6 citations · 14 across the 12 of their papers we have counts for
16 papers
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades
Miaobo Hu, Shuhao Hu, Xiaobo Guo +4
We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read semantics, binds successful read…
FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN
Adhwaa Al-Chaab, Ayman Younis, Dario Pompili
Open Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelli…
Quantizer Redundancy and Exact Index Recoding in a Frozen Neural Audio Codec
Youssef Hemimy
Residual scalar quantization can produce highly dependent indices when successive stages reuse identical, unscaled grids. This case study examines that behavior in a frozen neural…
VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Repeated verifier calls are useful only when they contribute conditional information. We introduce VStress, an auditable replay contract, and VStress-CA, a correlation-aware alloca…
SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text
Miaobo Hu, Xiaobo Guo, Shuhao Hu +5
Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already availa…
ECPO: Evidence-Coupled Policy Optimization for Verifier-Audited Candidate Ranking
Miaobo Hu, Shuhao Hu, BoKun Wang +5
Ranking systems used for decision support should expose evidence that can be audited independently of the model's ranking scores. We study a closed-roster setting in which a policy…
AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback
Miaobo Hu, Shuhao Hu, Bokun Wang +5
Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuning-heavy. We propose Adaptiv…
SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction
Miaobo Hu, Shuhao Hu, Bokun Wang +5
Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect to the text. In this settin…
Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning
Yue Zhou, Xiaobo Guo, Belhassen Bayar +1
Long-term conversational agents face a fundamental scalability challenge as interactions extend over time: repeatedly processing entire conversation histories becomes computational…
Neural Node Matching for Multi-Target Cross Domain Recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha +5
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) s…
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
Xuewen Tao, Mingming Ha, Xiaobo Guo +3
Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of mul…
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Ying Chen, Siwei Qiang, Mingming Ha +5
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse sce…
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Youru Li, Zhenfeng Zhu, Xiaobo Guo +3
Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied…
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +5
Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of…
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
Wujiang Xu, Runzhong Wang, Xiaobo Guo +6
Video summarization intends to produce a concise video summary by effectively capturing and combining the most informative parts of the whole content. Existing approaches for video…
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +6
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR…