Ai Ogura won the 2026 Dutch Grand Prix at Assen on June 28, completing 26 laps in 40 minutes, 21.905 seconds, officials said. Coverage on June 30 focused on the Assen MotoGP sprint event, held separately from the main race weekend, providing additional points and insights into the championship standings.
The 22-year-old rider, competing for Trackhouse Racing Team on an Aprilia machine, took command in the closing stages of the race, leading from lap 20 through the finish. The race featured a dynamic lead battle, with Jorge Martín initially leading the first 16 laps before Raúl Fernández briefly took over from laps 17 to 19. Fernández, Ogura?s teammate also riding an Aprilia, finished second, 2.004 seconds behind the winner, while Jorge Martín completed the podium in third place, 3.512 seconds adrift, the official results showed.
Ogura?s victory at the 2026 Dutch Grand Prix, held June 26?28 at the TT Circuit Assen, was secured with a total race time of 40 minutes, 21.905 seconds over 26 laps, according to official classification records published by Motorsport.com.
Additional top-five finishes included Fabio Di Giannantonio in fourth and Álex Márquez in fifth, rounding out the leading group at Assen. Enea Bastianini placed sixth, followed by Marc Márquez in seventh and Fabio Quartararo in eighth, according to the official race classification. The leaders maintained an average race pace near 175 kilometers per hour, with Ogura?s winning average speed recorded at 175.5 km/h, Motorsport.com?s detailed timing data indicated.
The race weekend at Assen, which concluded on June 28, was marked by multiple lead changes and strategic positioning among the top riders. Sources confirm that Martín?s early control of the race gave way to Fernández and ultimately Ogura?s late surge to the front. The Trackhouse Racing Team?s strong showing was underscored by the one-two finish of Ogura and Fernández on their Aprilia bikes.
June 30 coverage in the United States focused on the Assen MotoGP sprint event, a separate session from the main race weekend, which offered additional points and championship insights, according to Cycle News. The sprint event was broadcast on June 30 but was not a full Grand Prix race. Official MotoGP results pages and event schedules confirm that the primary Dutch Grand Prix race at Assen took place during the weekend ending June 28, with no new full race held on June 30.
The official MotoGP results index lists separate classifications for practice, qualifying, sprint, and race sessions, supporting the distinction between the completed main race and the sprint coverage dated June 30. The MotoGP website and affiliated sources do not provide a standalone main race result for June 30, further confirming that the June 30 date corresponds to post-race coverage and sprint event reporting rather than a new race.
The 2026 Dutch Grand Prix at Assen remains a key round in the MotoGP championship calendar, with Ogura?s victory contributing valuable points to the season standings. The race weekend?s competitive dynamics and the sprint event coverage on June 30 provide ongoing developments for teams and riders as the season progresses. Official results and standings are maintained on the MotoGP website, which tracks each round?s classification, including practice, qualifying, sprint, and race sessions.
Ai Ogura won the 2026 Dutch Grand Prix at Assen on June 28, completing 26 laps in 40 minutes, 21.905 seconds, officials said. Coverage on June 30 focused on the Assen MotoGP sprint event, held separately from the main race weekend, providing additional points and insights into the championship standings.
The 22-year-old rider, competing for Trackhouse Racing Team on an Aprilia machine, took command in the closing stages of the race, leading from lap 20 through the finish. The race featured a dynamic lead battle, with Jorge Martín initially leading the first 16 laps before Raúl Fernández briefly took over from laps 17 to 19. Fernández, Ogura?s teammate also riding an Aprilia, finished second, 2.004 seconds behind the winner, while Jorge Martín completed the podium in third place, 3.512 seconds adrift, the official results showed.
Ogura?s victory at the 2026 Dutch Grand Prix, held June 26?28 at the TT Circuit Assen, was secured with a total race time of 40 minutes, 21.905 seconds over 26 laps, according to official classification records published by Motorsport.com.
