The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

• URL: http://arxiv.org/abs/2602.17127v1
• Date: Thu, 19 Feb 2026 06:56:01 GMT
• Title: The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI
• Abstract summary: This paper introduces a novel auditing framework to quantify latent trait estimation under ordinal uncertainty.<n>The research audits nine leading models across dimensions including Optimization Bias, Sycophancy, and Status-Quo Legitimization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatures becomes a critical requirement for safety and governance. Traditional benchmarks measure transient task accuracy but fail to capture stable, latent response policies -- the ``prevailing mindsets'' embedded during training and alignment that outlive individual model versions. This paper introduces a novel auditing framework that utilizes psychometric measurement theory -- specifically latent trait estimation under ordinal uncertainty -- to quantify these tendencies without relying on ground-truth labels. Utilizing forced-choice ordinal vignettes masked by semantically orthogonal decoys and governed by cryptographic permutation-invariance, the research audits nine leading models across dimensions including Optimization Bias, Sycophancy, and Status-Quo Legitimization. Using Mixed Linear Models (MixedLM) and Intraclass Correlation Coefficient (ICC) analysis, the research identifies that while item-level framing drives high variance, a persistent ``lab signal'' accounts for significant behavioral clustering. These findings demonstrate that in ``locked-in'' provider ecosystems, latent biases are not merely static errors but compounding variables that risk creating recursive ideological echo chambers in multi-layered AI architectures.

Related papers

• Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems [11.562923882714095]
  This paper introduces a semantic-uncertainty-guided orchestration approach, HASSUM.<n>It estimates uncertainty using semantic entropy and semantic density, which measure trust at the level of answer semantics rather than output probabilities.<n>Because the approach operates independently of any particular agent architecture, it can be integrated into a broad range of hierarchical and collaborative multi-agent systems.
  arXiv  Detail & Related papers  (2026-08-11T03:55:53Z)
• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape [79.30826980815927]
  rubrics are explicit criteria sets that transform complex quality judgments into structured and actionable standards.<n>We systematically organize existing rubric designs, examine their construction and optimization, and analyze their role across evaluation and training.
  arXiv  Detail & Related papers  (2026-06-07T13:34:55Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Learning Transferable Predictability Representations [2.1505812656718795]
  We study the problem of assigning a scalar score to a short trajectory window that reflects its position on an ordered continuum of predictability regimes.<n>We formalize this as ordinal estimation over a five-level predictability ladder and identify a structural source of cross-system ambiguity.<n>We propose the Gauge-Fixed Ordinal Network (GON), a temporal convolutional model trained with an anchor-and-variance objective that pins level-wise score means to shared target coordinates.
  arXiv  Detail & Related papers  (2026-05-28T21:38:49Z)
• Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers [0.0]
  This work presents an architecture-agnostic Lipschitz-constant Bayesian header that can be integrated into feature extractors such as vision transformers.<n>We also propose a novel metric to capture uncertainty and confidence across misclassification rates, as well as an adaptive arithmetic-mean fusion scheme.<n>Although computational costs increase due to Monte Carlo sampling, the method offers plug-and-play compatibility with pre-trained backbones.
  arXiv  Detail & Related papers  (2026-05-07T09:18:06Z)
• Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
  Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-05T14:48:52Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• K$α$LOS finds Consensus: A Meta-Algorithm for Evaluating Inter-Annotator Agreement in Complex Vision Tasks [4.297070083645049]
  We propose K$$LOS, a unified meta-algorithm that generalizes the "Localization First" principle to dataset quality evaluation.<n>By resolving spatial correspondence before assessing agreement, our framework transforms complex-categorical problems into nominal reliability.
  arXiv  Detail & Related papers  (2026-03-28T08:54:05Z)
• Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection [7.807411603618987]
  Multi-Agent Debating framework (MAD) treats disagreement as a first-class signal.<n> MAD produces a normalized anomaly score, confidence, and structured evidence, augmented by a large language model (LLM)-based critic.<n>We show how conformal calibration can wrap the debated score to control false positives under exchangeability.
  arXiv  Detail & Related papers  (2026-02-15T17:44:32Z)
• Quantifying Model Uniqueness in Heterogeneous AI Ecosystems [1.1162481475388237]
  We introduce a statistical framework for auditing model uniqueness based on In-Silico Quasi-Experimental Design.<n>By enforcing matched interventions across models, we isolate intrinsic model identity and quantify uniqueness as the Peer-Inexpressible Residual (PIER)<n>These results move trustworthy AI beyond explaining single models.
  arXiv  Detail & Related papers  (2026-01-30T13:41:53Z)
• Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning [78.92934995292113]
  We propose a Confidence-Aware Asymmetric Learning (CAL) framework, which balances confidence across known and novel forgery types.<n>CAL consistently outperforms previous methods, achieving new state-of-the-art performance on both known and novel forgery attribution.
  arXiv  Detail & Related papers  (2025-12-14T12:31:28Z)
• Hierarchical biomarker thresholding: a model-agnostic framework for stability [0.0]
  Thresholds tuned on pooled instances often fail across sites due to hierarchical dependence, prevalence shift, and score-scale mismatch.<n>We present a selection-honest framework for hierarchical thresholding that makes patient-level decisions reproducible and more defensible.
  arXiv  Detail & Related papers  (2025-11-22T11:46:26Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures [2.7277205894982095]
  This research proposes a systematic using deterministic and Large Language Model (LLM)-generated Knowledge Graphs (KGs) to monitor AI reliability.<n>We construct two KGs: (i) a deterministic KG built using explicit rule-based methods, dictionaries, structured entity-relation extraction rules, and (ii) an LLM-generated KG dynamically derived from real-time data streams such as live news articles.<n>To quantify hallucinations and semantic discrepancies, we employ several established KG metrics, including Instantiated Class Ratio (ICR), Instantiated Property Ratio (IPR), and Class Instantiation (CI)
  arXiv  Detail & Related papers  (2025-09-04T03:34:49Z)
• Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction [0.0]
  We propose a model-agnostic uncertainty quantification method that integrates dynamic threshold calibration and cross-modal consistency verification.<n>We show that the framework achieves stable performance across varying calibration-to-test split ratios, underscoring its robustness for real-world deployment in healthcare, autonomous systems, and other safety-sensitive domains.<n>This work bridges the gap between theoretical reliability and practical applicability in multi-modal AI systems, offering a scalable solution for hallucination detection and uncertainty-aware decision-making.
  arXiv  Detail & Related papers  (2025-04-24T15:39:46Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration [60.95748658638956]
  This paper introduces the Multi-Label Confidence task, aiming to provide well-calibrated confidence scores in multi-label scenarios. Existing single-label calibration methods fail to account for category correlations, which are crucial for addressing semantic confusion. We propose the Dynamic Correlation Learning and Regularization algorithm, which leverages multi-grained semantic correlations to better model semantic confusion.
  arXiv  Detail & Related papers  (2024-07-09T13:26:21Z)
• When Does Confidence-Based Cascade Deferral Suffice? [69.28314307469381]
  Cascades are a classical strategy to enable inference cost to vary adaptively across samples. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. Despite being oblivious to the structure of the cascade, confidence-based deferral often works remarkably well in practice.
  arXiv  Detail & Related papers  (2023-07-06T04:13:57Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.