AI Research
Vietnam plans to introduce Law on Artificial Intelligence

This information was announced by Minister of Science and Technology Nguyen Manh Hung at a conference organised by the Ho Chi Minh National Academy of Politics in coordination with the Ministry of Public Security, the Ministry of National Defense, and the Central Theoretical Council in Hanoi on September 15.
Minister of Science and Technology Nguyen Manh Hung. Photo: MST |
At the event, experts, businesses, and managers shared their ideas in two discussion sessions. The first session focused on AI power, risks and control, analysing both positive and negative aspects, affirming the need to exploit potential and control ethics, safety, security, and social risks.
In the second session, they discussed national AI development strategy, from vision to actions, a specific roadmap to make AI a pillar in Vietnam’s socioeconomic development.
They agreed that for AI to truly become a driving force for development, Vietnam needs a comprehensive strategy: data infrastructure, high-quality human resources, a complete legal framework, and a dynamic innovation ecosystem. More importantly, AI must be oriented to serve people, protect human rights, and strengthen national security in the digital age.
According to Minister Hung, Vietnam issued its first AI Strategy in 2021, but AI is a rapidly changing field, so the strategy needed to be updated.
By the end of this year, the country will have an updated version of the National AI Strategy and the AI Law. This is not only a legal framework, but also a declaration of national vision. AI must become the country’s intellectual infrastructure, serving the people, developing sustainably, and enhancing national competitiveness.
Regarding open AI technology, Hung emphasised that Vietnam is committed to developing and mastering digital technology, including AI, based on open standards and open-source code. This is also Vietnam’s strategy to develop and master Vietnamese technology, implementing the “Make in Vietnam” programme.
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Experts, businesses, and managers share their ideas at the conference. Photo: MST |
Regarding creating a domestic AI market, he said that without applications, there will be no market. Without a market, Vietnamese AI enterprises will remain small. Therefore, promoting AI applications in enterprises, in state agencies and key areas is the fastest way to develop AI and create Vietnamese AI enterprises.
“The government will spend more on AI, the Natif Technology Innovation Fund of the Ministry of Science and Technology will spend at least 40 per cent to support AI applications, issue vouchers for small and medium-sized enterprises using Vietnamese AI. The domestic market is the cradle to create Vietnamese AI enterprises,” he noted.
In terms of policy and institutions, he added that Vietnam will issue a national AI ethics code that is in line with international standards but suitable for Vietnamese practice, and at the same time develop an AI Law and an AI strategy with core principles including risk-based management, transparency and accountability, putting people at the center, encouraging domestic AI development, AI autonomy, using AI as a driving force for rapid and sustainable growth, and protecting digital sovereignty based on three pillars: data, infrastructure, and AI technology.
According to the MST, Vietnam’s AI development will have to be based on four important pillars: transparent institutions, modern infrastructure, high-quality human resources, and humane culture.
Time for Vietnam to make breakthroughs
Speaking at the workshop, Luong Tam Quang, Minister of Public Security, said that AI is considered one of the key technologies, a factor that can lead to changes in the global order.
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Luong Tam Quang, Minister of Public Security. Photo: MST |
He added that with the ability to promote economic growth, optimise production, improve healthcare, innovate education, and enhance social governance capacity, AI helps countries save costs, increase efficiency, and expand knowledge. It is also a resource, and a driving force to affirm the country’s position in the digital age.
According to Minister Quang, Vietnam’s potential for AI development is huge, and is expected to contribute about $79.3 billion, equivalent to 12 per cent of Vietnam’s GDP in 2030 if widely applied. Under the leadership of the Party, legal regulations for the development of AI have gradually taken shape.
Prof. Dr. Nguyen Xuan Thang, director of the Ho Chi Minh National Academy of Politics, and chairman of the Central Theoretical Council, said that AI is becoming an indispensable part in the process of establishing a new growth model and the operation, governance, and management of the country’s society and economy.
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Prof. Dr. Nguyen Xuan Thang, director of the Ho Chi Minh National Academy of Politics, and chairman of the Central Theoretical Council. Photo: MST |
However, to turn potential into reality, it requires the support of the entire ecosystem, from national strategies and policies to implementation in businesses, institutes, schools, and the community.
“AI cannot develop sustainably without responsibility, ethics, and a clear humanistic orientation. Technology is the tool, while humans are the goal and the deciding factor, because even if it possesses unlimited power as many people believe, AI is still a product created by humans,” Thang emphasised.
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Spatially-Aware Image Focus for Visual Reasoning

View a PDF of the paper titled SIFThinker: Spatially-Aware Image Focus for Visual Reasoning, by Zhangquan Chen and 6 other authors
Abstract:Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods have tried to incorporate visual reasoning, however, they fail to leverage attention correction with spatial cues to iteratively refine their focus on prompt-relevant regions. In this paper, we introduce SIFThinker, a spatially-aware “think-with-images” framework that mimics human visual perception. Specifically, SIFThinker enables attention correcting and image region focusing by interleaving depth-enhanced bounding boxes and natural language. Our contributions are twofold: First, we introduce a reverse-expansion-forward-inference strategy that facilitates the generation of interleaved image-text chains of thought for process-level supervision, which in turn leads to the construction of the SIF-50K dataset. Besides, we propose GRPO-SIF, a reinforced training paradigm that integrates depth-informed visual grounding into a unified reasoning pipeline, teaching the model to dynamically correct and focus on prompt-relevant regions. Extensive experiments demonstrate that SIFThinker outperforms state-of-the-art methods in spatial understanding and fine-grained visual perception, while maintaining strong general capabilities, highlighting the effectiveness of our method. Code: this https URL.
Submission history
From: Zhangquan Chen [view email]
[v1]
Fri, 8 Aug 2025 12:26:20 UTC (5,223 KB)
[v2]
Thu, 14 Aug 2025 10:34:22 UTC (5,223 KB)
[v3]
Sun, 24 Aug 2025 13:04:46 UTC (5,223 KB)
[v4]
Tue, 16 Sep 2025 09:40:13 UTC (5,223 KB)
AI Research
[2506.08171] Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models

View a PDF of the paper titled Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models, by Daniel Koh and 4 other authors
Abstract:Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning over code still remains underexplored. We introduce the task of worst-case symbolic constraints analysis, which requires inferring the symbolic constraints that characterise worst-case program executions; these constraints can be solved to obtain inputs that expose performance bottlenecks or denial-of-service vulnerabilities in software systems. We show that even state-of-the-art LLMs (e.g., GPT-5) struggle when applied directly on this task. To address this challenge, we propose WARP, an innovative neurosymbolic approach that computes worst-case constraints on smaller concrete input sizes using existing program analysis tools, and then leverages LLMs to generalise these constraints to larger input sizes. Concretely, WARP comprises: (1) an incremental strategy for LLM-based worst-case reasoning, (2) a solver-aligned neurosymbolic framework that integrates reinforcement learning with SMT (Satisfiability Modulo Theories) solving, and (3) a curated dataset of symbolic constraints. Experimental results show that WARP consistently improves performance on worst-case constraint reasoning. Leveraging the curated constraint dataset, we use reinforcement learning to fine-tune a model, WARP-1.0-3B, which significantly outperforms size-matched and even larger baselines. These results demonstrate that incremental constraint reasoning enhances LLMs’ ability to handle symbolic reasoning and highlight the potential for deeper integration between neural learning and formal methods in rigorous program analysis.
Submission history
From: Daniel Koh [view email]
[v1]
Mon, 9 Jun 2025 19:33:30 UTC (1,462 KB)
[v2]
Tue, 16 Sep 2025 10:35:33 UTC (1,871 KB)
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