United States

Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as ``I don&#39;t know&quot;, RAIT enhances the reliability of LLMs and reduces their hallucination.
Generally, RAIT modifies training samples base on the correctness of initial LLM&#39;s response. However, this crude approach can cause LLMs to excessively refuse answering questions they could have correctly addressed, a problem we call over-refusal.
In this paper, we explore two primary causes of over-refusal:
Static conflict emerges when RAIT data is constructed solely on correctness criteria, causing similar samples in the LLM&#39;s feature space to be assigned different labels (original vs. modified &quot;I don&#39;t know&quot;).
Dynamic conflict occurs due to changes in LLM&#39;s knowledge state during fine-tuning, which transforms previous unknown questions into knowns, while the training data settled by initial LLM remains unchanged, resulting in conflicts.
These conflicts cause the trained LLM to misclassify known questions as unknown, resulting in over-refusal.
To address this issue, we introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Construction (CRaFT). CRaFT centers on two main contributions: First, we additionally incorporate response certainty to selectively filter and modify data, reducing static conflicts. Second, we implement preliminary rehearsal training to characterize changes in the LLM&#39;s knowledge state, which helps mitigate dynamic conflicts during the fine-tuning process. 
We conducted extensive experiments on open-ended question answering and multiple-choice question task. Experiment results show that CRaFT can improve LLM&#39;s reliability during the RAIT process. Source code and training data will be released at Github.

AAAI 2025

Utilize the Flow Before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning

snlp

language models

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.

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AAAI 2026

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.

Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as ``I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination.
Generally, RAIT modifies training samples base on the correctness of initial LLM's response. However, this crude approach can cause LLMs to excessively refuse answering questions they could have correctly addressed, a problem we call over-refusal.
In this paper, we explore two primary causes of over-refusal:
Static conflict emerges when RAIT data is constructed solely on correctness criteria, causing similar samples in the LLM's feature space to be assigned different labels (original vs. modified "I don't know").
Dynamic conflict occurs due to changes in LLM's knowledge state during fine-tuning, which transforms previous unknown questions into knowns, while the training data settled by initial LLM remains unchanged, resulting in conflicts.
These conflicts cause the trained LLM to misclassify known questions as unknown, resulting in over-refusal.
To address this issue, we introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Construction (CRaFT). CRaFT centers on two main contributions: First, we additionally incorporate response certainty to selectively filter and modify data, reducing static conflicts. Second, we implement preliminary rehearsal training to characterize changes in the LLM's knowledge state, which helps mitigate dynamic conflicts during the fine-tuning process. 
We conducted extensive experiments on open-ended question answering and multiple-choice question task. Experiment results show that CRaFT can improve LLM's reliability during the RAIT process. Source code and training data will be released at Github.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

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-25 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.



Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. Recently, great progress has been made on this problem. However, due to significant occlusions between hands and object as well as the high degree-of-freedom motions, it is still challenging to collect and annotate a high-quality, large-scale dataset, which prevents further improvement of bimanual hand-object interaction-related tasks. In this work, we propose a new 3D Gaussian Splatting (3DGS)-based data augmentation framework for bimanual hand-object interaction, which is capable of augmenting existing dataset to large-scale photorealistic data with various hand-object pose and viewpoints. First, we use mesh-based 3DGS to model objects and hands, and to deal with the rendering blur problem due to multi-resolution input images used, we design a super-resolution module. Second, we extend the single hand grasping pose optimization module for the bimanual hand object to generate various poses of bimanual hand-object interaction, which can significantly expand the pose distribution of the dataset. Third, we conduct an analysis for the impact of different aspects of the proposed data augmentation on the understanding of the bimanual hand-object interaction. We perform our data augmentation on two benchmarks, H2O and Arctic, and verify that our method can improve the performance of the baselines. We will release our code and dataset upon acceptance.

HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation

Simulating fuel sloshing within aircraft tanks during flight is crucial for aircraft safety research. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for simulating fuel sloshing in aircraft. This model is also the first deep learning model capable of stably modeling fluid particle dynamics in such complex scenarios. Our triangle feature fusion design achieves an optimal balance among fluid dynamics modeling, momentum conservation constraints, and global stability control. Additionally, we constructed the Fueltank dataset, the first dataset for aircraft fuel surface sloshing. It comprises 320,000 frames across four typical tank types and covers a wide range of flight maneuvers, including multi-directional rotations. We conducted comprehensive experiments on both our dataset and the take-off scenario of the aircraft. Compared to existing neural network-based fluid simulation algorithms, we significantly enhanced accuracy  while maintaining high computational speed. Compared to traditional SPH methods, our speed improved approximately 10 times. Furthermore, compared to traditional fluid simulation software such as Flow3D, our computation speed increased by more than 300 times.

A Pioneering Neural Network Method for Efficient and Robust Fuel Sloshing Simulation in Aircraft

Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic samples containing forgetting information but struggled to efficiently distill the retaining-related knowledge. In this work, we analyze that such a problem is due to over-filtering, which leads to a reduction in the synthesized retaining-related information. We propose a novel method, Inhibited Synthetic PostFilter (ISPF), to tackle this challenge from two perspectives: 1) Inhibited Synthetic, by reducing the synthesized forgetting information, and 2) PostFilter, by fully utilizing the retaining-related information in synthesized samples. Experimental results demonstrate that the proposed ISPF effectively tackles the challenge and outperforms existing methods.

Toward Efficient Data-Free Unlearning

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states consistent across different inputs in a nearby domain, reducing the number of unstable neurons and tightening the bounds of neurons thereby enhancing neural network verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets across various network architectures. The results of the experiment demonstrate that networks trained using our method are verification-friendly across different radii and different model architectures, whereas other tools fail to maintain verifiability as the radius increases. We also show that our method can be combined with existing methods to further improve the verifiability of networks.

Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

Audio-visual semantic segmentation (AVSS) has garnered significant interest in the multi-modal domain, which aims to segment the video objects producing specific sound in the corresponding audio. Despite notable progress, existing methods struggle with new classes not present in the original training set. Towards this issue, we introduce the few shot incremental learning (FSIL) to the AVSS task, whose goal is seamlessly integrating new classes with limited incremental samples while preserving existing knowledge of old classes. Two challenges emerge for this new setting: (1) To reduce labeling expenses, old classes within the incremental samples are treated akin to background, same as silent objects. Training the model directly with background annotations may exacerbate the loss of distinctive knowledge of old classes, such as outlines and sounds. (2) Most existing models adopt the early cross-modal fusion with the single-tower design. It involves more class characteristics to class representations, impeding the knowledge transfer between classes based on similarity. To address above issues, we propose a Few-shot Incremental learning framework via class-centric foregrouNd aggreGation and dual-tower knowlEdge tRansfer (FINGER) for the AVSS task, which comprises two targeted modules: (1) The class-centric foreground aggregation assembles class-specific features for each foreground class and disregards background features. The background class is thus excluded during training and inferred based on the foreground predictions. (2) The dual-tower knowledge transfer delays the cross-modal fusion to separately conduct knowledge transfer for each modality. Extensive experiments prove the effectiveness of the FINGER model by significantly surpassing the state-of-the-arts. The code is available (https://anonymous.4open.science/r/FINGER).

Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic Segmentation

Event cameras have recently been introduced into image semantic segmentation, owing to their high temporal resolution and other advantageous properties. However, existing event-based semantic segmentation methods often fail to fully exploit the complementary information provided by frames and events, resulting in complex training strategies and increased computational costs. To address these challenges, we propose an efficient hybrid framework for image semantic segmentation, comprising a Spiking Neural Network branch for events and an Artificial Neural Network branch for frames. Specifically, we introduce three specialized modules to facilitate the interaction between these two branches: the Adaptive Temporal Weighting (ATW) Injector, the Event-Driven Sparse (EDS) Injector, and the Channel Selection Fusion (CSF) module. The ATW Injector dynamically integrates temporal features from event data into frame features, enhancing segmentation accuracy by leveraging critical dynamic temporal information. The EDS Injector effectively combines sparse event data with rich frame features, ensuring precise alignment of temporal and spatial information. The CSF module selectively merges these features to optimize segmentation performance. Experimental results demonstrate that our framework not only achieves state-of-the-art accuracy across the DDD17-Seg, DSEC-Semantic, and M3ED-Semantic datasets but also significantly reduces energy consumption, achieving a 63\% reduction on the DSEC-Semantic dataset. The code and dataset will be made publicly available.

Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach

In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre-training framework, to integrate both the spatial information and the sequential temporal dynamics into the feature embeddings. Different from the existing fine-tuning approaches that capture temporal information by exploring the relationships among all the frames, our perceiver-based adapter recurrently captures the sequential dynamics alongside the timeline, which could perceive the order change. To obtain the discriminative representations for each class, we extend a textual corpus for each class derived from the large language models (LLMs) and enrich the visual prototypes by integrating the contextual semantic information. Besides, We introduce an unbalanced optimal transport strategy for feature matching that mitigates the impact of class-unrelated features, thereby facilitating more effective decision-making. Experimental results on five FSAR datasets demonstrate that our method set a new benchmark, beating the second-best competitors with large margins.

Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition

Large Language Models (LLMs) have demonstrated significant capabilities, particularly in the domain of question answering (QA). However, their effectiveness in QA is often undermined by the vagueness of user questions. %Given the inability to train black-box LLMs like GPT-4 to interpret ambiguous questions, it is essential to rewrite questions into a format that these LLMs can more easily understand. 
To address this issue, we introduce single-round instance-level prompt optimization, referred to as question rewriter.  By enhancing the intelligibility of human questions for black-box LLMs, our question rewriter improves the quality of generated answers. The rewriter is optimized using direct preference optimization based on feedback collected from automatic criteria for evaluating generated answers; therefore, its training does not require costly human annotations. The experiments across multiple black-box LLMs and long-form question answering (LFQA) datasets demonstrate the efficacy of our method. This paper provides a practical framework for training question rewriters and sets a precedent for future explorations in prompt optimization within LFQA tasks.

Putting People in LLMs’ Shoes: Generating Better Answers via Question Rewriter

Foreground segmentation is a fundamental task in computer vision, including various subdivision tasks. Previous research has typically designed task-specific architectures for foreground segmentation tasks, leading to a lack of unified frameworks. Moreover, they primarily focus on recognizing foreground objects without effectively distinguishing the foreground from the background. In this paper, we argue that the background and its relationship with the foreground matter in the foreground segmentation tasks. We introduce FOCUS, a Foreground ObjeCts Universal Segmentation framework that can handle multiple foreground tasks. We develop a multi-scale semantic network using the edge information of objects to enhance image features. To achieve boundary-aware segmentation, we propose a novel distillation method, integrating the contrastive learning strategy to refine the prediction mask in multi-modal feature space. We conduct extensive experiments on a total of 13 datasets across 5 tasks, and results show that our proposed FOCUS outperforms the state-of-the-art task-specific models on most metrics.

FOCUS: Towards Universal Foreground Segmentation

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by $>7$x ($>14$m vs $<2$m). Analysis shows the robustness of ConDo against computation budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, Condo achieves similar performance up to 25x faster. Code will be released upon acceptance.

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