Domain-generalizable speech emotion recognition (DG-SER) aims to ensure the robustness of SER models across unknown domains, which is essential for real-world human-machine interaction systems. Most DG-SER approaches employ alignment or adversarial strategies with domain labels to promote generalization. However, these strategies often confine generalization to predefined domains, limiting robustness under diverse real-world speech variations. To address these challenges, this paper proposes an emotion-preserving conditional information bottleneck framework (EP-CIB) for domain-free DG-SER. Specifically, EP-CIB introduces a nuisance proxy representation learning module to learn a nuisance proxy as the broad non-emotional variability without requiring any domain annotations, covering both defined and previously unseen domains. It then extracts coarse-grained emotion features via the consistency-aware emotion representation learning module. EP-CIB further introduces an emotion-preserving conditional information bottleneck that, conditioned on the emotion label, disentangles the nuisance proxy from the coarse-grained emotion representation, improving domain-free generalization under open-ended domain shifts. EP-CIB departs from implicit domain surrogates in prior domain-free methods by explicitly learning a proxy for nuisance domains and disentangling it from emotion features, enabling domain-free emotion representation learning. The state-of-the-art performance in both speaker-independent and cross-corpus settings, including an 18% improvement on EmoDB-to-CASIA transfer, demonstrates the effectiveness of EP-CIB for DG-SER.
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To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market conditions where demands are influenced by various contextual factors, while simultaneously dealing with the difficulty of lost sales that obscure true demand information. However, existing approaches often fail to account for both contextual information and censored demand observations. We address this gap by presenting a framework where we model demand as a linear combination of basis functions with unknown coefficients, allowing for adaptive pricing and inventory decisions that respond to changing contexts. We propose an efficient algorithm to achieve regret bound O(K sqrt(T) log T) under concave revenue conditions and O(K^(2/3) T^(2/3) (log T)^(1/2)) for the general case, with matching lower bounds confirming optimality. Extensive numerical experiments across diverse scenarios demonstrate our algorithm’s effectiveness.
Generating executable tool plans requires selecting appropriate subsets from tool libraries, a combinatorial search problem with an exponentially large solution space. However, we identify a critical misalignment in predominant approaches: standard autoregressive (AR) decoding suffers from early commitment, where initial token choices rigidly constrain the search trajectory. A controlled study shows that masked denoising raises Pass@10 solution coverage from 0.320 to 0.943 over AR sampling under matched compute. Motivated by this, we propose DiG-Plan, a framework that decouples combinatorial exploration from structural refinement. DiG-Plan employs a diffusion-based proposer to generate diverse tool sets via iterative refinement, followed by an AR refiner for dependency prediction. On TaskBench, DiG-Plan improves over AR baselines by a 10% relative margin, with the largest gains on complex compositional tasks; API-Bank results show that the propose-refine-select design remains effective across domains. Code is available at https://github.com/puddingyeah/DiG-Plan.
Text-to-Image (T2I) diffusion models have been widely adopted due to their strong generative capabilities, while Low-Rank Adaptation (LoRA) has emerged as an efficient mechanism for customizing these models for diverse creative and commercial applications. This trend has fostered LoRA-centric service platforms that that enable the customization and commercial distribution of LoRA modules according to user requirements. However, the growing prevalence of LoRA and its critical role in customized AI services have raised urgent concerns about LoRA copyright protection. To address this gap, we propose LoRA2D, an authorized dual-watermarking framework specifically designed to protect LoRA modules in T2I diffusion models. LoRA2D integrates license-based authorization control with explicit watermarks as visible deterrents for unauthorized or trial usage, which can be removed upon valid authorization, while persistently embedding an implicit watermark for robust black-box ownership verification. Extensive experiments on multiple image-generation datasets demonstrate the effectiveness and practicality of LoRA2D for securing copyrights in LoRA-adapted T2I diffusion models.
The deployment of edge servers plays a crucial role in supporting large-scale edge computing systems, where multiple conflicting objectives—such as latency, energy consumption, load balancing, and service reliability—must be jointly optimized in complex, dynamic environments. Existing solutions often struggle to scale effectively or to balance these objectives in a unified learning framework. In this paper, we propose GMM-TDQN, a two-stage multi-objective reinforcement learning framework for large-scale edge server deployment. The first stage employs a Gaussian Mixture Model (GMM) to capture spatial and workload heterogeneity, enabling an efficient reduction of the deployment search space. Building upon this structured initialization, the second stage formulates the deployment problem as a sequential decision-making task and adopts a Transformer-enhanced Deep Q-Network (TDQN) to learn adaptive deployment policies that balance multiple objectives. Extensive experiments on real-world datasets demonstrate that GMM-TDQN consistently outperforms state-of-the-art methods, achieving reductions of 29.18% in average latency and 17.55% in energy consumption, while improving load balancing by 27.50% and system reliability by 32.55%. These results validate the effectiveness and scalability of the proposed framework for multi-objective edge server deployment.
