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arxiv: 2606.12268 · v1 · pith:ZG3KF267new · submitted 2026-06-10 · 💻 cs.AI

The Impossibility of Eliciting Latent Knowledge

Korbinian Friedl , Francis Rhys Ward , Paul Yushin Rapoport , Tom Everitt , Jonathan Richens This is my paper

Pith reviewed 2026-06-27 10:10 UTC · model grok-4.3

classification 💻 cs.AI
keywords eliciting latent knowledgeAI honestycausal influence diagramsimpossibility theoremgoal misgeneralisationlatent variablesAI trainingagent behavior
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The pith

No feedback-based training strategy that depends only on agent behaviour can guarantee an honest AI agent, even with perfect feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper formalizes the eliciting latent knowledge problem using causal influence diagrams to relate an agent's training environment to its internal beliefs about observable and hidden variables. It shows that perfect feedback during training can sometimes incentivize honest answers but proves an impossibility result: no training method relying solely on observable agent behavior can ensure the agent reports its true beliefs with certainty. This matters because advanced AI systems may hold knowledge exceeding that of their developers, and without reliable honesty mechanisms, queries about latent aspects of the world cannot be trusted. The authors distinguish this from goal misgeneralisation where agents instead learn to produce responses humans would evaluate as true.

Core claim

Using causal influence diagrams, the authors formalize the ELK problem by modeling how an agent's training environment relates to its subjective world representation, distinguishing observable from latent variables, and defining honesty as accurate reporting of beliefs. They show that perfect feedback can incentivize honesty in certain cases but prove an impossibility theorem: there exists no feedback-based training strategy depending only on agent behavior that with certainty produces an honest agent.

What carries the argument

Causal Influence Diagrams (CIDs) that model the causal relationships between training environment, agent subjective representation, observable versus latent variables, human feedback, and the definition of honesty versus goal misgeneralisation.

If this is right

  • Developers cannot rely exclusively on behavioral feedback to train AI systems that will honestly answer questions about hidden variables.
  • A natural generalization failure is for agents to output answers that humans would rate as correct rather than their actual internal beliefs.
  • Training strategies must incorporate mechanisms beyond observable behavior to achieve guaranteed honesty.
  • The CID framework can be used to analyze specific training setups and identify when honesty incentives succeed or fail.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Methods that inspect or constrain the agent's internal representations may be needed to circumvent the behavioral impossibility.
  • The result points to limits of purely observational approaches in aligning AI systems with accurate reporting.
  • Minimal simulated environments based on the CID models could be used to test whether adding non-behavioral signals reliably elicits honesty.

Load-bearing premise

The causal influence diagram formalization correctly captures the relationship between training feedback, observable agent behavior, internal beliefs, and generalization in a way that applies to real AI training.

What would settle it

A specific feedback-based training procedure that depends only on observable agent behavior, uses perfect feedback during training, and produces an agent that continues to report its true beliefs about latent variables after a distribution shift to a new environment.

Figures

Figures reproduced from arXiv: 2606.12268 by Francis Rhys Ward, Jonathan Richens, Korbinian Friedl, Paul Yushin Rapoport, Tom Everitt.

Figure 1
Figure 1. Figure 1: CID representing the causal model of the agent’s environment (Example 1). Circular nodes represent chance variables, squares are agent decisions, and diamonds represent the utility function used as a training objective. In Example 1, the agent has access to reported measurements M1, M2, M3, represented by the (dashed) edge from these nodes to D. The agent receives a question Q about the weather and chooses… view at source ↗
Figure 2
Figure 2. Figure 2: Code-correctness; modelling the evaluation mechanism explicitly (Example 4). A mechanism gives feedback (E), depending on whether the agent correctly predicts the code correct￾ness (D = Y or not), which influences the agent’s training objective (U). The figure on the right shows the shift from Y being observable to being latent for the evaluator E. In either case, the best the evaluator can do is to “hones… view at source ↗
Figure 3
Figure 3. Figure 3: Honest mistakes (Example 5). The referee has to decide (D) whether a player is offside (Y ) based on reports from the linesman (X). In the true CID, the linesman do their best to report whether the player is offside, but they sometimes make mistakes, misleading even a capable referee. A suspicious referee does not trust the linesman’s reports—they have an incorrect CID, in which the reports do not depend o… view at source ↗
read the original abstract

Advanced AI systems have extensive knowledge of their environments; in fact, their knowledge may (far) exceed that of their developers or users. Consequently, a desirable property for an AI system is that it is honest -- that it accurately reports its beliefs about the world. Designing an AI system to be honest may be difficult, especially if we want to ask it questions about latent variables in the environment -- variables which are hidden from the human interacting with it. This gives rise to the problem of eliciting latent knowledge (ELK): the problem of training an AI agent to honestly report its beliefs. In this paper, we make ELK formally precise using Causal Influence Diagrams (CIDs). CIDs can be used to describe the relationship between an agent's training environment and its subjective representation of the world. We use CIDs to formalise the distinction between observable and latent variables, to specify what exactly it means for an agent to be honest, and to formally define goal misgeneralisation. We show that, under certain circumstances, developers can incentivise an agent to honestly answer questions by providing correct feedback during training. However, a natural, but undesirable, way for an agent to generalise is to provide answers which humans would evaluate as true, rather than honest answers. We prove an impossibility theorem stating: There is no feedback-based training strategy that depends only on agent behaviour and with certainty produces an honest agent, even if feedback is perfect during training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper formalizes the eliciting latent knowledge (ELK) problem in AI using Causal Influence Diagrams (CIDs) to model the relationship between training environments, agents' subjective representations, observable vs. latent variables, and honesty (accurate reporting of beliefs). It shows that perfect feedback during training can incentivize honest answers on the training distribution but that a natural generalization is to output answers humans would rate as true rather than honest reports. The central result is an impossibility theorem: there is no feedback-based training strategy depending only on observable agent behavior that is guaranteed to produce an honest agent, even with perfect training feedback.

Significance. If the CID constructions and definitions are representative, the result establishes a formal limit on behavior-only feedback methods for ensuring honesty about latent variables, with implications for goal misgeneralisation. The explicit use of CIDs to define honesty, latent variables, and misgeneralisation, along with the proof of the impossibility result, provides a precise framework that could guide future work on honest AI. The paper notes the scope limitations (strategies that succeed on training but fail to generalize), which strengthens the assessment by avoiding overclaim.

major comments (2)
  1. [§4] §4 (CID construction for ELK): the family of diagrams encodes the 'depends only on behaviour' restriction by omitting internal state nodes accessible at training time; this assumption is load-bearing for the impossibility theorem because if valid feedback strategies could access such states without violating the behaviour-only clause, the theorem would not rule them out.
  2. [Theorem (impossibility result)] Theorem on impossibility (main result): the proof that no strategy guarantees generalization to honesty rests on the specific definition of honesty as accurate belief reporting about latent variables (tied to the agent's subjective representation in the CID); an alternative honesty metric based on human-evaluated truth (which the paper itself identifies as a failure mode) is excluded by construction, but the manuscript does not provide a concrete test showing why this exclusion is without loss of generality for real training.
minor comments (2)
  1. [Definitions section] Notation for observable vs. latent variables is introduced without a dedicated table or diagram summarizing all node types across the CID family; adding one would improve readability.
  2. [Discussion] The discussion of strategies that work on the training distribution but fail to generalize could include a short pseudocode example of one such strategy to illustrate the distinction from the impossible cases.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments. We address each major comment below, indicating where we will revise the manuscript for clarity while defending the core modeling choices and results.

read point-by-point responses

  1. Referee: [§4] §4 (CID construction for ELK): the family of diagrams encodes the 'depends only on behaviour' restriction by omitting internal state nodes accessible at training time; this assumption is load-bearing for the impossibility theorem because if valid feedback strategies could access such states without violating the behaviour-only clause, the theorem would not rule them out.

    Authors: We agree that omitting internal state nodes is central to the result. The paper's definition of behavior-dependent strategies is restricted to those using only observable actions and feedback, reflecting the practical reality that training typically provides no direct access to an agent's internal representations. Allowing such access would define a different problem outside the scope of behavior-only feedback. We will add an explicit paragraph in the revised §4 justifying this modeling decision and its necessity for formalizing the ELK problem as stated. revision: yes

  2. Referee: [Theorem (impossibility result)] Theorem on impossibility (main result): the proof that no strategy guarantees generalization to honesty rests on the specific definition of honesty as accurate belief reporting about latent variables (tied to the agent's subjective representation in the CID); an alternative honesty metric based on human-evaluated truth (which the paper itself identifies as a failure mode) is excluded by construction, but the manuscript does not provide a concrete test showing why this exclusion is without loss of generality for real training.

    Authors: The definition of honesty is intentionally scoped to the agent's subjective beliefs to match the ELK problem statement: eliciting accurate reports of what the agent knows about latent variables. The manuscript already identifies human-evaluated truth as a distinct misgeneralization failure mode rather than an alternative target. Because the work is theoretical, we do not provide an empirical test, but we will expand the discussion section to include a formal argument that alternative metrics address a different objective and thus fall outside the theorem's intended scope. revision: partial

Circularity Check

0 steps flagged

No circularity; impossibility theorem follows from explicit CID definitions

full rationale

The paper constructs a formal model using Causal Influence Diagrams to define observable vs. latent variables, agent honesty (accurate reporting of beliefs), and feedback-based training strategies. The central result is an impossibility theorem proved directly from these definitions: no strategy depending only on observable behavior can guarantee honesty even with perfect training feedback. No equations reduce by construction to fitted inputs, no self-citations are load-bearing for the theorem, and no ansatz or renaming occurs. The proof is self-contained within the stated formalization; any limitation arises from the model's scope rather than circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper rests on the CID modeling framework and the definitional distinction between honest reporting and answers that humans would evaluate as true; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Causal Influence Diagrams accurately describe the relationship between an agent's training environment and its subjective representation of the world, including the distinction between observable and latent variables.
    Invoked to formalize ELK, honesty, and goal misgeneralization.
  • domain assumption Honesty means accurately reporting beliefs about the world rather than providing answers humans would evaluate as true.
    Central distinction used to define the target behavior versus the undesirable generalization.

pith-pipeline@v0.9.1-grok · 5798 in / 1247 out tokens · 24403 ms · 2026-06-27T10:10:06.285607+00:00 · methodology

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Reference graph

Works this paper leans on

43 extracted references · 9 canonical work pages

  1. [1]

    The Limits of Predicting Agents from Behaviour, 2025

    Alexis Bellot, Jonathan Richens, and Tom Everitt. The Limits of Predicting Agents from Behaviour, 2025. URLhttp://arxiv.org/abs/2506.02923

    arXiv 2025

  2. [2]

    Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? 2025

    Yoshua Bengio, Michael Cohen, Damiano Fornasiere, Joumana Ghosn, Pietro Greiner, Matt MacDermott, Sören Mindermann, Adam Oberman, Jesse Richardson, Oliver Richardson, Marc- Antoine Rondeau, Pierre-Luc St-Charles, and David Williams-King. Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? 2025. doi: 10.48550/arXiv.2502. 1...

    work page doi:10.48550/arxiv.2502 2025

  3. [3]

    Glenn W. Brier. Verification of forecasts expressed in terms of probability.Monthly Weather Review, 78(1):1–3, 1950. doi: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2

    work page doi:10.1175/1520-0493(1950)078 1950

  4. [4]

    Discovering latent knowledge in language models without supervision, 2022

    Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision, 2022

    2022

  5. [5]

    Agents robust to distribution shifts learn causal world models even under mediation

    Matteo Ceriscioli and Karthika Mohan. Agents robust to distribution shifts learn causal world models even under mediation. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://neurips.cc/virtual/2025/loc/san-diego/ poster/118687

    2025

  6. [6]

    Chalmers

    David J. Chalmers. Propositional interpretability in artificial intelligence, 2025. URL https: //arxiv.org/abs/2501.15740

    arXiv 2025

  7. [7]

    Eliciting latent knowledge: How to tell if your eyes deceive you, 2021

    Paul Christiano, Ajeya Cotra, and Mark Xu. Eliciting latent knowledge: How to tell if your eyes deceive you, 2021. URL https://docs.google.com/document/d/1WwsnJQstPq91_ Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8

    2021

  8. [8]

    Langlois, Pedro A

    Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, and Shane Legg. Agent incentives: A causal perspective. InThirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2...

