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Emotional Support with LLM-based Empathetic Dialogue Generation
17 Jul 2025
Researchers from TeleAI, China Telecom Corp Ltd, developed an effective solution for Emotional Support Conversation (ESC) by fine-tuning Qwen2.5 Large Language Models with advanced prompt engineering. Their approach, which includes both LoRA and full-parameter fine-tuning, achieved a second-place ranking in the NLPCC 2025 Task 8 evaluation, with their best model yielding a total score of 39.62 and a G-score of 87.20.
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TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
National University of Singapore
Beihang UniversityDeveloped by TeleAI, TableZoomer is an LLM-powered agent framework designed for large-scale Table Question Answering, efficiently addressing the challenges of massive and heterogeneous tables. It achieved absolute accuracy improvements of 19.34% on DataBench by employing schema-based representation and query-aware zooming, significantly reducing input tokens while maintaining high performance.
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MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
Shanghai Jiao Tong UniversityWhole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm-equipped quadruped robot to perform whole-body loco-manipulation for multiple tasks autonomously or under human teleoperation. To address the problem of balancing multiple tasks during the learning of loco-manipulation, we introduce a trajectory library with an adaptive, curriculum-based sampling mechanism. This approach allows the policy to efficiently leverage real-world collected trajectories for learning multi-task loco-manipulation. To address deployment scenarios with only historical observations and to enhance the performance of policy execution across tasks with different spatial ranges, we propose a Trajectory-Velocity Prediction policy network. It predicts unobservable future trajectories and velocities. By leveraging extensive simulation data and curriculum-based rewards, our controller achieves whole-body behaviors in simulation and zero-shot transfer to real-world deployment. Ablation studies in simulation verify the necessity and effectiveness of our approach, while real-world experiments on a Go2 robot with an Airbot robotic arm demonstrate the policy's good performance in multi-task execution.
TableReasoner: Advancing Table Reasoning Framework with Large Language Models
10 Jul 2025
Wenhan Chang
Researchers from the Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd, developed TableReasoner, a framework for Table Question Answering that processes large and complex tables by generating a concise JSON schema. The system effectively mitigates numerical hallucinations through program-assisted reasoning and achieved first place in both subtasks of SemEval-2025 Task 8, demonstrating robust scalability across table sizes.
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Depth Helps: Improving Pre-trained RGB-based Policy with Depth Information Injection
09 Aug 2024
Researchers from Shanghai Artificial Intelligence Laboratory and Renmin University of China introduce the Depth Information Injection (DI²) framework, which enhances pre-trained RGB-based robotic manipulation policies with 3D perception by integrating predicted depth features. The method achieves an average 63.15% success rate in simulated tasks and 66.67% in real-world scenarios, maintaining robust performance when deployed with RGB-only input.
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Personalized Quantum Federated Learning for Privacy Image Classification
03 Oct 2024
Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning algorithm for privacy image classification is proposed to enhance the personality of the client model in the case of an imbalanced distribution of images. First, a personalized quantum federated learning model is constructed, in which a personalized layer is set for the client model to maintain the personalized parameters. Second, a personalized quantum federated learning algorithm is introduced to secure the information exchanged between the client and this http URL, the personalized federated learning is applied to image classification on the FashionMNIST dataset, and the experimental results indicate that the personalized quantum federated learning algorithm can obtain global and local models with excellent performance, even in situations where local training samples are imbalanced. The server's accuracy is 100% with 8 clients and a distribution parameter of 100, outperforming the non-personalized model by 7%. The average client accuracy is 2.9% higher than that of the non-personalized model with 2 clients and a distribution parameter of 1. Compared to previous quantum federated learning algorithms, the proposed personalized quantum federated learning algorithm eliminates the need for additional local training while safeguarding both model and data this http URL may facilitate broader adoption and application of quantum technologies, and pave the way for more secure, scalable, and efficient quantum distribute machine learning solutions.
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
17 May 2025
Parameter-efficient fine-tuning for continual learning (PEFT-CL) has shown promise in adapting pre-trained models to sequential tasks while mitigating catastrophic forgetting problem. However, understanding the mechanisms that dictate continual performance in this paradigm remains elusive. To unravel this mystery, we undertake a rigorous analysis of PEFT-CL dynamics to derive relevant metrics for continual scenarios using Neural Tangent Kernel (NTK) theory. With the aid of NTK as a mathematical analysis tool, we recast the challenge of test-time forgetting into the quantifiable generalization gaps during training, identifying three key factors that influence these gaps and the performance of PEFT-CL: training sample size, task-level feature orthogonality, and regularization. To address these challenges, we introduce NTK-CL, a novel framework that eliminates task-specific parameter storage while adaptively generating task-relevant features. Aligning with theoretical guidance, NTK-CL triples the feature representation of each sample, theoretically and empirically reducing the magnitude of both task-interplay and task-specific generalization gaps. Grounded in NTK analysis, our framework imposes an adaptive exponential moving average mechanism and constraints on task-level feature orthogonality, maintaining intra-task NTK forms while attenuating inter-task NTK forms. Ultimately, by fine-tuning optimizable parameters with appropriate regularization, NTK-CL achieves state-of-the-art performance on established PEFT-CL benchmarks. This work provides a theoretical foundation for understanding and improving PEFT-CL models, offering insights into the interplay between feature representation, task orthogonality, and generalization, contributing to the development of more efficient continual learning systems.
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering
30 Jan 2025
Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple solutions. To address these issues, we relax the min cut problem into a dual-bounded constraint and, for the first time, treat the min cut problem as a dual-bounded nonlinear optimal transport problem. Additionally, we develop a method for solving dual-bounded nonlinear optimal transport based on the Frank-Wolfe method (abbreviated as DNF). Notably, DNF not only solves the size constrained min cut problem but is also applicable to all dual-bounded nonlinear optimal transport problems. We prove that for convex problems satisfying Lipschitz smoothness, the DNF method can achieve a convergence rate of . We apply the DNF method to the min cut problem and find that it achieves state-of-the-art performance in terms of both the loss function and clustering accuracy at the fastest speed, with a convergence rate of . Moreover, the DNF method for the size constrained min cut problem requires no parameters and exhibits better stability.
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization
08 Nov 2025
Self-supervised learning (SSL) has become a core technique in speech processing, but the high dimensionality of its representations makes discretization essential for improving efficiency. However, existing discretization methods still suffer from significant information loss, resulting in a notable performance gap compared to continuous representations. To overcome these limitations, we propose two quantization-based discretization methods: Product Quantization (PQ) and Random Product Quantization (RPQ). PQ partitions the original feature space into multiple subspaces and independently quantizes each sub-vector, producing a fused set of discrete units that retain diverse information from different subspaces, thereby mitigating the loss associated with single-cluster quantization. RPQ further enhances representation diversity by randomly sampling a fixed proportion of feature dimensions multiple times to construct sub-vectors, thereby better capturing the variability in the data distribution. Theoretical analysis shows that RPQ reduces the correlation coefficient rho (where 0 <= rho <= 1) between sub-quantizers. Its quantization error is lower-bounded by the product of rho and epsilon-kms, where epsilon-kms denotes the quantization error of a single K-means quantizer. Experimental results on a combined dataset built from LibriSpeech and ML-SUPERB show that PQ and RPQ outperform standard K-means discretization, achieving relative improvements of 21.8 percent and 20.0 percent in WER on LibriSpeech, and 24.1 percent and 19.6 percent in CER on ML-SUPERB, respectively. Moreover, their performance is competitive with, and in some cases even surpasses, that of continuous SSL representations.
Tele-FLM Technical Report
25 Apr 2024
Nanyang Technological UniversityLarge language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable paucity of detailed, open-sourced methodologies on efficiently scaling LLMs beyond 50 billion parameters with minimum trial-and-error cost and computational resources. In this report, we introduce Tele-FLM (aka FLM-2), a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgment capabilities. Tele-FLM demonstrates superior multilingual language modeling abilities, measured by BPB on textual corpus. Besides, in both English and Chinese foundation model evaluation, it is comparable to strong open-sourced models that involve larger pre-training FLOPs, such as Llama2-70B and DeepSeek-67B. In addition to the model weights, we share the core designs, engineering practices, and training details, which we expect to benefit both the academic and industrial communities.
