Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during adaptation. The complexity of scene information and lack of the source domain make SFDA a difficult task. Recent studies have shown promising results, but many approaches to domain adaptation concentrate on domain shift and neglect the effects of negative transfer, which may impede enhancements of model performance during adaptation. In this paper, addressing this issue, we propose a novel framework of Attention Residual Fusion Network (ARFNet) based on contrast learning for SFDA to alleviate negative transfer and domain shift during the progress of adaptation, in which attention residual fusion, global-local attention contrast, and dynamic centroid evaluation are exploited. Concretely, the attention mechanism is first exploited to capture the discriminative region of the target object. Then, in each block, attention features are decomposed into spatial-wise and channel-wise attentions. The spatial-wise attentions are aggregated with original semantic features to achieve the cross-layer attention residual fusion progressively while the channel-wise attentions are exploited for self-distillation. During adaptation progress, we contrast global and local representations to improve the perceptual capabilities of different categories, which enables the model to discriminate variations between inner-class and intra-class. Finally, a dynamic centroid evaluation strategy is exploited to evaluate the trustworthy centroids and labels for self-supervised self-distillation, which aims to accurately approximate the center of the source domain and pseudo-labels to mitigate domain shift. To validate the efficacy of our methods, we execute comprehensive experiments on five benchmarks of varying scales, i.e., Office-31, Office-Home, VisDA-C, DomainNet-126, Cub-Paintings. Experimental outcomes indicate that our method surpasses other techniques, attaining superior performance across SFDA benchmarks.

Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionable intelligence from this data remains challenging due to extreme noise, high throughput, and semantic complexity of diverse business lines. In this paper, we present TingIS, an end-to-end system designed for enterprise-grade incident discovery. At the core of TingIS is a multi-stage event linking engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging, enabling the stable extraction of actionable incidents from just a handful of diverse user descriptions. This engine is complemented by a cascaded routing mechanism for precise business attribution and a multi-dimensional noise reduction pipeline that integrates domain knowledge, statistical patterns, and behavioral filtering. Deployed in a production environment handling a peak throughput of over 2,000 messages per minute and 300,000 messages per day, TingIS achieves a P90 alert latency of 3.5 minutes and a 95% discovery rate for high-priority incidents. Benchmarks constructed from real-world data demonstrate that TingIS significantly outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.

We introduce the Markovian Pre-trained Transformer (MPT) for next-item recommendation, a transferable model fully pre-trained on synthetic Markov chains, yet capable of achieving state-of-the-art performance by fine-tuning a lightweight adaptor. This counterintuitive success stems from the observation of the `Markovian' nature: advanced sequential recommenders coincidentally rely on the latest interaction to make predictions, while the historical interactions serve mainly as auxiliary cues for inferring the user's general, non-sequential identity. This characteristic necessitates the capabilities of a universal recommendation model to effectively summarize the user sequence, with particular emphasis on the latest interaction. MPT inherently has the potential to be universal and transferable. On the one hand, when trained to predict the next state of Markov chains, it acquires the capabilities to estimate transition probabilities from the context (one adaptive manner for summarizing sequences) and attend to the last state to ensure accurate state transitions. On the other hand, unlike the heterogeneous interaction data, an unlimited amount of controllable Markov chains is available to boost the model capacity. We conduct extensive experiments on five public datasets from three distinct platforms to validate the superiority of Markovian pre-training over traditional recommendation pre-training and recent language pre-training paradigms.

