pwc-archive/papers-with-abstracts huggingface dataset:
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization
Authors: Dong Kyu Cho, Inwoo Hwang, Sanghack Lee
Date: 2025-05-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Data Augmentation, Domain Generalization, model, Model Selection, Single-Source Domain Generalization

Data augmentation is a popular tool for single source domain generalization, which expands the source domain by generating simulated ones, improving generalization on unseen target domains. In this work, we show that the performance of such augmentation-based methods in the target domains universally fluctuates during training, posing challenges in model selection under realistic scenarios. We argue that the fluctuation stems from the inability of the model to accumulate the knowledge learned from diverse augmentations, exacerbating feature distortion during training. Based on this observation, we propose a novel generalization method, coined Parameter-Space Ensemble with Entropy Regularization (PEER), that uses a proxy model to learn the augmented data on behalf of the main model. The main model is updated by averaging its parameters with the proxy model, progressively accumulating knowledge over the training steps. Maximizing the mutual information between the output representations of the two models guides the learning process of the proxy model, mitigating feature distortion during training. Experimental results demonstrate the effectiveness of PEER in reducing the OOD performance fluctuation and enhancing generalization across various datasets, including PACS, Digits, Office-Home, and VLCS. Notably, our method with simple random augmentation achieves state-of-the-art performance, surpassing prior approaches on sDG that utilize complex data augmentation strategies.
Incentivizing Multimodal Reasoning in Large Models for Direct Robot Manipulation
Authors: Weiliang Tang, Dong Jing, Jia-Hui Pan, Zhiwu Lu, Yun-hui Liu, Li Erran Li, Mingyu Ding, Chi-Wing Fu
Date: 2025-05-19 00:00:00
Tasks: Multimodal Reasoning, Robot Manipulation, Spatial Reasoning

Recent Large Multimodal Models have demonstrated remarkable reasoning capabilities, especially in solving complex mathematical problems and realizing accurate spatial perception. Our key insight is that these emerging abilities can naturally extend to robotic manipulation by enabling LMMs to directly infer the next goal in language via reasoning, rather than relying on a separate action head. However, this paradigm meets two main challenges: i) How to make LMMs understand the spatial action space, and ii) How to fully exploit the reasoning capacity of LMMs in solving these tasks. To tackle the former challenge, we propose a novel task formulation, which inputs the current states of object parts and the gripper, and reformulates rotation by a new axis representation instead of traditional Euler angles. This representation is more compatible with spatial reasoning and easier to interpret within a unified language space. For the latter challenge, we design a pipeline to utilize cutting-edge LMMs to generate a small but high-quality reasoning dataset of multi-round dialogues that successfully solve manipulation tasks for supervised fine-tuning. Then, we perform reinforcement learning by trial-and-error interactions in simulation to further enhance the model's reasoning abilities for robotic manipulation. Our resulting reasoning model built upon a 7B backbone, named ReasonManip, demonstrates three notable advantages driven by its system-2 level reasoning capabilities: i) exceptional generalizability to out-of-distribution environments, objects, and tasks; ii) inherent sim-to-real transfer ability enabled by the unified language representation shared across domains; iii) transparent interpretability connecting high-level reasoning and low-level control. Extensive experiments demonstrate the effectiveness of the proposed paradigm and its potential to advance LMM-driven robotic manipulation.
Dense Communication between Language Models
Authors: Shiguang Wu, Yaqing Wang, Quanming Yao
Date: 2025-05-19 00:00:00

As higher-level intelligence emerges from the combination of modular components with lower-level intelligence, many works combines Large Language Models (LLMs) for collective intelligence. Such combination is achieved by building communications among LLMs. While current systems primarily facilitate such communication through natural language, this paper proposes a novel paradigm of direct dense vector communication between LLMs. Our approach eliminates the unnecessary embedding and de-embedding steps when LLM interact with another, enabling more efficient information transfer, fully differentiable optimization pathways, and exploration of capabilities beyond human heuristics. We use such stripped LLMs as vertexes and optimizable seq2seq modules as edges to construct LMNet, with similar structure as MLPs. By utilizing smaller pre-trained LLMs as vertexes, we train a LMNet that achieves comparable performance with LLMs in similar size with only less than 0.1% training cost. This offers a new perspective on scaling for general intelligence rather than training a monolithic LLM from scratch. Besides, the proposed method can be used for other applications, like customizing LLM with limited data, showing its versatility.
EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting
Authors: Chenghua Gong, Rui Sun, Yuhao Zheng, Juyuan Zhang, Tianjun Gu, Liming Pan, Linyuan Lv
Date: 2025-05-19 00:00:00
Tasks: Prompt Learning

