RDMA is promising for enhancing the performance of cross-datacenter (DC) services. However, deploying RDMA over wide-area networks introduces severe congestion control unfairness, primarily due to asymmetric congestion feedback delays between inter-DC flows and intra-DC flows. As a result, intra-DC flows often bear the full burden of congestion response, leading to drastically increased flow completion times (FCT). In this work, we identify two key forms of unfairness — near-source and near-destination — depending on whether congestion occurs near the sender or receiver of inter-DC flows. Based on this, we propose THEMIS, a fairness maintenance patch for long-haul RDMA networks. To mitigate near-source unfairness, THEMIS devises a Proactive Notification Point to shorten the congestion feedback loop within a single DC. To alleviate near-destination unfairness, THEMIS introduces a Temporary Reaction Point to temporarily slow down the target inter-DC flow until the sender receives the corresponding congestion feedback. We implement an open-source prototype of THEMIS, and evaluate it on both real-world testbed and large-scale simulations. Compared to DCQCN, Annulus and BiCC, THEMIS reduces the intra-DC FCT by up to 79.2%, 63.6% and 55.6%, and decreases overall FCT by up to 61.2%, 31.9% and 59.5% respectively.

Social chatbots have become essential companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive response mechanisms usually rely on users to initiate or sustain dialogues by bringing up new topics, resulting in diminished engagement and shortened dialogue duration. In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. Specifically, PaRT first integrates user profiles and dialogue context into a large language model (LLM), which is initially prompted to refine user queries and recognize underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics as targeted queries to retrieve relevant passages from RedNote. Finally, we prompt LLMs with summarized passages to generate knowledge-grounded and engagement-optimized responses. Our approach has been running stably in a real-world production environment for more than 30 days, achieving a 21.77% improvement in the average duration of dialogues.

Despite the significant progress of large language models (LLMs) in various tasks, they often produce factual errors due to their limited internal knowledge.Retrieval-Augmented Generation (RAG), which enhances LLMs with external knowledge sources, offers a promising solution.However, these methods can be misled by irrelevant paragraphs in retrieved documents.Due to the inherent uncertainty in LLM generation, inputting the entire document may introduce off-topic information, causing the model to deviate from the central topic and affecting the relevance of the generated content.To address these issues, we propose the Retrieve-Plan-Generation (RPG) framework.RPG generates plan tokens to guide subsequent generation in the plan stage.In the answer stage, the model selects relevant fine-grained paragraphs based on the plan and uses them for further answer generation.This plan-answer process is repeated iteratively until completion, enhancing generation relevance by focusing on specific topics.To implement this framework efficiently, we utilize a simple but effective multi-task prompt-tuning method, enabling the existing LLMs to handle both planning and answering.We comprehensively compare RPG with baselines across 5 knowledge-intensive generation tasks, demonstrating the effectiveness of our approach.1

Recent years have witnessed the urgent request for multi-modal sarcasm detection in social media platforms. Though large efforts have been made with significant progress, prior arts may fail to fully integrate the commonsense knowledge, and struggle to model the incongruity within implicit meanings of multimodal cues. To address these limitations, we propose a novel Knowledge-Enhanced Multi-perspective Incongruity Perception network, named KEMIP. Specifically, we adopt generative language models to produce captions and commonsense for image and text respectively for comprehensive understanding. Subsequently, to exploit the essential cues from multiple perspectives, we customize an incongruity perception module, which utilizes several fusion networks to capture both literal and implicit inconsistencies. Afterwards, an ensemble weighting gate is employed to integrate the result of individual perspective. Experiments on a public multimodal sarcasm detection benchmark demonstrate the superiority of our proposed KEMIP framework.

Abstract The Cohenian paradox is one of the main themes of judicial probability theory and one of the core topics discussed by the new evidence scholarship. To resolve this paradox, evidence scholars nowadays have proposed various solutions, including legal probabilism, judicial Bayesian decision theory and relative plausibility theory. These three solutions can be classified into two approaches, i.e. the probabilism and the explanationism. Among them, the former includes legal probabilism and judicial Bayesian decision theory, and the latter includes the relative plausibility theory. However, the two approaches have recently begun to converge and become more understandable to each other. For example, Welch (2020, Int. J. Evid. Proof, 24, 351–373) has recently defended and improved the relative plausibility theory by substantially improving it with the help of Bayesian decision theory. In this paper, by contrast, we attempt to defend the probabilistic approach—legal probabilism and Bayesian decision theory on the basis of relative plausibility theory.