Singapore

Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks that require generating comprehensive and diverse answers. We attribute this limitation to \textbf{reasoning overconfidence}: a tendency to express undue certainty in an incomplete solution set. To examine the effect, we introduce \textit{MuSoBench}, a benchmark of multi-solution problems. Experiments show that the conventional short chain-of-thought (Short-CoT) prompting paradigm exhibits pronounced overconfidence, whereas the emerging long chain-of-thought (Long-CoT) approach mitigates it through iterative exploration and self-reflection. We further characterise observable behaviours and influential factors. To probe the underlying cause, we propose the \textbf{cognitive-rigidity hypothesis}, which posits that overconfidence arises when the reasoning process prematurely converges on a narrow set of thought paths. An attention-entropy analysis offers preliminary support for this view. These findings provide tools for assessing the completeness of LLM reasoning and highlight the need to move evaluation beyond single-answer accuracy toward comprehensive exploration.

AAAI 2026

Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks

nlp: safety and robustness

nlp: prompt engineering / prompting

nlp: (large) language models

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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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.

In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pre-trained models that struggle with fine-grained information and deterministic reasoning, leading to difficulties in aligning with complex or ambiguous moments. To overcome these limitations, we explore Deep Evidential Regression (DER) to construct a vanilla Evidential baseline. However, this approach encounters two major issues: the inability to effectively handle modality imbalance and the structural differences in DER's heuristic uncertainty regularizer, which adversely affect uncertainty estimation. This misalignment results in high uncertainty being incorrectly associated with accurate samples rather than challenging ones. Our observations indicate that existing methods lack the adaptability required for complex video scenarios. In response, we propose Debiased Evidential Learning for Moment Retrieval (DEMR), a novel framework that incorporates a Reflective Flipped Fusion (RFF) block for cross-modal alignment and a query reconstruction task to enhance text sensitivity, thereby reducing bias in uncertainty estimation. Additionally, we introduce a Geom-regularizer to refine uncertainty predictions, enabling adaptive alignment with difficult moments and improving retrieval accuracy. Extensive testing on standard datasets and debiased datasets ActivityNet-CD and Charades-CD demonstrates significant enhancements in effectiveness, robustness, and interpretability, positioning our approach as a promising solution for temporal-semantic robustness in moment retrieval.

Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval

Stickers are widely used in online communication to convey emotions and implicit intentions. The Sticker Response Selection (SRS) task aims to select the most contextually appropriate sticker based on the dialogue. However, existing methods typically rely on semantic matching and model emotional and intentional cues separately, which can lead to mismatches when emotions and intentions are misaligned. To address this issue, we propose **E**motion and **I**ntention **G**uided **M**ulti-Modal **L**earning (**EIGML**). This framework is the first to jointly model emotion and intention, effectively reducing the bias caused by isolated modeling and significantly improving selection accuracy. Specifically, we introduce Dual-Level Contrastive Framework to perform both intra-modality and inter-modality alignment, ensuring consistent representation of emotional and intentional features within and across modalities. In addition, we design an Intention-Emotion Guided Multi-Modal Fusion module that integrates emotional and intentional information progressively through three components: Emotion-Guided Intention Knowledge Selection, Intention-Emotion Guided Attention Fusion, and Similarity-Adjusted Matching Mechanism. This design injects rich, effective information into the model and enables a deeper understanding of the dialogue, ultimately enhancing sticker selection performance. Experimental results on two public SRS datasets show that EIGML consistently outperforms state-of-the-art baselines, achieving higher accuracy and a better understanding of emotional and intentional features. Code is provided in the supplementary materials.

Emotion and Intention Guided Multi-Modal Learning for Sticker Response Selection

In medical image classification, data privacy constraints and the high cost of expert annotations pose significant challenges to building generalizable models. Federated semi-supervised learning (FSSL), which combines the privacy-preserving nature of federated learning with the label efficiency of semi-supervised learning, offers a promising direction. However, in real-world deployments, client data often exhibits highly non-independent and identically distributed (Non-IID) characteristics. This distributional heterogeneity undermines the reliability of pseudo-labels generated by global models, ultimately limiting model generalization. A key limitation of existing FSSL approaches lies in their reliance on a static labeled set fixed prior to training. Such strategies lack the ability to adaptively correct pseudo-label noise or address class imbalance throughout training, particularly under Non-IID settings. To address this, we propose FSSAL, a novel framework that introduces an active learning component into the FSSL pipeline. By continuously identifying informative and representative samples during training, our method adaptively refines the labeled set and enhances the model’s robustness to distribution shifts. FSSAL employs client-private models for pseudo-label generation to reduce global bias, applies a class-aware dynamic thresholding mechanism to ensure more reliable and balanced label selection, and incorporates a sample selection strategy guided by both feature diversity and model uncertainty. Extensive experiments on four public medical image classification datasets demonstrate that FSSAL consistently outperforms competitive FSSL methods in accuracy and F1-score, especially under highly Non-IID conditions, highlighting its robustness and practical potential.

Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification

Asynchronous distributed learning is crucial for training large-scale deep models, especially when the computing capabilities of the workers in the cluster are heterogeneous. 
To reduce communication frequency, local updates are widely adopted in distributed learning. Meanwhile, momentum SGD (MSGD) serves as a foundational optimizer due to momentum's key role in accelerating convergence and enhancing generalization. However, how to implement asynchronous distributed MSGD with local updates remains unexplored.
To solve this problem, we propose a novel method, called \underline{or}dered \underline{lo}cal \underline{mo}mentum (OrLoMo), for asynchronous distributed learning. 
In OrLoMo, each worker runs MSGD locally. Then the local momentum from each worker will be aggregated by the server in order based on its global iteration index. To the best of our knowledge, OrLoMo is the first method to implement asynchronous distributed MSGD with local updates. We prove the convergence of OrLoMo for non-convex problems under arbitrary delays. Experiments validate that OrLoMo can outperform its synchronous counterpart and other asynchronous methods.

Ordered Local Momentum for Asynchronous Distributed Learning Under Arbitrary Delays

Auto-regressive (AR)-based decoders, owing to their flexibility in handling variable-length outputs and their strong capability in modeling character-level dependencies, have emerged as the predominant decoding paradigm in the field of scene text recognition (STR). However, AR-based decoders suffer from attention drift, slow decoding speed, and difficulty capturing global dependencies, restricting their performance in various scenarios. In this paper, we propose a novel paradigm for AR-based decoding, called One-Token to Sequence (One2Seq), to address the above issues. Unlike existing methods, we encode the semantic features into a single context token and design a One-Token Wise Decoder to perform the decoding, which alleviates the attention drift caused by the accumulation of semantic information. Moreover, we proposed Positioal-aware Hash Embedding to embed the decoded characters, ensuring the order information is obtained in the context token. By continuously updating this token, One2Seq fully leverages the decoded semantic information while avoiding the computational overhead associated with the growing query sequence. Furthermore, to leverage global information for decoding, we propose Dynamic Global Infusion to dynamically integrates global visual features into the context token. Equipped with the enriched context token, the model has an enhanced ability to extract discriminative local features under the guidance of global context, thereby enhancing recognition accuracy. Extensive experiments reveal that, with its ingenious design, One2Seq exhibits marked superiority on both accuracy and decoding speed compared to existing STR models.

One2Seq: One-Token Wise Decoder for Efficient Scene Text Recognition

Recent advances in multimodal large language models (MLLMs) have demonstrated strong capabilities in addressing open-world segmentation tasks. However, the substantial computational cost of the LLM components presents a significant challenge, especially in segmentation tasks, where efficiency has long been a central concern. Existing efficient MLLM approaches typically reduce computation cost by pruning visual tokens in the early layers, as they account for the majority of the input sequence. Despite their efficiency, this is incompatible with dense prediction tasks such as segmentation, since removing visual tokens leads to the loss of essential object parts and spatial details. To better understand the roles of visual tokens in segmentation, we analyze the attention weights of both image and mask tokens within LLM. We find that image tokens are important throughout all layers, whereas mask tokens only attend to image tokens at deeper layers. Based on the observation, we build an efficient segmentation framework based on MLLMs by introducing a sophisticated token routing strategy. This strategy dynamically determines when and how different tokens participate in computation: For mask tokens, they are only inserted at deeper layers of the LLM to reduce redundant computation, since they rarely attend to image tokens in early layers; For image tokens, only a small number of them, named proxies, are updated via full feedforward network (FFN) computation, while the update of the remaining tokens is guided by these proxies, i.e., efficiently computed through a lightweight projector applied on the difference of the proxies during their update. Our method achieves a 1.5$\times$ acceleration over the original LLM process by reducing its FLOPs to 56\%, while maintaining the same segmentation performance.

Efficient Segmentation with Multimodal Large Language Model via Token Routing

Cross-Domain Few-Shot Object Detection (CD-FSOD) faces significant challenges due to the dual issues of domain shift and limited labeled samples. One major challenge is style bias, caused by limited support samples that fail to represent the target domain’s style diversity. Another is feature confusion, which stems from distribution shifts and limited supervision, manifesting as both object-background ambiguity and object-object confusion. To address these challenges, we propose Style-Augmented Prototype Learning (StyleProto), which constructs style-aware prototypes from support samples with diverse visual styles, and refines them via spatial weighting and discriminative fusion. Specifically, our StyleProto consists of three components: (1) Style Generation Augmentation (SGA); (2) Semantic-Focused Prototype Construction (SPC); (3) Hierarchical Prototype Fusion Aggregator (HPFA). SGA synthesizes style-diverse yet semantically consistent training samples by recombining style statistics from the support set, thus improving robustness to unseen styles. SPC aggregates support features using spatial attention to highlight object semantics and suppress background noise, yielding cleaner and more distinctive class prototypes. HPFA leverages query-guided attention to integrate discriminative support features, enhancing prototype representations with richer class-specific details. Extensive experiments on multiple benchmarks demonstrate that StyleProto consistently outperforms existing state-of-the-art methods. The code is included in the \textit{supplementary material}.