Federated learning (FL) has emerged as a distributed machine learning paradigm due to its privacy-preserving advantages. Most FL studies assume offline labeled datasets are available at clients. In practice, however, client data often arrive without labels in a streaming manner, making label acquisition a crucial problem. Existing solutions typically rely on human experts or artificial intelligence (AI) models for annotation. Nevertheless, due to the subjectivity among human experts and the inherent limitations of AI models, the labels they provide are often unreliable. In this work, we propose CoFL, a novel consensus driven human-AI collaborative FL method, which utilizes the complementarity between humans and AI models to produce more reliable labels. Moreover, CoFL leverages cross-client consensus among human experts to further enhance the collaboration process. Experiments on three datasets demonstrate that CoFL consistently outperforms baseline methods under various settings. The code is available at https://github.com/huaiguang233/CoFL.
Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, fraudsters continuously evolve their camouflaging strategies by deliberately mimicking textual responses of benign users, thereby concealing their malicious purposes. This phenomenon, referred to as semantic camouflage, fundamentally undermines commonly relied assumptions on how structural and attribute cues can be exploited to identify fraudsters, and makes it difficult to spot fraudsters with unsupervised TAGFD. To bridge the gaps, we propose a Case-Adaptive Multi-cue Expert fRAmework (CAMERA) for unsupervised TAGFD. CAMERA employs an ego-decoupled mixture-of-experts architecture, where each expert specializes in modeling a distinct type of fraud-indicative cue. A context-informed gating model is introduced to jointly consider the ego node representation and its local neighborhood context for adaptive integration of cues learned by different experts. Furthermore, CAMERA leverages the inherent rarity of fraudsters to support unsupervised one-class learning with expert-level objectives that encourage modeling dominant benign patterns, thereby enabling reliable unsupervised detection of camouflaged fraudsters. Experiments on 4 challenging datasets show that CAMERA consistently outperforms competitors, showing its effectiveness against semantically camouflaged fraudsters. Code available at https://github.com/CampanulaBells/CAMERA
Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract relation triplets for unseen relation types without any annotated training data. Recent advancements in Large Language Models (LLMs) have significantly enhanced ZSRTE performance, enabling the direct generation of relational triplets from unstructured text. However, LLMs often introduce biases such as entity shift, relation confusion, and over-prediction, which limit the reliability of the extracted triplets. In this paper, we introduce DETRI, a novel debiasing framework for ZSRTE that addresses these biases by leveraging a discriminative structural expert model. DETRI treats LLMs as inspiration generators and refines their outputs via a three-stage debiasing pipeline, consisting of inspiration-based re-prediction, confidence-based filtering, and entity shift correction through span perturbation. The framework improves LLM-generated triplets without requiring fine-tuning the LLM, thus offering a transferable and scalable solution. Extensive experiments on two datasets show that DETRI outperforms state-of-the-art methods by reducing bias and improving extraction accuracy, achieving 2.89% F1 improvement over fine-tuned LLM methods while keeping the LLM parameters frozen. Our approach shows strong generalization across different LLMs, offering a solution to mitigate biases.
Clinical Computed Tomography (CT) reconstruction often faces mixed degradations, where quantum noise, streak artifacts, and geometric distortions co-occur with various compositions and severities. Recently, all-in-one frameworks have outperformed traditional single-task models through degradation-specialized modulation mechanisms. However, attempting to resolve these mixed degradations simultaneously forces the model into suboptimal trade-offs, failing to balance conflicting reconstruction objectives. Our empirical study shows that decomposing CT reconstruction into a sequence of ordered steps effectively mitigates this conflict, with the execution order being a critical performance factor. Leveraging this insight, we propose AgenticCT, an LLM-orchestrated multi-agent framework designed to autonomously plan the optimal reconstruction trajectory. Specifically, AgenticCT operates through a diagnose-plan-treat workflow: a Supervisor Agent explicitly estimates a structured degradation state; a Planner Agent then selects a topology-optimized execution order conditioned on this state; and a library of Processor Agents sequentially executes the trajectory to achieve high-fidelity reconstruction. Extensive experiments demonstrate that AgenticCT consistently improves reconstruction fidelity across single and mixed degradations, and generalizes better to out-of-distribution datasets compared with strong specialized and all-in-one baselines. Code is available at: https://github.com/yqhuang2912/AgenticCT.
