pwc-archive/papers-with-abstracts huggingface dataset:
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Authors: Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, ZiYi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan
Date: 2025-05-23 00:00:00
Tasks: Question Answering, Reinforcement Learning (RL)

Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the short-context reasoning tasks. In contrast, extending LRMs to effectively process and reason on long-context inputs via RL remains a critical unsolved challenge. To bridge this gap, we first formalize the paradigm of long-context reasoning RL, and identify key challenges in suboptimal training efficiency and unstable optimization process. To address these issues, we propose QwenLong-L1, a framework that adapts short-context LRMs to long-context scenarios via progressive context scaling. Specifically, we utilize a warm-up supervised fine-tuning (SFT) stage to establish a robust initial policy, followed by a curriculum-guided phased RL technique to stabilize the policy evolution, and enhanced with a difficulty-aware retrospective sampling strategy to incentivize the policy exploration. Experiments on seven long-context document question-answering benchmarks demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking, demonstrating leading performance among state-of-the-art LRMs. This work advances the development of practical long-context LRMs capable of robust reasoning across information-intensive environments.
GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking
Authors: Haibin He, Jing Zhang, Maoyuan Ye, Juhua Liu, Bo Du, DaCheng Tao
Date: 2025-05-28 00:00:00
Tasks: Benchmarking, Text Spotting

Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall short of the performance seen in ITS. This paper identifies a key limitation in current video text spotters: limited recognition capability, even after extensive end-to-end training. To address this, we propose GoMatching++, a parameter- and data-efficient method that transforms an off-the-shelf image text spotter into a video specialist. The core idea lies in freezing the image text spotter and introducing a lightweight, trainable tracker, which can be optimized efficiently with minimal training data. Our approach includes two key components: (1) a rescoring mechanism to bridge the domain gap between image and video data, and (2) the LST-Matcher, which enhances the frozen image text spotter's ability to handle video text. We explore various architectures for LST-Matcher to ensure efficiency in both parameters and training data. As a result, GoMatching++ sets new performance records on challenging benchmarks such as ICDAR15-video, DSText, and BOVText, while significantly reducing training costs. To address the lack of curved text datasets in VTS, we introduce ArTVideo, a new benchmark featuring over 30% curved text with detailed annotations. We also provide a comprehensive statistical analysis and experimental results for ArTVideo. We believe that GoMatching++ and the ArTVideo benchmark will drive future advancements in video text spotting. The source code, models and dataset are publicly available at https://github.com/Hxyz-123/GoMatching.
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
Authors: Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, Ningyu Zhang
Date: 2025-05-23 00:00:00
Tasks: Language Modeling, Language Modelling, Prompt Engineering

Precise control over language model generation is vital for ensuring both safety and reliability. Although prompt engineering and steering are commonly used to intervene in model behaviors, the vast number of parameters in models often results in highly intertwined internal representations. This interdependency can limit control precision and sometimes lead to unintended side effects. Recent research has explored the use of sparse autoencoders (SAE) to disentangle knowledge in high-dimensional spaces for steering. However, these applications have been limited to toy tasks owing to the nontrivial issue of locating atomic knowledge components. In this paper, we propose Steering Target Atoms (STA), a novel method that isolates and manipulates disentangled knowledge components to enhance safety. Comprehensive experiments demonstrate the effectiveness of our approach. Further analysis reveals that steering exhibits superior robustness and flexibility, particularly in adversarial scenarios. We also apply the steering strategy to the large reasoning model, confirming its effectiveness in precise reasoning control.
Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models
Authors: Yufei Zhan, Hongyin Zhao, Yousong Zhu, Shurong Zheng, Fan Yang, Ming Tang, Jinqiao Wang
Date: 2025-05-27 00:00:00
Tasks: Question Answering, Visual Reasoning

Large Multimodal Models (LMMs) have recently demonstrated remarkable visual understanding performance on both vision-language and vision-centric tasks. However, they often fall short in integrating advanced, task-specific capabilities for compositional reasoning, which hinders their progress toward truly competent general vision models. To address this, we present a unified visual reasoning mechanism that enables LMMs to solve complicated compositional problems by leveraging their intrinsic capabilities (e.g. grounding and visual understanding capabilities). Different from the previous shortcut learning mechanism, our approach introduces a human-like understanding-thinking-answering process, allowing the model to complete all steps in a single pass forwarding without the need for multiple inferences or external tools. This design bridges the gap between foundational visual capabilities and general question answering, encouraging LMMs to generate faithful and traceable responses for complex visual reasoning. Meanwhile, we curate 334K visual instruction samples covering both general scenes and text-rich scenes and involving multiple foundational visual capabilities. Our trained model, Griffon-R, has the ability of end-to-end automatic understanding, self-thinking, and reasoning answers. Comprehensive experiments show that Griffon-R not only achieves advancing performance on complex visual reasoning benchmarks including VSR and CLEVR, but also enhances multimodal capabilities across various benchmarks like MMBench and ScienceQA. Data, models, and codes will be release at https://github.com/jefferyZhan/Griffon/tree/master/Griffon-R soon.
Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems
Authors: Heng Tang, Feng Liu, Xinbo Chen, Jiawei Chen, Bohao Wang, Changwang Zhang, Jun Wang, Yuegang Sun, Bingde Hu, Can Wang
Date: 2025-05-27 00:00:00
Tasks: In-Context Learning, Recommendation Systems

