Singapore

To identify the root causes of attacks, behavior abstraction (BA) converts audit logs into multiple behavior graphs and finds similar ones, which has proven effective in bridging the semantic gap and reducing manual workload. Existing works fail to achieve both interpretability and generalization, while also exhibiting limited robustness when facing adversarial attacks. In this paper, we give the first attempt at interpretable and robust behavior abstraction and propose a novel method called 
$\textit{\textbf{E}nvironment-\textbf{D}isentangled \textbf{H}eterogeneous \textbf{G}raph \textbf{N}eural \textbf{N}etwork (\textbf{EDHGNN})}$. Motivated by Information Bottleneck (IB) principle, we propose a Heterogeneous Subgraph Disentanglement (HSD) module to disentangle label-relevant and environmental subgraphs through single optimization. We also introduce an Adapted Graph-Level Attention (AGLA) module to extract minimal sufficient representations from label-relevant subgraphs, a Label-Guided Graph Reconstructor (LGGR) to maximize environmental information coverage via reconstruction, and a Relevance Discriminator (RD) to enhance disentanglement quality. Additionally, we construct a new dataset contains ground-truth explanations and 4,160 behavior graphs. Extensive experiments demonstrate that EDHGNN outperforms the state-of-the-art methods in terms of interpretability and robustness against
adversarial attacks.

AAAI 2026

Interpretable and Robust Behavior Abstraction via Environment-Disentangled Heterogeneous Graph

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.<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.

Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs understand the skeleton? 2) How can LLMs distinguish among actions? To address these problems, we introduce a novel paradigm named learning Skeleton representation with visual-motion knowledge for Action Recognition (SUGAR). In our pipeline, we first utilize off-the-shelf large-scale video models as a knowledge base to generate visual, motion information related to actions. Then, we propose to supervise skeleton learning through this prior knowledge to yield discrete representations. Finally, we use the LLM with untouched pre-training weights to understand these representations and generate the desired action targets and descriptions. Notably, we present a Temporal Query Projection (TQP) module to continuously model the skeleton signals with long sequences. Experiments on several skeleton-based action classification benchmarks demonstrate the efficacy of our SUGAR. Moreover, experiments on zero-shot scenarios show that SUGAR is more versatile than linear-based methods

SUGAR: Learning Skeleton Representation with Visual-Motion Knowledge for Action Recognition

The Multi-Agent Path Finding (MAPF) problem is a computationally challenging task that involves coordinating collision-free trajectories for multiple cooperative agents. Although existing methods address corridor symmetry, where agents encounter repeated bidirectional conflicts in constrained environments, they typically focus exclusively on pairwise agent interactions. Our observations reveal that such pairwise symmetry frequently arises when multiple agents traverse shared corridors, necessitating repeated applications of the corridor reasoning technology over extended durations. To overcome this limitation, we propose a multi-agent corridor reasoning (MAC) technology capable of resolving group-level corridor symmetry in a single optimization step. Our theoretical analysis demonstrates that this technology preserves the completeness and optimality guarantees of Conflict-Based Search (CBS). By integrating MAC technology with CBSH-RTC, we developed CBSH-MACRT, which significantly outperforms state-of-the-art algorithms (CBSH-RTC and CBSH with mutex propagation) on standardized MAPF benchmarks, improving success rates by 8–40\% and cutting runtimes by 14–67\%.

Multi-Agent Corridor Reasoning for Multi-Agent Path Finding

In recent years, there has been growing interest in understanding the expressive power of graph neural networks (GNNs) by relating them to logical languages. This research has been been initialised by an influential result of Barceló et al. (2020), who showed that the graded modal logic (or a guarded fragment of the logic C2), characterises the logical expressiveness of aggregate-combine GNNs. As a ‘challenging open problem’ they left the question whether full C2 characterises the logical expressiveness of aggregate-combine-readout GNNs. This question has remained unresolved despite several attempts. In this paper, we solve the above open problem by proving that the logical expressiveness of aggregate-combine-readout GNNs strictly exceeds that of C2. This result holds over both undirected and directed graphs. Beyond its implications for GNNs, our work also leads to purely logical insights on the expressive power of infinitary logics.

