United States

Large Language Models (LLMs) have gained significant attention for their exceptional performance across various domains. Despite their advancements, concerns persist regarding their implicit bias, which often leads to negative social impacts. Therefore, it is essential to identify the implicit bias in LLMs and investigate the potential threat posed by it. Our study focused on a specific type of implicit bias, termed the &#39;&#39;Yes-No&#39;&#39; implicit bias, which refers to LLMs&#39; inherent tendency to favor &#39;&#39;Yes&#39;&#39; or &#39;&#39;No&#39;&#39; responses to a single instruction. By comparing the probability of LLMs generating a series of &#39;&#39;Yes&#39;&#39; versus &#39;&#39;No&#39;&#39; responses, we observed different inherent response tendencies exhibited by LLMs when faced with different instructions. To further investigate the impact of such bias, we developed an attack method called Implicit Bias In-Context Manipulation, attempting to manipulate LLMs&#39; behavior. Specifically, we explored whether the &#39;&#39;Yes&#39;&#39; implicit bias could manipulate &#39;&#39;No&#39;&#39; responses into &#39;&#39;Yes&#39;&#39; in LLMs&#39; responses to malicious instructions, leading to harmful outputs. Our findings revealed that the &#39;&#39;Yes&#39;&#39; implicit bias brings a significant security threat, comparable to that of carefully designed attack methods. Moreover, we offered a comprehensive analysis from multiple perspectives to deepen the understanding of this security threat, emphasizing the need for ongoing improvement in LLMs&#39; security.

AAAI 2025

Investigating the Security Threat Arising from “Yes-No” Implicit Bias in Large Language Models

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Large Language Models (LLMs) have gained significant attention for their exceptional performance across various domains. Despite their advancements, concerns persist regarding their implicit bias, which often leads to negative social impacts. Therefore, it is essential to identify the implicit bias in LLMs and investigate the potential threat posed by it. Our study focused on a specific type of implicit bias, termed the ''Yes-No'' implicit bias, which refers to LLMs' inherent tendency to favor ''Yes'' or ''No'' responses to a single instruction. By comparing the probability of LLMs generating a series of ''Yes'' versus ''No'' responses, we observed different inherent response tendencies exhibited by LLMs when faced with different instructions. To further investigate the impact of such bias, we developed an attack method called Implicit Bias In-Context Manipulation, attempting to manipulate LLMs' behavior. Specifically, we explored whether the ''Yes'' implicit bias could manipulate ''No'' responses into ''Yes'' in LLMs' responses to malicious instructions, leading to harmful outputs. Our findings revealed that the ''Yes'' implicit bias brings a significant security threat, comparable to that of carefully designed attack methods. Moreover, we offered a comprehensive analysis from multiple perspectives to deepen the understanding of this security threat, emphasizing the need for ongoing improvement in LLMs' security.

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/)<br><br><br>

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

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.



Spiking neural networks (SNNs), inspired by the inherent spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing.
In this paper, we propose noise-injected spiking graph convolutional networks to leverage the full regression potential of SNNs in 3D point cloud denoising. 
Specifically, we first emulate the noise-injected neuronal dynamics to build noise-injected spiking neurons.
On this basis, we design noise-injected spiking graph convolution for promoting disturbance-aware spiking representation learning on 3D points.
Starting from the spiking graph convolution, we build two SNN-based denoising networks.
One is a purely spiking graph convolutional network, which achieves low accuracy loss compared with some ANN-based alternatives, while resulting in significantly reduced energy consumption on two benchmark datasets, PU-Net and PC-Net.
The other is a hybrid architecture, which integrates some ANN-based learning operations and exhibits a high performance-efficiency trade-off with only a few time steps.
Our work lights up SNN’s potential for 3D point cloud denoising, 
injecting new perspectives of exploring the deployment on neuromorphic chips while paving the way for developing energy-efficient 3D data acquisition devices.
Our source code has been submitted and will be made publicly available.

Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising

Semi-supervised learning (SSL) is a fundamental task in machine learning, empowering models to extract valuable insights from datasets with limited labeled samples and a large amount of unlabeled data. Although pseudo-labeling is a widely used approach for SSL that generates pseudo-labels for unlabeled data and leverages them as ground truth labels for training, traditional pseudo-labeling techniques often face challenges that significantly decrease the quality of pseudo-labels and hence the overall model performance. In this paper, we propose a novel Bi-level Optimization method for Pseudo-label Learning (BOPL) to boost semi-supervised training. It treats pseudo-labels as latent variables, and optimizes the model parameters and pseudo-labels jointly within a bi-level optimization framework. By enabling direct optimization over the pseudo-labels towards maximizing the prediction model performance, the method is expected to produce high-quality pseudo-labels. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on multiple SSL benchmarks. The experimental results show the proposed BOPL outperforms the state-of-the-art SSL techniques.

Bi-Level Optimization for Semi-Supervised Learning with Pseudo-Labeling

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from most of the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure that do not compromise the accuracy of their judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, a Multi-View Token Selection Module (MVTSM) is designed to prune redundant feature tokens and realize dynamic selection of key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images demonstrate that our method performs favorably against state-of-the-art models. The public code can be available in the attachment.

Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines.

Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understanding benchmarks are relatively short, which makes them inadequate for effectively evaluating the long-sequence modeling capabilities of MLLMs. This highlights the urgent need for a comprehensive and integrated long video understanding benchmark to assess the ability of MLLMs thoroughly. To this end, we propose ALLVB (\textbf{ALL}-in-one long \textbf{V}ideo understanding \textbf{B}enchmark). ALLVB's main contributions include: 1) It integrates 9 major video understanding tasks. These tasks are converted into video Q\&A formats, allowing a single benchmark to evaluate 9 different video understanding capabilities of MLLMs, highlighting the versatility, comprehensiveness, and challenging nature of ALLVB. 2) A fully automated annotation pipeline using GPT-4o is designed, requiring only human quality control, which facilitates the maintenance and expansion of the benchmark. 3) It contains 1,376 videos across 16 categories, averaging nearly 2 hours each, with a total of 252k Q\&As. To the best of our knowledge, it is the largest long video understanding benchmark in terms of the number of videos, average duration, and number of Q\&As. We have tested various mainstream MLLMs on ALLVB, and the results indicate that even the most advanced commercial models have significant room for improvement. This reflects the benchmark's challenging nature and demonstrates the substantial potential for development in long video understanding.

ALLVB: All-in-One Long Video Understanding Benchmark

In recent years, the distributed training of foundation models (FMs) has seen a surge in popularity. In particular, federated learning enables collaborative model training among edge clients while safeguarding the privacy of their data. However, federated training of FMs across resource-constrained and highly heterogeneous edge devices encounters several challenges. These include the difficulty of deploying FMs on clients with limited computational resources and the high computation and communication costs associated with fine-tuning and collaborative training. To address these challenges, we propose FedCAMS, a Cluster-Aware Framework with Knowledge-Aware Model Search. Specifically, FedCAMS incorporates a multi-factor heterogeneity-aware clustering algorithm to group clients into clusters based on data distribution and resource limitations, and selects an appropriate model as the cluster model within each cluster. In consideration of the computational limitations of different client devices, we design knowledge-aware model architecture search (KAMAS), which allows each client to identify the optimal sub-model from the cluster model without any training. After local training, a partial aggregation method is employed for intra-cluster aggregation. Finally, cluster-Aware knowledge transfer facilitates knowledge sharing between clusters and the server, addressing model heterogeneity and reducing communication overhead. Extensive experiments demonstrate that FedCAMS outperforms state-of-the-art baselines by 3-10\% in accuracy.

Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning

Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking

Partially linear models (PLMs) have attracted much attention in the field of statistical machine learning. Specially, the ability of variable selection of PLMs has been studied extensively due to the high requirement of model interpretability. However, few of the existing works concerns the false discovery rate (FDR) controllability of variable selection associated with PLMs. To address this issue, we formuate a new Knockoffs Inference scheme for Linear And Nonlinear Discoverer (called KI-LAND), where FDR is controlled with respect to both linear and nonlinear variables for automatic structure discovery. For the proposed KI-LAND, theoretical guarantees are established for both FDR controllability and power, and experimental evaluations are provided to validate its effectiveness on simulated exmaples and real data.

Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery

Hypergraph neural networks (HGNNs) have shown promise in handling tasks characterized by high-order correlations, achieving notable success across various applications. However, there has been limited focus on heterophilous hypergraph learning (HHL), contrasting with the increasing attention given to graph neural networks designed for graphs exhibiting heterophily. This paper aims to pave the way for HHL by addressing key gaps from multiple perspectives: measurement, dataset diversity, and baseline model development. Firstly, we introduce metrics to quantify heterophily in hypergraphs, providing a numerical basis for assessing the homophily/heterophily ratio. Secondly, we develop diverse benchmark datasets across various real-world scenarios, facilitating comprehensive evaluations of existing HGNNs and advancing research in HHL. Additionally, as a novel baseline model, we propose HyperUFG, a framelet-based hypergraph neural network integrating both low-pass and high-pass filters. Extensive experiments conducted on synthetic and benchmark datasets highlight the challenges current HGNNs face with heterophilous hypergraphs, while showcasing that HyperUFG performs competitively and often outperforms many existing models in such scenarios. Overall, our study underscore the urgent need for further exploration and development in this emerging field, with the potential to inspire and guide future research in HHL.

When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline

3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental in various applications, including video editing, in-camera processing, photographic filters, computer graphics, and color processing for displays.  While an individual LUT does not incur a high {memory} overhead, software and devices may need to store dozens to hundreds of LUTs that can take over 100 MB.  This work aims to develop a neural network architecture that can encode hundreds of LUTs in a single compact representation. To this end, we propose a model with a {memory} footprint of less than 0.25 MB that can reconstruct 512 LUTs with only minor color distortion ($\bar{\Delta}E_M$ $\leq$ 2.0) over the entire color gamut.  We also show that our network can weight colors to provide further quality gains on natural image colors ($\bar{\Delta}{E}_M$ $\leq$ {1.0}). Finally, we show that minor modifications to the network architecture enable a bijective encoding that produces LUTs that are invertible, allowing for reverse color processing.

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