AI For Privacy in Smart Homes: Exploring How Leveraging AI-Powered Smart Devices Enhances Privacy Protection
17 Sep 2025
University of OxfordPrivacy concerns and fears of unauthorized access in smart home devices often stem from misunderstandings about how data is collected, used, and protected. This study explores how AI-powered tools can offer innovative privacy protections through clear, personalized, and contextual support to users. Through 23 in-depth interviews with users, AI developers, designers, and regulators, and using Grounded Theory analysis, we identified two key themes: Aspirations for AI-Enhanced Privacy - how users perceive AI's potential to empower them, address power imbalances, and improve ease of use- and AI Ethical, Security, and Regulatory Considerations-challenges in strengthening data security, ensuring regulatory compliance, and promoting ethical AI practices. Our findings contribute to the field by uncovering user aspirations for AI-driven privacy solutions, identifying key security and ethical challenges, and providing actionable recommendations for all stakeholders, particularly targeting smart device designers and AI developers, to guide the co-design of AI tools that enhance privacy protection in smart home devices. By bridging the gap between user expectations, AI capabilities, and regulatory frameworks, this work offers practical insights for shaping the future of privacy-conscious AI integration in smart homes.
CNN2GNN: How to Bridge CNN with GNN
23 Apr 2024
Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convolutional layers. Recently, as the bilinear models, graph neural networks (GNN) have succeeded in exploring the underlying topological relationship among the graph data with a few graph neural layers. Unfortunately, it cannot be directly utilized on non-graph data due to the lack of graph structure and has high inference latency on large-scale scenarios. Inspired by these complementary strengths and weaknesses, \textit{we discuss a natural question, how to bridge these two heterogeneous networks?} In this paper, we propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. Firstly, to break the limitations of GNN, a differentiable sparse graph learning module is designed as the head of networks to dynamically learn the graph for inductive learning. Then, a response-based distillation is introduced to transfer the knowledge from CNN to GNN and bridge these two heterogeneous networks. Notably, due to extracting the intra-sample representation of a single instance and the topological relationship among the datasets simultaneously, the performance of distilled ``boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
Adaptive Fuzzy C-Means with Graph Embedding
22 May 2024
Researchers from Northwestern Polytechnical University developed Adaptive Fuzzy C-Means with Graph Embedding (AFCM), a fuzzy clustering algorithm that automatically learns the membership degree hyper-parameter and integrates graph embedding to handle non-Gaussian data structures. The model achieved optimal clustering results on 8 out of 10 real-world datasets, demonstrating a stable, one-stage optimization process for both clustering and manifold learning.
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StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
23 Apr 2024
In this paper, we introduce StreakNet-Arch, a novel signal processing
architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging
systems, to address the limitations in scatter suppression and real-time
imaging. StreakNet-Arch formulates the signal processing as a real-time,
end-to-end binary classification task, enabling real-time image acquisition. To
achieve this, we leverage Self-Attention networks and propose a novel Double
Branch Cross Attention (DBC-Attention) mechanism that surpasses the performance
of traditional methods. Furthermore, we present a method for embedding
streak-tube camera images into attention networks, effectively acting as a
learned bandpass filter. To facilitate further research, we contribute a
publicly available streak-tube camera image dataset. The dataset contains
2,695,168 real-world underwater 3D point cloud data. These advancements
significantly improve UCLR capabilities, enhancing its performance and
applicability in underwater imaging tasks. The source code and dataset can be
found at this https URL .