Recent years have witnessed extensive exploration of Large Language Models (LLMs) on the field of Recommender Systems (RS). There are currently two commonly used strategies to enable LLMs to have recommendation capabilities: 1) The "Guidance-Only" strategy uses in-context learning to exploit and amplify the inherent semantic understanding and item recommendation capabilities of LLMs; 2) The "Tuning-Only" strategy uses supervised fine-tuning (SFT) to fine-tune LLMs with the aim of fitting them to real recommendation data. However, neither of these strategies can effectively bridge the gap between the knowledge space of LLMs and recommendation, and their performance do not meet our expectations. To better enable LLMs to learn recommendation knowledge, we combine the advantages of the above two strategies and proposed a novel "Guidance+Tuning" method called Self-Optimized Fine-Tuning (SOFT), which adopts the idea of curriculum learning. It first employs self-distillation to construct an auxiliary easy-to-learn but meaningful dataset from a fine-tuned LLM. Then it further utilizes a self-adaptive curriculum scheduler to enable LLMs to gradually learn from simpler data (self-distilled data) to more challenging data (real RS data). Extensive experiments demonstrate that SOFT significantly enhances the recommendation accuracy (37.59\% on average) of LLM-based methods. The code is available via https://anonymous.4open.science/r/Self-Optimized-Fine-Tuning-264E
What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals
Authors: Shahrooz Pouryousef
Date: 2025-05-27 00:00:00
Tasks: RAG, Recommendation Systems, Retrieval-augmented Generation

User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems. While recent work has explored the use of large language models (LLMs) for recommendation, it remains unclear whether LLMs can effectively reason over this type of collaborative information. In this paper, we conduct a systematic comparison between LLMs and classical matrix factorization (MF) models to assess LLMs' ability to leverage user-item interaction data. We further introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data. Our experiments reveal that current LLMs often fall short in capturing collaborative patterns inherent to MF models, but that our RAG-based approach substantially improves recommendation quality-highlighting a promising direction for future LLM-based recommenders.
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
Authors: Sijia Chen, Yanqiu Yu, En Yu, Wenbing Tao
Date: 2025-05-26 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking, Referring Multi-Object Tracking

Referring Multi-object tracking (RMOT) is an important research field in computer vision. Its task form is to guide the models to track the objects that conform to the language instruction. However, the RMOT task commonly requires clear language instructions, such methods often fail to work when complex language instructions with reasoning characteristics appear. In this work, we propose a new task, called Reasoning-based Multi-Object Tracking (ReaMOT). ReaMOT is a more challenging task that requires accurate reasoning about objects that match the language instruction with reasoning characteristic and tracking the objects' trajectories. To advance the ReaMOT task and evaluate the reasoning capabilities of tracking models, we construct ReaMOT Challenge, a reasoning-based multi-object tracking benchmark built upon 12 datasets. Specifically, it comprises 1,156 language instructions with reasoning characteristic, 423,359 image-language pairs, and 869 diverse scenes, which is divided into three levels of reasoning difficulty. In addition, we propose a set of evaluation metrics tailored for the ReaMOT task. Furthermore, we propose ReaTrack, a training-free framework for reasoning-based multi-object tracking based on large vision-language models (LVLM) and SAM2, as a baseline for the ReaMOT task. Extensive experiments on the ReaMOT Challenge benchmark demonstrate the effectiveness of our ReaTrack framework.
CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting
Authors: Lei Tian, Xiaomin Li, Liqian Ma, Hefei Huang, Zirui Zheng, Hao Yin, Taiqing Li, Huchuan Lu, Xu Jia
Date: 2025-05-26 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving

Recent advances in 3D reconstruction techniques and vision-language models have fueled significant progress in 3D semantic understanding, a capability critical to robotics, autonomous driving, and virtual/augmented reality. However, methods that rely on 2D priors are prone to a critical challenge: cross-view semantic inconsistencies induced by occlusion, image blur, and view-dependent variations. These inconsistencies, when propagated via projection supervision, deteriorate the quality of 3D Gaussian semantic fields and introduce artifacts in the rendered outputs. To mitigate this limitation, we propose CCL-LGS, a novel framework that enforces view-consistent semantic supervision by integrating multi-view semantic cues. Specifically, our approach first employs a zero-shot tracker to align a set of SAM-generated 2D masks and reliably identify their corresponding categories. Next, we utilize CLIP to extract robust semantic encodings across views. Finally, our Contrastive Codebook Learning (CCL) module distills discriminative semantic features by enforcing intra-class compactness and inter-class distinctiveness. In contrast to previous methods that directly apply CLIP to imperfect masks, our framework explicitly resolves semantic conflicts while preserving category discriminability. Extensive experiments demonstrate that CCL-LGS outperforms previous state-of-the-art methods. Our project page is available at https://epsilontl.github.io/CCL-LGS/.
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning
Authors: Jinbao Wang, Hanzhe Liang, Can Gao, Chenxi Hu, Jie zhou, Yunkang Cao, Linlin Shen, Weiming Shen
Date: 2025-05-27 00:00:00
Tasks: 3D Anomaly Detection, Anomaly Detection

Multimodal feature reconstruction is a promising approach for 3D anomaly detection, leveraging the complementary information from dual modalities. We further advance this paradigm by utilizing multi-modal mentor learning, which fuses intermediate features to further distinguish normal from feature differences. To address these challenges, we propose a novel method called Mentor3AD, which utilizes multi-modal mentor learning. By leveraging the shared features of different modalities, Mentor3AD can extract more effective features and guide feature reconstruction, ultimately improving detection performance. Specifically, Mentor3AD includes a Mentor of Fusion Module (MFM) that merges features extracted from RGB and 3D modalities to create a mentor feature. Additionally, we have designed a Mentor of Guidance Module (MGM) to facilitate cross-modal reconstruction, supported by the mentor feature. Lastly, we introduce a Voting Module (VM) to more accurately generate the final anomaly score. Extensive comparative and ablation studies on MVTec 3D-AD and Eyecandies have verified the effectiveness of the proposed method.
Generalizing Large Language Model Usability Across Resource-Constrained
Authors: Yun-Da Tsai
Date: 2025-05-13 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Large Language Model, model, Uncertainty Quantification

Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language tasks, and recent efforts have sought to extend their capabilities to multimodal domains and resource-constrained environments. However, existing approaches often rely on costly supervised fine-tuning or assume fixed training conditions, limiting their generalization when facing unseen modalities, limited data, or restricted compute resources. This dissertation presents a systematic study toward generalizing LLM usability under real-world constraints. First, it introduces a robust text-centric alignment framework that enables LLMs to seamlessly integrate diverse modalities-including text, images, tables, and any modalities - via natural language interfaces. This approach supports in-context adaptation to unseen or dynamically changing modalities without requiring retraining. To enhance robustness against noisy and missing modalities, an adversarial prompting technique is proposed, generating semantically challenging perturbations at the prompt level to stress-test model reliability. Beyond multimodal setting, the dissertation investigates inference-time optimization strategies for LLMs, leveraging prompt search and uncertainty quantification to improve performance without additional model training. This perspective offers an efficient alternative to scaling model parameters or retraining from scratch. Additionally, the work addresses low-resource domains such as Verilog code generation by designing correct-by-construction synthetic data pipelines and logic-enhanced reasoning models, achieving state-of-the-art performance with minimal data. Together, these contributions form a unified effort to enhance the adaptability, scalability, and efficiency of large language models under practical constraints.
2025 TGRS A Self-Supervised Method for Seismic Random Noise Attenuation under Non-Pixelwise Independent Assumption
Authors: Chuangji Meng; Jinghuai Gao; Wenting Shang; Yajun Tian
Date: 2025-05-11 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2025 5

The attenuation of seismic field noise using self-supervised deep learning has gained attention due to its label-free training process. However, common self-supervised methods are limited by the pixelwise independence assumption, which does not align with field seismic noise characteristics, and suffer from signal leakage due to receptive fields containing inherent blind spots or traces. In this paper, we propose a self-supervised random noise attenuation method based on the non-pixelwise independence assumption. By considering the spatial correlation map of field noise, we extend the blind spot to a generalized blind neighborhood, ensuring that the prediction pixel is not influenced by neighboring pixels with noise correlation greater than zero. The blind neighborhood size controls how much spatial correlation is disrupted, allowing our method to handle random noise with varying spatial correlation. Since larger blind neighborhoods may lead to signal loss, we introduce an automatic trade-off between noise correlation disruption and signal preservation during training. Experiments on real seismic noise attenuation (including random and tracewise coherent noise) demonstrate the superiority of our method in destroying the spatial coherence of noise and preventing useful signal leakage.
Streamlining Temporal Formal Verification over Columnar Databases
Authors: Giacomo Bergami
Date: 2024-01-08 00:00:00
Proceeding: Information 2024 1

