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Cognitive Diagnosis Models (CDMs) are designed to assess students&#39; cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often struggle with diagnosing infrequent students and exercises due to a lack of rich prior knowledge. 
    With the advancement in large language models (LLMs), which possess extensive domain knowledge, their integration into cognitive diagnosis presents a promising opportunity.
Despite this potential, integrating LLMs with CDMs poses significant challenges. LLMs are not well-suited for capturing the fine-grained collaborative interactions between students and exercises, and the disparity between the semantic space of LLMs and the behavioral space of CDMs hinders effective integration.
To address these issues, we propose a novel Knowledge-enhanced Cognitive Diagnosis (KCD) framework, which is a model-agnostic framework utilizing LLMs to enhance CDMs and compatible with various CDM architectures. The KCD framework operates in two stages: LLM Diagnosis and Cognitive Level Alignment. In the LLM Diagnosis stage, both students and exercises are diagnosed to achieve comprehensive and detailed modeling. In the Cognitive Level Alignment stage, we bridge the gap between the CDMs&#39; behavioral space and the LLMs&#39; semantic space using contrastive learning and mask-reconstruction approaches.
    Experiments on several real-world datasets demonstrate the effectiveness of our proposed framework.
    Our code and datasets are available at https://anonymous.4open.science/r/KCD-67DE/.

AAAI 2025

Knowledge Is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis

Cognitive Diagnosis Models (CDMs) are designed to assess students' cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often struggle with diagnosing infrequent students and exercises due to a lack of rich prior knowledge. 
    With the advancement in large language models (LLMs), which possess extensive domain knowledge, their integration into cognitive diagnosis presents a promising opportunity.
Despite this potential, integrating LLMs with CDMs poses significant challenges. LLMs are not well-suited for capturing the fine-grained collaborative interactions between students and exercises, and the disparity between the semantic space of LLMs and the behavioral space of CDMs hinders effective integration.
To address these issues, we propose a novel Knowledge-enhanced Cognitive Diagnosis (KCD) framework, which is a model-agnostic framework utilizing LLMs to enhance CDMs and compatible with various CDM architectures. The KCD framework operates in two stages: LLM Diagnosis and Cognitive Level Alignment. In the LLM Diagnosis stage, both students and exercises are diagnosed to achieve comprehensive and detailed modeling. In the Cognitive Level Alignment stage, we bridge the gap between the CDMs' behavioral space and the LLMs' semantic space using contrastive learning and mask-reconstruction approaches.
    Experiments on several real-world datasets demonstrate the effectiveness of our proposed framework.
    Our code and datasets are available at https://anonymous.4open.science/r/KCD-67DE/.

technical paper

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.



Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. 
  While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DIR.
  Moreover, such regularizers (e.g., contrastive penalties) merely focus on learning discriminative features of data, which inevitably results in ignorance of either continuity or similarity across the data.
  To address these issues, we first bridge the connection between the objectives of DIR and classification from a Bayesian perspective. 
  Consequently, this motivates us to decompose the objective of DIR into a combination of classification and regression tasks, which naturally guides us toward a divide-and-conquer manner to solve the DIR problem.
  Specifically, by aggregating the data at nearby labels into the same groups, we introduce an ordinal group-aware contrastive learning loss along with a multi-experts regressor to tackle the different groups of data thereby maintaining the data continuity.
  Meanwhile, considering the similarity between the groups, we also propose a symmetric descending soft labeling strategy to exploit the intrinsic similarity across the data, which allows classification to facilitate regression more effectively.
  Extensive experiments on real-world datasets also validate the effectiveness of our method.

Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

Majority illusion is a phenomenon in social networks wherein the decision by the majority of the network is not the same as one's personal social circle's majority, leading to an incorrect perception of the majority in a large network. For the model of [Grandi et al. 2023], we present polynomial-time algorithms which completely eliminate majority illusion by altering as few connections in the network as possible. Eliminating majority illusion ensures each neighbourhood in the network has at least a $\frac12$-fraction of the majority winner. This result is surprising as [Grandi et al. 2023] prove that partially eliminating majority illusion is NP-hard. We generalize the majority illusion problem to an arbitrary fraction $p$ and show that the problem of ensuring all neighbourhoods in the network contain at least a $p$-fraction of nodes consistent with a given preference is NP-hard, for nearly all values of $p$.