Advanced epidemic forecasting is critical for enabling precision containment strategies, highlighting its strategic importance for public health security. While recent advances in Large Language Models (LLMs) have demonstrated effectiveness as foundation models for domain-specific tasks, their potential for epidemic forecasting remains largely unexplored. In this paper, we introduce EpiLLM, a novel LLM-based framework tailored for spatio-temporal epidemic forecasting. Considering the key factors in real-world epidemic transmission: infection cases and human mobility, we introduce a dual-branch architecture to achieve fine-grained token-level alignment between such complex epidemic patterns and language tokens for LLM adaptation. To unleash the multi-step forecasting and generalization potential of LLM architectures, we propose an autoregressive modeling paradigm that reformulates the epidemic forecasting task into next-token prediction. To further enhance LLM perception of epidemics, we introduce spatio-temporal prompt learning techniques, which strengthen forecasting capabilities from a data-driven perspective. Extensive experiments show that EpiLLM significantly outperforms existing baselines on real-world COVID-19 datasets and exhibits scaling behavior characteristic of LLMs.
Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
Authors: Hongjoon Ahn, Heewoong Choi, Jisu Han, Taesup Moon
Date: 2025-05-19 00:00:00

Offline goal-conditioned reinforcement learning (GCRL) offers a practical learning paradigm where goal-reaching policies are trained from abundant unlabeled (reward-free) datasets without additional environment interaction. However, offline GCRL still struggles with long-horizon tasks, even with recent advances that employ hierarchical policy structures, such as HIQL. By identifying the root cause of this challenge, we observe the following insights: First, performance bottlenecks mainly stem from the high-level policy's inability to generate appropriate subgoals. Second, when learning the high-level policy in the long-horizon regime, the sign of the advantage signal frequently becomes incorrect. Thus, we argue that improving the value function to produce a clear advantage signal for learning the high-level policy is essential. In this paper, we propose a simple yet effective solution: Option-aware Temporally Abstracted value learning, dubbed OTA, which incorporates temporal abstraction into the temporal-difference learning process. By modifying the value update to be option-aware, the proposed learning scheme contracts the effective horizon length, enabling better advantage estimates even in long-horizon regimes. We experimentally show that the high-level policy extracted using the OTA value function achieves strong performance on complex tasks from OGBench, a recently proposed offline GCRL benchmark, including maze navigation and visual robotic manipulation environments.
SounDiT: Geo-Contextual Soundscape-to-Landscape Generation
Authors: JunBo Wang, Haofeng Tan, Bowen Liao, Albert Jiang, Teng Fei, QiXing Huang, Zhengzhong Tu, Shan Ye, Yuhao Kang
Date: 2025-05-19 00:00:00
Tasks: Image Generation

We present a novel and practically significant problem-Geo-Contextual Soundscape-to-Landscape (GeoS2L) generation-which aims to synthesize geographically realistic landscape images from environmental soundscapes. Prior audio-to-image generation methods typically rely on general-purpose datasets and overlook geographic and environmental contexts, resulting in unrealistic images that are misaligned with real-world environmental settings. To address this limitation, we introduce a novel geo-contextual computational framework that explicitly integrates geographic knowledge into multimodal generative modeling. We construct two large-scale geo-contextual multimodal datasets, SoundingSVI and SonicUrban, pairing diverse soundscapes with real-world landscape images. We propose SounDiT, a novel Diffusion Transformer (DiT)-based model that incorporates geo-contextual scene conditioning to synthesize geographically coherent landscape images. Furthermore, we propose a practically-informed geo-contextual evaluation framework, the Place Similarity Score (PSS), across element-, scene-, and human perception-levels to measure consistency between input soundscapes and generated landscape images. Extensive experiments demonstrate that SounDiT outperforms existing baselines in both visual fidelity and geographic settings. Our work not only establishes foundational benchmarks for GeoS2L generation but also highlights the importance of incorporating geographic domain knowledge in advancing multimodal generative models, opening new directions at the intersection of generative AI, geography, urban planning, and environmental sciences.
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing
Authors: Cheng Ji, Huaiying Luo
Date: 2025-05-16 00:00:00
Tasks: Anomaly Detection, Cloud Computing, Fault Detection, Natural Language Understanding

