Hello, ladies and gentlemen. Thank you for standing by, and welcome to Pony AI's Third Quarter 2025 earnings conference call. At this time, all participants are in a listen-only mode. After the management's prepared remarks, there will be a question-and-answer session. As a reminder, today's conference call is being recorded, and a webcast replay will be available on the company's investor relations website at ir.pony.ai under the news and events section. I will now turn the call over to your host, George Shao, Head of Capital Markets and Investor Relations at Pony AI. Please go ahead, George. Thank you, operator. Hello, everyone. We appreciate you joining us today for Pony AI's Third Quarter 2025 earnings call. Earlier today, we issued a press release with our financial and operating results, which is available on our investor relations website. An earnings presentation, which we'll refer to during this conference call, can also be accessed and downloaded on our investor relations website. Joining with me on the call today are Dr. James Peng, Chairman of the Board and Chief Executive Officer; Dr. Tiancheng Lou, Chief Technology Officer; and Dr. Leo Wang, Chief Financial Officer of the company. They will provide prepared remarks followed by a Q&A session. Before we begin, please refer to the safe harbor statement in our earnings press release, which applies to this call as we'll be making forward-looking statements. Please also note that we'll discuss non-GAAP measures today, which are more thoroughly explained and reconciled to the most comparable measures reported under GAAP in our earnings release, available on our investor relations website and filings with the SEC and Hong Kong Stock Exchange. I will now hand it over to our Chairman and CEO, Dr. James Peng. Please go ahead. Thank you, George. Hello, everyone. Thank you for joining our earnings call. I'm excited to share that we have successfully completed the dual primary listing on the Hong Kong Stock Exchange under stock code 2026 on November 6, just one year after our Nasdaq listing. With strong support from both international and domestic investors, we secured the largest IPO in the global autonomous driving sector this year, raising more than $800 million U.S. dollars. This significantly strengthens our balance sheet and provides the dry powder to accelerate mass production and large-scale commercialization. We now expect stronger growth, surpassing 1,000 robotaxi fleet plans by year-end and expanding to more than 3,000 vehicles for 2026. We have already seen the flywheel in action. Expanded fleet is driving higher user adoption, shorter wait time, more orders, and strong revenue growth. After launching Gen-7 Robotaxi, we have already seen a citywide unit economics break even. This, in turn, gives us more room to increase fleet size. The capital we raised also fuels our business development, research and development, market-making strategic investments in new markets, new applications, and attracting world-class AI talents. All these are set to further propel our technology leadership and long-term growth. Our Hong Kong IPO also powers our core mission: bringing autonomous mobility to everyone around the world. We're firmly delivering on this commitment. Earlier this month, we officially launched fully driverless commercial service for Gen-7 Robotaxis across Guangzhou, Shenzhen, and Beijing. Today, our management team, including myself, actually arrives at our Shenzhen office in a fully driverless Gen-7 Robotaxis to host this conference earnings call. This is more than just a normal ride for us. It actually marks a giant leap in autonomous driving's advancement. We are making level four autonomy more accessible than ever to a much broader user base. I'm excited to share a critical milestone. Our Gen-7 Robotaxis have reached city-level UE break-even in Guangzhou shortly after their official commercial launch. This is pivotal to validate our viable business model. It not only gives us strong confidence to further scale our fleet, but also attracts more and more third-party partners, enabling them to fund our fleet and support our asset-light model. The scaling up of a fleet is key to our growth, as large-scale operational footprint drives efficiency through the economy of scale. Our robotaxi vehicles are essentially moving billboards. In fact, many new users discover and download our Pony Pilot app after spotting our vehicles on the road for daily operation. Fleet expansion serves as a highly efficient self-reinforcing marketing engine, facilitating user adoption and strengthening brand recognition. This creates a powerful upward spiral: more vehicles generate greater visibility, which attracts more users and establishes network effects. The results are already evident. Building on that momentum, new registered users nearly doubled within just one week of launching Gen--7 from late October, reflecting robust user demand and an effective go-to-market strategy. Now, let me highlight some key advances we've made in recent months in executing our scale-up strategy. First, we have ramped up production at an accelerating pace since the start of production in the middle of this year. By November, more than 600 Gen-7 Robotaxis had rolled off our assembly lines, bringing the total fleet size to be over 900 vehicles. Thanks to the streamlined production process, we now expect to outperform our full-year target of 1,000 vehicles, delivering ahead of schedule. This gives us increasing confidence to sustain robust momentum, driving fleet size to surpass 3,000 vehicles in 2026. Second, in Q3, our robotaxi revenue surged by 90% year-over-year, with fare charging revenues delivering over 200% year-over-year growth. This was fueled by rising user adoption across all four Tier one cities, improved fleet operational efficiency, and tailored pricing strategies for diverse user segments. We have