生成式AI 3. 帕累托前沿反转证明参数不再是唯一真理，更便宜更快的模型现在也是更聪明的模型，打破"旗舰版迷信"。 https://mp.weixin.qq.com/s/DcSEhIQ9gt6L2pBLdmY3Uw 二、旧金山一场大停电，Waymo出租车罢工秒变「路障」 1. 旧金山停电导致红绿灯熄灭，Waymo无人驾驶出租车集体停摆秒变路障，多辆车停在十字路口和主干道上； 一、Gemini Flash表现超越Gemini Pro，帕累托前沿反转？ 1. Gemini 3 Flash在SWE-Bench Verified测试中获得78%分数，超越Pro版的76.2%，且速度是2.5 Pro的3倍， Token消耗量减少30%； 2. 谷歌团队解释Flash集成了大量Agentic RL研究成果，通过后训练算法实现小模型"降维打击"，Pro主要作用是蒸 馏Flash； 2. Waymo依赖多传感器融合和高精地图，当城市基础设施异常时系统无法确认安全边界选择停车，马斯克称特斯拉 FSD完全未受影响； 3. 事件凸显Waymo与特斯拉技术路线差异：前者重传感器地图规则，后者依赖视觉和AI，暴露了L4级无人驾驶在突 ...

Core Insights - The article discusses the competitive landscape of AI, particularly focusing on the advancements and challenges faced by large models like ChatGPT and Gemini 3, highlighting the ongoing debate about the scalability and limitations of AI models [2][3][4]. Group 1: AI Model Development and Scaling - The belief that increasing computational power and data will lead to exponential growth in AI intelligence is being challenged as the performance improvements of large models slow down [3]. - Gary Marcus argues that large models do not truly understand the world but merely fit language correlations, suggesting that future breakthroughs will come from better learning methods rather than just scaling [3][4]. - Despite criticisms, the Scaling Law remains a practical growth path for AI, as evidenced by the successful performance of Gemini 3 and ongoing investments in AI infrastructure in the U.S. [4][5]. Group 2: Data Challenges and Solutions - High-quality data is a critical challenge for the evolution of large models, with the industry exploring systematic methods to expand data sources beyond just internet corpora [5][7]. - The future of data generation will focus on creating scalable, controllable systems that can produce high-quality data through various modalities, including synthetic and reinforcement learning data [7][19]. Group 3: Multi-Modal AI and Its Implications - The emergence of multi-modal models like Google Gemini and OpenAI Sora marks a significant advancement, enabling deeper content understanding and the potential for non-linear leaps in AI intelligence [8][12]. - Multi-modal models can provide a more direct representation of the world, allowing for a more robust world model and the possibility of closing the perception-action loop in AI systems [12][13]. Group 4: Research and Innovation in AI - The article highlights the importance of research-driven approaches in the AI industry, with numerous experimental labs emerging to explore various innovative directions, including safety and multi-modal collaboration [15][16][17]. - Innovations in foundational architectures and learning paradigms are expected to yield breakthroughs in areas such as long-term memory mechanisms and agent-based systems [15][17]. Group 5: AI for Science (AI4S) and Industry Impact - AI for Science is transitioning from model-driven breakthroughs to system engineering, with significant implications for fields like drug development and materials science [24][25]. - The establishment of AI-driven automated research labs signifies a shift towards integrating AI into experimental processes, potentially accelerating scientific discovery [25][28]. Group 6: AI Glasses and Consumer Electronics - The rise of AI glasses is anticipated to reach a critical mass, with projections of significant sales growth, indicating a shift towards a new computing paradigm [46][47]. - The design philosophy of AI glasses focuses on lightweight, user-friendly devices that prioritize functionality over traditional display technologies, potentially transforming user interaction with technology [47][48]. Group 7: AI Safety and Governance - As AI capabilities advance, safety and ethical considerations are becoming increasingly important, with a growing emphasis on establishing safety protocols and governance structures within AI development [50][53]. - The establishment of AI safety committees and the allocation of computational resources for safety research are becoming essential components of responsible AI deployment [54][55].

