Singapore

Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributions and semantics across item domains make it difficult to construct a unified tokenization that preserves domain-specific information. To address these challenges, we propose **UniTok**, a **Uni**fied item **Tok**enization framework that integrates our own mixture-of-experts (MoE) architecture with a series of codebooks to convert items into discrete tokens, enabling scalable tokenization while preserving semantic information across *multiple item domains*. Specifically, items from different domains are first projected into a unified latent space through a shared encoder. They are then routed to *domain-specific* experts to capture the *unique* semantics, while a *shared* expert, which is always active, encodes common knowledge transferable across domains. Additionally, to mitigate *semantic imbalance* across domains, we present a mutual information calibration mechanism, which guides the model towards retaining similar levels of semantic information for each domain. Comprehensive experiments on wide-ranging real-world datasets demonstrate that the proposed UniTok framework is **(a) highly effective:** achieving up to 51.89\% improvements over strong benchmarks, **(b) theoretically sound:** showing the analytical validity of our architectural design and optimization; and **(c) highly generalizable:** demonstrating robust performance across diverse domains without requiring per-domain retraining, a capability not supported by existing baselines.

AAAI 2026

Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation

recommender system

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.

poster

Automated planning involves finding a sequence of actions that changes the world from an initial state to a final state with goals satisfied. The general problem is PSPACE-hard. Nevertheless, many restricted variants are NP-complete or even in P. Existing complexity work focuses mostly on plan existence, or plan with minimal plan length. Little is known about optimization variants that aim to satisfy as many goal conditions as possible. In this paper, we aim to fill this gap by providing a first inapproximability study of goal-maximization using the classical STRIPS formalism. For MAXPLANSAT and its length-bounded counterpart MAXPLANSAT(K), we prove tight constant-factor lower bounds. More specifically, through performing $L$-reductions from MAXE3SAT and MAX3DM, we show several of these problems are inapproximable by a constant factor, unless P=NP.

Inapproximability of STRIPS Planning

This paper examines a variety of classical optimization problems, including well-known minimization tasks and more general variational inequalities. We consider a stochastic formulation of these problems and, unlike most previous work, we take into account the complex Markov nature of the noise. We also consider the geometry of the problem in an arbitrary non-Euclidean setting and propose four methods based on the Mirror Descent iteration technique. The theoretical analysis is provided for smooth and convex minimization problems and variational inequalities with Lipschitz and monotone operators. The convergence guarantees obtained are optimal for first-order stochastic methods, as evidenced by the lower bound estimates provided in this paper. In order to validate the theoretical results, we present the relevant numerical experiments on various reinforcement learning tasks.

Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry

Multi-task learning (MTL) is critical in real-world applications such as autonomous driving and robotics, enabling simultaneous handling of diverse tasks. However, obtaining fully annotated data for all tasks is impractical due to labeling costs. Existing methods for partially labeled MTL typically rely on predictions from unlabeled tasks, making it difficult to establish reliable task associations and potentially leading to negative transfer and suboptimal performance. To address these issues, we propose a prototype-based knowledge retrieval framework that achieves robust MTL instead of relying on predictions from unlabeled tasks. Our framework consists of two key components: (1) a task prototype embedding task-specific characteristics and quantifying task associations, and (2) a knowledge retrieval transformer that adaptively refines feature representations based on these associations. To achieve this, we introduce an association knowledge generating (AKG) loss to ensure the task prototype consistently captures task-specific characteristics. Extensive experiments demonstrate the effectiveness of our framework, highlighting its potential for robust multi-task learning, even when only a subset of tasks is annotated.

Task Prototype-Based Knowledge Retrieval for Multi-Task Learning from Partially Annotated Data

Recent Large Reasoning Language Models (LRLMs) employ long chain-of-thought reasoning with complex reflection behaviors, typically signaled by specific trigger words (e.g., "Wait" and "Alternatively") to enhance performance. However, these reflection behaviors can lead to the overthinking problem where the generation of redundant reasoning steps that unnecessarily increase token usage, raise inference costs, and reduce practical utility. In this paper, we propose Certainty-Guided Reflection Suppression (CGRS), a novel method that mitigates overthinking in LRLMs while maintaining reasoning accuracy. CGRS operates by dynamically suppressing the model's generation of reflection triggers when it exhibits high confidence in its current response, thereby preventing redundant reflection cycles without compromising output quality. Our approach is model-agnostic, requires no retraining or architectural modifications, and can be integrated seamlessly with existing autoregressive generation pipelines.
Extensive experiments across four reasoning benchmarks (i.e., AIME24, AMC23, MATH500, and GPQA-D) demonstrate CGRS's effectiveness: it reduces token usage by an average of 18.5% to 41.9% while preserving accuracy and also achieves the optimal balance between length reduction and performance compared to state-of-the-art baselines. These results hold consistently across model architectures (e.g., DeepSeek-R1-Distill series, QwQ-32B, and Qwen3 family) and scales (4B to 32B parameters), highlighting CGRS's practical value for efficient reasoning.

Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression

Multivariate Time Series Forecasting (MTSF) aims to capture the dependencies among multiple variables and their temporal dynamics to predict future values. In recent years, Large Language Models (LLMs) have set a new paradigm for MTSF, incorporating external knowledge into the modeling process through textual prompts. However, we observe that current LLM-based methods fail to exploit these priors due to their coarse-grained representation of time series data, which hinders effective alignment of the two modals. To address this, we propose M3Time, a multi-modal, multi-scale, and multi-frequency framework for multivariate time series forecasting. It enhances the quality of time series representations and facilitates the integration of LLM semantic priors with fine-grained temporal features. Additionally, M3Time further improved training stability and model robustness with an adaptive mixed loss function, which dynamically balances L1 and L2 error terms. Experiment results on seven real-world public datasets show that M3Time consistently outperforms state-of-the-art methods, underscoring its effectiveness.