seen that the higher order density leads to lower users' average waiting time and, in turn, higher vehicle utilization rate. This allows us to continuously optimize our pricing strategy. Third, we have continued to expand our operational footprint. For example, in Shanghai, we became the city's first company to launch fully driverless commercial robotaxi operations earlier this July, covering the Jinqiao and Huamu areas of Pudong. In Shenzhen, we extended commercial fully driverless operations to more and bigger city areas, including Shekou and Overseas Chinese Town. Fourth, we're taking major steps toward scalable mobility. Excuse me, I believe there has been an interruption. Just one moment, please. Excuse me, I've rejoined management. Please continue. Thank you. Sure. I was talking about the scale-up strategy. Following our collaboration with Sihu in June, we recently forged another partnership with Sunlight Mobility. These alliances reflect growing market recognition of our business model, with an increasing number of third parties wanting to fund fleet deployment. This actually enables us to speed up further fleet expansion. Now, let me turn to our global expansion. We are deeply dedicated to advanced robotaxi services while strategically expanding our international fleet. Now, we have robotaxi presence established in eight countries across China, the Middle East, East Asia, Europe, and the U.S. We entered a new market in the Middle East, Qatar, through a partnership with Mowasalat in the third quarter. Mowasalat is the country's largest transportation service provider. As part of this collaboration, our robotaxis have recently begun testing on public roads in Doha, the capital of Qatar. We have also advanced our presence in South Korea by securing nationwide robotaxi permits, enabling operation across the country's autonomous testing and operational zones. Our collaboration with local partners continues to deepen. We are collaborating closely with Mowasalat, the country's largest transportation service provider, to begin road testing. In Luxembourg, we plan to deploy testing vehicles based on the Peugeot E -Traveler through our alliance with Stellantis. It's a European leader in light commercial vehicles. This effort will initially focus on vehicles designed for Europeans' diverse mobility needs to enable a range of use cases. In addition, we have partnered with global ride-hailing platforms that also participated in our Hong Kong IPO. Those platforms include Uber and Bolt. Bolt is an Estonia-based mobility company operating in over 50 countries and 600 cities. Built upon our collaboration with Uber, we aim to leverage Uber's robust ecosystem to enter the Middle East and then scale into additional international markets. Last but not least, we recently released our fourth-generation Robotruck, with production and initial fleet deployment expected in 2026. Featuring fully automotive-grade components, optimized software/hardware integration, and a transition from internal combustion engine vehicles to electric vehicles, the Gen- 4 Robotruck delivers a significantly more efficient cost structure and greater energy savings. The new platform fully leverages the technological foundation and operational expertise developed through our Gen-7 Robotaxi vehicles. In addition, we deepened our collaboration with SANY Group and added Liuzhou Motor as a new partner to have multiple vehicles to support our further operations. To sum up, 2025 is a critical year of mass production and commercialization for Pony AI. We take pride in the progress we have made and are steadily delivering on the promise we have made to our shareholders at the time of our U.S. IPO last year. Our recent Hong Kong Stock Exchange listing not only marks a major milestone for our company, but also underscores the promising future of the industry. Moving forward, we will drive technological innovation and create lasting values by scaling fast, efficient, and comfortable autonomous mobility services toward our mission: autonomous mobility everywhere. With that, now I'll hand it over to our CTO, Dr. Tiancheng Lou, to share more about our technology strategies. Tiancheng, please go ahead. Thanks, James. Hello everyone. This is Tiancheng. Let me first share my thoughts on our autonomous driving technology stack. From day one, we believed that full-stack integration across software, hardware, and operations was the only way to build a truly scalable autonomous mobility. That conviction has been validated again and again, especially for this critical year of scaling up. With the achievements we've made, it is clear the over-early technology bets help us achieve the leading position, and it will further accelerate our future growth. Our deep foresight into tech stack is what is positioning us as a leader in the industry today, as we become one of the few companies to operate large-scale fully driverless robotaxi services. As early as 2020, we recognized the importance of training code through based on reinforcement learning using simulation. In that year, we transitioned our tech stack into a world model, which is what we call the Pony World today. Through years of R&D effort and real-world validation, our autonomous driving model has evolved into a closed-loop training. We achieved unsupervised, self-improving iterations. In recent years, we are seeing the broader autonomous and robotic industry coverage converge on the world model, validating the approach we adopt today. This foresight in AI tech stack has given us a meaningful head start, and we're confident that we will stay ahead for multiple years. Let me dive into the three criteria that put us at the frontier forefront of world model development. First, the high-fidelity interactive simulation. This is far beyond the ability to just generate scenarios and render sensor data. Driving is by nature interactive. The robotaxi's actions directly affect how surrounding agents behave, such as other vehicles and pedestrians need to react to our driving behavior. It must understand and adapt to new situations and complex physical interactions in real time, mirroring true on-road interactions. It enables robotaxi operations that are safe, smooth, and social-aware. Of the 10 billion kilometers of test miles that Pony World generates each week, more than 99% capture vehicle agent interactions, while less than 1% are substatic environments such as sensor rendering. Okay, second, the ability to reproduce scale and realistic corner cases. While these long-tail scenarios do not occur frequently, they are critical to safety in autonomous driving. More importantly, every scenario must be something that could really happen in the real world, not those useless edge cases with no basic in reality. The third, the AI-based learning evaluator. This is a reward-based evaluation mechanism. Driving is a multiple object optimization problem. What is considered as good driving also changes in various driving scenarios. Within the closed-loop training environment, Pony World and our Virtual Driver are continuously evaluated on key driving metrics. This assessment does not rely on real-world data, human-labeled data, or rules. Instead, it uses AI-powered models to learn what good driving looks like directly from outcomes, turning real and simulated experiences into a powerful cycle of self-improvement. A best-in-class world model must meet all three criteria to enable truly unsupervised and self-improving closed-loop training. This is critical to realizing large-scale driverless autonomous driving. Leveraging our full-stack technology as a core strength, I will now turn to how to drive business progress during the third quarter. First, on cost and operational efficiency. We pioneered 100% automotive-grade Autonomous Driving Kits for Gen-7 Robotaxis. We've optimized the design, reducing bottom cost by 70% compared with the previous generation. The Gen-7 vehicle has been officially operating for the public in Guangzhou, Shenzhen, and Beijing, fully validating our safety standards and operational efficiency. We built on our momentum and delivered further progress. Driven by scaled production and enhanced R&D, we've already realized an additional 20% reduction in the autonomous driving kit bottom cost for the Gen-7 platform designed for 2026 production compared with the 2025 baseline. This lies in the foundation for sustained cost savings. Our robust AI algorithm and fleet management experts have proven effective at driving operational efficiency. To better identify user demand in hotspot areas during rush hours, we enhance our algorithm for order dispatch, matching, and scheduling, thereby ensuring sustained efficient robotaxi utilization. We have also improved our virtual driver to recognize more and more complex scenarios. This allows us to improve our remote assistance to vehicle ratios substantially on the track to reach 1,00 0- 1,230 by year-end. Our superior service experience has become the key reason users choose Pony AI Robotaxi. After the launch of Gen-7 Robotaxis, we've earned widespread positive feedback and generated great social media buzz from users. As we deliver high-quality experience, users are increasingly willing to pay a premium for the enhanced effort, reliability, and safety of our autonomous journey. For ride comfort, our advanced interactive planning capability is intelligently optimized for the frequency and magnitude of acceleration, braking, and steering. This delivers smooth, natural motion control tailored to the electric vehicles and the ride-sharing market, offering consistent comfort experience for every Pony AI Robotaxi ride. This enhancement has reflected near-measurable improvements for Gen-7, such as emergency brakes and steering over the past few months. Additionally, our robotaxi features are superior in cabin experience. We also pioneered the innovative smart repositioning feature. With one tap, users can remotely adjust their vehicle position for more convenient pickup and drop-off. We introduced voice-activated features we call the Pony AI Voice Assist, allowing users to do star trips and control air conditioning, etc. We will continue to upgrade the cabin into an AI-powered mobility terminal. Together, this upgrade creates a more accessible and streamlined user experience. Third, our tech stack is also built for generalization. The L4 native tech architecture allows us to adapt quickly to new markets and platforms. In terms of cross-region generalization, our Virtual Driver and showings can quickly understand and adapt to diverse traffic conditions around the world. For example, leveraging our high-fidelity training environment and evaluation mechanism powered by Pony World, we extend our fully driverless coverage in Pudong District in just a few weeks. In addition, when expanding to Europe, the system intelligently identifies and adapts to key differences in local road conditions, such as unique traffic signal configurations and various driving patterns. Our technology boosts generalization power across platforms as well. The latest-generation robot truck will come into production and operations from next year. This demonstrates our capability to create synergy between robotaxi and robot truck tech stack. Looking ahead, we will leverage our success at Hong Kong listing to reinforce our technological leadership, increasing R&D investment, and attract top AI talent to advance our robotaxi, robot truck, and new market initiatives. We will continue pushing the frontier of autonomous mobility and refining what is possible in transportation. Okay, this concludes my prepared remarks. I will now pass the call over to our CFO, Dr. Leo Wang, for a closer look at our financial results. Leo, please go ahead. Thank you, Tiancheng. Hello everyone. This