Group 1: Moore Threads Technology Roadmap - Moore Threads has unveiled its new generation full-featured GPU architecture "Huagang," which boasts a 50% increase in computing density and a 10-fold improvement in energy efficiency, supporting full precision calculations from FP4 to FP64 and capable of supporting over 100,000 card intelligent computing clusters [1] - The company is set to release the "Huashan" AI training and inference integrated chip and the "Lushan" high-performance graphics rendering GPU, with a computing power of 10 EFLOPS for the Wan Card intelligent computing cluster, and the S5000 single card inference sets a new record for domestic GPU performance [1] - The AI computing book MTT AIBOOK, equipped with the "Yangtze River" SoC chip, offers 50 TOPS heterogeneous AI computing power and can locally run large models with up to 30 billion parameters, now available for pre-sale on JD.com [1] Group 2: OpenAI's GPT-5.2-Codex Launch - OpenAI has launched GPT-5.2-Codex, which is considered the most advanced intelligent coding model to date, achieving state-of-the-art performance in SWE-Bench Pro and Terminal-Bench 2.0 benchmark tests [2] - Compared to GPT-5.2, it has improved instruction-following capabilities, long context understanding, and network security features, with better performance in Windows environments and significant improvements in token efficiency at mid-high inference levels [2] - The model is now available to paid ChatGPT users across all Codex platforms, with plans to open access to API users in the coming weeks and provide more lenient access for defensive cybersecurity professionals [2] Group 3: Google's Gemma Models - Google has open-sourced two models from the Gemma 3 family, T5Gemma 2 and FunctionGemma, with T5Gemma 2 being the first multi-modal long-context encoder-decoder model, available in sizes of 270M-270M, 1B-1B, and 4B-4B [3] - FunctionGemma is optimized for function calls, running on just 270 million parameters, suitable for mobile and browser devices, and supports precise structured data output for external API calls, making it ideal for edge AI agent applications [3] - T5Gemma 2 returns to the classic Encoder-Decoder architecture, surpassing similarly sized Gemma 3 models in multi-modal performance, code reasoning, and long context capabilities, while FunctionGemma can be reduced to 135MB for operation through quantization [3] Group 4: NVIDIA's NitroGen Model - NVIDIA has open-sourced the NitroGen foundational model, designed to play over 1,000 games, using game video frames as input to output real controller operation signals, and supports rapid adaptation to new games through post-training [4] - The model is based on the GR00T N1.5 architecture and utilizes 500 million parameters, trained by automatically extracting action labels from 40,000 hours of publicly available game videos, covering various game types including RPGs, platformers, and racing [4] - It can accomplish non-trivial tasks without fine-tuning, achieving a task success rate improvement of up to 52% compared to models trained from scratch, and the dataset, evaluation suite, and model weights have been made open-source [4] Group 5: OpenAI's Codex Agent Skills Support - OpenAI has announced that Codex now fully supports Agent Skills, integrating with industry-standard specifications led by Anthropic, which include markdown commands and optional script resources [5] - It allows for explicit calls (via /skills command or $selection) and implicit calls (automatically matching descriptions based on tasks), with skill storage prioritized from the current working directory to the user's personal directory [5] - Built-in tools like $skill-creator and $skill-installer are provided to automatically generate skill frameworks or install skills from third-party repositories like GitHub, with an official Skill library released by OpenAI [5] Group 6: Luma AI's Ray3 Modify - Luma AI has launched the Ray3 Modify feature, emphasizing a "real person first, AI follows" approach to video production, where actor performances and camera movements serve as the foundational input for AI processing [6] - It supports keyframe control (start and end frames), character reference capabilities, and retains the integrity of performances, allowing the same performance to be placed in different scenes for various content versions without reshooting [6] - Integrated into the Dream Machine platform, it targets film production, advertising creativity, and post-production processes, enabling creators to maintain control without the need for repeated filming [6] Group 7: METR Report on Claude Opus 4.5 - The METR report indicates that Claude Opus 4.5 can sustain coding for approximately 4 hours and 49 minutes, marking the longest time span reported to date, surpassing GPT-5.1-Codex-Max's 2 hours and 53 minutes [9] - The task duration for AI coding agents is showing exponential growth, doubling every 7 months from 2019 to 2024, and expected to double every 4 months from 2024 to 2025, with predictions that AI will complete a full workday's tasks by April 2026 [9] - The industry views long-term memory as the final challenge towards achieving AGI, as current models rely on retrieval tools and context compression, lacking true self-learning and persistent memory capabilities [9] Group 8: Google AI's Success Story - Josh Woodward, the head of Google AI products, has driven the Gemini application’s monthly active users from 350 million in March to 650 million in October, surpassing ChatGPT to top the App Store rankings [10] - At 42 years old and from Oklahoma, he joined Google through an internship in 2009, contributing to Chromebook development, founding the NBU initiative, and leading the expansion of Google Pay, before taking over as Gemini application head in April 2025 [10] - He has promoted the NotebookLM project to break Google's traditional practices by utilizing Discord for community engagement, establishing a "Block" ticketing system to eliminate bureaucratic obstacles, and initiating the "Papercuts" plan to address minor issues, emphasizing the balance between AI innovation and social responsibility [10]

Group 1: Core Insights - The article presents a weekly roundup of the top 50 keywords in the AI sector, highlighting significant developments and trends in the industry [2]. - Key players mentioned include Google, Apple, ByteDance, NVIDIA, and OpenAI, indicating a competitive landscape in AI technology and applications [3][4]. Group 2: Chip Developments - Google is advancing its AI chip technology with the introduction of TorchTPU [3]. - Apple is focusing on AI server chips, which may enhance its capabilities in AI applications [3]. Group 3: Model Innovations - Google has launched the Gemini 3 Flash model, while ByteDance introduced Seed1.8, showcasing ongoing innovation in AI models [3]. - Other notable models include MiMo-V2-Flash from Xiaomi and Nemotron 3 from NVIDIA, indicating a diverse range of AI model developments [3]. Group 4: Application Trends - OpenAI is expanding its ecosystem with the ChatGPT application store and various applications like ChatGPT Images and SAM Audio [3][4]. - Companies like Tencent and xAI are also developing unique applications, such as the writing mode and Grok Voice, respectively [3][4]. Group 5: Technological Insights - The article discusses various technological insights, including AI memory systems and recursive self-improvement, which are critical for future AI advancements [4]. - The AI adult content market and AGI predictions are also highlighted, reflecting the broader implications of AI technology [4].

Core Insights - Generative AI (GenAI) is revolutionizing content production, breaking barriers in high-quality dynamic content generation and pushing complex creative work into the realm of machines [2] - The cultural industry faces both strategic anxiety and opportunity desire due to the disruptive potential of GenAI, prompting a comprehensive reshaping of existing value chains and business models [2] - The "Dawn" research project by Tencent Research Institute and Communication University of China aims to explore the systematic transformation of the cultural industry in the AI era, focusing on applications in long videos, short videos, music, animation, and online literature [2] Group 1: AI Tools and Their Impact - Reading Group has launched AI tools such as Writer Assistant, Comic Assistant, and Copyright Assistant, covering the entire process from writing assistance to IP adaptation [6] - AI cannot replace the emotional and personal expression of excellent writers; the soul and value of a work ultimately depend on human creativity [6][11] - The future online literature ecosystem may present an "olive-shaped" structure, where GenAI serves as a powerful creative "auxiliary wheel," primarily enhancing the "mid-tier" group while the top tier still relies on the talent and effort of writers [6][12] Group 2: Content Creation and Quality - Text and video have structural differences in expression forms, carriers, channels, and audiences, making complete integration unlikely; however, online literature is rapidly evolving into a form that integrates multimodal elements [6][14] - Originality remains the "first principle" of online literature, and the industry must maintain a focus on quality and individual style rather than standardization and maximum efficiency [8][19] - AI tools can assist in visualizing online literature IP and addressing traditional adaptation bottlenecks, but human artistic judgment and decision-making remain central [7][17] Group 3: User Acceptance and Market Dynamics - User acceptance of AI-generated content varies, with some users preferring content created by emotional writers, while others focus on the story itself [20] - The cultural industry must prioritize quality over quantity, as excessive low-quality content can drive users away [19] - The rise of GenAI presents new opportunities for online literature to expand into visual content, enhancing its reach in overseas markets [21][22]

