Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-art performance on benchmarks like RelBench. However, the current leading model, RelGT, suffers from two key limitations: its random local sampler yields loosely connected...
It is found that workspace topology measurably affects IPI success, and changes in codebase modularity can significantly alter the Attack Success Rate (ASR), with highly modular environments demonstrating significantly lower attack success rates.
A. R. Day, Pradeep Yadlapalli, Sriram Venkatapathy et al.· 0 citations
This work proposes Sequential Epistemic and Action-Level Validation (SEAV), a verification-centric jailbreak evaluation framework that decomposes responses into ordered steps and evaluates both validity and correctness, and shows that enforcing correctness substantially reshapes measured robustness.
Qilong Wu, Sahil Wadhwa, Pranab Mohanty et al.· 0 citations
REPREC is a lightweight framework that conditions a frozen LLM using compact user-level representations and consistently improves recommendation performance across different sequential encoders, LLM backbones, and user activity levels.