is Leo. I will focus on year-over-year comparisons for the third quarter, unless otherwise noted. Q3 2025 was a landmark quarter. We delivered robust revenue growth, specifically with solid progress in robotaxi large-scale commercialization. We now expect to outperform our full-year fleet target of 1,000 vehicles. Moreover, our newly deployed Gen-7 robotaxi fleet has reached a pivotal city-wide unit economic break-even milestone. This lays out a solid foundation for further scaling up and the implementation of our satellite business model, which will be further accelerated by our successful Hong Kong IPO capital raise. In this quarter, revenue finished at $25.4 million, growing by 72%. This strong performance was primarily driven by the continuous optimization of our robotaxi services and the sustained demand in our licensing and application business. Firstly, robotaxi services revenue reached $6.7 million, representing a remarkable growth of 89.5% year-over-year and 338.7% quarter-over-quarter. Specifically, fare charging revenue continued to deliver a triple-digit growth, surging 233.3%. This was achieved even before the commercial rollout of our Gen-7 Robotaxi. Supported by a stable commercial fleet of our Gen-5 and Gen-6 vehicles, the strong growth during Q2 and Q3 stemmed from growing user demand in Tier one cities in China. Our continuous effort to optimize fleet operation and pricing strategy altogether led to increased fleet utilization and efficiency. This is a testament to growing user recognition and brand loyalty to Pony Pilot service. Going forward, as we follow this strong momentum towards a significant fleet expansion of over 3,000 vehicles by 2026, we expect robotaxi revenue growth to accelerate even further, driving more orders and a higher operational efficiency. In Q3, another key robotaxi update is the implementation of our satellite model for fleet expansion. As we have shown promising numbers in vehicle unit economics, we received strong interest from third parties who are willing to purchase Gen- 7 vehicles to run as robotaxi operators. Such partners include but are not limited to leading ride-hailing or taxi operators, for instance, Shenzhen Sihu Group and Sunlight Mobility. The satellite model has contributed revenues through technology licensing fees and vehicle sales, while giving us further leverage and capital efficiency for further fleet expansion. Aside from strong top-line growth domestically, we are also seeing fast growth of robotaxi revenues from overseas markets. Moving forward, we expect robotaxi revenues from overseas markets to continue to grow. Currently, our robotaxi footprint has already expanded into eight countries globally, serving as a promising foundation in our exploration of the international opportunities. Secondly, moving to robot truck. Robot truck service revenues were $10.2 million, growing by 8.7%. Moreover, as we launch our Gen4 fully auto-grade robot truck, we expect to reduce the bottom cost of its ADK, autonomous driving hardware kit, by 70% and reach a 1,000-unit scale of robot truck fleet going forward. This new generation of robot truck will powerfully accelerate the progress of robot truck commercialization at scale. Thirdly, licensing and application revenues were $8.6 million, growing significantly by 354.6%. We continue to see robust and growing demand for our autonomous domain controller, primarily from robot delivery clients. Turning to gross margin, we delivered a significant gross profit margin improvement from 9.2% in Q3 2024 to 18.4% in Q3 2025, with gross profit of $4.7 million in the third quarter. This remarkable improvement was firstly driven by our strategic initiatives to optimize the revenue mix, and secondly, by a greater contribution from robotaxi services, which carry a relatively higher margin. The unit economic break-even achievement validates our dual focus on go-to-market execution and optimized operational efficiency. Since the launch of Gen-7 commercial operations in Guangzhou, the daily net revenue per vehicle has reached CNY 299. The net revenue refers to the total CNY value generated from ride-hailing services after deducting discounts and refunds. Notably, daily average orders per vehicle have reached 23, fueled by robust widespread user demands and our operational optimization. Meanwhile, we have also optimized hardware depreciation as well as operational costs, including charging, remote assistance, ground support, service and maintenance, insurance, parking, and network costs. This will further improve our margin down the road. The total operating expenses were $74.3 million, up by 76.7%. Excluding share-based compensation expenses, non-GAAP operating expenses were $67.7 million, up 63.7%. The increase primarily reflects the win of R&D investment in Gen-7 vehicles and the expansion of our R&D personnel, critical to securing and extending our technological leadership. Specifically, approximately half of the increase in research and development expenses stemmed from one-time customized development fee of $12.7 million for vehicles. Net loss for the third quarter was $61.6 million, compared to $42.1 million in the same period of last year. Non-GAAP net loss was $55 million, compared to $41.4 million last year. Looking ahead, we expect to sustain disciplined investment to accelerate large-scale commercial deployment. Turning to the balance sheet, our cash and cash equivalents, short-term investments, restricted cash, and long-term debt instrument for wealth management were $587.7 million as of September 30, 2025, compared to the balance as of June 30, 2025, of $747.7 million. Around half of this decrease comes from one-off cash outflow, including capital injection to JFeng, our joint venture with Toyota, to support Gen - 7 mass production and deployment. All of the capital commitment in JFeng has been completed. The remaining cash balance