United States

Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. These models encapsulate and facilitate the modification of detailed parameters in construction processes, which is essential for creating precise 3D shapes. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain and not cost-effective. Recent advances in Multimodal Large Language Models (MLLMs) have inspired researchers to use natural language instructions and images for CAD model construction. However, these models still struggle with inferring 3D spatial location and orientation, leading to inaccuracies in determining the spatial 3D starting points and extrusion directions for constructing geometries. This work introduces CAD-GPT, a CAD synthesis method with spatial reasoning-enhanced MLLM. In this method, we propose a 3D Modeling Spatial Mechanism for accurately inferring three types of spatial information: 1) 3D global location, thereby ensuring the correct spatial position of each 3D shape; 2) 3D sketch plane angles, which enables accurate orientation of the 2D sketch plane each time it is constructed; 3) 2D sketch location translation, through which the precision of the size and shape of the 2D sketches is guaranteed. Extensive experiments demonstrate that CAD-GPT consistently outperforms existing state-of-the-art methods in CAD model synthesis, both quantitatively and qualitatively.

AAAI 2025

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs

snlp

multi-modal nlp

language grounding

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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ACL 2025

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

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.



While Neural Radiance Fields (NeRFs) have advanced the frontiers of novel view synthesis (NVS) using LiDAR data, they still struggle in dynamic scenes. Due to the low frequency and sparsity characteristics of LiDAR point clouds, it is challenging to spontaneously learn a dynamic and consistent scene representation from posed scans. In this paper, we propose STGC-NeRF, a novel LiDAR NeRF method that combines spatial-temporal geometry consistency to enhance the reconstruction of dynamic scenes. First, we propose a temporal geometry consistency regularization to enhance the regression of time-varying scene geometries from low-frequency LiDAR sequences. By estimating the pointwise correspondences between synthetic (or real) and real frames at different times, we convert them into various forms of temporal supervision. This alleviates the inconsistency caused by moving objects in dynamic scenes. Second, to improve the reconstruction of sparse LiDAR data, we propose spatial geometric consistency constraints. By computing multiple neighborhood feature descriptors incorporating geometric and contextual information, we capture structural geometry information from sparse LiDAR data. This helps encourage consistent direction, smoothness, and detail of the local surface. Extensive experiments on the KITTI-360 and nuScenes datasets demonstrate that STGC-NeRF outperforms state-of-the-art methods in both geometry and intensity accuracy for dynamic LiDAR scene reconstruction.

STGC-NeRF: Spatial-Temporal Geometric Consistency for LiDAR Neural Radiance Fields in Dynamic Scenes

As AI-based decision-makers increasingly influence decisions that affect humans, it is crucial to ensure their decisions are fair and unbiased. Most algorithms for fair decision-making provide probabilistic guarantees of fairness over the long run, not providing any guarantees at specific intervals, such as yearly or quarterly. In this paper, we introduce a novel neurosymbolic approach to guarantee fairness in every finite run through the use of a symbolic runtime enforcer called a *fairness shield*. The fairness shield monitors and minimally intervenes in the decision-maker’s decisions to ensure that fairness criteria are met either within a bounded horizon or periodically, while also minimizing the costs associated with such interventions as specified by a given cost function. Given a distribution over future decisions and their costs, we present algorithms to compute fairness shields by solving a bounded-horizon optimal control problem. We present synthesis algorithms for four types of fairness shields, each tailored to different operational settings. Our empirical evaluation demonstrates the effectiveness of these shields in ensuring fairness while maintaining cost efficiency across various scenarios.

