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Woojin Kim

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

Woojin Kim
Changkwon Lee
Hyeoncheol Kim

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

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NeurIPS Conference 2025 Conference Paper

Don’t Let It Fade: Preserving Edits in Diffusion Language Models via Token Timestep Allocation

Woojin Kim
Jaeyoung Do

Classifier guidance is a widely adopted technique in diffusion language models, used to steer generation toward desired attributes. However, such guidance often introduces instability during the generation process, where token-level updates fluctuate across timesteps. We identify and formally characterize this phenomenon as update-forgetting. This instability disrupts the refinement process by overwriting semantic edits, ultimately degrading fluency and coherence, which is particularly problematic in tasks like controllable text generation. To address this, we propose TTA-Diffusion, a novel inference-time approach that dynamically allocates timesteps per token based on refinement needs. Unlike conventional diffusion models that apply uniform updates, TTA-Diffusion employs structured timestep allocation, preserving stable tokens while allowing uncertain tokens to undergo progressive adjustment. Experimental results across diverse tasks demonstrate that TTA-Diffusion significantly outperforms both diffusion-based and auto-regressive baselines in fluency and control accuracy while improving computational efficiency by reducing the number of required timesteps. On the sentiment control task, TTA-Diffusion achieves over 20\% higher accuracy and nearly half the perplexity of prior diffusion models, using less than one-fifth the denoising steps. This work highlights the importance of mitigating fluctuations in token updates and promoting a balanced refinement process, thereby enhancing stability and controllability in controllable language modeling.

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NeurIPS Conference 2025 Conference Paper

MMPB: It’s Time for Multi-Modal Personalization

Jaeik Kim
Woojin Kim
Woohyeon Park
Jaeyoung Do

Visual personalization is essential in user-facing AI systems such as smart homes and healthcare, where aligning model behavior with user-centric concepts is critical. However, recent large Vision-Language Models (VLMs), despite their broad applicability, remain underexplored in their ability to adapt to individual users. In this paper, we introduce MMPB, the first extensive benchmark for evaluating VLMs on personalization. MMPB comprises 10k image-query pairs and includes 111 personalizable concepts across four categories: humans, animals, objects, and characters, with the human category enriched with preference-grounded queries. We structure personalization into three main task types, each highlighting a different key property of VLMs. Using 23 widely used VLMs including both open- and closed-source models, we evaluate personalization performance via a three-stage protocol: concept injection, multi-turn dialogue, and personalized querying. Our findings indicate that most VLMs (including some closed-source models) struggle with personalization, particularly in maintaining consistency over dialogue, handling user preferences, and adapting to visual cues. Our analysis reveals that the challenges in VLM personalization (such as refusal behaviors and long-context forgetting) highlight substantial room for improvement. By identifying these limitations and offering a scalable benchmark, MMPB offers valuable insights and a solid foundation for future research toward truly personalized multi-modal AI.

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ICML Conference 2025 Conference Paper

SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

Woohyeon Park
Woojin Kim
Jaeik Kim
Jaeyoung Do

Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a critical challenge to achieving accurate visual understanding. To address this issue, we propose SECOND: Selective and Contrastive Decoding, a novel approach that enables VLMs to effectively leverage multi-scale visual information with an object-centric manner, closely aligning with human visual perception. SECOND progressively selects and integrates multi-scale visual information, facilitating a more precise interpretation of images. By contrasting these visual information iteratively, SECOND significantly reduces perceptual hallucinations and outperforms a wide range of benchmarks. Our theoretical analysis and experiments highlight the largely unexplored potential of multi-scale application in VLMs, showing that prioritizing and contrasting across scales outperforms existing methods.

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ICRA Conference 2013 Conference Paper

Joint detection and tracking of boundaries using cooperative mobile sensor networks

Woojin Kim
Dong Jun Kwak
H. Jin Kim

This paper considers a boundary tracking problem using mobile sensor networks, in which we design controllers for the mobile sensors to obtain the boundary of physical events. We set the boundary estimation problem as a classification problem of the region in which the physical events occurs, and employ support vector learning (SVL). By using the hyper-dimensional radius function obtained from SVL, we build the hyper-potential field to generate a velocity vector field which is globally attractive to a desired closed path with circulation at the desired speed. We also study stabilizing the collective configuration of the multiple mobile sensors. To coordinate the mobile sensors in the formation that encloses the boundary, we define virtual phases of mobile sensors and compute the desired speed of each mobile sensors minimizing the level of synchrony of the virtual phases. Both a simulation and an experiment is performed and the results demonstrate that this study provides good performance of the collective boundary tracking.

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ICRA Conference 2012 Conference Paper

Multi-target tracking using distributed SVM training over wireless sensor networks

Woojin Kim
Jae Hyun Yoo
H. Jin Kim

In this paper, we propose to use distributed support vector machine (SVM) training to solve a multi-target tracking problem in wireless sensor networks. We employ gossip-based incremental SVM to obtain the discriminant function. By gossiping the support vectors with neighboring sensor nodes, the local SVM training results can achieve the agreement of the sub-optimal discriminant planes. After training the local SVM at each node, we can calculate the posterior probability of the existence of the targets using Platt's method. By maximum a posterior (MAP), the target trajectories are estimated. In order to validate the proposed tracking framework in wireless sensor networks, we perform two different target-tracking experiments. The experimental results demonstrate that the proposed procedure provides a good estimator, and supports the feasibility of applying the distributed SVM training to the target tracking problems.

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IROS Conference 2011 Conference Paper

Event-driven Gaussian process for object localization in wireless sensor networks

Jae Hyun Yoo
Woojin Kim
H. Jin Kim

Object localization using wireless sensor networks (WSN) often requires data from many sensor nodes and different types of sensors for position estimation. This incurs a heavy communication load, which can cause packet loss, communication delay and much energy consumption, deteriorating the performance of object localization. Here we employ an event-driven Gaussian process in order to learn the position of an unknown object using WSN with multiple types of sensors. In the event-driven framework, each sensor node transmits data only when decision criteria are satisfied. We consider the error-bounded algorithm as the decision criteria based on the measurement history of each sensor node. The overall communication between sensor nodes is reduced, thus increasing energy-efficiency of the network and relieving the concentration of communication traffic at the base node. Experiments to track the position of a mobile robot are conducted using a multi-sensor WSN, and the comparison is made between the event-driven framework and the conventional approach in which sensors transmit data at a constant sampling rate. Experimental results demonstrate the efficiency and accuracy of the proposed event-driven Gaussian process approach.

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