Singapore

IT environments typically have logging mechanisms to monitor system health and detect issues. However, the huge volume of generated logs makes manual inspection impractical, highlighting the importance of automated log analysis in IT Software Support. In this paper, we propose a log analytics tool that leverages Large Language Models (LLMs) for log data processing and issue diagnosis, enabling the generation of automated insights and summaries. We further present a novel approach for efficiently running LLMs on CPUs to process massive log volumes in minimal time without compromising output quality. We share the insights and lessons learned from deployment of the tool - in production since March 2024 - scaled across 70 software products, processing over 2000 tickets for issue diagnosis, achieving a time savings of 300+ man hours and an estimated $15,444 per month in manpower costs compared to the traditional log analysis practices.

AAAI 2026

Scalable and Efficient Large-Scale Log Analysis with LLMs: An IT Software Support Case Study

it software support

ai in it and infrastructure

log analysis

llms

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

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We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

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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.

The Knowledge of Optimization And Learning Algorithms (KOALA) group studies how to integrate optimization, machine learning, and generative modeling to enable data-driven decision-making under uncertainty. We study decision-focused learning, embedding optimization as a differentiable layer to train models end-to-end for decision quality. We design scalable reinforcement learning algorithms for population and personalized healthcare, and develop efficient bilevel optimization methods for nested and multi-agent decision-making. These directions form a unified framework linking optimization and learning for impactful AI in healthcare. Through collaborations with hospitals and NGOs, our group designs and deploys algorithms for pediatric, diabetes, maternal, and mental health applications. Looking ahead, we aim to unite these foundations with generative AI to build theoretically grounded and socially responsible algorithms that advance trustworthy, real-world AI for health and beyond.

KOALA: Knowledge of Optimization and Learning Algorithms for Healthcare

The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. 
This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention—a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) Dataset. We further propose a novel social-aware coarse-to-fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets—a consequence of autistic children's tendency to show reduced gaze to faces. 
It utilizes a two-pathway architecture with expert models specialized in social and non-social gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze.

Toward Gaze Target Detection of Young Autistic Children

As a retina-inspired sensor with ultra-high temporal resolution, spike cameras can continuously capture dynamic scenes with high-speed motion. It is a key task to restore clear images from spike streams. The quantization effects in spike readout bring degradation to the visual quality of restored images. To tackle the degradation without introducing motion blur, existing methods often employ a short-term temporal window to infer the light intensity at a certain time point. However, these methods only focus on the spike signals within the current window, which limits their performance. Motivated by the human-like memory mechanism for visual signals from the retina, we explore Spike Stream Memory Transfer (SSMT) to restore the dynamic scenes, considering spike signals beyond the window. Specifically, we design a framework that leverages temporal memory by transferring previously inferred light intensity and motion to enhance current reconstruction. The framework enables a long-term temporal perception of spike streams to handle the spike quantization effects. Besides, we utilize the estimated motion to suppress the potential blur from inter-stream clips, considering the underlying motion of spike streams. We also develop a spike interval-guided alignment module to tackle the blur from intra-stream clips. Experiments on both synthetic and real-captured data show that our method can restore high-quality images from spike streams. The source code will be publicly available.

Spike Stream Memory Transfer for Dynamic Scene Reconstruction

Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically assume homogeneous streams and employ static architectures with indiscriminate knowledge fusion, limiting generalizability in dynamic hybrid environments. To tackle this gap, we propose CAMEL, a dynamic Collaborative Assistance Mixture of Experts Learning framework. CAMEL addresses heterogeneity by assigning each stream an independent system with a dedicated feature extractor and task-specific head. Meanwhile, a dynamic pool of specialized private experts captures stream-specific idiosyncratic patterns. Crucially, collaboration across these heterogeneous streams is enabled by a dedicated assistance expert. This expert employs a multi-head attention mechanism to distill and integrate relevant context autonomously from all other concurrent streams. It facilitates targeted knowledge transfer while inherently mitigating negative transfer from irrelevant sources. Furthermore, we propose an Autonomous Expert Tuner (AET) strategy, which dynamically manages expert lifecycles in response to drift. It instantiates new experts for emerging concepts (freezing prior ones to prevent catastrophic forgetting) and prunes obsolete ones. This expert-level plasticity provides a robust and efficient mechanism for online model capacity adaptation. Extensive experiments demonstrate CAMEL's superior generalizability across diverse hybrid multistreams and exceptional resilience against complex concept drifts.

Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning

Aerial Vision-and-Language Navigation (AVLN) requires Unmanned Aerial Vehicle (UAV) agents to localize targets in complex urban environments using linguistic instructions. While successful navigation demands both global environmental reasoning and fine-grained scene comprehension, existing UAV agents typically rely on single-step planning paradigms that struggle to balance these two aspects. To address this limitation, this work proposes a History-Enhanced Two-Stage Transformer (HETT) framework, which navigates in a coarse-to-fine manner. Specifically, HETT first predicts coarse-grained target positions using spatial landmarks and historical context, then refines actions through fine-grained visual analysis. Moreover, a historical grid map is designed to dynamically aggregate and organize visual features into a structured spatial memory, enhancing comprehensive scene awareness. Additionally, the CityNav dataset annotations are manually refined to enhance data quality. Experimental results demonstrate that HETT achieves state-of-the-art Success Rate (SR) on our refined CityNav dataset. The refined dataset and code will be released.

History-Enhanced Two-Stage Transformer for Aerial Vision-and-Language Navigation

A safe Pareto improvement (SPI) [41] is a modification of a game that leaves all players better off with certainty. 
SPIs are typically proven under qualitative assumptions about the way different games are played. 
For example, we assume that strictly dominated strategies can be iteratively removed and that isomorphic games are played isomorphically.
In this work, we study SPIs achieved through three types of \textit{ex post} verifiable commitments -- promises about player behavior from which deviations can be detected by observing the game. 
First, we consider disarmament -- commitments not to play certain actions. 
Next, we consider SPIs based on \textit{token games}. A token game is a game played by simply announcing an action (via cheap talk). As such, its outcome is intrinsically meaningless. However, we assume the players commit in advance to play specific (pure or correlated) strategy profiles in the original game as a function of the token game outcome. Under such commitments, the token game becomes a new, meaningful normal-form game.
Finally, we consider default-conditional commitment: SPIs in settings where the players' default ways of playing the original game can be credibly revealed and hence the players can commit to act as a function of this default. 
We characterize the complexity of deciding whether SPIs exist in all three settings, giving a mixture of characterizations and efficient algorithms and \NP- and \textsc{Graph Isomorphism}-hardness

Promises Made, Promises Kept: Safe Pareto Improvements via Ex Post Verifiable Commitments

A fundamental challenge in visual reinforcement learning (RL) is achieving robust generalization across environments with varying visual distractions. Current RL methods struggle with generalization due to their inability to differentiate foreground and background features during augmentation,while their Q-consistency mechanisms rely on outdated actions from replay buffers that drift from the current policy.In this paper, we present PQDA, a novel framework that addresses generalization challenges in RL through two key innovations: (1) Foreground-Background Decoupled Augmentation leverages Gaussian mixture model-based segmentation to efficiently generate and cache masks in replay buffers, applying differentiated augmentation strategies to foreground and background regions, thereby enhancing data diversity while maintaining task-relevant features. (2) Policy-Aligned Q-Consistency enforces policy alignment by sampling actions from the current policy for Q-regularization, achieving faster and more stable convergence. Notably, PQDA eliminates auxiliary tasks entirely through a unified architecture that co-optimizes the encoder and RL components directly. Extensive experiments on DMControl benchmarks (including our newly proposed CVDMC benchmark) and robotic manipulation tasks demonstrate PQDA's superior generalization performance, outperforming state-of-the-art methods.The code and new CVDMC benchmark will be released to facilitate reproducibility.

