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cs.LG2026

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

Mohsen Hariri, Weicong Chen, Nahal Shahini +11

Large language models can solve substantially harder reasoning problems with more inference-time compute. The term "test-time scaling," however, now covers diverse inference algori…

cs.LG2026

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation

Osama Zafar, Alexander Nemecek, Yiqian Zhang +5

Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals. We introduce a Privacy Poli…

cs.LG2026

CausalGuard: Conformal Inference under Graph Uncertainty

Vikash Singh, Weicong Chen, Debargha Ganguly +12

Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause…

cs.LG2026

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation

Vikash Singh, Debargha Ganguly, Weicong Chen +8

Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a frozen source checkpoint. This anchor is usef…

cs.LG2025

: Online Log Anomaly Detection Via Unsupervised Typicality Learning

Weicong Chen, Vikash Singh, Zahra Rahmani +3

Existing Log Anomaly Detection (LogAD) methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. We introduce , an unsupervised and p…