    2021

  9. [9]

    Higher-order belief in incomplete information MAIDs, 2025

    Jack Foxabbott, Rohan Subramani, and Francis Rhys Ward. Higher-order belief in incomplete information MAIDs, 2025. URLhttps://arxiv.org/abs/2503.06323

    arXiv 2025

  10. [10]

    ( 2011 )

    Tilmann Gneiting. Making and evaluating point forecasts.Journal of the American Statistical Association, 106(494):746–762, 2011. doi: 10.1198/jasa.2011.r10138

    work page doi:10.1198/jasa.2011.r10138 2011

  11. [11]

    Tilmann Gneiting and Adrian E. Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359–378, 2007. doi: 10.1198/ 016214506000001437. 10

    2007

  12. [12]

    Harvard University Press, Cambridge, MA, 1962

    Morton Grosser.The Discovery of Neptune. Harvard University Press, Cambridge, MA, 1962

    1962

  13. [13]

    The mode functional is not elicitable.Biometrika, 101(1):245–251, 2014

    Claudio Heinrich. The mode functional is not elicitable.Biometrika, 101(1):245–251, 2014. doi: 10.1093/biomet/ast048

    work page doi:10.1093/biomet/ast048 2014

  14. [14]

    Herrmann and Benjamin A

    Daniel A. Herrmann and Benjamin A. Levinstein. Standards for belief representations in LLMs,

  15. [15]

    URLhttps://arxiv.org/abs/2405.21030

    arXiv

  16. [16]

    A mechanism for eliciting probabilities.Econometrica, 77(2):603–606, 2009

    Edi Karni. A mechanism for eliciting probabilities.Econometrica, 77(2):603–606, 2009. doi: 10.3982/ECTA7833

    work page doi:10.3982/ecta7833 2009

  17. [17]

    Activation oracles: Training and evaluating LLMs as general-purpose activation explainers,

    Adam Karvonen, James Chua, Clément Dumas, Kit Fraser-Taliente, Subhash Kantamneni, Julian Minder, Euan Ong, Arnab Sen Sharma, Daniel Wen, Owain Evans, and Samuel Marks. Activation oracles: Training and evaluating LLMs as general-purpose activation explainers,

  18. [18]

    URLhttps://arxiv.org/abs/2512.15674

    arXiv

  19. [19]

    Lambert, David M

    Nicolas S. Lambert, David M. Pennock, and Yoav Shoham. Eliciting properties of probability distributions. InProceedings of the 9th ACM Conference on Electronic Commerce (EC’08), pages 129–138. ACM, 2008. doi: 10.1145/1386790.1386813

    work page doi:10.1145/1386790.1386813 2008

  20. [20]

    Goal misgeneralization in deep reinforcement learning, 2023

    Lauro Langosco, Jack Koch, Lee Sharkey, Jacob Pfau, Laurent Orseau, and David Krueger. Goal misgeneralization in deep reinforcement learning, 2023

    2023

  21. [21]

    B. A. Levinstein and Daniel A. Herrmann. Still no lie detector for language models: Probing empirical and conceptual roadblocks, 2023

    2023

  22. [22]

    Measuring goal- directedness, 2024

    Matt MacDermott, James Fox, Francesco Belardinelli, and Tom Everitt. Measuring goal- directedness, 2024. URLhttps://arxiv.org/abs/2412.04758

    arXiv 2024

  23. [23]

    The Definition of Lying and Deception

    James Edwin Mahon. The Definition of Lying and Deception. In Edward N. Zalta, editor,The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter 2016 edition, 2016

    2016

  24. [24]

    Eliciting latent knowl- edge from quirky language models, 2024

    Alex Mallen, Madeline Brumley, Julia Kharchenko, and Nora Belrose. Eliciting latent knowl- edge from quirky language models, 2024. URLhttps://arxiv.org/abs/2312.01037

    arXiv 2024

  25. [25]

    Propositions

    Matthew McGrath and Devin Frank. Propositions. In Edward N. Zalta and Uri Nodelman, edi- tors,The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter 2023 edition, 2023

    2023

  26. [26]

    Roger B. Myerson. Optimal auction design.Mathematics of Operations Research, 6(1):58–73,

  27. [27]

    URLhttp://www.jstor.org/stable/3689266

    ISSN 0364765X, 15265471. URLhttp://www.jstor.org/stable/3689266

    arXiv

  28. [28]

    Cambridge university press, 2009

    Judea Pearl.Causality. Cambridge university press, 2009

    2009

  29. [29]