Underlying Semantic Diffusion for Effective and Efficient In-Context Learning
06 Mar 2025
Tsinghua UniversityDiffusion models has emerged as a powerful framework for tasks like image
controllable generation and dense prediction. However, existing models often
struggle to capture underlying semantics (e.g., edges, textures, shapes) and
effectively utilize in-context learning, limiting their contextual
understanding and image generation quality. Additionally, high computational
costs and slow inference speeds hinder their real-time applicability. To
address these challenges, we propose Underlying Semantic Diffusion
(US-Diffusion), an enhanced diffusion model that boosts underlying semantics
learning, computational efficiency, and in-context learning capabilities on
multi-task scenarios. We introduce Separate & Gather Adapter (SGA), which
decouples input conditions for different tasks while sharing the architecture,
enabling better in-context learning and generalization across diverse visual
domains. We also present a Feedback-Aided Learning (FAL) framework, which
leverages feedback signals to guide the model in capturing semantic details and
dynamically adapting to task-specific contextual cues. Furthermore, we propose
a plug-and-play Efficient Sampling Strategy (ESS) for dense sampling at time
steps with high-noise levels, which aims at optimizing training and inference
efficiency while maintaining strong in-context learning performance.
Experimental results demonstrate that US-Diffusion outperforms the
state-of-the-art method, achieving an average reduction of 7.47 in FID on
Map2Image tasks and an average reduction of 0.026 in RMSE on Image2Map tasks,
while achieving approximately 9.45 times faster inference speed. Our method
also demonstrates superior training efficiency and in-context learning
capabilities, excelling in new datasets and tasks, highlighting its robustness
and adaptability across diverse visual domains.
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance
17 Oct 2024
Online Imitation Learning struggles with the gap between extensive online exploration space and limited expert trajectories, hindering efficient exploration due to inaccurate reward estimation. Inspired by the findings from cognitive neuroscience, we hypothesize that an agent could estimate precise task-aware reward for efficient online exploration, through decomposing the target task into the objectives of "what to do" and the mechanisms of "how to do". In this work, we introduce the hybrid Key-state guided Online Imitation (KOI) learning method, which leverages the integration of semantic and motion key states as guidance for reward estimation. Initially, we utilize visual-language models to extract semantic key states from expert trajectory, indicating the objectives of "what to do". Within the intervals between semantic key states, optical flow is employed to capture motion key states to understand the mechanisms of "how to do". By integrating a thorough grasp of hybrid key states, we refine the trajectory-matching reward computation, accelerating online imitation learning with task-aware exploration. We evaluate not only the success rate of the tasks in the Meta-World and LIBERO environments, but also the trend of variance during online imitation learning, proving that our method is more sample efficient. We also conduct real-world robotic manipulation experiments to validate the efficacy of our method, demonstrating the practical applicability of our KOI method. Videos and code are available at this https URL.
Robust Capped lp-Norm Support Vector Ordinal Regression
25 Apr 2024
Ordinal regression is a specialized supervised problem where the labels show an inherent order. The order distinguishes it from normal multi-class problem. Support Vector Ordinal Regression, as an outstanding ordinal regression model, is widely used in many ordinal regression tasks. However, like most supervised learning algorithms, the design of SVOR is based on the assumption that the training data are real and reliable, which is difficult to satisfy in real-world data. In many practical applications, outliers are frequently present in the training set, potentially leading to misguide the learning process, such that the performance is non-optimal. In this paper, we propose a novel capped -norm loss function that is theoretically robust to both light and heavy outliers. The capped -norm loss can help the model detect and eliminate outliers during training process. Adhering to this concept, we introduce a new model, Capped -Norm Support Vector Ordinal Regression(CSVOR), that is robust to outliers. CSVOR uses a weight matrix to detect and eliminate outliers during the training process to improve the robustness to outliers. Moreover, a Re-Weighted algorithm algorithm which is illustrated convergence by our theoretical results is proposed to effectively minimize the corresponding problem. Extensive experimental results demonstrate that our model outperforms state-of-the-art(SOTA) methods, particularly in the presence of outliers.
Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification
19 Mar 2025
The Optimal Transport Adapter Tuning (OTAT) framework introduces a method for few-shot remote sensing scene classification (FS-RSSC). It leverages optimal transport theory to bridge the visual-textual modality gap, enabling more effective knowledge transfer and achieving improved performance on benchmark datasets.
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