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In recent years, learning-based underwater image enhancement (UIE) techniques have rapidly evolved. However, distribution shifts between high-quality enhanced outputs and natural images can hinder semantic cue extraction for downstream vision tasks, thereby limiting the adaptability of existing enhancement models. To address this challenge, this work proposes a new learning mechanism that leverages Vision-Language Models (VLMs) to empower UIE models with semantic-sensitive capabilities. To be concrete, our strategy first generates textual descriptions of key objects from a degraded image via a VLM. Subsequently, a text-image alignment model remaps these relevant descriptions back onto the image to produce a spatial semantic guidance map. This map then steers the UIE network through a dual-guidance mechanism, which combines cross-attention and an explicit alignment loss. This forces the network to focus its restorative power on semantic-sensitive regions during image reconstruction, rather than pursuing a globally uniform improvement, thereby ensuring the faithful restoration of key object features. Experiments confirm that when our strategy is applied to different UIE baselines, significantly boosts their performance on perceptual quality metrics as well as enhances their performance on detection and segmentation tasks, validating its effectiveness and adaptability.

AAAI 2026

Empowering Semantic-Sensitive Underwater Image Enhancement with VLM

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.

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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) demonstrate strong reasoning
capabilities, yet the inference-time performance of
existing solutions remains limited by self-biases,
coordination inefficiencies, lack of robust error
detection, and dependency on high-quality verifiers. To
address these challenges, we propose Adaptive Coopetition
(AdCo), a lightweight, multi-agent multi-round
inference-time framework that enhances collective reasoning
through adaptive decision-making guided by coarse verifier
signals. Without relying on high-performance verifiers,
AdCo achieves a 20% relative accuracy improvement on math
reasoning benchmarks, with consistent performance on
different sample sizes and agent configurations. This
adaptive, signal-guided ‘coopetition’ framework (Tran et
al. 2025) enhances reasoning robustness by leveraging
diverse model knowledge and reasoning traces, while also
promoting uncertainty-driven exploration, especially when
participants have comparable capabilities.

Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning (Student Abstract)

This paper addresses the nonparametric estimation of the drift function over a compact domain for a time-homogeneous diffusion process, based on high-frequency discrete observations from $N$ independent trajectories. We propose a neural network-based estimator and derive a non-asymptotic convergence rate, decomposed into a training error, an approximation error, and a diffusion-related term scaling as ${\log N}/{N}$. For compositional drift functions, we establish an explicit rate. In the numerical experiments, we consider a drift function with local fluctuations generated by a double-layer structure and show that the empirical convergence rate becomes independent of the input dimension $d$. Compared to the $B$-spline method proposed by Denis et al., the neural network estimator achieves better convergence rates and more effectively captures local features, particularly in higher-dimensional settings.

Drift Estimation for Diffusion Processes Using Neural Networks Based on Discretely Observed Independent Paths

Deep Learning techniques are nowadays pervasive in AI. However, these approaches suffer of the lack of transparency for justifying their output and for helping users in believing in their decisions. For these reasons alternative approaches to learning deserve to be explored either for developing new tools with autonomous learning capability or for explaining the results of black-box predictors.
Among them an important role is assumed since the Nineties by Inductive Logic Programming and, in particular, recently by the approaches of Learning from Answer Sets (LAS). 
Computing inductive solutions for LAS tasks is known to be $\Sigma_2^P$-hard. In this work, we tackle this problem using a single-shot disjunctive ASP encoding based on the saturation technique originally proposed by Eiter and Gottlob. We prove that, when the background knowledge and hypothesis space form a tight program (a syntactical property) our encoding is linear in the size of the task. This approach contrasts with the state-of-the-art ILASP system, which relies on multiple iterative calls to an ASP solver. As a result, it can be directly evaluated by modern disjunctive ASP solvers, leveraging decades of research and optimization in the ASP community. 
We implement our method in a system named LASCO. Experimental results on a diverse set of benchmarks demonstrate that LASCO outperforms all versions of ILASP on many instances and it scales if run on multi-threaded machines.

Learning from Answer Sets via Single-Shot Disjunctive ASP Encoding

The spatial reasoning task aims to reason about the spatial relationships in 2D and 3D space, which is a fundamental capability for Visual Question Answering (VQA) and robotics. Although vision language models (VLMs) have developed rapidly in recent years, they are still struggling with the spatial reasoning task. In this paper, we introduce a method that can enhance Spatial reasoning through Visual and Textual thinking Simultaneously (SpatialVTS). In the spatial visual thinking phase, our model is trained to generate location-related specific tokens of important targets automatically. Not only are the objects mentioned in the problem addressed, but also the potential objects related to the reasoning are considered. During the spatial textual thinking phase, our model conducts long-term thinking based on visual cues and dialogues and gradually inferences the answers to spatial reasoning problems. To effectively support the model's training, we made manual corrections to the existing spatial reasoning dataset, eliminating numerous incorrect labels resulting from automatic annotation, restructuring the data input format to enhance generalization, and developing a reasoning framework for model thinking. Without introducing any additional information (such as masks or depth), our model's overall average level in several spatial understanding tasks has significantly improved compared with other models.

Enhancing Spatial Reasoning Through Visual and Textual Thinking

This paper bridges two perspectives: it studies the multi-secretary problem through the fairness lens of social choice, and examines multi-winner elections from the viewpoint of online decision making. After identifying the limitations of the prominent proportionality notion of Extended Justified Representation (EJR) in the online domain, the work proposes a set of mechanisms that merge techniques from online algorithms with rules from social choice---such as the Method of Equal Shares and the Nash Rule---and supports them through both theoretical analysis and extensive experimental evaluation.

