Singapore

We study a class of two-player zero-sum Colonel Blotto games in which, after allocating soldiers across battlefields, players engage in (possibly distinct) normal-form games on each battlefield. Per-battlefield payoffs are parameterized by the soldier allocations. This generalizes the classical Blotto setting, where outcomes depend only on relative soldier allocations. We consider both discrete and continuous allocation models and examine two types of aggregate objectives: linear aggregation and worst-case battlefield value. For each setting, we analyze the existence and computability of Nash equilibrium. The general problem is not convex-concave, which limits the applicability of standard convex optimization techniques. However, we show that in several settings it is possible to reformulate the strategy space in a way where convex-concave structure is recovered. We evaluate the proposed methods on synthetic and real-world instances inspired by security applications, suggesting that our approaches scale well in practice.

AAAI 2026

Colonel Blotto with Battlefield Games

colonel blotto

nash equilibrium

game theory

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

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AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Vision-Language Models (VLMs) encode images into lengthy
sequences of visual tokens, leading to excessive
computational overhead and limited inference efficiency. In
this paper, we study the hierarchical attention pattern in
vision encoders and propose HiPrune, a training-free and
model-agnostic token Pruning framework for VLMs. We
identify that middle layers in the vision encoder attend to
object-centric regions, while deep layers capture global
contextual features. Based on this observation, HiPrune
selects tokens based on the attention score from the middle
and deep layers. Our method requires no retraining and
integrates seamlessly with any ViT-based VLM. Experiments
demonstrate that HiPrune achieves outstanding pruning
performance, maintaining a balance between efficiency and
efficacy.

HiPrune: Training-Free Visual Token Pruning via Hierarchical Attention in Vision-Language Models (Student Abstract)

Autonomous computer-use agents (ACUAs) enable end-to-end
computer operation with human-like capabilities, executing
commands across applications and making independent
decisions. However, their real-world effectiveness and
security remain largely untested. A systematic evaluation
of ACUAs from Anthropic, OpenAI, and open-source projects
categorized them into full-computer-access and
browser-based agents. Findings reveal substantial
limitations, with success rates dropping as low as 28% in
some cases. Additionally, a 100% rate of unauthorized
software installation was observed in certain tasks. The
agents also demonstrated clear susceptibility to prompt
injection attacks. The impact of varied prompting
strategies on performance was also examined. Building on
these weaknesses, development of a specialized agent for
office tasks is proposed. This work bridges agentic AI,
human-computer interaction (HCI), and security to address
the observed limitations of ACUAs, prioritizing both
capability and safety.

Towards Capable and Secure Autonomous Computer-Use Agents (Student Abstract)

Rip currents cause over 100 drowning deaths and more than 30,000 rescues annually in the United States, posing a severe threat to beach safety worldwide. However, most existing detection methods are reactive, identifying rip currents only after they form, leaving limited time for intervention. We propose RipAlert, a future-frame-aware framework that forecasts near-future coastal dynamics and proactively identifies rip current risks. We design a region-sensitive optical flow prediction method with a novel entropy-based object detector to capture early-stage reverse-flow anomalies. Unlike static-image approaches, RipAlert leverages temporal motion patterns to detect rip currents up to 5 seconds before they visibly form. To support real-world deployment, we design a lightweight mobile application and release a curated dataset with over 2,000 annotated images. Experiments on the RipVIS benchmark show that our approach achieves state-of-the-art performance. The system has been deployed at high-risk beaches in China, issuing successful early warnings over real-world events. Our work advances AI-driven coastal safety and contributes to SDG 3 (Good Health and Well-Being) and SDG 13 (Climate Action).

