arXiv subjects

Xuejian Xu

Publications and source records attributed to Xuejian Xu.

3 recordsLinked to original sources

arXiv2026

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon.

cs.LG↗

arXiv2026

C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding

Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation modeling among state, action, and return-to-go (RTG) sequences, and indiscriminate learning of optimal/suboptimal behaviors. To address these, we propose C2, a novel framework enhancing DT with two core innovations: (1) a Cross Learning Block (CLB) via cross-attention to strengthen inter-sequence correlation modeling; (2) a Constraint-aware Loss (CL) incorporating budget and Cost-Per-Acquisition (CPA) constraints for selective learning of optimal trajectories. Extensive offline evaluations on the AuctionNet dataset demonstrate consistent performance gains (up to 3.2% over state-of-the-art method) across diverse budget settings; ablation studies verify the complementary synergy of CLB and CL, confirming C2's superiority in auto-bidding. The code for reproducing our results is available at: https://github.com/Dingjinren/C2.

cs.LG↗

arXiv2017

Modeling, Analysis, and Optimization of Coded Caching in Small-Cell Networks

Coded caching is able to exploit accumulated cache size and hence superior to uncoded caching by distributing different fractions of a file in different nodes. This work investigates coded caching in a large-scale small-cell network (SCN) where the locations of small base stations (SBSs) are modeled by stochastic geometry. We first propose a content delivery framework, where multiple SBSs that cache different coded packets of a desired file transmit concurrently upon a user request and the user decodes the signals using successive interference cancellation (SIC). We characterize the performance of coded caching by two performance metrics, average fractional offloaded traffic (AFOT) and average ergodic rate (AER), for which a closed-form expression and a tractable expression are derived, respectively, in the high signal-to-noise ratio region. We then formulate the coded cache placement problem for AFOT maximization as a multiple-choice knapsack problem (MCKP). By utilizing the analytical properties of AFOT, a greedy but optimal algorithm is proposed. We also consider the coded cache placement problem for AER maximization. By converting this problem into a standard MCKP, a heuristic algorithm is proposed. Analytical and numerical results reveal several design and performance insights of coded caching in conjunction with SIC receiver in interference-limited SCNs.

cs.IT↗