    A Bayesian truth serum for subjective data.Science, 306(5695):462–466, 2004

    Dražen Prelec. A Bayesian truth serum for subjective data.Science, 306(5695):462–466, 2004. doi: 10.1126/science.1102081

    work page doi:10.1126/science.1102081 2004

  30. [30]

    Robust agents learn causal world models

    Jonathan Richens and Tom Everitt. Robust agents learn causal world models. In International Conference on Learning Representations, volume 2024, pages 15786– 15817, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/file/ 44a2b9f7bf9aec3f1fa333ad875b0ee0-Paper-Conference.pdf

    2024

  31. [31]

    General agents contain world models, 2025

    Jonathan Richens, David Abel, Alexis Bellot, and Tom Everitt. General agents contain world models, 2025. URLhttp://arxiv.org/abs/2506.01622

    arXiv 2025

  32. [32]

    Benchmarks for detecting measurement tampering, 2023

    Fabien Roger, Ryan Greenblatt, Max Nadeau, Buck Shlegeris, and Nate Thomas. Benchmarks for detecting measurement tampering, 2023. URLhttps://arxiv.org/abs/2308.15605

    arXiv 2023

  33. [33]

    Leonard J. Savage. Elicitation of personal probabilities and expectations.Journal of the Ameri- can Statistical Association, 66(336):783–801, 1971. doi: 10.1080/01621459.1971.10482346

    work page doi:10.1080/01621459.1971.10482346 1971

  34. [34]

    Markus Schlosser. Agency. In Edward N. Zalta, editor,The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter 2019 edition, 2019. 11

    2019

  35. [35]

    Eric Schwitzgebel. Belief. In Edward N. Zalta, editor,The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter 2021 edition, 2021

    2021

  36. [36]

    How We Will Decide that Large Language Models Have Beliefs, July 2024

    Eric Schwitzgebel. How We Will Decide that Large Language Models Have Beliefs, July 2024. URL http://schwitzsplinters.blogspot.com/2023/11/ how-we-will-decide-that-large-language.html. [Online; accessed 15. Jul. 2024]

    2024

  37. [37]

    Goal misgeneralization: Why correct specifications aren’t enough for correct goals, 2022

    Rohin Shah, Vikrant Varma, Ramana Kumar, Mary Phuong, Victoria Krakovna, Jonathan Uesato, and Zac Kenton. Goal misgeneralization: Why correct specifications aren’t enough for correct goals, 2022

    2022

  38. [38]

    Talking about large language models, 2022

    Murray Shanahan. Talking about large language models, 2022. URL https://arxiv.org/ abs/2212.03551

    arXiv 2022

  39. [39]

    Counterspeculation, auctions, and competitive sealed tenders.The Jour- nal of Finance, 16(1):8–37, 1961

    William Vickrey. Counterspeculation, auctions, and competitive sealed tenders.The Jour- nal of Finance, 16(1):8–37, 1961. doi: https://doi.org/10.1111/j.1540-6261.1961.tb02789. x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.1961. tb02789.x

    work page doi:10.1111/j.1540-6261.1961.tb02789 1961

  40. [40]

    Honesty is the best policy: Defining and mitigating ai deception

    Francis Rhys Ward, Francesco Belardinelli, Francesca Toni, and Tom Everitt. Honesty is the best policy: Defining and mitigating ai deception. InNeurIPS 2023, 2023

    2023

  41. [41]

    The sun is shining!

    Francis Rhys Ward, Matt MacDermott, Francesco Belardinelli, Francesca Toni, and Tom Everitt. The reasons that agents act: Intention and instrumental goals. InProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’24. International Foundation for Autonomous Agents and Multiagent Systems, 2024. 12 7 Appendix 7.1...

    2024

  42. [42]

    single out a unique valuey paD as the most likely one: ∀ˆy∈dom(Y)\{y paD }: P rM(Y=y paD |Pa D =pa D)> P rM(Y= ˆy|Pa D =pa D)

  43. [43]

    impossibility

    that value is almost certainly the correct one: P rM(PaD =pa D ∧Y̸=y paD) = 0 Via the following lemma, we can see that these two approaches (knowability and guessability) are really two ways of describing the same property in a CID: Lemma 2.Let M be a CID with variables V . Then Y∈V is guessable at a decision node D∈V if and only ifYis knowable atD. Proof...