StyleProto: Style-Augmented Prototype Learning for Cross-Domain Few-Shot Object Detection

Computational fluid dynamics (CFD) simulations traditionally require extensive computational resources, limiting their utility in many scientific and engineering applications at scale. We introduce Physically-Informed Flow Matching Graph Networks (PIFM-GN), a novel generative framework that directly samples fluid states under specified physical conditions without requiring expensive time-stepping simulations. The key innovation of our approach is the incorporation of incompressibility constraints directly into the flow matching transport process by parameterizing velocity fields through vector potentials, with graph-based curl operators ensuring divergence-free predictions without requiring global pressure-Poisson solves. Experiments on diverse fluid dynamics problems -- ranging from two-dimensional surface pressure distributions and complete flow fields, to complex three-dimensional airflow fields -- demonstrate that PIFM-GN generates high-fidelity samples with significantly fewer sampling steps than diffusion-based alternatives. Most notably, our model maintains competitive performance even with a single sampling step, a regime where diffusion models completely fail. Our generated samples accurately reproduce the statistical characteristics of target flows, successfully capturing multi-modal pressure distributions across various flow conditions, while achieving significant computational speedups compared to diffusion-based methods. PIFM-GN thus enables efficient generation of fluid states for downstream analysis and design tasks in scientific and engineering applications. The code is available at https://anonymous.4open.science/r/pifm-gn-F75F/.

Physically-Informed Flow Matching with Graph Neural Networks for Complex Fluid Dynamics

Since next-scale prediction was introduced as a new paradigm for autoregressive image generation, it has attracted extensive research interest. By progressively increasing resolution in a draft-to-refinement process, next-scale prediction demonstrates great potential in both generation quality and efficiency.However, at high resolutions, this paradigm faces a fundamental challenge: token sequences grow quadratically and accumulate across multiple scales, resulting in a key performance bottleneck. Our systematic study uncovers two critical observations: (1) most image regions have stabilized during early drafting stages, making later refinement across the full-scale image token-inefficient; (2) different scales inherently trade off efficiency and fidelity, suggesting that adaptive token dispatch on different scales can focus resources where they yield the greatest quality gains. Motivated by these insights, we propose a training-free \textbf{M}ixture \textbf{o}f \textbf{S}cale\textbf{s} (\textbf{MoSs}) method for efficient high-resolution autoregressive image generation. MoSs breaks the strict causal dependency across scales in the final refinement steps by parallelizing scales of different resolutions, each responsible for a subset of spatial regions. A lightweight frequency-based token dispatcher, analyzes the drafted image and assigns regions to the appropriate scale. The outputs are then composited over the draft to produce the final high-resolution image. The scale-mixture method exhibits remarkable efficiency with little impact on generation quality on various models. For instance, our implementaion achieves \textbf{2.05-4.96$\times$ speedup} on transformer backbone, up to \textbf{85.62\% KV cache reduction}, incurring only \textbf{0.1-2.4\%} loss on GenEval\citep{ghosh2023geneval} quality, based on state-of-the-art Infinity\citep{han2024infinity} model.

MoSs: Mixture of Scales for Efficient High-Resolution Autoregressive Image Generation

Vision-Language Continual Learning (VLCL) has attracted significant research attention for its robust capabilities, and the adoption of Parameter-Efficient Fine-Tuning (PEFT) strategies is enabling these models to achieve competitive performance with substantially reduced resource consumption. However, dominated First-Order (FO) optimization is prone to trap models in suboptimal local minima, especially in limited exploration subspace within PEFT. To overcome this challenge, this paper pioneers a systematic exploration of adopting Zeroth-Order (ZO) optimization for PEFT-based VLCL. We first identify the incompatibility of naive full-ZO adoption in VLCL due to optimization process instability. We then investigate the application of ZO optimization from a modality branch-wise to a fine-grained layer-wise across various training units to identify an optimal strategy. Besides, a key theoretical insight reveals that vision modality exhibit higher variance than language counterparts in VLCL during the ZO optimization process, and we propose a modality-aware stabilized ZO strategy, which adopts gradient sign normalization in ZO and constrains vision modality perturbation to further improve performance. Benefiting from the adoption of ZO optimization, PEFT-based VLCL fulfills better ability to escape local minima during the optimization process, extensive experiments on four benchmarks demonstrate that our method achieves state-of-the-art results. Code will be available upon publication.

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