With the rapid development of cloud computing systems and the increasing complexity of their infrastructure, intelligent mechanisms to detect and mitigate failures in real time are becoming increasingly important. Traditional methods of failure detection are often difficult to cope with the scale and dynamics of modern cloud environments. In this study, we propose a novel AI framework based on Massive Language Model (LLM) for intelligent fault detection and self-healing mechanisms in cloud systems. The model combines existing machine learning fault detection algorithms with LLM's natural language understanding capabilities to process and parse system logs, error reports, and real-time data streams through semantic context. The method adopts a multi-level architecture, combined with supervised learning for fault classification and unsupervised learning for anomaly detection, so that the system can predict potential failures before they occur and automatically trigger the self-healing mechanism. Experimental results show that the proposed model is significantly better than the traditional fault detection system in terms of fault detection accuracy, system downtime reduction and recovery speed.
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps
Authors: Jie Ou, Jinyu Guo, Shuaihong Jiang, Zhaokun Wang, Libo Qin, Shunyu Yao, Wenhong Tian
Date: 2025-05-19 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has emerged as a pivotal method for expanding the knowledge of large language models. To handle complex queries more effectively, researchers developed Adaptive-RAG (A-RAG) to enhance the generated quality through multiple interactions with external knowledge bases. Despite its effectiveness, A-RAG exacerbates the pre-existing efficiency challenges inherent in RAG, which are attributable to its reliance on multiple iterations of generation. Existing A-RAG approaches process all retrieved contents from scratch. However, they ignore the situation where there is a significant overlap in the content of the retrieval results across rounds. The overlapping content is redundantly represented, which leads to a large proportion of repeated computations, thus affecting the overall efficiency. To address this issue, this paper introduces a model-agnostic approach that can be generally applied to A-RAG methods, which is dedicated to reducing the redundant representation process caused by the overlapping of retrieval results. Specifically, we use cache access and parallel generation to speed up the prefilling and decoding stages respectively. Additionally, we also propose an instruction-driven module to further guide the model to more effectively attend to each part of the content in a more suitable way for LLMs. Experiments show that our approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality.
Shadow-FT: Tuning Instruct via Base
Authors: Taiqiang Wu, Runming Yang, Jiayi Li, Pengfei Hu, Ngai Wong, Yujiu Yang
Date: 2025-05-19 00:00:00

Large language models (LLMs) consistently benefit from further fine-tuning on various tasks. However, we observe that directly tuning the INSTRUCT (i.e., instruction tuned) models often leads to marginal improvements and even performance degeneration. Notably, paired BASE models, the foundation for these INSTRUCT variants, contain highly similar weight values (i.e., less than 2% on average for Llama 3.1 8B). Therefore, we propose a novel Shadow-FT framework to tune the INSTRUCT models by leveraging the corresponding BASE models. The key insight is to fine-tune the BASE model, and then directly graft the learned weight updates to the INSTRUCT model. Our proposed Shadow-FT introduces no additional parameters, is easy to implement, and significantly improves performance. We conduct extensive experiments on tuning mainstream LLMs, such as Qwen 3 and Llama 3 series, and evaluate them across 19 benchmarks covering coding, reasoning, and mathematical tasks. Experimental results demonstrate that Shadow-FT consistently outperforms conventional full-parameter and parameter-efficient tuning approaches. Further analyses indicate that Shadow-FT can be applied to multimodal large language models (MLLMs) and combined with direct preference optimization (DPO). Codes and weights are available at \href{https://github.com/wutaiqiang/Shadow-FT}{Github}.
Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
Authors: Qichen Sun, Zhengrui Guo, Rui Peng, Hao Chen, Jinzhuo Wang
Date: 2025-05-19 00:00:00
Tasks: Survival Prediction, Triplet, whole slide images

Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.g., genomics) for pathological diagnosis. Although deep learning has demonstrated strong potential in pathology, several key challenges persist: (1) fusing heterogeneous data types requires sophisticated strategies beyond simple concatenation due to high computational costs; (2) common scenarios of missing modalities necessitate flexible strategies that allow the model to learn robustly in the absence of certain modalities; (3) the downstream tasks in CPath are diverse, ranging from unimodal to multimodal, cnecessitating a unified model capable of handling all modalities. To address these challenges, we propose ALTER, an any-to-any tri-modal pretraining framework that integrates WSIs, genomics, and pathology reports. The term "any" emphasizes ALTER's modality-adaptive design, enabling flexible pretraining with any subset of modalities, and its capacity to learn robust, cross-modal representations beyond WSI-centric approaches. We evaluate ALTER across extensive clinical tasks including survival prediction, cancer subtyping, gene mutation prediction, and report generation, achieving superior or comparable performance to state-of-the-art baselines.
PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI
Authors: Yingchen He, Christian D. Weilbach, Martyna E. Wojciechowska, Yuxuan Zhang, Frank Wood
Date: 2025-05-19 00:00:00
Tasks: Benchmarking, Minecraft, Object Recognition