Throughout history, transformative technologies have repeatedly reshaped production systems and economic activity. In the current wave of AI development, Agentic AI systems are emerging as a new class of productive instruments capable of goal perception, multi-step planning, environmental interaction, and autonomous workflow coordination. Beyond automating routine tasks, these systems have the potential to augment knowledge work, reorganize decision-making, and reshape the relationship between human labor, organizational routines, and machine intelligence. This development gives rise to the emerging research direction of AI for Productivity (AI4Productivity), which examines how advanced AI systems affect productivity across economic and social contexts. In this paper, we conducts an in-depth investigation for explaining how Agentic AI creates productivity. We define AI4Productivity as the use of Agentic AI in real-world workflows to deliver economically valuable work with less human effort, and identify two core mechanisms of productivity gains: Efficiency, which reduces the time and labor required for existing tasks, and Expansion, which makes previously infeasible work economically viable. We further characterize the evolution of AI systems through four stages: conversational assistant, reactive operator, adaptive coordinator, and self-governing production system. Building on this framework, we review representative AI productivity tools and examine the sectoral diffusion of Agentic AI across three levels of adoption: early-stage exploration in manufacturing, agriculture, real estate, and government services; broader integration in trade, media, education, and legal services; deeper workflow orchestration in information technology, finance, and healthcare. Finally, we discuss key challenges including safety, long-horizon reliability, academia-industry gaps, adoption barriers, governance, and labor-market uncertainty. This paper provides a structured account of how Agentic AI is reshaping productivity and outlines future directions for reliable and socially beneficial AI-driven production systems. We hope this paper will serve as a foundation for advancing AI4Productivity research and guiding the design of AI systems that deliver reliable, scalable, and broadly beneficial productivity gains.

Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rating prediction and explanation generation in a multi-task manner. However, these works suffer from incoherence between predicted ratings and explanations. To address the issue, we propose a novel framework that employs a large language model (LLM) to generate a rating, transforms it into a rating vector, and finally generates an explanation based on the rating vector and user-item information. Moreover, we propose utilizing publicly available LLMs and pre-trained sentiment analysis models to automatically evaluate the coherence without human annotations. Extensive experimental results on three datasets of explainable recommendation show that the proposed framework is effective, outperforming state-of-the-art baselines with improvements of 7.3% in explainability and 4.4% in text quality.

Source-free domain adaptation (SFDA) aims to address the challenge of adapting to a target domain without accessing the source domain directly. However, due to the inaccessibility of source domain data, deterministic invariable features cannot be obtained. Current mainstream methods primarily focus on evaluating invariant features in the target domain that closely resemble those in the source domain, subsequently aligning the target domain with the source domain. However, these methods are susceptible to hard samples and influenced by domain bias. In this paper, we propose a Consistent Assistant Domains Transformer for SFDA, abbreviated as CADTrans, which solves the issue by constructing invariable feature representations of domain consistency. Concretely, we develop an assistant domain module for CADTrans to obtain diversified representations from the intermediate aggregated global attentions, which addresses the limitation of existing methods in adequately representing diversity. Based on assistant and target domains, invariable feature representations are obtained by multiple consistent strategies, which can be used to distinguish easy and hard samples. Finally, to align the hard samples to the corresponding easy samples, we construct a conditional multi-kernel max mean discrepancy (CMK-MMD) strategy to distinguish between samples of the same category and those of different categories. Extensive experiments are conducted on various benchmarks such as Office-31, Office-Home, VISDA-C, and DomainNet-126, proving the significant performance improvements achieved by our proposed approaches. Code is available at https://github.com/RoryShao/CADTrans.git.

Fine-tuning large language models (LLMs) is challenged by the presence of noisy data and the high computational cost when training on large-scale datasets. While data selection has emerged as a promising approach to reduce training cost and improve data quality, existing methods often rely on static heuristics or manual metrics. These approaches struggle to adapt to the model’s evolving capabilities during training, as its understanding of tasks improves. As the model becomes more powerful, its requirements for data that can enhance performance also change, making it crucial to incorporate this dynamic into the data selection process. Moreover, ensuring data diversity throughout different stages of training is essential for preventing redundancy, reducing overfitting. To address these issues, we propose DSP, a Diversity-Aware Self-Paced data selection framework that evolves with the model. DSP progressively selects training samples based on the model’s own outputs and incorporates a diversity-aware mechanism to enhance generalization and mitigate overfitting. Unlike prior static or rule-based strategies, DSP adaptively adjusts to the model’s internal feedback and training stage. Experiments on two public benchmarks demonstrate that DSP consistently outperforms static and heuristic-based baselines across multiple datasets and backbone models. Our findings highlight the critical role of dynamic, diversity-aware data selection in effective LLM fine-tuning.

Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where triggers inserted into original graphs cause adversary-determined predictions. Backdoor attacks on GNNs, typically focusing on node classification tasks, are categorized by dirty- and clean-label attacks and pose challenges due to the interconnected nature of normal and poisoned nodes. Current defenses are indeed circumvented by sophisticated triggers and often rely on strong assumptions borrowed from other domains (e.g., rapid loss drops on poisoned images). They lead to high attack risks, failing to effectively protect against both dirty- and clean-label attacks simultaneously. To tackle these challenges, we propose DShield, a comprehensive defense framework with a discrepancy learning mechanism to defend against various graph backdoor attacks. Specifically, we reveal two vital facts during the attacking process: semantic drift where dirty-label attacks modify the semantic information of poisoned nodes, and attribute overemphasis where clean-label attacks exaggerate specific attributes to enforce adversary-determined predictions. Motivated by those, DShield employs a self-supervised learning framework to construct a model without relying on manipulated label information. Subsequently, it utilizes both the self-supervised and backdoored models to analyze discrepancies in semantic information and attribute importance, effectively filtering out poisoned nodes. Finally, DShield trains normal models using the preserved nodes, thereby minimizing the impact of poisoned nodes. Compared with 6 state-of-the-art defenses under 21 backdoor attacks, we conduct evaluations on 7 datasets with 2 victim models to demonstrate that DShield effectively mitigates backdoor threats with minimal degradation in performance on normal nodes. For instance, on the Cora dataset, DShield reduces the attack success rate to 1.33% from 54.47% achieved by the second-best defense Prune while maintaining an 82.15% performance on normal nodes. The source code is available at https://github.com/csyuhao/DShield.

Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addresses the challenges of achieving high long-context performance, low computational complexity, and compatibility with pretrained models – collectively termed the “impossible triangle”. We introduce E2LLM (Encoder Elongated Large Language Models), a novel approach that effectively navigates this paradox. E2LLM divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter. To enhance the LLM’s reasoning with these soft prompts, we employ two training objectives: encoder output reconstruction and long-context instruction fine-tuning. Extensive experiments reveal that E2LLM not only outperforms 8 state-of-the-art (SOTA) methods in effectiveness and efficiency for document summarization and question answering, but also achieves the best performance on LongBench v2 among models of comparable size. The source code is available at https://github.com/codefuse-ai/E2LLM .

This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge graph exploration, ignoring sequential patterns present in the session history. Therefore, we propose a generalized hierarchical reinforcement learning framework for SR, which improves the explainability of existing SR models via Path Reasoning, namely PR4SR. Considering the different importance of items to the session, we design the session-level agent to select the items in the session as the starting point for path reasoning and the path-level agent to perform path reasoning. In particular, we design a multi-target reward mechanism to adapt to the skip behaviors of sequential patterns in SR, and introduce path midpoint reward to enhance the exploration efficiency in knowledge graphs. To improve the completeness of the knowledge graph and to diversify the paths of explanation, we incorporate extracted feature information from images into the knowledge graph. We instantiate PR4SR in five state-of-the-art SR models (i.e., GRU4REC, NARM, GCSAN, SR-GNN, SASRec) and compare it with other explainable SR frameworks, to demonstrate the effectiveness of PR4SR for recommendation and explanation tasks through extensive experiments with these approaches on four datasets.

The adversarial robustness of a neural network mainly relies on two factors: model capacity and antiperturbation ability. In this article, we study the antiperturbation ability of the network from the feature maps of convolutional layers. Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling. It brings new inspiration to the design of robust neural networks and urges us to apply these findings to improve existing architectures. The proposed modifications are very simple and only require upsampling the inputs or slightly modifying the stride configurations of downsampling operators. We verify our approaches on several benchmark neural network architectures, including AlexNet, VGG, RestNet18, and PreActResNet18. Nontrivial improvements in terms of both natural accuracy and adversarial robustness can be achieved under various attack and defense mechanisms. The code is available at https://github.com/MTandHJ/rcm.

Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already. However, these claims are drawn from unobjective and unfair comparisons. In view of the substantial quantity of items in reality, conventional recommenders typically adopt a pointwise/pairwise loss function instead for training. This substitute however causes severe performance degradation, leading to under-estimation of conventional methods and over-confidence in the ranking capability of LLMs. In this work, we theoretically justify the superiority of cross-entropy, and showcase that it can be adequately replaced by some elementary approximations with certain necessary modifications. The remarkable results across three public datasets corroborate that even in a practical sense, existing LLM-based methods are not as effective as claimed for next-item recommendation. We hope that these theoretical understandings in conjunction with the empirical results will facilitate an objective evaluation of LLM-based recommendation in the future.

Automated code generation is a pivotal capability of large language models (LLMs). However, assessing this capability in real-world scenarios remains challenging. Previous methods focus more on low-level code generation, such as model loading, instead of generating high-level codes catering for real-world tasks, such as image-to-text, text classification, in various domains. Therefore, we construct AICoderEval, a dataset focused on real-world tasks in various domains based on HuggingFace, PyTorch, and TensorFlow, along with comprehensive metrics for evaluation and enhancing LLMs' task-specific code generation capability. AICoderEval contains test cases and complete programs for automated evaluation of these tasks, covering domains such as natural language processing, computer vision, and multimodal learning. To facilitate research in this area, we open-source the AICoderEval dataset at . After that, we propose CoderGen, an agent-based framework, to help LLMs generate codes related to real-world tasks on the constructed AICoderEval. Moreover, we train a more powerful task-specific code generation model, named AICoder, which is refined on llama-3 based on AICoderEval. Our experiments demonstrate the effectiveness of CoderGen in improving LLMs' task-specific code generation capability (by 12.00% on pass@1 for original model and 9.50% on pass@1 for ReAct Agent). AICoder also outperforms current code generation LLMs, indicating the great quality of the AICoderEval benchmark.

Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue flow, with agent tuning being a crucial optimization technique that involves human adjustments to the model for better response to such guidance.Addressing this dependency, our work introduces the TinyAgent model, trained on a meticulously curated high-quality dataset. We also present the Collaborative Multi-Agent Tuning (CMAT) framework, an innovative system designed to augment language agent capabilities through adaptive weight updates based on environmental feedback. This framework fosters collaborative learning and real-time adaptation among multiple intelligent agents, enhancing their context-awareness and long-term memory. In this research, we propose a new communication agent framework that integrates multi-agent systems with environmental feedback mechanisms, offering a scalable method to explore cooperative behaviors. Notably, our TinyAgent-7B model exhibits performance on par with GPT-3.5, despite having fewer parameters, signifying a substantial improvement in the efficiency and effectiveness of LLMs.

Sequential recommendation is a critical but challenging task in capturing users' potential preferences due to inherent biases in the data. Existing debiasing recommendation methods aim to eliminate biases from historical interaction data collected by recommender systems and have shown promising results. However, there is another significant bias that hinders the improvement of sequential recommendation models: dynamic item tendency bias. This bias arises because a period might have some unique tendencies consisting of items interacted with by users with the same intent, leading to a dynamic tendency distribution that biases the model training towards these tendencies. To address this issue, we propose a causal approach to model dynamic item tendency bias in sequential recommendation. We first extract tendencies on carefully designed item-item graphs through community detection. We then use causal intervention to conduct deconfounded training to capture true user preferences and introduce the beneficial item tendency bias to the inference process through optimal transport techniques. Experimental results on four real-world datasets demonstrate that our proposed method consistently outperforms state-of-the-art debiasing recommendation methods, confirming that our model is effective in reducing dynamic item tendency bias and dealing with tendency drifts.