Group 1 - Google is advancing the "TorchTPU" strategy to enable PyTorch to run smoothly on TPU chips, aiming to eliminate migration barriers for developers and considering partial open-sourcing of the software [1] - Google is negotiating a collaboration with Meta to provide Meta with more TPU access, allowing Meta to reduce inference costs and dependence on NVIDIA by adapting software for TPU [1] - Wall Street analysts believe that CUDA is NVIDIA's strongest defense, and Google's previous reliance on its internal Jax framework has widened the gap with external customer usage habits [1] Group 2 - The ChatGPT app store has officially launched, categorizing applications like Adobe Photoshop and Canva, with users triggering them via "@app name" [2] - Developers can submit applications for review on the OpenAI developer platform, which offers a comprehensive resource system including best practice guides and open-source sample applications [2] - OpenAI plans to raise new funding at an estimated valuation of around $750 billion, potentially reaching $1 trillion, attempting to replicate the Apple App Store model in the AI era [2] Group 3 - Google has released the Gemini 3 Flash model, achieving a score of 33.7% on the Humanity's Last Exam benchmark, while Gemini 3 Pro scored 37.5% and GPT-5.2 scored 34.5% [3] - This model maintains the Flash series' extreme native speed, outperforming Gemini 2.5 Pro while tripling the speed, priced at $0.50 per million tokens for input and $3 for output [3] - Gemini 3 Flash is now the default model for Gemini applications and search AI modes, with response times generally under one second, available globally through Google AI Studio and Vertex AI [3] Group 4 - ByteDance has launched the universal Agent model Seed1.8, which integrates search, code, and GUI Agent capabilities, automatically adjusting processing methods based on task complexity [4] - In GUI Agent evaluations, Seed1.8 surpassed Seed1.5-VL, demonstrating reliability in multi-step tasks across computer, web, and mobile environments, scoring 67.6 on the BrowseCompen benchmark [4] - The model achieved a top score of 11.0 on ZeroBench and 87.8 on VideoMME for long video understanding, incorporating the "VideoCut" video tool [4] Group 5 - The Step-GUI cloud model has been fully upgraded, supporting over 200 task scenarios and usable across mobile, PC, and automotive platforms, with deployment of an "AI phone" possible in as little as 10 minutes [5][6] - This model features longer reasoning steps, enhanced semantic understanding, and generalization capabilities, autonomously asking questions when user instructions are vague [6] - The GUI-MCP protocol is open for end-cloud collaboration, with APIs temporarily available for free, and a call for users to create showcases and develop applications [6] Group 6 - xAI has officially released the Grok Voice Agent API, making its real-time voice capabilities available to developers for voice-first application scenarios [7] - The API includes various built-in voices and companion personalities, allowing developers to finely control system commands and behavior parameters [7] - It supports real-time voice recognition and synthesis with a streaming audio design, enabling search capabilities during conversations and significantly reducing interaction latency [7] Group 7 - Apple is reportedly abandoning its VR headset project in favor of developing AI smart glasses, with a projected launch in late 2026 or 2027 [8] - The company has paused its AR/VR headset initiatives and plans to reintroduce the iMac Pro, which has been off the market for over four years, potentially featuring the M5 Max chip [8] - A 20th-anniversary edition iPhone is expected in 2027, featuring a curved design that wraps around the device edges and a front camera positioned under the display [8] Group 8 - a16z partners assert that the AI bubble has not yet burst, as it has not reached a point where investments are wasted [9] - They believe that if companies cease developing larger models and rely solely on existing models, they could quickly achieve profitability at current profit margins [9] - Predictions indicate that GDP could grow by several percentage points by 2030, with a reasonable lower limit of 30% growth if AGI is achieved, though outcomes could vary widely [9]

