# Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems

arXiv: https://arxiv.org/abs/2604.17805 · HTML: https://mlsanity.com/paper/2604.17805/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion](https://mlsanity.com/paper/2503.17551/) (2025)
- [PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference](https://mlsanity.com/paper/2509.04467/) (2025)
- [TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts](https://mlsanity.com/paper/2512.22748/) (2025)
- [MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks](https://mlsanity.com/paper/2503.19134/) (2025)
- [DRF: LLM-AGENT Dynamic Reputation Filtering Framework](https://mlsanity.com/paper/2509.05764/) (2025)
- [HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance](https://mlsanity.com/paper/2510.02630/) (2025)
- [IPS: In-Prompt Process Supervision for Short Video Content Moderation](https://mlsanity.com/paper/2412.15251/) (2024)
- [Robust Voting Rules from Algorithmic Robust Statistics](https://mlsanity.com/paper/2112.06380/) (2021)

## Built on by (most cited first, 2 of 2)

- [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026)
- [Harnessing LLMs as Agents: What Does It Cost?](https://mlsanity.com/paper/2610.02488/) (2026)

## Abstract (arXiv)

Maximum-likelihood pairwise ranking is a com- mon computational mechanism for prioritization, reputation estimation, and comparison-driven decision support. Despite its broad use, the perturbation sensitivity of this estimator under structured changes in comparison data remains insufficiently characterized. We study this question as an applied-mathematics and computational-science problem in stability analysis. We for- mulate coordinated perturbation as a budgeted subset-selection problem over pairwise observations and introduce an Adaptive Subset Selection Attack (ASSA) as a scalable search heuristic for probing high-impact perturbation sets. Through experiments on synthetic and observed preference datasets, we show that MLE-based ranking can exhibit pronounced regime-dependent sensitivity: relatively small but coordinated perturbations may in- duce meaningful changes in output orderings, while the response profile varies across budgets and data conditions. By comparing ASSA with random, greedy, and randomized subset baselines under repeated trials, we characterize both the magnitude and the variability of perturbation-induced ranking shifts. These results position pairwise ranking sensitivity as a problem in computational reliability, numerical stability, and robustness auditing for engineering systems built on comparison-driven inference.