Eliminating Majority Illusion Is Easy

Issue salience is a major determinant in voters' decisions.  Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the dynamics, strategies and equilibria of campaign spending for voter priming in multi-issue multi-party settings. We consider both parliamentary elections, where parties aim to maximize their share of votes, and various settings for presidential elections, where the winner takes all. For parliamentary elections, we show that pure equilibrium spending always exists and can be computed in time linear in the number of voters. For two parties and all settings, a spending equilibrium exists such that each party invests only in a single issue, and an equilibrium can be computed in time that is polynomial in the number of issues and linear in the number of voters. We also show that in most presidential settings no equilibrium exists. Additional properties of optimal campaign strategies are also studied.

Voter Priming Campaigns: Strategies, Equilibria, and Algorithms

The connection between inconsistent databases and Dung’s abstract argumentation framework has recently drawn growing interest. Specifically, an inconsistent database, involving certain types of integrity constraints such as functional and inclusion dependencies, can be viewed as an argumentation framework in Dung’s setting. Nevertheless, no prior work has explored the exact expressive power of Dung’s theory of argumentation when compared to inconsistent databases and integrity constraints. In this paper, we close this gap by arguing that an argumentation framework can also be viewed as an inconsistent database. We first establish a connection between subset-repairs for databases and extensions for AFs considering conflict-free, naive, admissible, and preferred semantics. Further, we define a new family of attribute-based repairs based on the principle of maximal content preservation. The effectiveness of these repairs is then highlighted by connecting them to stable, semi-stable, and stage semantics. Our main contributions include translating an argumentation framework into a database together with integrity constraints. Moreover, this translation can be achieved in polynomial time, which is essential in transferring complexity results between the two formalisms.

Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases

Safety alignment is indispensable for Large language models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue, limiting their helpfulness. In this paper, we propose a $\underline{\textbf{S}}$afety-$\underline{\textbf{C}}$onscious $\underline{\textbf{A}}$ctivatio$\underline{\textbf{n}}$ $\underline{\textbf{S}}$teering ($\textbf{SCANS}$) method to mitigate the exaggerated safety concerns in aligned LLMs. First, SCANS extracts the refusal steering vectors within the activation space and utilizes vocabulary projection to anchor some specific safety-critical layers which influence model refusal behavior. Second, by tracking the hidden state transition, SCANS identifies the steering direction and steers the model behavior accordingly, achieving a balance between exaggerated safety and adequate safety. Experiments show that SCANS achieves new state-of-the-art performance on XSTest and OKTest benchmarks, without impairing their defense capability against harmful queries and maintaining almost unchanged model capability.

SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering

Enabling object detectors to recognize out-of-distribution (OOD) objects is essential for building reliable AI systems. A primary obstacle stems from the fact that models lack supervision signals from unknown data, which can produce overconfident predictions on OOD objects. Despite previous progress that estimates OOD uncertainty based on the detection model and in-distribution (ID) samples, we explore using pre-trained vision-language representations for object-level OOD detection. We first discuss the limitations of applying image-level CLIP-based OOD detection methods to object-level scenarios. Building upon these insights, we propose RUNA, a novel framework that leverages a dual encoder architecture to capture rich contextual information and employs a regional uncertainty alignment mechanism to distinguish ID from OOD objects effectively. We introduce a few-shot fine-tuning approach that focuses on aligning region-level semantic representations to enhance further the model's ability to discriminate between similar objects. Our experiments demonstrate that RUNA significantly outperforms state-of-the-art methods in object-level OOD detection, particularly in challenging scenarios with diverse and complex object instances.