Research Background and Significance - The rapid flow of information has led to unprecedented speed and influence of rumor dissemination, necessitating a stable and professional fact-checking capability to maintain a healthy information ecosystem and protect user rights [7][9]. Methodology and Data Sources - The report employs quantitative statistics, trend comparisons, questionnaire surveys, and case studies to analyze the evolution of rumors from 2015 to 2025, focusing on total volume, type distribution, and dissemination patterns [10][11]. Ten Core Findings 1. The rumor ecosystem has evolved through three phases: 2015-2019 focused on health and food safety rumors; 2020-2022 centered on major public events; and 2023-2025 characterized by AI integration and content diversification [13]. 2. The dissemination of rumors has undergone a "channel revolution," transitioning from text-based to video and algorithm-driven platforms, significantly altering the spread and impact of misinformation [13]. 3. AI technology has lowered the barriers for producing false information, leading to a "probability truth" challenge where the focus shifts from content authenticity to source credibility [13]. 4. Public trust and recognition have deteriorated as highly realistic content blurs the lines of truth, increasing anxiety and apathy towards the concept of "truth" [13]. 5. A significant portion of the public exhibits cognitive closure, with 93% of respondents preferring clear answers, making them susceptible to emotionally driven misinformation [14]. 6. The public's ability to identify health-related rumors is notably weak, with 55.63% of respondents scoring low due to the complexity and misleading nature of health misinformation [14]. 7. Fact-checking methods have evolved through three stages: manual verification, algorithmic identification, and AI-driven real-time verification, enhancing efficiency by up to 90% [14]. 8. The governance model has shifted from independent platform efforts to a collaborative ecosystem involving multiple stakeholders, including authoritative institutions [14]. 9. The effectiveness of fact-checking has been quantitatively assessed, showing a 36.5% reduction in public panic during the pandemic due to effective rumor management [14]. 10. "Pre-fact-checking" strategies have proven effective, particularly for policy-related rumors, with 92% of respondents showing a habit of verifying through official channels [15]. Evolution of Rumors Over the Past Decade - The chapter outlines the significant changes in the forms, media, and social impacts of rumors over the past decade, reflecting technological advancements and societal developments [19]. Phases of Rumor Evolution - The rumor landscape is divided into three phases: - 2015-2019 saw a rise in everyday life rumors amid a lack of scientific literacy [22]. - 2020-2022 shifted focus to major public health events, with video content becoming the dominant form of rumor [26]. - 2023-2025 is marked by AI's role in content creation, complicating the identification of misinformation [28]. Lifecycle of Rumors - The lifecycle of rumors includes four stages: emergence, diffusion, peak, and decline, with some rumors re-emerging periodically [37]. Types of Rumors - "Evergreen" rumors maintain relevance due to their broad applicability, while "flash-in-the-pan" rumors arise from specific events and have shorter lifespans [39][40]. Upgrading of Rumor Dissemination Methods and Public Perception Changes - The evolution of media technology has restructured rumor dissemination, transitioning from text-based platforms to visual and algorithm-driven short video platforms, significantly altering public attitudes and behaviors towards misinformation [45]. Channels of Rumor Dissemination - The transition from traditional text-based platforms to visual content and algorithm-driven platforms has redefined the mechanisms of rumor spread [46]. Public Attitudes and Behavioral Changes - Public attitudes towards rumors have shifted significantly over the past decade, influenced by life anxieties and the trust dynamics within social networks [58].

Group 1: OpenAI Developments - OpenAI launched a new image generation model, ChatGPT Images, which enhances image generation speed by 4 times and allows for precise editing while maintaining detail [1] - The model supports various editing types such as adding, removing, and combining elements, with improved text rendering capabilities for handling dense and small text [1] - The new Images feature is available to all ChatGPT users, with the API offered at a 20% lower price than the previous version [1] Group 2: Meta Innovations - Meta has open-sourced the audio segmentation model SAM Audio, which can separate any sound from complex audio mixes using text, visual, and time span prompts [2] - The core engine PE-AV is based on Perception Encoder and has been trained on over 100 million videos, achieving a processing speed faster than real-time [2] - SAM Audio-Bench and SAM Audio Judge have been released for benchmarking and evaluation, achieving state-of-the-art performance in various audio separation tasks [2] Group 3: Xiaomi's AI Model - Xiaomi released and open-sourced the MiMo-V2-Flash model, featuring 309 billion total parameters and 15 billion active parameters, surpassing all open-source models with a SWE-bench Verified score of 