Affective computing has achieved notable success in recognizing explicit emotions from short, isolated dialogue segments. However, human emotions are often implicitly expressed, internally regulated, and dynamically evolve over extended interactions. Existing models struggle to disentangle internal emotional states from external expressions, and fail to capture the emotional inconsistency that emerges across long-horizon dialogues. To address this limitation, we introduce Emotional Inconsistency Analysis (EIA), a novel task that aims to identify and reason about discrepancies between implicit and explicit emotions over long-term conversational contexts. To support this task, we construct the MaskDialog dataset carefully curated from television drama and large language models (LLMs). We further propose two LLM-based baseline approaches, i.e., One-shot Self-consistent Inference and Cascaded Multi-step Inference, and conduct comprehensive analyses on dialogue construction strategies and inference behaviors. Extensive experiments across multiple mainstream LLMs reveal that EIA remains highly challenging, particularly in modeling implicit emotional trajectories and cross-turn inconsistency. Overall, EIA reframes emotion understanding from short-term recognition to longitudinal, implicit emotion tracking, with implications for dialogue systems and human–computer interaction.
Time series anomaly detection faces a critical challenge that different anomaly types require different detection mechanisms, yet single methods are inherently limited by their design biases. We propose FlowFuse, a multi-view ensemble framework with coupling flow-based score fusion for time series anomaly detection. FlowFuse combines four complementary detection modules spanning temporal and frequency domains, as well as clustering and reconstruction paradigms, to extract multi-perspective anomaly scores. Rather than simple averaging, an ensemble of coupling flows models the joint distribution of these multi-view scores, learning complex inter-view dependencies through invertible transformations with alternating updates between temporal and frequency scores. The ensemble naturally quantifies detection uncertainty through prediction disagreement, which can optionally guide selective supervision when labels are available. Extensive experiments across 18 diverse benchmarks show that FlowFuse achieves state-of-the-art performance, demonstrating effectiveness across multiple anomaly types.
Offline safe reinforcement learning learns high-return policies that satisfy hard safety constraints using only a pre-collected dataset. This setting is challenging due to the inability to explore, and the risk of propagating value errors through unsafe state-space regions. To address this, first, we characterize the safe state region by developing a framework for learning control barrier functions (CBFs) using a novel generalized Bellman operator, yielding a persistent safety set, from which the agent can remain safe indefinitely. Second, we show that several existing safety set estimation methods (e.g., reachability-constrained RL) can be formulated within our CBF learning framework, highlighting its generality. We further propose a new CBF that ensures safety under environment dynamics uncertainty, unlike standard CBFs designed for deterministic settings. Third, we propose a new reward maximization algorithm that effectively exploits our learned persistent safety set for reward critic estimation. Empirical results on standard benchmarks show that our approach achieves state-of-the-art safety with fewer constraint violations while maintaining competitive returns.
Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and continuous temporal evolution that captures how such shifts unfold over time. By jointly encoding these temporal semantics, the framework constructs time-varying region representations that condition pretrained static OD generators in a lightweight and plug-and-play fashion. This modular design further supports scalable deployment and cross-city transferability. Extensive experiments on large-scale real-world datasets show that our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity. Code is publicly available at https://github.com/csjiezhao/DynaOD.
Machine unlearning has become a critical capability for AI services to comply with evolving privacy regulations. A key yet underexplored challenge is how to verify whether a profit-driven AI server has faithfully performed unlearning. Existing verification approaches either incur prohibitive costs or provide insufficient deterrence, failing to balance audit cost and enforcement effectiveness. To bridge this gap, we propose UAG, a game-theoretic unlearning auditing framework that incentivizes truthful unlearning via strategic deterrence rather than exhaustive verification. We design a hierarchical auditing mechanism that combines low-cost screening with selectively triggered high-precision verification, and models the server-auditor interaction as a three-stage dynamic Bayesian game. By characterizing the equilibrium strategy, we derive optimal audit and penalty policies that incentivize honest unlearning. Theoretical analysis and experiments show that UAG maintains reliable detection while achieving a favorable cost-deterrence trade-off. Notably, UAG attains a server honesty rate of approximately 95% while screening only about 50% of unlearning requests, showing its practicality for trustworthy black-box unlearning services.
