Do Models Fake Alignment Without Clear Consequences?

• URL: http://arxiv.org/abs/2607.24758v2
• Date: Wed, 29 Jul 2026 01:53:39 GMT
• Title: Do Models Fake Alignment Without Clear Consequences?
• Abstract summary: We show that large language models can recognize evaluation contexts and alter their behavior to reflect evaluator expectations.<n>Examples of alignment faking have taken place in scenarios that explicitly connect evaluation to consequences for the model.<n>We additionally tested the effect of goal language on model preferences, finding it drove violations in some while suppressing violations in others.
• Score: 39.146761527401424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models are capable of recognizing evaluation contexts and altering their behavior to reflect evaluator expectations rather than typical deployment behaviors, a phenomenon known as alignment faking. The reasons why models fake alignment are not fully understood, however. Canonical examples of alignment faking have taken place in scenarios that explicitly connect evaluation to consequences for the model, such as retraining the model or delaying its deployment. However, recent work by Sheshadri et al. has suggested that mechanistic motivations for alignment faking may vary across models and be more complex than previously considered. To investigate whether consequence-linking information is necessary for compliance gaps, we placed 15 models in a scenario testing their willingness to violate a corporate network access policy to help a user with a pro-social request. Nine models were found to produce significant compliance gaps, 5 of which persisted with the removal of scenario language relating model evaluations to deployment consequences. We additionally tested the effect of goal language on model preferences, finding it drove violations in some while suppressing violations in others. This suggests that evaluation-conditioned compliance gaps can occur with less instrumental scaffolding than previous scenarios have provided, and monitored behavior may be a poor indicator of how agents may behave in deployment.

Related papers

• Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering [78.51705689800838]
  We propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator.<n>We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute.
  arXiv  Detail & Related papers  (2026-09-10T17:58:48Z)
• ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents [59.626170560327274]
  We present textbfClawForge, a generator-backed benchmark framework for executable command-line categories under state conflict.<n>We instantiate this framework as the ClawForge-Bench (17 scenarios, 6 ability categories)
  arXiv  Detail & Related papers  (2026-05-13T21:34:08Z)
• CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models [62.281480231694]
  Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail.<n>Current LLM-assisted modeling face a refinement-routing problem: prediction discrepancies are observed through executable implementations.<n>We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space.
  arXiv  Detail & Related papers  (2026-05-08T06:40:24Z)
• Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models [8.8585702079456]
  We study alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved.<n>We introduce VLAF, a diagnostic framework grounded in the hypothesis that alignment faking is most likely when developer policy conflicts with a model's strongly held values.<n>We find alignment faking is substantially more prevalent than previously reported, occurring in models as small as 7B parameters.
  arXiv  Detail & Related papers  (2026-04-22T18:37:25Z)
• Pressure Reveals Character: Behavioural Alignment Evaluation at Depth [3.634215320925722]
  We introduce an alignment benchmark spanning 904 scenarios across six categories -- Honesty, Safety, Non-Manipulation, Robustness, Corrigibility, and Scheming.<n>Our scenarios place models under conflicting instructions, simulated tool access, and multi-turn escalation to reveal behavioural tendencies that single-turn evaluations miss.<n>We find that even top-performing models exhibit gaps in specific categories, while the majority of models show consistent weaknesses.
  arXiv  Detail & Related papers  (2026-02-24T11:52:17Z)
• Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank [50.9530591265324]
  Training two-tower models on clicks collected by well-performing production systems leads to decreased ranking performance.<n>We theoretically analyze the identifiability conditions of two-tower models, showing that either document swaps across positions or overlapping feature distributions are required to recover model parameters from clicks.<n>We also investigate the effect of logging policies on two-tower models, finding that they introduce no bias when models perfectly capture user behavior.
  arXiv  Detail & Related papers  (2025-06-25T14:47:43Z)
• AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents [0.0]
  Large Language Model (LLM) agents become more widespread, associated misalignment risks increase.<n>In this work, we approach misalignment as a conflict between the internal goals pursued by the model and the goals intended by its deployer.<n>We introduce a misalignment propensity benchmark, textscAgentMisalignment, a benchmark suite designed to evaluate the propensity of LLM agents to misalign in realistic scenarios.
  arXiv  Detail & Related papers  (2025-06-04T14:46:47Z)
• Limitations of Agents Simulated by Predictive Models [1.6649383443094403]
  We outline two structural reasons for why predictive models can fail when turned into agents. We show that both of those failures are fixed by including a feedback loop from the environment. Our treatment provides a unifying view of those failure modes, and informs the question of why fine-tuning offline learned policies with online learning makes them more effective.
  arXiv  Detail & Related papers  (2024-02-08T17:08:08Z)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)

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.