Singapore

Partially observable Markov decision processes (POMDPs) present significant challenges for reinforcement learning, as agents must learn optimal policies while maintaining belief states over unobserved environment states based on partial observations. 
We observe a compelling analogy: large language models (LLMs) autoregressively generate token probability distributions based on preceding context, mirroring how belief states are maintained and updated in POMDPs. 
This insight motivates leveraging the rich prior knowledge embedded in pre-trained LLMs for latent states estimation from observation-action histories. 
However, two critical challenges emerge: on the one hand, modality misalignment prevents LLMs from directly encoding visual observations and discrete actions; on the other hand, semantic misalignment exists between observation-action sequences and token sequences. 
To address these challenges, we introduce a novel framework ELSLLM that employs a Johnson-Lindenstrauss projection (JLP) module to transform input dimensions while preserving state similarity with theoretical guarantees, and utilizes modern Hopfield networks (MHN) to store all word embeddings from pre-trained LLMs as a knowledge repository. 
Through retrieval and querying mechanisms, ELSLLM achieves token-level knowledge alignment without requiring fine-tuning of the pre-trained LLMs. 
Extensive experiments on partially observable environments demonstrate that ELSLLM achieves state-of-the-art performance, significantly outperforming baseline methods with and without LSTM memory mechanisms. 
Our work opens new avenues for integrating pre-trained LLMs with reinforcement learning in partially observable settings.

AAAI 2026

From Tokens to Latent States: Leveraging Pre-trained Language Models for Improving Partially Observable Reinforcement Learning

ml: reinforcement learning

ml: deep learning algorithms

ml: representation learning

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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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 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.

Regulatory compliance checking for online medical advertisements poses a critical public safety challenge distinct from traditional fact-checking, particularly in low-resource languages. Existing automated systems are ill-suited for the authorization-based, evidence-grounded, and explainable reasoning this task demands. To address this gap, we introduce \texttt{VietCheckMed}, a novel retrieval-augmented framework, and \texttt{VietAestheticAds}, the first large-scale, expert-validated benchmark for this task, comprising \textbf{8,329 advertisements} paired with an authoritative regulatory corpus of \textbf{9,978 facilities}. Comprehensive experiments demonstrate that our evidence-grounded approach is essential, substantially outperforming powerful unassisted LLM baselines by over 0.3805 F1-score. A detailed analysis reveals that the primary remaining challenges are nuanced failures in semantic and logical reasoning, defining a clear frontier for future research. To promote advances in regulatory technology and responsible AI, our dataset, code, and evaluation scripts will be made publicly available. This work contributes a foundational methodology and a vital public resource for developing responsible AI in high-stakes regulatory domains.

VietCheckMed: Explainable Regulatory Compliance Checking for Medical Advertisements on Vietnamese Social Media

Large vision-language models (LVLMs) have shown impressive performance across a broad range of multimodal tasks. However, robust image caption evaluation using LVLMs remains challenging, particularly under domain-shift scenarios. To address this issue, we introduce the Distribution-Aware Score Decoder (DISCODE), a novel finetuning-free method that generates robust evaluation scores better aligned with human judgments across diverse domains. The core idea behind DISCODE lies in its test-time adaptive evaluation approach,
which introduces the Adaptive Test-Time (ATT) loss, leveraging a Gaussian prior distribution to improve robustness in evaluation score estimation. This loss is efficiently minimized at test time using an analytical solution that we derive. Furthermore, we introduce the Multi-domain Caption Evaluation (MCEval) benchmark, a new image captioning evaluation benchmark covering six distinct domains, designed to assess the robustness of evaluation metrics. In our experiments, we demonstrate that DISCODE achieves state-of-the-art performance as a reference-free evaluation metric across MCEval and four representative existing benchmarks. Our code and dataset will be made publicly available.

DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioning

In the field of audio generation, 
signal-to-noise ratio (SNR) has long served as an objective metric for evaluating audio quality. Nevertheless, recent studies have shown that SNR and its variants are not always highly correlated with human perception, prompting us to raise the questions: \textit{Why does SNR fail in measuring audio quality?} And \textit{how to improve its reliability as an objective metric?} In this paper, we identify the inadequate measurement of phase distance as a pivotal factor and propose to reformulate SNR with specially designed phase-distance terms, yielding an improved metric named GOMPSNR. We further extend the newly proposed formulation to derive two novel categories of loss function, corresponding to magnitude-guided phase refinement and joint magnitude-phase optimization, respectively. Besides, extensive experiments are conducted for an optimal combination of different loss functions. Experimental results on advanced neural vocoders demonstrate that our proposed GOMPSNR exhibits more reliable error measurement than SNR. Meanwhile, our proposed loss functions yield substantial improvements in model performance, and our well-chosen combination of different loss functions further optimizes the overall model capability.

GOMPSNR: Reflourish the Signal-to-Noise Ratio Metric for Audio Generation Tasks

Text-guided image inpainting endeavors to generate new content within specified regions of images using textual prompts from users. The primary challenge is to accurately align the inpainted areas with the user-provided prompts while maintaining a high degree of visual fidelity. While existing inpainting methods have produced visually convincing results by leveraging the pre-trained text-to-image diffusion models, they still struggle to uphold both prompt alignment and visual rationality simultaneously. In this work, we introduce FreeInpaint, a plug-and-play tuning-free approach that directly optimizes the diffusion latents on the fly during inference to improve the faithfulness of the generated images. Technically, we introduce a prior-guided noise optimization method that steers model attention towards valid inpainting regions by optimizing the initial noise. Furthermore, we meticulously design a composite guidance objective tailored specifically for the inpainting task. This objective efficiently directs the denoising process, enhancing prompt alignment and visual rationality by optimizing intermediate latents at each step. Through extensive experiments involving various inpainting diffusion models and evaluation metrics, we demonstrate the effectiveness and robustness of our proposed FreeInpaint.

FreeInpaint: Tuning-free Prompt Alignment and Visual Rationality Enhancement in Image Inpainting

3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation in both efficiency and quality. Recent adaptations of Gaussian splatting specifically tailored for computed tomography (CT) have shown promising results but still suffer from severe artifacts under highly sparse-view X-ray conditions and lack robustness in dynamic scenarios. To tackle these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored specifically for sparse-view and dynamic CT reconstruction. A hash encoder is introduced to explicitly capture spatial geometric relationships among Gaussian primitives, significantly regularizing their spatial distribution under ultra-sparse conditions. We further extend this framework to dynamic reconstruction by introducing time-conditioned representations. To alleviate hash collisions and temporal inconsistencies caused by joint spatiotemporal encoding, a spatiotemporal attention module is proposed, which adaptively recalibrates and optimizes Gaussian features across frames. Moreover, we incorporate a motion-flow network to model fine-grained respiratory motion, enabling accurate tracking of local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions. We will release source codes.

TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction

Recent LLMs like DeepSeek-R1 have demonstrated state-of-the-art performance by integrating deep thinking and complex reasoning during generation. However, the internal mechanisms behind these reasoning processes remain unexplored. We observe *reasoning* LLMs consistently use vocabulary associated with human reasoning processes. We hypothesize these words correspond to specific reasoning moments within the models' internal mechanisms. To test this hypothesis, we employ Sparse Autoencoders (SAEs), a technique for sparse decomposition of neural network activations into human-interpretable features. We introduce *ReasonScore*, an automatic metric to identify active SAE features during these reasoning moments. We perform manual and automatic interpretation of the features detected by our metric, and find those with activation patterns matching uncertainty, exploratory thinking, and reflection. Through steering experiments, we demonstrate that amplifying these features increases performance on reasoning-intensive benchmarks ($+2.2$%) while producing longer reasoning traces ($+20.5$%). Using model diffing technique, we provide evidence that these features are present only in models with reasoning capabilities. Our work provides the first step towards a mechanistic understanding of reasoning in LLMs.