Additional top-five finishes included Fabio Di Giannantonio in fourth and Álex Márquez in fifth, rounding out the leading group at Assen. Enea Bastianini placed sixth, followed by Marc Márquez in seventh and Fabio Quartararo in eighth, according to the official race classification. The leaders maintained an average race pace near 175 kilometers per hour, with Ogura?s winning average speed recorded at 175.5 km/h, Motorsport.com?s detailed timing data indicated.
The race weekend at Assen, which concluded on June 28, was marked by multiple lead changes and strategic positioning among the top riders. Sources confirm that Martín?s early control of the race gave way to Fernández and ultimately Ogura?s late surge to the front. The Trackhouse Racing Team?s strong showing was underscored by the one-two finish of Ogura and Fernández on their Aprilia bikes.
June 30 coverage in the United States focused on the Assen MotoGP sprint event, a separate session from the main race weekend, which offered additional points and championship insights, according to Cycle News. The sprint event was broadcast on June 30 but was not a full Grand Prix race. Official MotoGP results pages and event schedules confirm that the primary Dutch Grand Prix race at Assen took place during the weekend ending June 28, with no new full race held on June 30.
The official MotoGP results index lists separate classifications for practice, qualifying, sprint, and race sessions, supporting the distinction between the completed main race and the sprint coverage dated June 30. The MotoGP website and affiliated sources do not provide a standalone main race result for June 30, further confirming that the June 30 date corresponds to post-race coverage and sprint event reporting rather than a new race.
The 2026 Dutch Grand Prix at Assen remains a key round in the MotoGP championship calendar, with Ogura?s victory contributing valuable points to the season standings. The race weekend?s competitive dynamics and the sprint event coverage on June 30 provide ongoing developments for teams and riders as the season progresses. Official results and standings are maintained on the MotoGP website, which tracks each round?s classification, including practice, qualifying, sprint, and race sessions.
Developers working with large language models (LLMs) have increasingly adopted the DSML tool calls parser convention in recent system updates, according to technical documentation released this year. This structured wrapper, which maps executable calls to XML-based parsing semantics, was implemented to standardize agent and tool-calling pipelines across specific model formats, sources confirmed.
This convention maps executable calls to XML-based parsing semantics, specifically employing tags such as `<|DSML|invoke name="...">` and `<|DSML|parameter name="...">`, according to documentation cited by vLLM and other technical sources released in 2024. The parser treats the DSML wrapper as a shell alias, while the internal parsing logic remains XML-driven, officials said.
The DSML tool calls parser convention, identified by the markup `<|DSML|tool_calls>`, is primarily a structured wrapper used in large language model (LLM) systems to standardize the execution of external tool or API calls, sources confirmed.
The preferred DSML markup form begins with `<|DSML|tool_calls>` and is followed by nested `<|DSML|invoke>` and `<|DSML|parameter>` tags, according to a parser source referenced in recent technical literature. A legacy XML form using `
Other tool-call syntaxes, including `
The tool-calling workflow enabled by DSML conventions allows LLM agents to output structured data that invokes external APIs or functions. Typically, a tool definition includes a name, description, and parameters, which the model generates in a tool-call message. This message is then executed by the server, with the results fed back into the model for further synthesis, according to research and talk materials from 2023 and 2024. This process is fundamental for agents requiring external actions such as retrieving weather data or querying databases.
Recent research, including a 2026 paper titled *W&D: Scaling Parallel Tool Calling for Efficient Deep Research Agents*, highlights advances in parallel and delegated tool use. These approaches enable models to generate code that chains multiple function calls and leverage sandboxed code execution tools that act as callers for other tools, reducing the need for the LLM itself to orchestrate every step. A YouTube talk from late 2023 further detailed these developments, describing multi-step orchestration and delegated execution as emerging standards.
Additionally, tool retrieval and context-window optimization have become key focuses in tool-calling systems. Anthropic?s introduction of a tool search mechanism, as described in the same 2023 talk, reduces the need to load extensive tool schemas into the model?s context window. This approach reportedly uses only about 500 tokens in some scenarios, saving up to 80% of context window usage. Deferred loading, where tools are dynamically retrieved rather than statically injected, is part of this trend toward more efficient and scalable tool-calling pipelines.