Fairness Shields: Safeguarding against Biased Decision Makers

Despite the widespread use of LLMs due to their superior performance in various tasks, their high computational costs often lead potential users to opt for the pretraining-finetuning pipeline. However, biases prevalent in manually constructed datasets can introduce spurious correlations between tokens and labels, creating the so-called *shortcuts* and hindering the generalizability of fine-tuned models. Existing debiasing methods often rely on prior knowledge of specific dataset biases, which is challenging to acquire a priori. We propose RAZOR (Rewriting And Zero-bias Optimization Refinement), a novel, unsupervised, and data-focused debiasing approach based on text rewriting for shortcut mitigation. RAZOR leverages LLMs to iteratively rewrite potentially biased text segments by replacing them with heuristically selected alternatives in a shortcut space defined by token statistics and positional information. This process aims to align surface-level text features more closely with diverse label distributions, thereby promoting the learning of genuine linguistic patterns. Compared with unsupervised SoTA models, RAZOR improves by $3.5$% on the FEVER and $6.5$% on MNLI and SNLI datasets according to the F1 score. Additionally, RAZOR effectively mitigates specific known biases, reducing bias-related terms by $\times 2$ without requiring prior bias information, a result that is on par with SoTA models that leverage prior information. Our work prioritizes data manipulation over architectural modifications, emphasizing the pivotal role of data quality in enhancing model performance and fairness. This research contributes to developing more robust evaluation benchmarks for debiasing methods by incorporating metrics for bias reduction and overall model efficacy.

RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting

Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly and involves extensive computations or physical experiments. Current Pareto set learning methods for such problems often rely on surrogate models like Gaussian processes to approximate the objective functions. These surrogate models can become fragmented, resulting in numerous small uncertain regions between explored solutions. When using acquisition functions such as the Lower Confidence Bound (LCB), these uncertain regions can turn into pseudo-local optima, complicating the search for globally optimal solutions. To address these challenges, we propose a novel approach called SVH-PSL, which integrates Stein Variational Gradient Descent (SVGD) with Hypernetworks for efficient Pareto set learning. Our method addresses the issues of fragmented surrogate models and pseudo-local optima by collectively moving particles in a manner that smooths out the solution space. The particles interact with each other through a kernel function, which helps maintain diversity and encourages the exploration of underexplored regions. This kernel-based interaction prevents particles from clustering around pseudo-local optima and promotes convergence towards globally optimal solutions. Our approach aims to establish robust relationships between trade-off reference vectors and their corresponding true Pareto solutions, overcoming the limitations of existing methods. Through extensive experiments across both synthetic and real-world MOO benchmarks, we demonstrate that SVH-PSL significantly improves the quality of the learned Pareto set, offering a promising solution for expensive multi-objective optimization problems.

Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks

Video Action Detection (VAD) involves localizing and categorizing action instances in videos. Videos inherently contain various information sources, including audio, visual cues, and surrounding scene contexts. Effectively leveraging this multi-modal information for VAD is challenging, as the model must accurately focus on action-relevant cues. In this study, we introduce a novel multi-modal VAD architecture called the Joint Actor-centric Visual, Audio, Language Encoder (JoVALE). JoVALE is the first VAD method to integrate audio and visual features with scene descriptive context derived from large image captioning models. The core principle of JoVALE is the actor-centric aggregation of audio, visual, and scene descriptive contexts, where action-related cues from each modality are identified and adaptively combined. We propose a specialized module called the Actor-centric Multi-modal Fusion Network, designed to capture the joint interactions among actors and multi-modal contexts through Transformer architecture. Our evaluation conducted on three popular VAD benchmarks, AVA, UCF101-24, and JHMDB51-21, demonstrates that incorporating multi-modal information leads to significant performance gains. JoVALE achieves state-of-the-art performances.

JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts

Iterative self-improvement, a concept extending beyond personal growth, has found powerful applications in machine learning, particularly in transforming weak models into strong ones. While recent advances in natural language processing have shown its efficacy through iterative preference optimization, applying this approach to Video Large Multimodal Models (VLMMs) remains challenging due to modality misalignment. VLMMs struggle with this misalignment during iterative preference modeling, as the self-judge model often prioritizes linguistic knowledge over visual information.
Additionally, iterative preference optimization can lead to visually hallucinated verbose responses due to length bias within the self-rewarding cycle. To address these issues, we propose Iterative Self-Retrospective Direct Preference Optimization (ISR-DPO), a method that uses self-retrospection to enhance preference modeling. This approach enhances the self-judge’s focus on informative video regions, resulting in more visually grounded preferences. In extensive empirical evaluations across diverse video question answering benchmarks, the ISR-DPO significantly outperforms the state of the art. We are committed to open-sourcing our code, models, and datasets to encourage further investigation.

ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO

Given the ubiquity of multi-task in practical systems, Multi-Task Learning (MTL) has found widespread application across diverse domains. In real-world scenarios, these tasks often have different priorities. For instance, In web search, relevance is often prioritized over other metrics, such as click-through rates or user engagement. Existing frameworks pay insufficient attention to the prioritization among different tasks, which typically adjust task-specific loss function weights to differentiate task priorities. However, this approach encounters challenges as the number of tasks grows, leading to exponential increases in hyperparameter tuning complexity. Furthermore, the simultaneous optimization of multiple objectives can negatively impact the performance of high-priority tasks due to interference from lower-priority tasks.

In this paper, we introduce a novel multi-task learning framework employing Lagrangian Differential Multiplier Methods for step-wise multi-task optimization. It is designed to boost the performance of high-priority task without interference from other tasks. Its primary advantage lies in its ability to automatically optimize multiple objectives without requiring balancing hyper-parameters for different tasks, thereby eliminating the need for manual tuning. Additionally, we provide a theoretical proof demonstrating that our method ensures optimization guarantees, enhancing the reliability of the process. We demonstrate its effectiveness through experiments on multiple public datasets and its application in a large-scale industrial search ranking system, resulting in significant improvements across various business metrics.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods

As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains insufficient, leading to potential issues in gaining the trust of actual users.
Here, we propose a novel module that constructs a fair latent space, enabling faithful explanation while ensuring fairness. The fair latent space is constructed by disentangling and redistributing labels and sensitive attributes, allowing the generation of counterfactual explanations for each type of information. Our module is attached to a pretrained generative model, transforming its biased latent space into a fair latent space. Additionally, since only the module needs to be trained, there are advantages in terms of time and cost savings, without the need to train the entire generative model. We validate the fair latent space with various fairness metrics and demonstrate that our approach can effectively provide explanations for biased decisions and assurances of fairness.

Constructing Fair Latent Space for Intersection of Fairness and Explainability

In the Noisy Intermediate-Scale Quantum (NISQ) era, using variational quantum algorithms (VQAs) to solve optimization problems has become a key application. However, these algorithms face significant challenges, such as choosing an effective initial set of parameters and the limited quantum processing time that restricts the number of optimization iterations.
In this study, we introduce a new framework for optimizing parameterized quantum circuits (PQCs) that employs a classical optimizer, inspired by Model-Agnostic Meta-Learning (MAML) technique. This approach aim to achieve better parameter initialization that ensures fast convergence.
Our framework features a classical neural network, called ${Learner}$, which interacts with a PQC using the output of ${Learner}$ as an initial parameter. During the pre-training phase, ${Learner}$ is trained with the meta objective function. In the adaptation phase, the framework requires only a few PQC updates to converge to a more accurate value, while the learner remains unchanged. 
This method is highly adaptable and is effectively extended to various Hamiltonian optimization problems. We validate our approach through experiments, including distribution function mapping and optimization of the Heisenberg XYZ Hamiltonian. 
The result implies that the ${Learner}$ successfully estimates initial parameters that generalize across the problem space, enabling fast adaptation.

Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms

Post-processing networks (PPNs) are components that modify the outputs of arbitrary modules in task-oriented dialogue systems and are optimized using reinforcement learning (RL) to improve the overall task completion capability of the system. However, previous PPN-based approaches have been limited to handling only a subset of modules within a system, posing a significant limitation on improving system performance. In this paper, we propose a novel joint optimization method for post-processing the outputs of all modules using universal post-processing networks (UniPPNs), which are language model-based networks capable of modifying the outputs of arbitrary modules in the system as a sequence transformation task. Moreover, our RL algorithm, which employs a module-level Markov decision process, enables fine-grained value and advantage estimation for each module, stabilizing the joint learning for post-processing the outputs of all modules. Through both simulation-based and human evaluation experiments using the MultiWOZ dataset, we demonstrate that UniPPN outperforms conventional PPNs in the task completion capability of task-oriented dialogue systems.

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