Vikash Singh

Current approaches to LLM safety rely on a brittle pattern of identifying and blocking known threats via guardrails. This paper introduces Trust The Typical (T3), a framework that reframes safety as an out-of-distribution detection problem, learning the distribution of acceptable prompts in a semantic space and flagging significant deviations as potential threats. Unlike prior methods, T3 requires no training on harmful examples yet achieves state-of-the-art performance across 18 benchmarks spanning toxicity, jailbreaking, multilingual harms, and over-refusal—reducing false positive rates by up to 40× relative to specialized safety models. A single model trained on safe English text transfers effectively to over 14 languages without retraining.

Existing log anomaly detection methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. This paper introduces K4 (Knowing the Unknown by Knowing only the Known), a fully unsupervised, parser-independent framework that transforms arbitrary log embeddings into compact four-dimensional descriptors—Precision, Recall, Density, Coverage—using efficient k-nearest neighbor statistics. Under a realistic online chunk-based evaluation protocol, K4 achieves state-of-the-art AUROC of 0.995–0.999 across HDFS, BGL, and Thunderbird datasets, with training under 4 seconds and inference as low as 4 μs.

Large language models show remarkable promise for automated reasoning by generating formal specifications, but a fundamental tension exists between their probabilistic nature and the deterministic guarantees required by formal verification. This paper comprehensively investigates failure modes and uncertainty quantification in LLM-generated formal artifacts, revealing that SMT-based autoformalization has highly domain-specific accuracy impacts ranging from +34.8% on logical tasks to −44.5% on factual ones. A probabilistic context-free grammar (PCFG) framework is introduced to model LLM outputs and yield a refined uncertainty taxonomy, finding that uncertainty signals are task-dependent—for example, grammar entropy for logic achieves AUROC > 0.93.

Training large language models is one of the most compute-intensive tasks in HPC, and predicting end-to-end training time for multi-billion parameter models across hundreds of GPUs is challenging due to complex interactions between transformer components, parallelism strategies, and multi-tier communication. This paper addresses this by decomposing LLMs into core computational primitives and modeling them with operator-level decomposition, lightweight hardware-aware prediction models for key operations, and an end-to-end prediction system integrating these across complex parallelization strategies. The resulting framework enables accurate distributed LLM training performance prediction without costly full-scale sampling.