Clippers beat the Jazz 115-103 behind Leonard's 25th straight game with more than 20 points

SALT LAKE CITY (AP) — Kawhi Leonard scored 21 points, James Harden had 16 points and 10 assists, and the Los Angeles Clippers beat the Utah Jazz 115-103 on Tuesday night for their ninth win in 10 games.

Kris Dunn added 14 points and a season-best five steals for the Clippers (22-24), who have the best record in the NBA since Christmas. They have won 16 of their last 19 games — immediately after going 3-16 in their previous 19.

The Clippers took control during a 10-2 run in the third quarter powered powered by Dunn’s two steals and layups, along with a 3-pointer that made it 73-61. Los Angeles led by double figures the rest of the way.

Utah's Lauri Markkanen returned after missing seven games due to illness to score 19 points on 5-for-15 shooting.

Ace Bailey scored 20 points and Brice Sensabaugh added 13 but the Jazz (15-32) have dropped seven of their last eight games.

With Leonard on the court, the Clippers are 14-2 in their last 16 games and have won seven straight.

Leonard has scored at least 20 points in 25 consecutive games. He joins Bob McAdoo, World B. Free and Blake Griffin as the only players in franchise history with such a streak.

The Jazz were missing their top two playmakers in Jusuf Nurkic (illness), who has had three straight triple-doubles, and Keyonte George (rest), who has averaged 28.8 points in the last five games.

The Jazz scored the first 10 points of the game and led 54-53 at halftime, but the Clippers defense tightened up and forced 10 steals and several shot clock violations.

Utah committed 17 turnovers which led to 23 points for Los Angeles. Although the Clippers play at the league's slowest pace, they still topped the Jazz in fast-break points 12-8.

Clippers: Visit Denver on Friday.

Jazz: Host Golden State on Wednesday.

AP NBA: https://apnews.com/hub/nba

Utah Jazz guard Walter Clayton Jr. (13) and forward Lauri Markkanen, right, attempt to defend Los Angeles Clippers guard James Harden (1) as he shoots during the second half of an NBA basketball game, Tuesday, Jan. 27, 2026, in Salt Lake City. (AP Photo/Anna Fuder)

Utah Jazz forward Cody Williams, left, defends Los Angeles Clippers guard James Harden, right, during the second half of an NBA basketball game, Tuesday, Jan. 27, 2026, in Salt Lake City. (AP Photo/Anna Fuder)

Los Angeles Clippers forward Kawhi Leonard (2) shoots over Utah Jazz forward Ace Bailey (19) during the second half of an NBA basketball game, Tuesday, Jan. 27, 2026, in Salt Lake City. (AP Photo/Anna Fuder)

Los Angeles Clippers guard Kris Dunn (8) and center Brook Lopez (11) celebrate a play during the second half of an NBA basketball game against the Utah Jazz, Tuesday, Jan. 27, 2026, in Salt Lake City. (AP Photo/Anna Fuder)

SHANGHAI--(BUSINESS WIRE)--Jan 28, 2026--

Robbyant, an embodied AI company within Ant Group, today announced the open-source release of LingBot-VLA, a vision-language-action (VLA) model designed to serve as a “universal brain” for real-world robotics, which helps reduce post-training costs and accelerate the path to scalable deployment.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20260127455032/en/

So far, LingBot-VLA has been successfully adapted to robots from leading manufacturers, including Galaxea Dynamics and AgileX Robotics, demonstrating strong cross-morphology transfer capabilities across diverse robot platforms.

The model’s performance was evaluated on the GM-100 benchmark, a comprehensive evaluation suite open-sourced by Shanghai Jiao Tong University that comprises 100 real-world tasks. In tests conducted across three distinct physical robot platforms, LingBot-VLA achieved higher task success rates than other evaluated models. Notably, when depth information was included, the model’s spatial perception improved significantly, setting a new record on task success rate.

Additionally, on the RoboTwin 2.0 simulation benchmark, which features 50 challenging tasks under intense environmental randomization, including varying lighting, clutter, and height perturbations, LingBot-VLA leveraged its learnable query alignment mechanism to integrate depth cues effectively and achieved a higher task success rate in complex scenarios, demonstrating robust performance on both simulation and real-world deployment.

To date, the deployment of embodied AI has been hampered by cross-platform generalization challenges stemming from differences in robot morphology, task definitions and operating environments. Developers are often forced to repeatedly collect data, retrain models, and fine-tune parameters for each new deployment, leading to high costs, low reusability, and limited scalability.

To address these challenges, LingBot-VLA was pre-trained on over 20,000 hours of large-scale real-world interaction data, covering nine mainstream dual-arm robot configurations, including AgileX, Galaxea R1Pro, RILite, and AgiBot G1. This enables a single model, or a universal brain, to be deployed across a wide range of robotic morphologies, including single-arm, dual-arm, and humanoid platforms, while maintaining high success rates and robustness despite variations in tasks, environments, or hardware configurations.

Beyond generalization, LingBot-VLA also demonstrates strong data and computational efficiency. With comprehensive optimizations to its underlying codebase, LingBot-VLA achieves a 1.5x to 2.8x improvement in training speed compared with other frameworks such as StarVLA and OpenPI.

Notably, this open-source release includes not only the model weights but also a complete, production-ready codebase, featuring tools for data processing, efficient fine-tuning, and automated evaluation. This toolchain can help shorten training cycles and reduces both compute requirements and time cost to commercial deployment, allowing developers to rapidly adapt LingBot-VLA to their own robots and use cases with minimal overhead.

Zhu Xing, CEO of Robbyant, said: “For embodied intelligence to achieve large-scale adoption, we need highly capable and cost-effective foundation models that work reliably on real hardware. With LingBot-VLA, we aim to push the limits of reusable, verifiable, and scalable embodied AI for real-world deployment. Our goal is to accelerate the integration of AI into the physical world so it can serve everyone sooner.”

“LingBot-VLA is Ant Group’s first open-source embodied AI model and marks another milestone in our efforts toward Artificial General Intelligence (AGI),” Zhu added. “Ant Group is committed to advancing AGI through an open and collaborative approach. To this end, we’ve launched InclusionAI, a comprehensive technological ecosystem spanning foundational models, multimodal intelligence, reasoning, novel architectures, and embodied AI. The open-sourcing of LingBot-VLA is a key step in this initiative. We look forward to working with developers worldwide to accelerate the development and large-scale adoption of embodied intelligence and help advance progress toward AGI.”

The announcement was made as part of Robbyant’s “Evolution of Embodied AI Week” initiative. On January 27, Robbyant unveiled LingBot-Depth, a high-precision spatial perception model. When paired with LingBot-Depth, LingBot-VLA can leverage higher-quality depth representations, effectively upgrading the system’s “vision” and enabling robots to “see more clearly and act more intelligently”.

To learn more about LingBot-VLA, please visit:

About Robbyant

Robbyant is an embodied intelligence company within Ant Group, dedicated to advancing embodied intelligence through cutting-edge software and hardware technologies. Robbyant independently develops foundational large models for embodied AI and actively explores next-generation intelligent devices, aiming to create robotic companions and caregivers that truly understand and enhance people’s everyday lives and deliver reliable intelligent services across key use cases, such as elderly care, medical assistance, and household tasks.

To learn more about Robbyant, please visit: www.robbyant.com

On the RoboTwin 2.0 simulation benchmark, LingBot-VLA outperformed other models in cross-task generalization

On the GM-100 real-robot benchmark, LingBot-VLA outperformed other models in cross-morphology generalization