamingo PPO baselines, achieving task success rates exceeding 70% across all environments, with improvements ranging from 66.67% to 1114.29% compared to baseline. By processing task observations and instructions once per episode to generate high-level planning tokens, our method efficiently combines medical expertise with real-time visual feedback. Our results highlight the value of specialized medical knowledge in robotic surgical planning and decision-making.

Abstract
Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.

Abstract
This study explores how different managerial behaviors influence team effectiveness and organizational outcomes, using Kenneth Blanchard's situational leadership model as a diagnostic tool. Conducted across ten companies, the research evaluates leadership adaptability through a scenario-based questionnaire identifying instructional, teaching, supportive, and delegating styles. Results show that the supportive (affiliative) style is dominant, present in 60 percent of surveyed companies, with delegating being second. Correlation analysis reveals strong negative relationships between certain styles, particularly instructional and supportive, indicating that flexibility in leadership is crucial. The findings suggest that over-reliance on any one style may lead to inefficiencies, while a balanced, situational approach enhances decision-making, morale, and adaptability. The research contributes to leadership theory by demonstrating how behavioral combinations, not static traits, influence outcomes. It offers practical implications for managerial training, recommending the integration of diagnostic tools like the Blanchard test to improve style awareness and behavioral flexibility. Limitations include reliance on self-assessment data and a small sample size. Future research should explore longitudinal and cross-industry analyses to assess how leadership behaviors evolve over time or under pressure.

AI Insights

• A strong negative correlation between instructional and supportive styles shows the need for fluid switching.
• Supportive rapport plus assertive delegation beats single‑style leadership, boosting morale and decision speed.
• The Blanchard test maps situational variables, letting leaders adjust behavior—key for training.
• Findings echo emotional‑intelligence research: trust and psychological safety lift motivation.
• Self‑assessment bias may skew results; future work should add observer ratings.
• Situational Leadership tailors style to team maturity and task complexity; Affiliative Leadership values cohesion.
• Read Goleman’s “Emotional Intelligence” and Koźmiński’s “Zarządzanie. Teoria i praktyka” for adaptive leadership depth.