Fairness in the Multi-Secretary Problem

Neural coupling is a fundamental mechanism in neuroscience that facilitates the emergence of cognitive functions through dynamic interactions and synchronization among distributed brain regions. Inspired by this principle, we pose the question: Might the biological mechanism of neural oscillatory synchronization inspire the feature representation learning for neuroscience? By addressing this question through the Kuramoto model, renowned for simulating oscillatory dynamics, we present a novel physics-informed deep model, `SyncBrain`, it models brain regions as interacting oscillatory units and simulates their temporal dynamics and synchronization patterns to distinguish cognitive states. Furthermore, inspired by the brain's inherent ability to dynamically attend to critical temporal information, we incorporate an adaptive control module that introduces an attention-like mechanism to guide information flow. We evaluate our model on multiple functional neuroimaging datasets, it demonstrates promising performance and enhanced interpretability in both cognitive state decoding and early disease diagnosis, outperforming existing computational methods. These results demonstrate the effectiveness of neural oscillatory mechanisms in shaping robust and interpretable machine learning models for neuroscience applications.

SyncBrain: Exploring Brain Functional Dynamics Through Neural Oscillatory Synchronization

Vietnamese exhibits extensive dialectal variation, posing challenges for NLP systems trained predominantly on standard Vietnamese. Such systems often underperform on dialectal inputs, especially from underrepresented Central and Southern regions. Previous work on dialect normalization has focused narrowly on Central-to-Northern dialect transfer using synthetic data and limited dialectal diversity. These efforts exclude Southern varieties and intra-regional variants within the North. We introduce ViDia2Std, the first manually annotated parallel corpus for dialect-to-standard Vietnamese translation covering all 63 provinces. Unlike prior datasets, ViDia2Std includes diverse dialects from Central, Southern, and non-standard Northern regions often absent from existing resources, making it the most dialectally inclusive corpus to date. The dataset consists of over 13,000 sentence pairs sourced from real-world Facebook comments and annotated by native speakers across all three dialect regions. To assess annotation consistency, we define a semantic mapping agreement metric that accounts for synonymous standard mappings across annotators. Based on this criterion, we report agreement rates of 86% (North), 82% (Central), and 85% (South). We benchmark several sequence-to-sequence models on ViDia2Std. mBART-large-50 achieves the best results (BLEU 0.7801, ROUGE-L 0.9285, METEOR 0.8689), while ViT5-base offers competitive performance with fewer parameters. ViDia2Std demonstrates that dialect normalization substantially improves downstream tasks, highlighting the need for dialect-aware resources in building robust Vietnamese NLP systems.

ViDia2Std: A Parallel Corpus and Methods for Low-Resource Vietnamese Dialect-to-Standard Translation

Alignment of large language models (LLMs) with human preferences typically relies on supervised reward models or external judges, which in turn require abundant preference data. We propose a generative preference modeling approach for low-resource and domain-specific scenarios, reframing preference learning as an inverse reinforcement learning problem. Instead of training a discriminative reward model, we train the LLM itself to infer and maximize an implicit reward function underlying high-quality reasoning. Specifically, we leverage Chain-of-Thought (CoT) sampling to generate diverse candidate solutions for each query and refine fine-grained preferences from these without additional human labels. We also introduce an entropy-guided token scoring mechanism to rank and weight the sampled CoTs, boosting the importance of high-confidence answers and strategically high-entropy tokens. Building on this, we train the model with our Self-Evaluated Group Advantage (SEGA) algorithm. Compared with other methods, this algorithm efficiently utilizes the fine-grained preference information in group candidate solutions to update the strategy. Our method eliminates dependence on external judges or reward classifiers, instead relying on the generative model’s own judgments. Experiments on general benchmarks and domain-specific tasks—such as mathematical reasoning and medical question answering—demonstrate that our generative preference model achieves significant improvements with limited data.

GEM: Generative Entropy-Guided Preference Modeling for Few-Shot Alignment of LLMs

Agents are rapidly advancing in automating digital work, but enterprises face a harder challenge: moving beyond prototypes to deployed systems that deliver measurable business value. This path is complicated by fragmented frameworks, slow development, and the absence of standardized evaluation practices. Generalist agents have emerged as a promising direction, excelling on academic benchmarks and offering flexibility across task types, applications, and modalities. Yet, evidence of their deployment in production enterprise settings is still limited. This paper reports IBM’s experience developing and deploying the \textbf{Computer Using Generalist Agent (CUGA)}. CUGA adopts a hierarchical planner--executor architecture with strong analytical foundations, achieving state-of-the-art performance on AppWorld and WebArena. Beyond benchmarks, it was deployed in the Business-Process-Outsourcing talent acquisition domain, meeting enterprise requirements for scalability, auditability, safety, and governance. To support evaluation, we introduce \textbf{BPO-TA}, a 26-task benchmark spanning 13 analytics endpoints. In deployment, CUGA matched the accuracy of specialized agents while reducing development time by 91.9\% and cost by 52.3\%. Our contribution is twofold: demonstrating generalist agents operating at enterprise scale, and distilling technical and organizational lessons from this deployment. We outline requirements and next steps for advancing research-grade architectures like CUGA into robust, enterprise-ready systems.

From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production

Explanation generation frameworks aim to make AI systems’ decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.

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Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning (Student Abstract)

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