2 papers
cs.LG2026
How Much Rank Does LoRA Need? Rank-Error Bounds for Transformer Attention
Gerard Conangla Planes
Choosing the rank of a low-rank adaptation (LoRA) update is usually an empirical task. In this paper, we provide a task-dependent theory of the approximation error achievable at ea…
cs.CE2026
TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
Qianggang Ding, Haochen Shi, Luis Castejón Lozano +7
We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies symbolic relational priors, dynamic graph…