RUNA: Object-Level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations

Identifying and linking the same users across different social platforms is crucial for understanding user behavior and preferences. However, cross-domain datasets exhibit diverse characteristics, such as varying check-in frequencies, significant disparities in data precision, and distinct distributions. Existing trajectory representations rely on recurrent neural network, which fails to dynamically learn multi-dimensional feature relations and capture high-order associations. Furthermore, current methods for integrating trajectory information fails to capture the complex relations and dynamic variations among cross-domain mobility trajectories. To this end, we propose the Hierarchical Spatio-Temporal Enhanced Attention Hypergraph Network (StarNet). This model dynamically regulates the multi-dimensional features of trajectories through a locally enhanced spatiotemporal graph neural network. Meanwhile, StarNet employs a hypergraph network enhanced by a global spatiotemporal to capture high-order associations between cross-domain trajectories. The fusion enhancement association integrates local and global information, which enables this model to link user identities. Extensive experiments on two well-known LBSN cross-domain datasets reveal that StarNet outperforms state-of-the-art baselines in the accuracy of user identity linkage.

Cross-Domain Trajectory Association Based on Hierarchical Spatiotemporal Enhanced Attention Hypergraph

The Large Vision-Language Model (LVLM) integrates computer vision and natural language processing techniques, offering substantial application potential. However, these models demand extensive resources during inference. Adaptive attention techniques can dynamically reduce computational redundancy and thus improve efficiency. Although current adaptive attention methods significantly reduce the memory requirements of Transformer-based language models, they are not tailored for LVLMs. We observe that LVLMs generate responses from both remote image tokens and local text tokens, and different modalities have different attention patterns. This observation inspires us to manage the attention for each modality separately. Specifically, for visual input, we store the cache of potentially useful information but only compute the most critical parts. For language input, we care more about local information. Based on our observation and analysis of vision-language attention patterns, we develop A-VL, a plug-and-play adaptive attention tailored for LVLM inference. Extensive evaluations on three vision-language tasks and five datasets show the effectiveness of our designs. Our approach A-VL outperforms existing adaptive attention methods in reducing memory usage and computational load without compromising performance.

A-VL: Adaptive Attention for Large Vision-Language Models

Vehicle-to-infrastructure (V2I) cooperative perception systems can enhance the sensing abilities of autonomous vehicles. Existing V2I solutions often consider LiDARs devices instead of cameras, the most prevalent sensors with low cost and wide installation. In addition, a major challenge that has been underexplored is the time asynchrony between image frames from different sources. This asynchrony arises because of clock differences, varying times involved in data processing and transmission, causing uncertain delays that complicate data alignment and potentially reduce perception accuracy. We propose BEVSync, a camera-based V2I cooperative perception system that adaptively aligns frames from the ego-vehicle and infrastructure by compensating for motion deviations. Specifically, we develop an extractor-compensator model to extract and predict perceptual features using historical frames, thereby smoothing out the data misalignment. Experiments on the real-world dataset DAIR-V2X show that our approach surpasses existing methods in terms of performance and robustness.

BEVSync: Asynchronous Data Alignment for Camera-based Vehicle-Infrastructure Cooperative Perception Under Uncertain Delays

Existing ToM benchmarks for LLMs diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states, such as beliefs, 2) false beliefs are not comprehensively explored, and 3) the diverse personality traits of characters are overlooked.
To address these challenges, we introduce ToMATO, a new ToM benchmark that utilizes LLM-LLM conversations featuring information asymmetry.
ToMATO is formulated as multiple-choice QA over conversations generated by LLMs.
By employing a prompting method that requires LLMs to verbalize their thoughts before each utterance, we capture both first- and second-order mental states across five categories: beliefs, intentions, desires, emotions, and knowledge.
These verbalized thoughts serve as answers to questions designed to assess the mental states of characters within conversations.
Furthermore, the information asymmetry introduced by concealing thoughts from others induces the generation of false beliefs about various mental states.
Assigning distinct personality traits to LLM agents further diversifies both utterances and thoughts.
ToMATO consists of 5.4k multiple-choice questions, 753 conversations, and 15 personality trait patterns.
Our analysis shows that this dataset construction approach frequently generates false beliefs due to the information asymmetry between LLM agents, and effectively reflect diverse personalities.
We evaluate nine LLMs on ToMATO and find that even GPT-4o mini lags behind human performance, especially in understanding false beliefs, and lacks robustness to various personality traits.

Knowledge Is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis

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Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

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Knowledge Is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis

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Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

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