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher's knowledge may not always be reliable; 2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher's knowledge; 3) divergence in semantic space poses a challenge to distill the knowledge from embeddings. To tackle these challenges, this work proposes a novel distillation strategy, DLLM2Rec, specifically tailored for knowledge distillation from LLM-based recommendation models to conventional sequential models. DLLM2Rec comprises: 1) Importance-aware ranking distillation, which filters reliable and student-friendly knowledge by weighting instances according to teacher confidence and student-teacher consistency; 2) Collaborative embedding distillation integrates knowledge from teacher embeddings with collaborative signals mined from the data. Extensive experiments demonstrate the effectiveness of the proposed DLLM2Rec, boosting three typical sequential models with an average improvement of 47.97%, even enabling them to surpass LLM-based recommenders in some cases.

Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' potential intentions. Consequently, machines need to interact with users multiple rounds to better understand users' intents. The unpredictable costs of using or learning image generation models through multiple feedback interactions hinder their widespread adoption and full performance potential, especially for non-expert users. In this research, we aim to enhance the user-friendliness of our image generation system. To achieve this, we propose a reflective human-machine co-adaptation strategy, named RHM-CAS. Externally, the Agent engages in meaningful language interactions with users to reflect on and refine the generated images. Internally, the Agent tries to optimize the policy based on user preferences, ensuring that the final outcomes closely align with user preferences. Various experiments on different tasks demonstrate the effectiveness of the proposed method.

Vision transformers (ViT) usually extract features via forwarding all the tokens in the self-attention layers from top to toe. In this paper, we introduce dynamic token-pass vision transformers (DoViT) for semantic segmentation, which can adaptively reduce the inference cost for images with different complexity. DoViT gradually stops partial easy tokens from self-attention calculation and keeps the hard tokens forwarding until meeting the stopping criteria. We employ lightweight auxiliary heads to make the token-pass decision and divide the tokens into keeping/stopping parts. With a token separate calculation, the self-attention layers are speeded up with sparse tokens and still work friendly with hardware. A token reconstruction module is built to collect and reset the grouped tokens to their original position in the sequence, which is necessary to predict correct semantic masks. We conduct extensive experiments on two common semantic segmentation tasks, and demonstrate that our method greatly reduces about 40% ∼ 60% FLOPs and the drop of mIoU is within 0.8% for various segmentation transformers. The throughput and inference speed of ViT-L/B are increased to more than 2× on Cityscapes. Code is available at https://github.com/FLHonker/DoViT-code.

Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models. In this paper, we propose a novel embedding update mechanism, Structure-aware Embedding Evolution (SEvo for short), to encourage related nodes to evolve similarly at each step. Unlike GNN (Graph Neural Network) that typically serves as an intermediate module, SEvo is able to directly inject graph structural information into embedding with minimal computational overhead during training. The convergence properties of SEvo along with its potential variants are theoretically analyzed to justify the validity of the designs. Moreover, SEvo can be seamlessly integrated into existing optimizers for state-of-the-art performance. Particularly SEvo-enhanced AdamW with moment estimate correction demonstrates consistent improvements across a spectrum of models and datasets, suggesting a novel technical route to effectively utilize graph structural information beyond explicit GNN modules.

Personalized exercise recommendation is a challenging task in the field of artificial intelligence in education due to several problems. First, the mainstream approaches focus more on the exercises that students have not mastered, while overlooking their long-term needs during the learning process. Second, it is difficult to capture students' knowledge states caused by sparse interactions with exercises. Moreover, most recommendation methods are dedicated to the performance of the recommendation w.r.t. accuracy, disregarding the students' learning ability. We introduce a new framework called contrastive personalized exercise recommendation with reinforcement learning (RCL4ER) to tackle these issues. Our framework augments the standard recommendation model with an output layer of self-supervised learning and reinforcement learning. The reinforcement allows the supervised layer to focus on specific rewards, acting as a regularizer. The self-supervised layer provides a powerful signal for parameter updating. Three data augmentation methods are used to provide additional data, which are leveraged to conduct contrastive learning and incorporated into reinforcement learning as supplementary information. In addition, we adopt a trained deep knowledge tracing model to capture the changes in students' knowledge states, so as to establish a dynamic reward mechanism. Experiments of our framework based on four recommendation backbones on several public datasets demonstrate the effectiveness of our RCL4ER, which successfully promotes students' capacity and improves the recommendation performance.