Aggregate-Combine-Readout GNNs Can Express Logical Classifiers Beyond the Logic C2

This paper proposes a framework for improving the operational efficiency of automated storage systems under uncertainty.
Recent years have seen a rise in automated grid-based storage for uniform-sized \emph{loads}, e.g., containers, pallets, totes.
Such systems face a fundamental tradeoff between maximizing space utilization and minimizing costly load relocation efforts during storage and retrieval operations. 
The focus here is on a setting with unique loads that can move along cardinal directions using a single mobile manipulator, such as a robot.
The setting consists of two distinct phases, common in some logistics applications, such as last-mile distribution centers and shipyards: i) storage of all the loads, followed by ii) their retrieval.
The goal is to minimize relocations for both phases, especially when the storage system is at capacity.
Previous efforts have shown that with known storage and retrieval orders, zero relocations can be achieved for storage at full capacity, provided that the size of the opening through which loads are stored and retrieved (grid width) is at least 3.

In realistic scenarios, however, schedules may be uncertain, i.e., loads may be stored or retrieved out of order, rendering previous approaches suboptimal.
The model of uncertainty in this work assumes that any two departing loads may depart out of order if they are originally at most $k$ positions apart. Under this model, this work
generalizes the previous result and proves that a grid width of $\Theta(k)$ is necessary and sufficient for eliminating relocations via robust storage arrangements.
An efficient solver is introduced to find such robust arrangements.
Furthermore, when relocations become inevitable, such as when loads are retrieved out of order by more than $k$, a strategy is introduced that effectively minimizes total relocations.
Extensive experiments show that, for $k$ up to half the grid width, the proposed storage and retrieval approaches essentially eliminate relocations.
For high uncertainty, i.e., $k$ values up to the full grid width, relocations are reduced by $50\%+$.

Robust Out-of-Order Retrieval for Grid-Based Storage at Maximum Capacity

Chain-of-Thought (CoT) reasoning is a critical capability for large language models (LLMs), enabling them to tackle complex multi-step tasks. While base LLMs, pre-trained on general text corpora, often struggle with reasoning due to a lack of specialized training, recent studies reveal their latent reasoning potential tied to hidden states. However, existing hidden state manipulation methods, such as linear activation steering, suffer from limitations due to their rigid and unconstrained nature, often leading to distribution shifts and degraded text quality. In this work, we propose a novel approach for eliciting CoT reasoning from base LLMs through hidden state manipulation grounded in probabilistic conditional generation. By reformulating the challenge as an optimization problem with a balanced likelihood and prior regularization framework, our method guides hidden states toward reasoning-oriented trajectories while preserving linguistic coherence. Extensive evaluations across mathematical, commonsense, and logical reasoning benchmarks demonstrate that our approach consistently outperforms existing steering methods, offering a theoretically principled and effective solution for enhancing reasoning capabilities in base LLMs.

Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization

Large Language Models (LLMs) have recently emerged as a leading approach for multivariate time series forecasting. However, their effectiveness is hampered by a fundamental architectural mismatch: the permutation-invariant self-attention of Transformers lacks inductive biases for the strict temporal order and complex cross-variable dependencies inherent in time series. Existing methods often sidestep this issue with input-level alignment techniques rather than endowing the model itself with structural awareness. To address this gap, we introduce \textbf{GraFT} (\textbf{Gra}ph-infused \textbf{F}orecasting \textbf{T}ransformer), a framework that systematically embeds relational priors into a pre-trained backbone by constructing a heterogeneous patch relation graph, which represents both universal temporal principles with static edges and instance-specific patterns with dynamic adaptive edges. To process this multi-relational structure, a relational graph convolutional network generates structure-aware representations, which are infused into the patch embeddings to provide explicit structural guidance to the Transformer's attention mechanism. Extensive experiments show that GraFT achieves state-of-the-art performance on long-term forecasting and zero-shot learning, outperforming leading LLM-based methods on eight standard benchmarks with an average Mean Squared Error (MSE) reduction of 14.4\%.