M3Time: LLM-Enhanced Multi-Modal, Multi-Scale, and Multi-Frequency Multivariate Time Series Forecasting

Pretrained equivariant graph neural networks based on spherical harmonics offer efficient and accurate alternatives to computationally expensive ab-initio methods, yet adapting them to new tasks and chemical environments still requires fine-tuning. Conventional parameter-efficient fine-tuning (PEFT) techniques, such as Adapters and LoRA, typically break symmetry, making them incompatible with those equivariant architectures. ELoRA, recently proposed, is the first equivariant PEFT method. It achieves improved parameter efficiency and performance on many benchmarks. However, the relatively high degrees of freedom it retains within each tensor order can still perturb pretrained feature distributions and ultimately degrade performance. To address this, we present Magnitude-Modulated Equivariant Adapter (MMEA), a novel equivariant fine-tuning method which employs lightweight scalar gating to modulate feature magnitudes on a per-order and per-multiplicity basis. We demonstrate that MMEA preserves strict equivariance and, across multiple benchmarks, consistently improves energy and force predictions to state-of-the-art levels while training fewer parameters than competing approaches. These results suggest that, in many practical scenarios, modulating channel magnitudes is sufficient to adapt equivariant models to new chemical environments without breaking symmetry, pointing toward a new paradigm for equivariant PEFT design.

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks

The task of stochastic human motion prediction has attracted significant attention in recent years due to its wide-ranging applications in robotics, animation, and human-computer interaction. While diffusion models have demonstrated promising progress in this domain, they remain hindered by two critical limitations: (1) slow inference speeds due to their reliance on iterative sampling, and (2) performance degradation resulting from suboptimal sample allocation during generation. To overcome these challenges, we propose SPARD (Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction), a novel framework that achieves efficient single-step inference while maintaining high predictive accuracy. Furthermore, we introduce a novel adaptive noise predictor module that dynamically samples latent representations based on observed motion sequences, ensuring both accuracy and plausibility in generated motions. Extensive experiments on benchmark datasets demonstrate that SPARD significantly outperforms state-of-the-art methods in both inference efficiency and motion quality, achieving a 15× to 18× speedup in sampling time compared to conventional diffusion-based baselines while preserving generation quality.

SPARD: Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction

Although deep learning-based image retouching has made significant progress, its inherent subjectivity renders current black-box methods limited in interactivity and explainability. Among existing efforts, parameter-controlled methods aim to improve interactivity, but often suffer from ambiguous semantics and lack support for natural language control. Reinforcement learning–based explainability methods are constrained by low-dimensional and limited action spaces, which result in suboptimal performance. To address the above issues, we propose RetouchAgent, a novel framework that leverages collaboration among multiple MLLM agents for image retouching. Our method consists of the following key steps: (1) Retrieval: By constructing a multimodal retouching database, we enable an ICL sample retrieval mechanism guided by retouching intent. (2) Engine: Leveraging the vision-language understanding capabilities of MLLM, a carefully designed prompting strategy, and a dedicated operation library, we enable precise and controllable image retouching. (3) Reflection: We evaluate each retouching interaction and optimize the retouching process for progressive result refinement. Finally, through multiple rounds of collaboration among MLLM agents, RetouchAgent achieves state-of-the-art performance in quantitative and qualitative evaluations.

RetouchAgent: Towards Interactive and Explainable Image Retouching with MLLM Agents

In multi-agent systems, explicit cognition of teammates' decision logic serves as a critical factor in facilitating coordination. Communication (i.e., "Tell") can assist in the cognitive development process by information dissemination, yet it is inevitably subject to real-world constraints such as noise, latency, and attacks. Therefore, building the understanding of teammates' decisions without communication remains challenging. To address this, we propose a novel non-communication MARL framework that realizes the construction of cognition through local observation-based modeling (i.e., "Think"). Our framework enables agents to model teammates' active inference process. At first, the proposed method produces three teammate portraits: perception-belief-action. Specifically, we model the teammate's decision process as follows: 1) Perception: observing environments; 2) Belief: forming beliefs; 3) Action: making decisions. Then, we selectively integrate the belief portrait into the decision process based on the accuracy and relevance of the perception portrait. This enables the selection of cooperative teammates and facilitates effective collaboration. Extensive experiments on the SMAC, SMACv2, MPE, and GRF benchmarks demonstrate the superior performance of our method.

Think How Your Teammates Think: Active Inference Can Benefit Decentralized Execution

Dynamic graph learning focuses on representing time-varying graphs, enabling the modeling of evolving relationships between nodes. This approach is essential for applications such as traffic systems, social networks, and recommendation engines, where interactions shift dynamically. While existing methods often utilize temporal modules and transformer networks to capture these changes, a major challenge lies in the high computational demands of self-attention mechanisms, which scale quadratically with the number of nodes.
To address this, we propose a novel transformer-based framework for dynamic graph learning that incorporates a more efficient token mixer. Our key insight is that the Transformer's performance primarily stems from its architecture rather than the self-attention mechanism itself. Thus, we introduce an adaptive token mixer, which aggregates tokens based on their order and timing within a sliding window. Furthermore, we design a hierarchical learning module to capture long-term dependencies by leveraging long-range neighbor information across layers.
Our approach significantly reduces computational complexity while preserving the ability to model both short-term and long-term dependencies in dynamic graphs effectively. Experimental results demonstrate that our framework achieves robust performance, showing that the simplified architectures can deliver competitive results without the resource-intensive requirements of traditional Transformers.

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Inapproximability of STRIPS Planning

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