reduction primarily reflects our mass production and large-scale deployment status, including, firstly, ongoing operational cash outflow, and secondly, capital expenditure for the procurement of Gen - 7 vehicles in Q3 to support our goal of 1,000 vehicle fleets by year-end. For the nine months ending September 30, 2025, we have an accumulated free cash outflow of $173.6 million. With the completion of our recent Hong Kong IPO, we have over $800 million cash newly added, providing us with substantial fuel for the next phase of growth. The IPO proceeds will help us accelerate fleet expansion into key addressable markets, further optimize our platform for scale, and deepen our R&D investments to further solidify our technology moat. Looking ahead, our mass production momentum continues to strengthen, and we are on track to exceed our full-year vehicle target of 1,000, achieving this milestone ahead of schedule. This acceleration reinforces our confidence in scaling rapidly, and we now anticipate to grow our fleet to be more than 3,000 vehicles by 2026. In addition, we've already transitioned to a satellite model for a meaningful portion of our new vehicles. This will enhance our capital expenditure efficiency and provide greater leverage for scalable fleet expansion. With the proven operational model and the financial runway from the recent Hong Kong IPO, we are uniquely positioned to accelerate our business plan, turning momentum into sustained profitable growth. I will now turn the call over to the operator to begin our Q&A session. Thank you. We will now begin the question and answer session. To ask a question, you may press star then one on your telephone keypad. If you're using a speakerphone, please pick up your handset before pressing the keys. To withdraw your question, please press star then two. For the benefit of all participants on today's call, please limit yourself to one question. If you have more questions, please re-enter the question queue. If you ask questions in Chinese, please repeat them in English. The first question comes from Ming Hsun Lee with Bank of America. Please go ahead. Thank you, thank you, management, to give the opportunity for me to ask a question. I just have one question. Could the management team give us more updates on the fleet size for this year and also the outlook in 2026? For the new vehicles added, what is the fleet deployment plan across different cities? Thank you. This is James. I'll take this one. As you can see, since the launch of our Gen-7 Robotaxi, we actually have seen a much faster-than-expected production and deployment. For this year, we certainly expect to outperform our previous target of 1,000 robotaxis by the year-end. We certainly expect this strong momentum to continue into 2026, now with a conservative target of over 3,000 vehicles. This is mainly because we have already seen an upward spiral with the launch of our Gen - 7 vehicles. Essentially, the fleet density creates a much shorter wait time for the passengers. That creates a better user experience. The user experience leads to much higher utilization for our vehicles. We can actually then charge a better pricing. This spiral really created a strong momentum for us to expand much faster. In addition, we also started experimenting with the satellite model by collaborating with fleet managers such as Sihu, Sunlight, and certainly will add more partners. This satellite model allows us to deploy at a much larger fleet with less CapEx. This is our growth plan. In terms of the fleet deployment plan, we'll go deeper on our existing markets. At the same time, we'll go much wider to explore some new opportunities. The city-wide UE break-even for the Gen - 7 in Guangzhou, in my view, is a pivotal milestone to validate our business model. This gives us huge confidence and allows us to deepen our collaboration and our operation in the existing markets, which are the Tier 1 cities in China. This is because, as I already mentioned, expanded fleet size creates an upward spiral. At the same time, we also expand into many more domestic cities and also the overseas markets. We see those for our future growth. Our go-to-market strategy on those markets is that we'll collaborate deeply with the local partners and the local government agencies to establish presence and prepare for our future growth. Stay tuned. I think we'll have great news ahead of us. With that, back to the operator. Thank you. The next question comes from Ben Wang with Deutsche Bank. Please go ahead. Hi, management. Thank you for taking my question. I just have one question, which is about the fare charging. I'd like to know fare charging revenues will leverage another growth in Q2 2025. What is the outlook for fare charging revenues as we deploy more vehicles? Thank you. Yeah, this is Leo. I'll take this question. Yes, in Q3, our fare charging revenue actually surged even faster. It was growing about 233%. Though at that time, our fleet were still with the Gen-5 and Gen-6 vehicles. We believe such growth was driven by both the demand side as well as the operational side. On the demand side, we have been continuously to do our effort to improve the whole riding experience and also the user experience. With this effort, we've seen robust and organic user demand in Tier 1 cities. This is also a signal of a strong consumer adoption of our robotaxi service. Giving you an example that the total registered user was more than doubled year over year in Q3. On the operational side, we have also been optimizing the fleet operation to improve our vehicle utilization and order fulfillment, as Tiancheng already mentioned in his remarks. For example, we enhanced our fleet dispatching and deployment. This has consistently reduced our wait time. It is approximately 50% shorter compared to the same period in 2024. We also continue to expand our pickup and drop-off points to create a much