GraFT: Infusing Pre-trained Transformers with Relational Structure for Time Series Forecasting

In the referenced manuscript, we introduced SUNT, to our
knowledge the richest open urban mobility dataset,
integrating continuously collected passenger and vehicle
data across three public transport systems in Salvador,
Brazil, namely regular buses, subway, and BRT. SUNT covers
daily activity for about 700,000 passengers and
approximately 2,000 vehicles operating over nearly 400
lines and close to 3,000 stops and stations, with
collection from March 2024 to March 2025 at subminute
resolution. The article details data acquisition and
organization, including technical validation through
descriptive statistics and quality checks, as well as the
implementation of a trip chaining to build
Origin–Destination flows. We present a large scale, real
world testbed for reasoning over graphs, sequences, and
uncertainty in continuous time, supporting research in
scalable online learning, distribution shift adaptation,
and decision making under constraints across logistics,
energy, and other infrastructure domains. At the conference
we will show results with new methods and recent state of
the art in Graph Neural Networks, Transformer based and
Recurrent models, Reinforcement Learning, Multiagent
methods, Continual Learning, and Foundation models.
Researchers across AI can use SUNT and the accompanying
models as a shared benchmark, enabling comparable,
reproducible, and transferable evaluation and accelerating
progress in temporal modeling, relational reasoning, and
decision making under uncertainty.

Salvador Urban Network Transportation (SUNT): A Landmark
Spatiotemporal Dataset for Public Transportation

The class PLS (Polynomial Local Search) captures the complexity of finding a solution that is locally optimal and has proven to be an important concept in the theory of local search. It has been shown that local search versions of various combinatorial optimization problems, such as Maximum Independent Set and Max Cut, are complete for this class. Such computational intractability typically arises in local search problems allowing arbitrary weights; in contrast, for unweighted problems, locally optimal solutions can be found in polynomial time under standard settings. In this paper, we pursue the complexity of local search problems from a different angle: We show that computing two locally optimal solutions is NP-hard for various natural unweighted local search problems, including Maximum Independent Set, Minimum Dominating Set, Max SAT, and Max Cut. We also discuss several tractable cases for finding two (or more) local optimal solutions.

Finding One Local Optimum Is Easy—but What About Two?

In the user growth scenario, Internet companies invest heavily in paid acquisition channels to acquire new users. But sustainable growth depends on acquired users' generating lifetime value (LTV) exceeding customer acquisition cost (CAC). In order to maximize LTV/CAC ratio, it is crucial to predict channel-level LTV in an early stage for further optimization of budget allocation. The LTV forecasting problem is significantly different from traditional time series forecasting problems, and there are three main challenges. Firstly, it is an unaligned multi-time series forecasting problem that each channel has a number of LTV series of different activation dates. Secondly, to predict in the early stage, it faces the imbalanced short-input long-output (SILO) challenge. Moreover, compared with the commonly used time series datasets, the real LTV series are volatile and non-stationary, with more frequent fluctuations and higher variance. In this work, we propose a novel framework called Trapezoidal Temporal Fusion (TTF) to address the above challenges. We introduce a trapezoidal multi-time series module to deal with data unalignment and SILO challenges, and output accurate predictions with a multi-tower structure called MT-FusionNet. The framework has been deployed to the online system for Douyin. Compared to the previously deployed online model, $MAPE_{p}$ decreased by 4.3\%, and $MAPE_{a}$ decreased by 3.2\%, where $MAPE_{p}$ denotes the point-wise MAPE of the LTV curve and $MAPE_{a}$ denotes the MAPE of the aggregated LTV.

TTF: A Trapezoidal Temporal Fusion Framework for LTV Forecasting in Douyin

Long-form science fiction generation demands rigorous maintenance of narrative coherence across evolving plots, character dynamics, and speculative world-building. We propose Octopus, an entropy-controlled neural framework with persistent memory-context binding that addresses these challenges through two key innovations: 1) dynamic entropy regulation balancing creativity and structural stability via narrative divergence thresholds, and 2) hierarchical memory architecture preserving character states, plot events, and scientific rules over 10K+ token spans. Evaluations across 12 sci-fi subgenres demonstrate Octopus's superiority over GPT-4 and ReAlign baselines, achieving 15.2\% higher coherence scores (SciClarity) and 62\% fewer contextual contradictions in extended narratives. Human evaluations confirm its effectiveness in maintaining speculative logic (4.7/5 vs. 3.1/5 baseline) while preserving creative diversity. The framework resolves the "hard sci-fi paradox" of enforcing scientific rigor without compromising narrative flexibility, establishing new capabilities for AI-assisted cross-media universe development.

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