It is important to distinguish that the acronym DSML traditionally stands for domain-specific modeling language in academic literature. Formal modeling papers from 2022 to 2024 define DSML as a four-tuple comprising a domain and its interpretations, often used for software agents based on belief?desire?intention frameworks. The parser-related usage of DSML as a tool-call shell alias represents a different operational meaning specific to LLM runtime environments, according to sources analyzing both academic and technical documents.
These developments reflect ongoing efforts within the AI research community to standardize and optimize how language models interact with external tools. The DSML tool calls parser convention, while not a universal protocol, serves as a practical standard within certain model ecosystems to ensure consistent and unambiguous tool invocation. Further updates and refinements to these conventions are expected as LLM architectures and agent workflows evolve.
Developers working with large language models (LLMs) have increasingly adopted the DSML tool calls parser convention in recent system updates, according to technical documentation released this year. This structured wrapper, which maps executable calls to XML-based parsing semantics, was implemented to standardize agent and tool-calling pipelines across specific model formats, sources confirmed.
This convention maps executable calls to XML-based parsing semantics, specifically employing tags such as `<|DSML|invoke name="...">` and `<|DSML|parameter name="...">`, according to documentation cited by vLLM and other technical sources released in 2024. The parser treats the DSML wrapper as a shell alias, while the internal parsing logic remains XML-driven, officials said.
The DSML tool calls parser convention, identified by the markup `<|DSML|tool_calls>`, is primarily a structured wrapper used in large language model (LLM) systems to standardize the execution of external tool or API calls, sources confirmed.
The preferred DSML markup form begins with `<|DSML|tool_calls>` and is followed by nested `<|DSML|invoke>` and `<|DSML|parameter>` tags, according to a parser source referenced in recent technical literature. A legacy XML form using `
Other tool-call syntaxes, including `
The tool-calling workflow enabled by DSML conventions allows LLM agents to output structured data that invokes external APIs or functions. Typically, a tool definition includes a name, description, and parameters, which the model generates in a tool-call message. This message is then executed by the server, with the results fed back into the model for further synthesis, according to research and talk materials from 2023 and 2024. This process is fundamental for agents requiring external actions such as retrieving weather data or querying databases.
Recent research, including a 2026 paper titled *W&D: Scaling Parallel Tool Calling for Efficient Deep Research Agents*, highlights advances in parallel and delegated tool use. These approaches enable models to generate code that chains multiple function calls and leverage sandboxed code execution tools that act as callers for other tools, reducing the need for the LLM itself to orchestrate every step. A YouTube talk from late 2023 further detailed these developments, describing multi-step orchestration and delegated execution as emerging standards.
Additionally, tool retrieval and context-window optimization have become key focuses in tool-calling systems. Anthropic?s introduction of a tool search mechanism, as described in the same 2023 talk, reduces the need to load extensive tool schemas into the model?s context window. This approach reportedly uses only about 500 tokens in some scenarios, saving up to 80% of context window usage. Deferred loading, where tools are dynamically retrieved rather than statically injected, is part of this trend toward more efficient and scalable tool-calling pipelines.
It is important to distinguish that the acronym DSML traditionally stands for domain-specific modeling language in academic literature. Formal modeling papers from 2022 to 2024 define DSML as a four-tuple comprising a domain and its interpretations, often used for software agents based on belief?desire?intention frameworks. The parser-related usage of DSML as a tool-call shell alias represents a different operational meaning specific to LLM runtime environments, according to sources analyzing both academic and technical documents.
These developments reflect ongoing efforts within the AI research community to standardize and optimize how language models interact with external tools. The DSML tool calls parser convention, while not a universal protocol, serves as a practical standard within certain model ecosystems to ensure consistent and unambiguous tool invocation. Further updates and refinements to these conventions are expected as LLM architectures and agent workflows evolve.