more smooth user experience. For example, in Shenzhen, now we have more than 10,000 such points, more than 300% increase since the end of June this year. With all this demand side and operational side improvement, I believe we could see sustained strong growth momentum through the continuous fleet expansion with more and more Gen - 7 vehicles into our service. First of all, we expect that our fleet has been growing exponentially from 270 last year and to be more than 1,000 this year, and a target of more than 3,000 next year. This scaling up would also create a better network effect, which means shorter wait time and higher vehicle utilization and higher user adoption. We would also progressively expand our service area in cities such as Shanghai, Shenzhen. We've already been doing so today. We would increase the population coverage and expand to more drivable mileages, etc., etc. With all these being done, I think we can boost the average order value per trip. Okay, now I'll get back to the operator. Thank you, sir. The next question comes from Kyle Wu with City Research. Please go ahead. Thanks for taking my questions. This is Kyle from City Research. Congratulations on achieving the milestone of city ride UE break-even. Could you elaborate more about the assumption behind the UE break-even, including daily order pricing, daily operating hours, and the ratio of remote assistance? Thank you. Yes, I'll take this question. Like you said, we all believe the city-wide unit economic break-even is a pivotal milestone for the company and also for the industry. First of all, we achieved this pivotal milestone in Guangzhou city since our Gen - 7 vehicle has been put into commercial service. We always believe China is the largest market of global ride-hailing market. For the Tier one cities, the total TAM accounts for a huge percent of ride-hailing market in China. Achieving this milestone in this market is far more meaningful from a commercial perspective. If we talk about the unit economics, there is the revenue side. There is always the cost side. On the revenue side, first of all, on the daily net revenue per vehicle. As I mentioned, our daily net revenue per vehicle has hit CNY 299. It is based on a two-week daily average figures as of November 23, following the launch of our Gen - 7 vehicle in Guangzhou. This net revenue also refers to the total CNY value generated from ride-hailing service after deducting discounts and refunds. In terms of daily orders from this CNY 299 number, it was average 23 orders per day. It is fueled by robust widespread of user demand. Now let's look into the cost side. The cost side of the unit economics basically has two major components. First of all, it's the hardware depreciation. For Gen - 7 vehicles, the annual vehicle depreciation is based on a six-year useful life. The other major component on the cost side is the operational cost, which includes the charging, remote assistant, and the ground supporting staff, vehicle service and maintenance, insurance, parking, internet network costs. Regarding the remote assistant, we are on track to achieve our 1 over 30 vehicles. From this milestone that we achieved, we are very confident to capture the China huge TAM. Meanwhile, it also established a strategic foundation for further scaling up domestically and internationally. This not only gives us strong confidence to further scale our fleet, but we also see more and more third-party companies are enabled to fund their fleet and helping us to transition into a satellite model. All these together, we believe will drive our top-line growth and also the cost optimization. Okay, I'll get back to the operator. Thank you. The next question comes from Purdy Ho with Huatai Securities. Please go ahead. Hello, James, Dr. Lou, and Leo. Thank you for taking my question and congratulations on the results. We've observed a surge in diverse players attempting to enter into the robotaxi operations, particularly the EV makers. What's your take on these new entrants in the Level 4 autonomous driving space? Also, specifically, could you elaborate on the main technical and operational challenges such as tackling corner cases and fleet management for these new e-commerce? Thank you. Basically, James, I'll take this one. First and foremost, I think it's definitely. As we see more and more companies announcing that they're going to enter into the robotaxi industry, I think itself is actually a great thing because it indicates increasing recognition and confidence in robotaxi imminent potential for the large-scale commercialization. As the awareness increases, more resources, more companies come in, more resources will pour into this robotaxi industry to actually accelerate its development. Overall, I view this as a good thing. On the flip side, the robotaxi industry is actually not one that any new player can easily enter because, as you can see, the fact is that currently none of the new entrants being an OEM maker or being ride-hailing platforms, none of them have fully driverless vehicles deployed on the open road. It is clear evidence this is not an easy industry to be entered. I think there are certainly three huge hurdles for any new players. Those hurdles are business side, regulatory side, and also technical challenges. Let's probably look at the business challenges first because robotaxi, as you see, it's not just about airflow driving itself. It also has many more aspects such as user acquisition, vehicle production, fleet dispatching, fleet maintenance, such as the cleaning, charging, and everything else. As a leader and the first mover in this industry, we certainly enjoyed the early mover advantages as we have a much bigger L4 fleet on the road. We generated better brand awareness. We have optimized the cost on every aspect of the business, as Leo already mentioned in his answer to the last question. Because of early