I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic relationships. While existing two-stage methods mitigate this by first refining hash centers with semantics and then training the hash function, they introduce additional complexity, computational overhead, and suboptimal performance due to stage-wise discrepancies.
To address these limitations, we propose $\textbf{Center-Reassigned Hashing (CRH)}$, an end-to-end framework that $\textbf{dynamically reassigns hash centers}$ from a preset codebook while jointly optimizing the hash function. Unlike previous methods, CRH adapts hash centers to the data distribution $\textbf{without explicit center optimization phases}$, enabling seamless integration of semantic relationships into the learning process. Furthermore, $\textbf{a multi-head mechanism}$ enhances the representational capacity of hash centers, capturing richer semantic structures. Extensive experiments on three benchmarks demonstrate that CRH learns semantically meaningful hash centers and outperforms state-of-the-art deep hashing methods in retrieval tasks.

Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function

Efficient visual backbone design remains crucial for resource-constrained computer vision applications. Inspired by the adaptive continuous-time dynamics observed in biological neurons, we propose FVNet, a novel lightweight architecture that integrates liquid neural dynamics for efficient and dynamic visual feature extraction. Central to FVNet is the Fluid Temporal Flow Unit (FTFU), which employs continuous-time equations with learnable time constants to capture spatio-temporal dependencies adaptively. By further stacking these units in a Multi-Phase Fluid Block (MPFB), our model processes features across parallel temporal scales, enabling context-aware feature encoding without incurring excessive computational overhead. Through a discrete closed-form solution, FVNet achieves the representational power of continuous-time models while avoiding the instability and overhead of iterative numerical solvers. Extensive experiments on various vision tasks demonstrate that FVNet achieves superior performance and efficiency over existing state-of-the-art lightweight networks.

FVNet: Harnessing Liquid Neural Dynamics for Lightweight Visual Representation

Generative diffusion models show promise for data augmentation. However, applying them to fine-grained tasks presents a significant challenge: ensuring synthetic images accurately capture the subtle, category-defining features critical for high fidelity. Standard approaches, such as text-based Classifier-Free Guidance (CFG), often lack the required specificity, potentially generating misleading examples that degrade fine-grained classifier performance. To address this, we propose Hierarchically Guided Fine-grained Augmentation (HiGFA). HiGFA leverages the temporal dynamics of the diffusion sampling process. It employs strong text and transformed contour guidance with fixed strengths in the early-to-mid sampling stages to establish overall scene, style, and structure. In the final sampling stages, HiGFA activates a specialized fine-grained classifier guidance and dynamically modulates the strength of all guidance signals based on prediction confidence. This hierarchical, confidence-driven orchestration enables HiGFA to generate diverse yet faithful synthetic images by intelligently balancing global structure formation with precise detail refinement. Experiments on several FGVC datasets demonstrate the effectiveness of HiGFA.

HiGFA: Hierarchical Guidance for Fine-grained Data Augmentation with Diffusion Models

Recent advances in text-to-image (T2I) diffusion models have significantly improved semantic image editing, yet most methods fall short in performing 3D-aware object manipulation. In this work, we present ***FFSE***, a 3D-aware autoregressive framework designed to enable intuitive, physically-consistent object editing directly on real-world images. Unlike previous approaches that either operate in image space or require slow and error-prone 3D reconstruction, ***FFSE*** models editing as a sequence of learned 3D transformations, allowing users to perform arbitrary manipulations, such as translation, scaling, and rotation, while preserving realistic background effects (e.g. shadows, reflections) and maintaining global scene consistency across multiple editing rounds. To support learning of multi-round 3D-aware object manipulation, we introduce ***3DObjectEditor***, a hybrid dataset constructed from simulated editing sequences across diverse objects and scenes, enabling effective training under multi-round and dynamic conditions. Extensive experiments show that the proposed ***FFSE*** significantly outperforms existing methods in both single-round and multi-round 3D-aware editing scenarios.

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VietCheckMed: Explainable Regulatory Compliance Checking for Medical Advertisements on Vietnamese Social Media

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