We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for visual language models (VLMs). Existing state-of-the-art UQ methods rely on consistency among multiple responses, but often report higher confidence when the model is consistently incorrect, leading to poorly calibrated confidence. To address this, we leverage cross-modal consistency by grounding back the textual responses to visual inputs. The confidence from the grounding model (GM) is used to calibrate the overall confidence. Given that using a GM adds its own uncertainty, we apply temperature scaling to calibrate the GM’s confidence in the accuracy of the generated responses. We comprehensively evaluate the proposed approach across diverse range of settings, including datasets with varying visuo-linguistic complexity, Q&A and caption generation tasks, general visual and specialized medical contexts, VLMs differing in scale, openness, and different categories of GMs. Experimental results show that our approach consistently improves confidence calibration, with expected calibration error (ECE) improvements ranging from 32.5% to 96.7% over both self and non-self consistency-based baselines. Code is available here https://github.com/trilokpadhi/Multimodal-Uncertainty-Quantification.

Chain-of-thought (CoT) prompting makes language models write step-by-step explanations, but these steps may not match what the model actually used to choose its answer. Existing faithfulness checks often only test whether changing the written chain changes the answer, without verifying whether the steps are truly supported by the given evidence, or they require special prompts that do not generalize well. We present NSF-CoT, a neuro-symbolic formal verification method that checks CoT faithfulness step by step for contextual question answering. NSF-CoT (1) converts the provided context facts and each reasoning step into simple logical statements, (2) uses counterfactual attribution to estimate which context facts the model relied on while generating each step, and (3) verifies each step using a hybrid checker that combines an SMT solver with an LLM-based entailment judge. For every step, we score groundedness (supported by the full context), validity (supported by the facts the model relied on), and utility (helps reach the final answer), and combine them into a faithfulness score. Across OpenBookQA, QASC, and HotpotQA, NSF-CoT consistently outperforms causal mediation, perturbation probes, and behavioral monitoring, and it identifies reasoning steps that are not only unfaithful but also harmful to the model’s final decision.

Hierarchical Instruction Following (HIF) refers to the problem of prompting large language models with a priority-ordered stack of instructions. Standard methods like RLHF and DPO typically fail in this problem since they mainly optimize for a single objective, failing to explicitly enforce system prompt compliance. Meanwhile, supervised fine-tuning relies on mimicking filtered, compliant data, which fails to establish the priority asymmetry at the algorithmic level. In this paper, we introduce HIPO, a novel alignment framework that formulates HIF as a Constrained Markov Decision Process. HIPO elevates system prompts from mere input context to strict algorithmic boundaries. Using a primal-dual safe reinforcement learning approach, the algorithm dynamically enforces system prompt compliance as an explicit constraint, maximizing user utility strictly within this feasible region. Extensive evaluations across diverse model architectures (e.g., Qwen, Phi, Llama) demonstrate that HIPO significantly improves both system compliance and user utility. Furthermore, mechanistic analysis reveals that this constrained optimization autonomously drives the model to shift its attention toward long-range system tokens, providing a principled foundation for reliable LLM deployment in complex workflows.