mover, we also have secured more partners. I think all those are important and create a big hurdle for any new entrants. The second hurdle that I want to mention is on the regulatory front because L4, a robotaxi, needs very high safety requirements. All the policymakers worldwide have fundamentally will require much, much higher safety requirements for the robotaxis compared with the traditional taxi. That means in any city, a new player needs to prove its safety step by step before they can expand even into a fully driverless fleet. Typically, a new player will start with testing with just a few dozen or maybe even less vehicles. Then once those vehicles prove to be safe, they add more vehicles and then expand operational areas after they can accumulate the safety records. Along the way, they also need to acquire all the required licenses and permits. This in itself is actually a lengthy process. Overall, the whole process takes time. This cold starting process cannot be easily accelerated. That is the second challenge. The third challenge, certainly in my view, is on the technical side. Probably for this one, I will turn to Tiancheng to elaborate. Yeah, sure. I am Tiancheng. Let me continue from a technology perspective. As I said in my preparatory remarks, we are now seeing the broader industry starting to use world models such as robotaxi players and automakers. Essentially, they are all about using reinforcement learning based on simulation training environments. First and foremost, I will say we started developing reinforcement learning for autonomous driving five years ago. This gives us an early mover advantage. We have one of the most experienced companies in the world model. We believe that we will continue to stay ahead as more players follow the same path. Once the world model matured, I would say human feedback and real-world data are no longer used for further iterations. At the stage of training closed loop, the world model and the virtual driver co-evolve into a dual spiral cycle. This means the world model is training the virtual driver. At the same time, the world model improves itself through feedback of the virtual driver. This sharply reduces reliance on the real-world data. The question touched on the technical challenge for the meeting of corner cases. Maybe an example here that when the virtual driver meets some corner cases, this can give feedback to the world model. The world model will improve its distribution of the corner cases. The next generation of next version of the world model will be able to create or generate and testing and also improving the capability of the virtual driver to handle the corner cases. Okay, looking ahead, our real advantage lies in the ability to validate new technology safely and deploy them at scale. Based on our proving track record of scaling robotaxi operations, we believe we can quickly capture the next wave of innovation. Also, last but not least, our current Hong Kong IPO will further accelerate R&D and iteration cycles, reinforcing our technical leadership and widening our competitive moat. Yeah, with that, I'll back to the operator. Thank you. The next question comes from Zhao Yili with Jefferies. Please go ahead. Thanks for taking my question. I have one as well. My question is about what do you see as the main factors behind the faster expansion of your operational areas? Beyond technology, what else do you think really matters? From the technical perspective, are you using large language models? If so, how are they helping push L4 autonomy forward? Thank you. Thank you. This is Tiancheng. I will continue to answer this question. I think your question consists of two parts. Let me answer your question on generalization first. I will address the other one on large language model later. For generalization, I would say technically, our tech stack is by nature built for generalization. A good example is that our operational area expansion into new areas in Shanghai, Pudong, and Shenzhen, Nanshan District in the South Quarter. In both cases, it only took us a few weeks from verifying the safety to truly realizing fully driverless operations to the public. There was no need for additional model training. The key reason is that L4 native architecture is built for handling corner cases and extreme cases, while these cases are actually very consistent across different regions. They are really nothing more than things like small obstacles, boxes on the road, pedestrians suddenly crossing, and sudden lane changes from other cars without looking at the vehicle behind, etc. It is just about the likelihood and the probabilities of each one happening. I hope that can help understand why the L4 tech stack is by nature built for generalization. At this moment, I will say the key to our new area expansion is the number of robotaxi vehicles. If we expand to too many areas without adding more cars, it will instead dilute the density. That is the reason why the speed of operational area expansion cannot significantly be faster than that of the size. Let me share my thought on the second part, that's a large language model. First, I will say first and foremost, there are two non-negotiable requirements for L4 onboard driving model: uncompromising safety and also low latency. There are the large language model and chatbot do not meet, and they are not designed to meet as well. For safety, large language models generally have issues like model health in nature, which is unacceptable for L4 in terms of safety. For latency, large language models are optimized for throughput like tokens per second. In contrast, L4 is optimized for low latency and the ability to run fully driverless robotaxi on chips that are both low power consumption and cost-efficient. Moreover, large language models overly rely on human data, which fundamentally limits them to the boundary of the existing human knowledge, as