RipAlert: A Future-Frame-Aware Framework for Rip Current Forecasting and Early Alerting

Sequential recommendation (SR) aims to predict users' next action based on their historical behavior, and is widely adopted by a number of platforms. The performance of SR models relies on rich interaction data. However, in real-world scenarios, many users only have a few historical interactions, leading to the problem of data sparsity. Data sparsity not only leads to model overfitting on sparse sequences, but also hinders the model’s ability to capture the underlying hierarchy of user intents. This results in misinterpreting the user's true intents and recommending irrelevant items. Existing data augmentation methods attempt to mitigate overfitting by generating relevant and varied data. However, they overlook the problem of reconstructing the user's intent hierarchy, which is lost in sparse data. Consequently, the augmented data often fails to align with the user's true intents, potentially leading to misguided recommendations. To address this, we propose the Adaptive Diffusion Augmentation for Recommendation (ADARec) framework. Critically, instead of using a diffusion model as a black-box generator, we use its entire step-wise denoising trajectory to reconstruct a user's intent hierarchy from a single sparse sequence. To ensure both efficiency and effectiveness, our framework adaptively determines the required augmentation depth for each sequence and employs a specialized mixture-of-experts architecture to decouple coarse- and fine-grained intents. Experiments show ADARec outperforms state-of-the-art methods by 2-9\% on standard benchmarks and 3-17\% on sparse sequences, demonstrating its ability to reconstruct hierarchical intent representations from sparse data. Our code is available at: https://anonymous.4open.science/r/ADARec-6C8D.

De-collapsing User Intent: Adaptive Diffusion Augmentation with Mixture-of-Experts for Sequential Recommendation

Visualizations play a crucial part in effective communication of concepts and information. Recent advances in reasoning and retrieval augmented generation have enabled Large Language Models (LLMs) to perform deep research and generate comprehensive reports. Despite its progress, existing deep research frameworks primarily focus on generating text-only content, leaving the automated generation of interleaved texts and visualizations underexplored. This novel task poses key challenges in designing informative visualizations and effectively integrating them with text reports. To address these challenges, we propose Formal Description of Visualization (FDV), a structured textual representation of charts that enables LLMs to learn from and generate diverse, high-quality visualizations. Building on this representation, we introduce Multimodal DeepResearcher, an agentic framework that decomposes the task into four stages: (1) researching, (2) exemplar report textualization, (3) planning and (4) multimodal report generation. For the evaluation of the generated reports, we develop MultimodalReportBench which contains 100 diverse topics as inputs, and a set of dedicated metrics for report and chart evaluation. Extensive experiments across models and evaluation methods demonstrate the effectiveness of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet model, Multimodal DeepResearcher achieves an 82\% overall win rate over the baseline method.

Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports from Scratch with Agentic Framework

Uncertainty visualizations, such as hurricane cones and ensemble tracks, are vital for risk communication but are frequently misinterpreted, leading to dangerous outcomes. No current visualization reliably conveys forecast uncertainty without cognitive bias. As AI assistants like large language models (LLMs) increasingly support public understanding and decision-making, they offer a promising pathway to enhance the interpretation of such complex visualizations. We present HurricaneTrust, the first benchmark to systematically compare how humans and LLMs reason about hurricane forecast uncertainty visualizations. HurricaneTrust spans two escalating phases and includes seven representative visualization formats, six real hurricane scenarios, and three agent types (humans, LLMs with context, and LLMs without context), resulting in 880 visualizations and over 117,600 structured question–answer pairs under matched evaluation conditions. Phase 1 contrasts LLM and human performance across implicit and explicit uncertainty encodings; Phase 2 examines reasoning under single- versus multi-dimensional uncertainty representations. Our evaluation includes a thorough analysis of damage estimation, reasoning strategies, and comprehension patterns. Results reveal that LLMs have a stronger semantic and conceptual understanding of uncertainty, and are less misled by visual variability, but still replicate key human biases during decision-making. Our findings offer insights into aligning LLM behavior with human cognition in uncertainty-rich visual reasoning tasks.

Do Large Language Models Reason About Uncertainty Like Humans? A Benchmark on Hurricane Forecast Visualization Comprehension

Building on VeriX (Verified eXplainability), a system for producing *optimal verified explanations* for machine learning models, we present VeriX+, which significantly improves both the *size* and the generation *time* of formal explanations. We introduce a bound propagation-based sensitivity technique to improve the size, and a binary search-based traversal with confidence ranking for improving time---the two techniques are orthogonal and can be used independently or together. We also show how to adapt the QuickXplain algorithm to our setting to provide a trade-off between size and time. Experimental evaluations on standard benchmarks demonstrate significant improvements on both metrics, e.g., a size reduction of $38$% on the GTSRB dataset and a time reduction of $90$% on MNIST. We demonstrate that our approach is scalable to transformers and real-world scenarios such as autonomous aircraft taxiing and sentiment analysis. We conclude by showcasing several novel applications of formal explanations.