Advances in deep generative modelling have made it increasingly plausible to train human-level embodied agents. Yet progress has been limited by the absence of large-scale, real-time, multi-modal, and socially interactive datasets that reflect the sensory-motor complexity of natural environments. To address this, we present PLAICraft, a novel data collection platform and dataset capturing multiplayer Minecraft interactions across five time-aligned modalities: video, game output audio, microphone input audio, mouse, and keyboard actions. Each modality is logged with millisecond time precision, enabling the study of synchronous, embodied behaviour in a rich, open-ended world. The dataset comprises over 10,000 hours of gameplay from more than 10,000 global participants.\footnote{We have done a privacy review for the public release of an initial 200-hour subset of the dataset, with plans to release most of the dataset over time.} Alongside the dataset, we provide an evaluation suite for benchmarking model capabilities in object recognition, spatial awareness, language grounding, and long-term memory. PLAICraft opens a path toward training and evaluating agents that act fluently and purposefully in real time, paving the way for truly embodied artificial intelligence.
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Authors: Joel Jang, Seonghyeon Ye, Zongyu Lin, Jiannan Xiang, Johan Bjorck, Yu Fang, Fengyuan Hu, Spencer Huang, Kaushil Kundalia, Yen-Chen Lin, Loic Magne, Ajay Mandlekar, Avnish Narayan, You Liang Tan, Guanzhi Wang, Jing Wang, Qi Wang, Yinzhen Xu, Xiaohui Zeng, Kaiyuan Zheng, Ruijie Zheng, Ming-Yu Liu, Luke Zettlemoyer, Dieter Fox, Jan Kautz, Scott Reed, Yuke Zhu, Linxi Fan
Date: 2025-05-19 00:00:00
Tasks: Video Generation

We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - synthetic robot data generated from video world models. DreamGen leverages state-of-the-art image-to-video generative models, adapting them to the target robot embodiment to produce photorealistic synthetic videos of familiar or novel tasks in diverse environments. Since these models generate only videos, we recover pseudo-action sequences using either a latent action model or an inverse-dynamics model (IDM). Despite its simplicity, DreamGen unlocks strong behavior and environment generalization: a humanoid robot can perform 22 new behaviors in both seen and unseen environments, while requiring teleoperation data from only a single pick-and-place task in one environment. To evaluate the pipeline systematically, we introduce DreamGen Bench, a video generation benchmark that shows a strong correlation between benchmark performance and downstream policy success. Our work establishes a promising new axis for scaling robot learning well beyond manual data collection. Code available at https://github.com/NVIDIA/GR00T-Dreams.
Counterfactual Explanations for Continuous Action Reinforcement Learning
Authors: Shuyang Dong, Shangtong Zhang, Lu Feng
Date: 2025-05-19 00:00:00
Tasks: counterfactual, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual explanations, which address "what if" scenarios, provide a promising avenue for understanding RL decisions but remain underexplored for continuous action spaces. We propose a novel approach for generating counterfactual explanations in continuous action RL by computing alternative action sequences that improve outcomes while minimizing deviations from the original sequence. Our approach leverages a distance metric for continuous actions and accounts for constraints such as adhering to predefined policies in specific states. Evaluations in two RL domains, Diabetes Control and Lunar Lander, demonstrate the effectiveness, efficiency, and generalization of our approach, enabling more interpretable and trustworthy RL applications.
Bullying the Machine: How Personas Increase LLM Vulnerability
Authors: Ziwei Xu, Udit Sanghi, Mohan Kankanhalli
Date: 2025-05-19 00:00:00

Large Language Models (LLMs) are increasingly deployed in interactions where they are prompted to adopt personas. This paper investigates whether such persona conditioning affects model safety under bullying, an adversarial manipulation that applies psychological pressures in order to force the victim to comply to the attacker. We introduce a simulation framework in which an attacker LLM engages a victim LLM using psychologically grounded bullying tactics, while the victim adopts personas aligned with the Big Five personality traits. Experiments using multiple open-source LLMs and a wide range of adversarial goals reveal that certain persona configurations -- such as weakened agreeableness or conscientiousness -- significantly increase victim's susceptibility to unsafe outputs. Bullying tactics involving emotional or sarcastic manipulation, such as gaslighting and ridicule, are particularly effective. These findings suggest that persona-driven interaction introduces a novel vector for safety risks in LLMs and highlight the need for persona-aware safety evaluation and alignment strategies.