73.4% [3] - Key innovations include a 5:1 hybrid sliding window attention mechanism and lightweight multi-token prediction, improving inference speed by 2 to 2.6 times [3] - The post-training process uses a multi-teacher online distillation strategy, requiring only 1/50th of the computational power to achieve peak teacher performance [3] Group 4: Tencent's Real-Time Model - Tencent officially released and open-sourced the HY WorldPlay model, enabling real-time interactive 3D world creation from text or image inputs at 24 FPS and 720P video quality [4] - Innovations include a memory reconstruction mechanism for geometric consistency and a 3D autoregressive diffusion model for enhanced learning [4] - The model provides a comprehensive real-time world model training system, covering data, training, and streaming inference deployment [4] Group 5: Vidu Agent Launch - Vidu Agent has opened global beta testing, focusing on "one-click video creation" capabilities, allowing users to upload product images and information to generate ready-to-launch advertisements [6] - Highlights include storyboard-level control, fine editing capabilities, and multi-language customization [6] - The platform supports video replication, enabling bulk production of high-quality videos based on popular one-minute videos and product images [6] Group 6: Google's Gemini Updates - Google introduced the Super Gems feature in Gemini, integrating Opal applications with the Gems manager, making the Opal workflow directly accessible in the Labs area [7] - The new Workflow Builder allows for automatic generation of complete workflow steps and visual elements based on scene descriptions [7] - Workflows can be shared via links without relying on Google Drive permissions, enhancing user accessibility [7] Group 7: OpenAI's FrontierScience Benchmark - OpenAI launched the FrontierScience benchmark to assess expert-level scientific capabilities, featuring over 700 physics, chemistry, and biology questions [8] - GPT-5.2 scored 77% in the Olympiad track and 25% in the research track, outperforming other leading models [8] - The research track uses a 10-point scale focusing on reasoning correctness, revealing issues in logical reasoning and understanding of professional concepts [8] Group 8: Xiaomi's Future Plans - Xiaomi's Luo Fuli made her first public appearance, discussing the MiMo-V2-Flash model's core directions, emphasizing the need for models that can interact with the physical world [9] - She highlighted that computational power and data are not the ultimate moat; the true moat lies in scientific research culture and the ability to turn unknown problems into usable products [9] - Xiaomi plans to invest over 200 billion yuan in R&D over the next five years, with an estimated 40 billion yuan allocated for 2026 [9]

阿兰·麦克法兰 （Alan Macfarlane）教授，拥有很长一串头衔：社会人类学学家、历史学家、剑桥大学 国王学院终身院士、英国国家学术院院士…… 参与方式 因为他每次的视频都在书房里录制，再加上慈眉善目的老教授形象，让很多人觉得非常亲切，认为他 是" 从霍格沃茨走出的教授 "。 在视频里，他回答粉丝对于人工智能及相关技术的疑惑、不安，也耐心地回应读者的日常困惑与人生问 题。 现在有一个机会，你可以向他提问一个关于AI时代的问题，你想问什么？ 来自麦克法兰教授的一封信： 他以跨学科视角著称，善于将历史、人类学与当代技术问题结合，探讨社会长期演化中的关键转折点。 他也长期关注中国技术与经济发展，在中国的社交媒体平台有接近百万的粉丝关注。 请将你的提问直接发在评论区，可包含1-3个问题 。可以是关于AI的，可以是关于社会的，可以是你当 下的人生困惑。 问题征集截止至2025年12月27日。 我们会选取其中有趣、有代表性的问题，请麦克法兰教授亲自录制视频答复，并将于 2026年1月27号腾 讯研究院科技向善创新节 公开放出。 推 荐 阅 读 艾伦·麦克法兰 ： 《我们很可能正走向一个"无工作社会"｜腾研对话海外 ...

Core Viewpoint - The article discusses the launch of the "AI for Good Corpus," a collaborative initiative aimed at creating an AI training dataset that emphasizes humanistic care for marginalized groups, such as the elderly, disabled, and at-risk children [2][3]. Group 1: Project Overview - The first phase of the AI for Good Corpus focuses on the elderly, gathering over 8,047 expert Q&A entries and 1,408 high-quality entries written by elderly individuals, making it the first publicly co-created AI training corpus in the world [3]. - The project aims to address the needs of vulnerable populations by fostering a more compassionate AI that can provide meaningful support [2][3]. Group 2: Target Audience and Themes - The next focus will be on "at-risk children and adolescents," with an emphasis on understanding how AI can effectively respond to their unique challenges, such as loneliness and body awareness during adolescence [7][11]. - Two critical topics identified for exploration are: 1. Providing sensitive and respectful support for sexual education discussions with AI [13][14]. 2. Understanding the specific needs and contexts of left-behind children to ensure AI responses are relevant and helpful [16][17]. Group 3: Collaboration and Partnerships - The initiative seeks to partner with organizations that have expertise in child-related fields, AI technology, and open-source governance to co-create the corpus [21][22][23]. - Strategic partners will gain early access to resources, participate in various events, and receive recognition for their contributions, enhancing collaboration efficiency [28][29].