Efficiently Computing Compact Formal Explanations

The growing misuse of Vision-Language Models (VLMs) has led providers to deploy multiple safeguards—alignment tuning, system prompts, and content moderation.
Yet the real-world robustness of these defenses against adversarial attack remains underexplored. 
We introduce MFA, a framework that systematically uncovers general safety vulnerabilities in leading defense-equipped VLMs, including GPT-4o, Gemini-Pro, and Llama 4.1, etc. Central to MFA is the Attention-Transfer Attack (ATA), which conceals harmful instructions inside a meta task with competing objectives. We offer a theoretical perspective grounded in reward-hacking to explain why such an attack can succeed. To maximize cross-model transfer, we introduce a lightweight transfer-enhancement algorithm combined with a simple repetition strategy that jointly evades both input- and output-level filters—without any model-specific fine-tuning. 
We empirically show that adversarial images optimized for one vision encoder transfer broadly to unseen VLMs, indicating that shared visual representations create a cross-model safety vulnerability.
Combined, our techniques reach a 58.5% overall success rate, consistently outperforming existing methods. 
Notably, on state-of-the-art commercial models, our attack achieves a 52.8% success rate, outperforming the second-best attack by 34%. These findings challenge the perceived robustness of current defensive mechanisms, systematically expose general safety loopholes within defense-equipped VLMs, and offer a practical probe for diagnosing and strengthening the safety of VLMs.

Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models

The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to ad-hoc, non-standardized scripts, as common metrics are often unsuitable for specialized, structured outputs (e.g., automated plans, time-series) or holistic comparison across modalities (e.g., text, audio, and image). This fragmentation hinders comparability and slows AI system development. To address this challenge, we present GAICo (Generative AI Comparator): a deployed, open-source Python library that streamlines and standardizes GenAI output comparison. GAICo provides a unified, extensible framework supporting a comprehensive suite of reference-based metrics for unstructured text, specialized structured data formats, and multimedia (images, audio). Its architecture features a high-level API for rapid, end-to-end analysis, from multi-model comparison to visualization and reporting, alongside direct metric access for granular control. We demonstrate GAICo's utility through a detailed case study evaluating and debugging complex, multi-modal AI Travel Assistant pipelines. GAICo empowers AI researchers and developers to efficiently assess system performance, make evaluation reproducible, improve development velocity, and ultimately build more trustworthy AI systems, aligning with the goal of moving faster and safer in AI deployment. **Since its release on PyPI in Jun 2025, the tool has been downloaded over 13K times, across versions, by Aug 2025, demonstrating growing community interest.**

GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs

Stable models of logic programs have been studied and characterized in relation with other formalisms by many researchers. As already argued in previous papers, such characterizations are interesting for diverse reasons, including theoretical investigations and the possibility of leading to new algorithms for computing stable models of logic programs. At the theoretical level, complexity and expressiveness comparisons have brought about fundamental insights. Beyond that, practical implementations of the developed reductions enable the use of existing solvers for other logical formalisms to compute stable models. In this paper, we first provide a simple characterization of stable models that can be viewed as a proof-theoretic counterpart of the standard model-theoretic definition. We further show how it can be naturally encoded in difference logic. Such an encoding, compared to the existing reductions to classical logics, does not require Boolean variables. Then, we implement our novel translation to a Satisfiability Modulo Theories (SMT) formula. We finally compare our approach, employing the SMT solver yices, to the translation-based ASP solver lp2diff and to clingo on domains from the “Basic Decision” track of the 2017 Answer Set Programming competition. The results show that our approach is competitive to and often better than lp2diff, and that it can also be faster than clingo on non-tight domains.

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HiPrune: Training-Free Visual Token Pruning via Hierarchical Attention in Vision-Language Models (Student Abstract)

Similar lecture

Equilibria of the Colonel Blotto Games with Costs