mlsanityData to 14 Oct 2026

Towards Integrated Alignment

arXiv 2508.06592 ↗First posted 8 Aug 2025cs.CY cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

As AI adoption expands across human society, the problem of aligning AI models to match human preferences remains a grand challenge. Currently, the AI alignment field is deeply divided between behavioral and representational approaches, resulting in narrowly aligned models that are more vulnerable to increasingly deceptive misalignment threats. In the face of this fragmentation, we propose an integrated vision for the future of the field. Drawing on related lessons from immunology and cybersecurity, we lay out a set of design principles for the development of Integrated Alignment frameworks that combine the complementary strengths of diverse alignment approaches through deep integration and adaptive coevolution. We highlight the importance of strategic diversity - deploying orthogonal alignment and misalignment detection approaches to avoid homogeneous pipelines that may be "doomed to success". We also recommend steps for greater unification of the AI alignment research field itself, through cross-collaboration, open model weights and shared community resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 59 of its 96 references that are in the map. First posted on arXiv 8 Aug 2025.

Builds on

59 of 96 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025PRISM: Perspective Reasoning for Integrated Synthesis and Mediation as a Multi-Perspective Framework for AI Alignment5 Feb
  2. 2023A Multi-Level Framework for the AI Alignment Problem10 Jan

Other references · 57

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2016Understanding intermediate layers using linear classifier probescited by 1,353
  4. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  5. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,154
  6. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  7. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  8. 2022Toy Models of Superpositioncited by 971
  9. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
  10. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 724
  11. 2024Scaling and evaluating sparse autoencoderscited by 553
  12. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 529
38 more
  1. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  2. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 435
  3. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 422
  4. 2018AI safety via debatecited by 400
  5. 2024Alignment faking in large language modelscited by 360
  6. 2023Language Models Represent Space and Timecited by 316
  7. 2016Cooperative Inverse Reinforcement Learningcited by 300
  8. 2023AI Alignment: A Comprehensive Surveycited by 263
  9. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  10. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 245
  11. 2024Not All Language Model Features Are One-Dimensionally Linearcited by 212
  12. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 188
  13. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 185
  14. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 178
  15. 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 172
  16. 2020Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?cited by 151
  17. 2018Supervising strong learners by amplifying weak expertscited by 145
  18. 2023Getting aligned on representational alignmentcited by 143
  19. 2024Analyzing the Generalization and Reliability of Steering Vectorscited by 143
  20. 2022Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goalscited by 126
  21. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  22. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
  23. 2025Auditing language models for hidden objectivescited by 83
  24. 2024RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representationscited by 79
  25. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
  26. 2022Polysemanticity and Capacity in Neural Networkscited by 77
  27. 2023Alignment for Honestycited by 74
  28. 2023Eliciting Latent Knowledge from Quirky Language Modelscited by 62
  29. 2020Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesiscited by 39
  30. 2023Fully Autonomous Programming with Large Language Modelscited by 31
  31. 2025Mitigating Deceptive Alignment via Self-Monitoringcited by 29
  32. 2018AGI Safety Literature Reviewcited by 28
  33. 2024Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignmentcited by 27
  34. 2024Robust Reinforcement Learning from Corrupted Human Feedbackcited by 25
  35. 2020An overview of 11 proposals for building safe advanced AIcited by 18
  36. 2024Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Biascited by 15
  37. 2023Deceptive Alignment Monitoringcited by 14
  38. 2022Engineering Monosemanticity in Toy Modelscited by 13

50 most cited of 57 All 57 (JSON)

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Position: Towards Bidirectional Human-AI Alignment
  2. 2024On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
  3. 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process
  4. 2024The AI Alignment Paradox
  5. 2025Out of Control -- Why Alignment Needs Formal Control Theory (and an Alignment Control Stack)
  6. 2026Automated alignment is harder than you think
  7. 2025Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
  8. 2026Align AI to Dynamic Human-AI Workflows