Singapore

Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of multiple future events on a horizon with high accuracy and diversity. Our method optimally aligns predictions with ground truth events during training by using a novel matching-based loss function. We establish a new state-of-the-art in long-horizon event prediction, achieving up to a 50% relative improvement over existing temporal point processes and event prediction models. Furthermore, we achieve state-of-the-art performance in next-event prediction tasks while demonstrating high computational efficiency during inference.

AAAI 2026

Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching

long horizon forecasting

marked temporal point processes

event sequences

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.<br><br>

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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.

technical paper

Jigsaw puzzle solving remains difficult because models must
reconcile local fragment cues with global structure. Most
prior work leans solely on visual signals (edge or texture
coherence) and rarely exploits natural-language
descriptions, which are especially helpful for puzzles with
eroded gaps. We introduce a vision–language framework that
uses textual context to guide assembly. At its core, the
Vision–Language Hierarchical Semantic Alignment (VLHSA)
module aligns image patches with text via multi-level
matching—from local tokens to global summaries—within a
multimodal design that couples dual visual encoders with
language features for cross-modal reasoning. Across
multiple datasets, the method surpasses the state of the
art, including a 14.2 percentage point gain in piece
accuracy; ablations identify VLHSA as the principal source
of improvement. These results suggest a practical shift for
jigsaw solving: augmenting vision with language to resolve
ambiguous placements

VLHSA: Vision-Language Hierarchical Semantic Alignment for Jigsaw Puzzle Solving with Eroded Gaps (Student Abstract)

Hand-craft reward engineering requires domain knowledge
with numerous trials and errors, while Preference-based
Reinforcement Learning (PbRL) avoids manual reward design
but often suffers from limited interpretability and
unstable training. To address these issues, we propose a
novel preference alignment framework. Our approach
leverages large language models to generate sub-reward
functions informed by prior knowledge and further align
human preferences by optimizing the weights combining these
sub-rewards. For policy learning, we introduce Policy
Optimization via Pareto Regularization (POPR) which
regularizes updates along Pareto-optimal directions.
Experiments show that our framework improves reward quality
and policy stability, achieving superior performance to
expert-designed rewards across most tasks.

Efficient Preference Alignment via Pareto Exploration (Student Abstract)

We present AniTales, a system designed to generate multimodal visual novels from natural language prompts. Our system integrates large language models for story generation, diffusion models for character art, and text-to-speech for voice acting. This paper describes the system's architecture and presents findings from a pilot user study. We evaluated the system with general users (n=10) and domain experts (n=5), focusing on usability, coherence, and visual consistency. General users reported high usability (SUS: 84/100) and strong character-dialogue consistency (4.2/5), along with an average score of 82/100 for their intention to continue using the platform. These initial results suggest AniTales is a promising approach for bridging the gap between text-based AI storytelling and end-to-end multimedia content creation.

AniTales: End-to-End Multimodal Story Generation Through Natural Language Prompting (Student Abstract)

Citation recommendation aims to provide researchers with the most relevant references for their manuscripts, helping them swiftly discover pertinent studies and bolster the reliability of their arguments. However, some individuals manipulate these recommendation systems by injecting false information, such as deliberately inflating the citation count of their own papers, to obtain favorable recommendations and ratings. This form of attack, commonly termed “shilling attack”, is not only highly concealed but also has an unimaginable impact on all scientific research. To address this problem, we theoretically reveal the impact of shilling attacks on citation recommendation and propose three feasible resistance strategies: historical collaborations, significant citations and content constraints. Based on these insights, we introduce RSA-CR, a robust and hybrid citation recommendation algorithm resistant to shilling attacks. The algorithm constructs a two-layer academic graph and uses random and content generation strategies to initialize author and paper embeddings. Confidence-guided inductive aggregations based on collaboration and citation relationships are then performed at the author and paper sides, where author aggregation results directly influences the paper aggregation strength. Finally, recommendations are made by measuring the distances between the fused paper embeddings. The entire learning process resembles a dumbbell, hence termed “dumbbell inductive learning”. Experiments on four academic datasets demonstrate that our method outperforms baselines in both effectiveness and robustness. Code will be released upon acceptance.

RSA-CR: Resisting Shilling Attacks in Citation Recommendation via Dumbbell Inductive Learning

We present RENEW, a novel global path planning framework for Autonomous Surface Vehicle (ASV) operating in dynamic environments with external disturbances (e.g., water currents). These disturbances significantly affect both the risk and energy cost of navigation, particularly in constrained coastal waterways, by dynamically reshaping the navigable area. 
RENEW addresses this challenging scenario through a unified, risk- and energy-aware planning strategy that guarantees safety by explicitly identifying states at risk of entering non-navigable regions and enforcing adaptive safety constraints. Our planner incorporates a best-effort strategy under worst-case scenarios, inspired by contingency planning concepts from maritime domains, to ensure feasible control actions even under adverse conditions. RENEW employs a hierarchical architecture: a high-level planner explores topologically distinct paths via constrained triangulation, while a low-level planner selects an energy-efficient and kinematically feasible trajectory within a safe corridor. We validate our approach through extensive simulations using both custom realistic scenarios and real-world ocean current data. To our knowledge, this is the first global planning framework to jointly address the adaptive identification of non-navigable areas and topological diversity within a risk-aware paradigm, enabling robust navigation in maritime environments.

RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways

More and more organizations are relying on Machine Learning (ML) models to support internal decision-making processes. To better support such processes, it would be highly beneficial to contextualize the inductively acquired knowledge encoded in these models and enable formal reasoning over it. Despite significant progress in Neural-Symbolic AI, this specific challenge remains largely under-explored. We propose a framework that allows to integrate the knowledge induced by ML classifiers with the knowledge specified by logic-based formalisms. The framework is based on the novel notion of Hybrid Knowledge Base (HKB), consisting of two components: an ontology and a set of ML binary classifiers. As usual, the ontology provides an intensional representation of the modeled domain through logic-based axioms, while the binary classifiers implicitly encode the extensional knowledge. Specifically, a HKB associates to each concept and role mentioned in the ontology a classifier based on a set of features deemed to be relevant for the application domain, thereby virtually populating the concepts and roles with the instances and pairs of instances from the feature space. Besides the definition of the new framework, as a more technical contribution we show how to reason in this framework by studying query answering over HKBs. In particular, we investigate the computational complexity of query answering in a rich language over HKBs in which the ontology is specified in (the Description Logic counterpart of) RDFS, while the binary classifiers are represented by Multi-Layer Perceptrons.

Foundations of Formal Reasoning over Knowledge Bases Combining Symbolic and Sub-Symbolic Knowledge

Over recent decades, the tourism industry has demonstrated progressive expansion, driven by advancements in aviation technologies and shifting consumer interests. In this context, online flight itinerary ranking has become a pivotal business for Online Travel Platforms (OTPs), which aim to rank flight itineraries by synthesizing real-time flight data provided by airlines with users' individual travel preferences.
Currently, most OTPs rely on rule-based methodologies or rudimentary user preference-driven models to address this task. However, these methods are inherently limited by their insufficient consideration of delayed booking behaviors and their neglect of dynamic contextual attributes associated with flight itineraries, thereby undermining their ability to effectively handle the intricacies of flight ranking.
To address these shortcomings, this paper introduces the $\textbf{D} $elayed $\textbf{C} $onversion Modeling based Personalized Flight Itinerary $\textbf{R}$anking $\textbf{Net} $work ( $\textbf{DCRNet} $), designed to improve ranking accuracy by integrating delayed booking patterns and contextual dependencies into the modeling framework. 
Specifically, DCRNet explores the dynamic associations between users' current contextual information and their historical travel records, and models users' delayed booking behaviors via a masked attention mechanism. Moreover, an enhanced multi-task learning framework is employed to effectively integrate traditional behavioral modeling with delay-aware modeling, thereby improving the overall prediction accuracy and enhancing the system's personalized recommendation capabilities.
Extensive offline experiments conducted on real-world datasets from Amadeus and Fliggy demonstrate the superior performance of DCRNet. Furthermore, its successful deployment on Fliggy's online itinerary search system has yielded significant improvements, underscoring its practical effectiveness and scalability.

DCRNet: Delayed Conversion Modeling Based Personalized Flight Itinerary Ranking Network

In-context learning (ICL) with large language models (LLMs) has emerged as a promising paradigm for named entity recognition (NER) in low-resource scenarios. However, existing ICL-based NER methods suffer from three key limitations: (1) reliance on dynamic retrieval of annotated examples, which is problematic when annotated data is scarce; (2) limited generalization to unseen domains due to the LLM's insufficient internal domain knowledge; and (3) failure to incorporate external knowledge or resolve entity ambiguities. To address these challenges, we propose **KDR-Agent**, a novel multi-agent framework for multi-domain low-resource in-context NER that integrates **Knowledge retrieval**, **Disambiguation**, and **Reflective analysis**. KDR-Agent leverages natural-language type definitions and a static set of entity-level contrastive demonstrations to reduce dependency on large annotated corpora. A central planner coordinates specialized agents to (i) retrieve factual knowledge from Wikipedia for domain-specific mentions, (ii) resolve ambiguous entities via contextualized reasoning, and (iii) reflect on and correct model predictions through structured self-assessment. Experiments across ten datasets from five domains demonstrate that KDR-Agent significantly outperforms existing zero-shot and few-shot ICL baselines across multiple LLM backbones.

A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysis

Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive capabilities, they remain vulnerable to adversarial attacks, as even minor meaning-preserving changes such as synonym substitutions can lead to incorrect predictions. As a result, certifying the robustness of LLMs against such adversarial prompts is of vital importance. Existing approaches focused on word deletion or simple denoising strategies to achieve robustness certification. However, these methods face two critical limitations: (1) they yield loose robustness bounds due to the lack of semantic validation for perturbed outputs and (2) they suffer from high computational costs due to repeated sampling. To address these limitations, we propose CluCERT, a novel framework for certifying LLM robustness via clustering-guided denoising smoothing. Specifically, to achieve tighter certified bounds, we introduce a semantic clustering filter that reduces noisy samples and retains meaningful perturbations, supported by theoretical analysis. Furthermore, we enhance computational efficiency through two mechanisms: a refine module that extracts core semantics, and a fast synonym substitution strategy that accelerates the denoising process. Finally, we conduct extensive experiments on various downstream tasks and jailbreak defense scenarios. Experimental results demonstrate that our method outperforms existing certified approaches in both robustness bounds and computational efficiency.

CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothing

Understanding human actions in videos requires robust
integration of multimodal cues beyond raw pixels. This work
introduces a deep self-supervised action recognition
framework that jointly predicts action concepts and
auxiliary features from RGB video, then hallucinates
missing modalities at test time to improve recognition
without added runtime cost. Two new domain-specific
descriptors, Object Detection Features (ODF) and Saliency
Detection Features (SDF), are proposed to capture spatial
context and motion saliency, integrating them with other
modalities such as optical flow, skeleton, audio, and
improved dense trajectories. The framework incorporates
aleatoric uncertainty modeling to handle noisy or
unreliable features, along with a robust loss for stable
multimodal fusion. Compatible with popular architectures
including I3D, AssembleNet, Video Transformer Network,
VideoMAE V2, and InternVideo2, the approach achieves
state-of-the-art results on Kinetics-400, Kinetics-600, and
Something-Something V2.

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