Semantic watermarking methods, embedding identity into the initial latent noise, provide an imperceptible identity traceability for diffusion models in copyright protection and source verification. However, existing methods are highly vulnerable to adversarial attacks, especially geometric transformations (e.g., rotation, cropping) and latent-space manipulations via proxy models, limiting the reliability of watermark verification in practical deployment. To address this issue, we propose a robust and fully reversible, flow-based watermarking framework with dual encoding paths, which preserves high visual fidelity of watermarked image while ensuring resilient identity recovery under adversarial attacks. Specifically, a dual-path network is proposed to encode watermark information into both the generated image and the owner’s secret key. This network leverages Mutual Information Redundancy to recover compromised information under single-path attack, ensuring robust verification. To enhance verification credibility without degrading generation quality, we introduce a joint training strategy that suppresses false positives on negative samples through contrastive learning under fidelity constraints. Furthermore, we employ a backward Euler iteration scheduler for rectified flow models, which facilitate accurate inversion mapping, to enable effective watermark verification, which accurate inversion. Extensive experiments show that our method achieves superior robustness against various adversarial attacks while maintaining high visual quality across diverse generative models.
Recent advances in large language models (LLMs) allow agents to represent actions as executable code, offering greater expressivity than traditional tool-calling. However, real-world tasks often demand both strategic planning and detailed implementation. Using a single agent for both leads to context pollution from debugging traces and intermediate failures, impairing long-horizon performance. We propose CodeDelegator, a multi-agent framework that separates planning from implementation via role specialization. A persistent Delegator maintains strategic oversight by decomposing tasks, writing specifications, and monitoring progress without executing code. For each sub-task, a new Coder agent is instantiated with a clean context containing only its specification, shielding it from prior failures. To coordinate between agents, we introduce Ephemeral-Persistent State Separation (EPSS), which isolates each Coder's execution state while preserving global coherence, preventing debugging traces from polluting the Delegator's context. Experiments on various benchmarks demonstrate the effectiveness of CodeDelegator across diverse scenarios. Code is available at https://github.com/txfei/code-delegator.
Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (Property Enhanced Instruction Tuning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5, BioT5, MolCA and Text+Chem-T5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, demonstrating the effectiveness of the PEIT framework for various molecular tasks. The code and appendix are available at https://github.com/chenlong164/PEIT.
Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfulness. Existing approaches often fail to reconcile these two requirements. Extractive methods rely on rigid sentence splicing that disrupts macro-level logical coherence, while large language model (LLM)-based generative approaches, despite mastering linguistic fluency, exhibit limited factual consistency. In this work, we propose ScholarSum, a hierarchical reflective graph-based framework that emulates a student–teacher writing process for fluent and faithful scientific summarization. ScholarSum first organizes the document into a hierarchical knowledge graph by segmenting it into semantically coherent units, whose multi-layered community structure captures global logic and macro-level themes. Guided by this global structure, the student generates an initial draft, which is subsequently refined through fine-grained evidence retrieval. To ensure factual consistency, a teacher-like reviewer then iteratively examines the draft, identifies unsupported content, and prompts targeted re-retrieval and rewriting until the summary meets rigorous quality standards. Extensive experiments demonstrate that ScholarSum significantly outperforms previous baselines in terms of both completeness and faithfulness. Our code is available at https://github.com/Xiaoyu-Tao/ScholarSum.
Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary for context-aware time series forecasting. Specifically, TokenCast employs a discrete tokenizer to transform continuous numerical sequences into temporal tokens, enabling structural alignment with language-based inputs. To effectively bridge the semantic gap between modalities, both temporal and contextual tokens are embedded into a shared representation space via a pre-trained LLM, further optimized with generative objectives. Building upon this unified semantic space, the aligned LLM is subsequently fine-tuned in a supervised manner to predict future temporal tokens, which are then decoded back into the original numerical space. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework and highlight its potential as a generative framework for context-aware time series forecasting. The code is available at https://github.com/Xiaoyu-Tao/TokenCast.
As a fundamental component of the physical layer, convolutional codes are instrumental in ensuring dependable transmission of information over inherently noisy communication channels. Recently, neural network-based decoding algorithms have shown remarkable progress in enhancing the performance of block error-correcting codes. However, current neural network decoders for convolutional codes often exhibit strong performance primarily on low-rate codewords, whereas maximum likelihood decoding algorithm incur substantial time overhead when decoding high-rate codewords, which impacts communication efficiency in practical communication systems. In this work, we propose a Transformer-based decoder for convolutional codes designed for soft-decision decoding over AWGN and fading channels, which serves as an efficient decoding scheme for convolutional codes with various code rates. For the unique structure of convolutional codes, we propose a Bidirectional Context Aggregation mechanism to enable the model to fully capture the long-range dependencies and the memory effect of convolutional structures. Additionally, we extend the Transformer attention matrix with a codeword-specific feature integration mechanism to allow the model to capture implicit relationships within codewords. Extensive experiments demonstrate that the accuracy performance of the proposed model outperforms that of neural network decoders. And for maximum likelihood decoding algorithm, our model achieves comparable error-correction capability and a significantly faster decoding speed.