it inevitably makes them pick up human errors and bad habits from human drivers. We also extensively use large language models in the R&D effort, such as AI-enhanced human-machine interaction, engineering productivity tools for coding and documentation, and analysis for the rider feedback for experience improvement. However, due to the multiple reasons mentioned above, large language models are by nature not built for driving models onboard. With that, back to the operators. Thank you. Thank you. That's very helpful. The next question comes from Xinyu Fang with UBS. Please go ahead. Hi. Thank you, Mancheng, for taking my questions. I have one question here. It is currently that Pony cooperates with multiple OEMs for robotaxi manufacturing, including BAIC, GAC, and Toyota. Does Mancheng see potential for improving operating leverage through working with only one OEM team staff? Thank you. This is James. I'll take this one. The matter of the reality is that in the whole global taxi industry, local governments and the local residents actually have strong preferences for the local branded taxi vehicles. That is the reality. Typically, when a robotaxi fleet is relatively small, the brand does not really matter much. If we need to deploy a significant fleet size, the requirements certainly are no longer true. The local branded OEMs are much more preferred. It is necessary for us to cooperate with multiple local OEMs in different regions. It actually can help us to expand into different markets much quickly. That is why we are now collaborating with three OEMs to produce our Gen - 7 Robotaxis. It is true that feeding our autonomous driving kit into different vehicles actually poses a huge technical challenge. If you look at it from the other side, the mere fact that we were able to standardize our technology and being able to fit our setup into different vehicles, that shows our technical generalization. Down the road, it actually can create a huge competitive edge. As a result, we can add new models much faster to accelerate our expansion into new regions. For example, in Europe, we currently added the partnership with Stellantis. With that, I'll back to the operator. The next question comes from Tang Zhu Jia with Guoshen. Please go ahead. Thanks for taking my question. I have one question. Why can Pony use remote assistant on robotaxi when the car meets difficulty instead of remote control or human takeover? What is the technology difference behind that? This is Tiancheng. I will take this one. I think one of the previous questions also touched on the remote assistant for robotaxi. Let me elaborate on that in a little more detail. First, I feel that the remote assist never controls the vehicle through the steering wheel or pedal. Instead, they provide remote support and suggestions by responding to service requests. For all the time, the vehicle can independently drive and independently make decisions without remote assistance. Assistance only initiates when a vehicle requests it rather than through remote driving. When a vehicle receives the assistance response, the onboard driving system will still make timely decisions based on the actual situation. Because the vehicle never waits for remote command to act, it remains safe operating without any dependence on network latency. One typical example of remote assistance is the situation of a temporary traffic control. In such cases, the system may request remote assist, which can provide high-level suggestions to confirm the car's decision, navigating through the scenario. Also, as I mentioned, we have continuously improved AI algorithms and also leveraged general AI capability to recognize more and more complex contexts. This allows us to improve remote assist to vehicle ratio in the third quarter to reach 1 to 30 by year-end. I hope that can answer your question. Back to the operator. Thank you. The next question comes from Serena Li with China Securities. Please go ahead. Thank you for taking my question. This is Serena Li from China Securities. As far as we know, some countries in the Middle East have issued fully driverless robotaxi licenses recently. What's our view on that? What's Pony's overseas strategy? Sure. This is Tiancheng again. Let me take this one. Our company's mission has always been autonomous mobility everywhere. We certainly have the global ambition, since our funding, to actually utilize our technology to benefit the local societies worldwide. Currently, our global efforts are focused on the markets with hyper-growth potential. Those are the markets with typically strong mobility demand, well-developed infrastructure, and a supportive regulatory environment. When we evaluate a potential market to enter, on a high level, three factors we will consider. One is the addressable market size, which is 10. Second is the openness and the execution of the local government to support and issue permits for the fully driverless commercial operation. Third is how strong is the local partner for their on-the-ground resources and operational capacities. As you can see, our current global expansion status is that we have already entered eight countries for our robotaxi. For example, in Q3, we added Qatar as a new market by collaborating with Mowasalat. In Q3, we also saw rapid revenue growth, especially for the robotaxi from our overseas markets. We certainly expect this momentum to continue. Going forward, we will enter other global markets if we see there are good growth opportunities. This is our overseas strategy. With this, back to the operator. As there are no further questions, I'd like to turn the call back over to the company for closing remarks. Thank you, operator. This is George again. If anyone has any more questions, feel free to contact our team. We will conclude our call today. Thank you, everyone. This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your line.