Recent findings demonstrate how database technology enhances the computation of formal verification tasks expressible in linear time logic for finite traces (LTLf). Human-readable declarative languages also help the common practitioner to express temporal constraints in a straightforward and accessible language. Notwithstanding the former, this technology is in its infancy, and therefore, few optimization algorithms are known for dealing with massive amounts of information audited from real systems. We, therefore, present four novel algorithms subsuming entire LTLf expressions while outperforming previous state-of-the-art implementations on top of KnoBAB, thus postulating the need for the corresponding, leading to the formulation of novel xtLTLf-derived algebraic operators.
Pause Tokens Strictly Increase the Expressivity of Constant-Depth Transformers
Authors: Charles London, Varun Kanade
Date: 2025-05-27 00:00:00

Pause tokens, simple filler symbols such as "...", consistently improve Transformer performance on both language and mathematical tasks, yet their theoretical effect remains unexplained. We provide the first formal separation result, proving that adding pause tokens to constant-depth, logarithmic-width Transformers strictly increases their computational expressivity. With bounded-precision activations, Transformers without pause tokens compute only a strict subset of $\mathsf{AC}^0$ functions, while adding a polynomial number of pause tokens allows them to express the entire class. For logarithmic-precision Transformers, we show that adding pause tokens achieves expressivity equivalent to $\mathsf{TC}^0$, matching known upper bounds. Empirically, we demonstrate that two-layer causally masked Transformers can learn parity when supplied with pause tokens, a function that they appear unable to learn without them. Our results provide a rigorous theoretical explanation for prior empirical findings, clarify how pause tokens interact with width, depth, and numeric precision, and position them as a distinct mechanism, complementary to chain-of-thought prompting, for enhancing Transformer reasoning.
Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistance
Authors: Semanto Mondal
Date: 2025-05-24 00:00:00
Tasks: Large Language Model, object-detection, Object Detection, RAG, Retrieval, Retrieval-augmented Generation

As a social being, we have an intimate bond with the environment. A plethora of things in human life, such as lifestyle, health, and food are dependent on the environment and agriculture. It comes under our responsibility to support the environment as well as agriculture. However, traditional farming practices often result in inefficient resource use and environmental challenges. To address these issues, precision agriculture has emerged as a promising approach that leverages advanced technologies to optimise agricultural processes. In this work, a hybrid approach is proposed that combines the three different potential fields of model AI: object detection, large language model (LLM), and Retrieval-Augmented Generation (RAG). In this novel framework, we have tried to combine the vision and language models to work together to identify potential diseases in the tree leaf. This study introduces a novel AI-based precision agriculture system that uses Retrieval Augmented Generation (RAG) to provide context-aware diagnoses and natural language processing (NLP) and YOLOv8 for crop disease detection. The system aims to tackle major issues with large language models (LLMs), especially hallucinations and allows for adaptive treatment plans and real-time disease detection. The system provides an easy-to-use interface to the farmers, which they can use to detect the different diseases related to coffee leaves by just submitting the image of the affected leaf the model will detect the diseases as well as suggest potential remediation methodologies which aim to lower the use of pesticides, preserving livelihoods, and encouraging environmentally friendly methods. With an emphasis on scalability, dependability, and user-friendliness, the project intends to improve RAG-integrated object detection systems for wider agricultural applications in the future.
Genetic Influences on Brain Aging: Analyzing Sex Differences in the UK Biobank using Structural MRI
Authors: Karen Ardila, Aashka Mohite, Abdoljalil Addeh, Amanda V. Tyndall, Cindy K. Barha, Quan Long, M. Ethan MacDonald
Date: 2025-05-25 00:00:00

Brain aging trajectories differ between males and females, yet the genetic factors underlying these differences remain underexplored. Using structural MRI and genotyping data from 40,940 UK Biobank participants (aged 45-83), we computed Brain Age Gap Estimates (BrainAGE) for total brain, hippocampal, and ventricular volumes. We conducted sex-stratified genome-wide association studies (GWAS) and Post-GWAS analyses to identify genetic variants associated with accelerated brain aging. Distinct gene sets emerged by sex: in females, neurotransmitter transport and mitochondrial stress response genes were implicated; in males, immune and inflammation-related genes dominated. Shared genes, including GMNC and OSTN, were consistently linked to brain volumes across sexes, suggesting core roles in neurostructural maintenance. Tissue expression analyses revealed sex-specific enrichment in pathways tied to neurodegeneration. These findings highlight the importance of sex-stratified approaches in aging research and suggest genetic targets for personalized interventions against age-related cognitive decline.