Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks.However, highquality human-annotated preference data remains expensive and scarce.Existing methods address this challenge through either selfrewarding, which scales by using purely AIgenerated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data.In this paper, we present COACT, a novel framework that synergistically combines selfrewarding and active learning through strategic human-AI collaboration.COACT leverages self-consistency to identify both reliable selflabeled data and samples that are requiring oracle verification.Additionally, oracle feedback guides the model to generate new instructions within its solvable capability.Evaluated on three reasoning benchmarks across two model families, COACT achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.1

With the large-scale deployment of IPv6, it is becoming more and more important to probe active IPv6 addresses on the global Internet. However, the vast address space and the random distribution of active addresses make the probing process full of challenges, especially for the probing of IPv6 prefixes without seed addresses. Furthermore, the widespread existence of IPv6 aliased prefixes also causes significant trouble for probing. In this paper, we presentAddrProbe, an active IPv6 address probing system, which dynamically probes all global routing prefixes based on learned fine-grained address patterns from limited seed addresses and quickly detects aliased prefixes during probing. The evaluation results show thatAddrProbeachieves a hit rate of 23%-45% with all routing prefixes announced by the BGP system, which is 6.6-13× that of current state-of-the-art approaches (no more than 4%). Moreover, we find 1.2×1033aliased addresses characterized by the detected aliased prefixes, covering 6,412 routing prefixes, which is a 107× and 5.9× improvement over existing methods, respectively. Finally, an IPv6 Hitlist is constructed based on the long-term probing results, which contains 562M addresses covering 190K routing prefixes and 29K ASes. These widely distributed addresses are meaningful for analyzing IPv6 address assignments and some other IPv6 measurement activities.

Out-of-distribution (OOD) detection is crucial for ensuring the robustness of machine learning models by identifying samples that deviate from the training distribution. While traditional OOD detection has predominantly focused on single-modality inputs, such as images, recent advancements in multimodal models have shown the potential of utilizing multiple modalities (e.g., video, optical flow, audio) to improve detection performance. However, existing approaches often neglect intra-class variability within in-distribution (ID) data, assuming that samples of the same class are perfectly cohesive and consistent. This assumption can lead to performance degradation, especially when prediction discrepancies are indiscriminately amplified across all samples. To address this issue, we propose Dynamic Prototype Updating (DPU), a novel plug-and-play framework for multimodal OOD detection that accounts for intra-class variations. Our method dynamically updates class center representations for each class by measuring the variance of similar samples within each batch, enabling tailored adjustments. This approach allows us to intensify prediction discrepancies based on the updated class centers, thereby enhancing the model’s robustness and generalization across different modalities. Extensive experiments on two tasks, five datasets, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performances, setting a new state-of-the-art in multimodal OOD detection, including improvements up to 80% in Far-OOD detection. To improve accessibility and reproducibility, our code is released at https://github.com/lili0415/DPU-OOD-Detection.

To ensure the performance of large-scale datacenters, operators need to monitor up to tens of millions of various-type KPIs, e.g., CPU utilization, memory utilization. For each KPI, it is crucial but challenging to detect outliers that deviate from its historical patterns or the patterns of other KPIs in the same period. In this work, we proposeOutSpot, an unsupervised outlier detection framework that integrates hierarchical agglomerative clustering (HAC) with conditional variational autoencoder (CVAE), which significantly improves computational efficiency and comprehensively learns the above two patterns. Additionally, two simple yet effective techniques, soft threshold and median filter, are applied to precisely determine outlier KPIs. Using two real-world datasets collected from the datacenters owned by a top-tier global short video service provider and a top-tier domestic operator,respectively. It demonstrates thatOutSpotachieves the best F1 score of 0.95 and 0.91, AUC of 0.99 and 0.99 on the two datasets, significantly outperforming seven baseline outlier detection methods.