PQDA:Policy-Aligned Q-Consistency Meets Decoupled Augmentation for Generalizable Visual RL

Hypergraph contrastive learning has emerged as a powerful unsupervised paradigm for hypergraph representation learning. Traditional hypergraph contrastive learning methods typically leverage neighbor aggregation strategy to obtain entity (node and hyperedge) representations within each connected component, and then utilize contrastive losses (e.g., node- or hyperedge-level) to update the encoders. However, since entities are usually focused equally on their respective losses, large connected components with numerous entities tend to provide a dominant contribution to the whole learning process, which inevitably hinders the effective learning of entity representations within small connected components. To address this issue, we propose a novel Connected-Component-Aware Hypergraph Contrastive Learning method (CCAHCL). Different from previous methods that only construct node or hyperedge representations, our method additionally constructs the connected component representations, and accordingly designs a hierarchical contrastive loss to balance the model's focus on different scales of connected components. Specifically, we first use the traditional neighbor aggregation strategy to aggregate and update entity (node and hyperedge) representations. Then, these entity representations are further aggregated to generate the connected component representations, where entity features are incorporated into connected components and their structural information is propagated back to enrich their corresponding entities. Afterwards, we employ node-level and hyperedge-level losses to learn the enriched entity representations, and further propose a novel connected-component-level contrastive loss to balance the model's focus on all different connected components, naturally avoiding the learning bias on large connected components. Extensive experiments on various datasets demonstrate that our proposed model achieves superior performance against other state-of-the-art methods.

CCAHCL: Multi-Level Hypergraph Contrastive Learning for Connected Component Awareness

With the advancement of meteorological instruments, abundant data has become available. 
However, due to instruments’ intrinsic limitations such as environmental sensitivity and orbital constraints, raw data often suffer from temporal or spatial gaps, making it urgent to leverage data synthesis techniques to fill in missing information. 
Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. 
This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and often leads to over-smoothed results. 
To address above challenges, we introduce SynWeather, the first dataset designed for \textbf{Unified Multi-region and Multi-variable Weather Observation Data Synthesis}. 
SynWeather covers four representative regions: the Continental United States, Europe, East Asia, and Tropical Cyclone regions, as well as provides high-resolution observations of key weather variables, including Composite Radar Reflectivity, Hourly Precipitation, Visible Light, and Microwave Brightness Temperature. 
In addition, we introduce SynWeatherDiff, a general and probabilistic weather synthesis model built upon the Diffusion Transformer framework to address the over-smoothed problem. 
Experiments on the SynWeather dataset demonstrate the effectiveness of our network compared with both task-specific and general models. 
Moreover, SynWeatherDiff is able to generate results that are both fine-grained and accurate in high-value regions.
Through the dataset and baseline model, we aim to advance meteorological downstream tasks and promote the development of general models for weather variable synthesis.

SynWeather: Weather Observation Data Synthesis Across Multiple Regions and Variables via a General Diffusion Transformer

Graph neural networks (GNNs) excel at processing non-Euclidean data, privacy concerns often hinder data sharing, leading to data isolation. Although federated learning (FL) provides a privacy-preserving framework for distributed GNN training, combining FL with GNNs presents challenges such as data heterogeneity and client resource constraints. This paper introduces DA-DFGAS: a federated graph neural network architecture search algorithm that minimises resource consumption while maintaining search flexibility through a directed tree topology and path constraint mechanisms. Additionally, it employs a self-attention joint aggregation mechanism based on predicted probability distribution to adjust the discrepancy between the client and global output distributions. A client selection strategy combined with a global-local two-level objective optimisation balances global consistency and local flexibility. Experimental results demonstrate that DA-DFGAS surpasses existing baseline methods across multiple datasets, achieving an accuracy enhancement of 0.5–3.0\% compared to graph learning baselines and 0.5–5.0\% compared to other federated graph learning baselines.

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