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arxiv: 2605.20258 · v1 · pith:RUVUOS5Snew · submitted 2026-05-18 · 💻 cs.LG · cs.AI· cs.CR

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs

Sangwoo Park , Woongyeong Yeo , Seanie Lee , Yumin Choi , Hyomin Lee , Kangsan Kim , Jinheon Baek , Seong Joon Oh
show 1 more author
Sung Ju Hwang
This is my paper

Pith reviewed 2026-05-21 08:01 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CR
keywords contextual integrityself-distillationprivacyLLMsalignmentproduct of expertsutility trade-offfeedback
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The pith

SELFCI aligns large language models to contextual integrity by optimizing two complementary self-distillation objectives from feedback.

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

The paper proposes a training approach for large language models that serve as personal agents handling sensitive information. It targets the challenge of making appropriate disclosure decisions that respect context-specific norms while keeping the model effective at its core tasks. Rather than using a single training signal, the method applies two separate objectives: one that retains details needed for successful task completion and another that restricts unnecessary or inappropriate revelations. These objectives are combined through a product-of-experts mechanism to produce a unified target distribution. A sympathetic reader would care because existing approaches often force a choice between privacy compliance and performance, whereas this method claims to improve both using only internal feedback.

Core claim

SELFCI jointly optimizes two independent reverse KL divergences over distinct teacher distributions derived from feedback: one encourages preserving task-relevant information for utility, while the other enforces minimal and appropriate disclosure. This complementary formulation induces a Product-of-Experts (PoE) target, aligning the policy with the intersection of capability and privacy requirements.

What carries the argument

Complementary self-distillation that decouples suppression from task resolution by jointly optimizing two reverse KL divergences over feedback-derived teachers to induce a product-of-experts alignment target.

If this is right

  • SELFCI outperforms online reinforcement learning baselines such as GRPO on privacy and utility metrics.
  • The method achieves these gains without requiring costly external supervision.
  • The alignment improvements hold in out-of-domain settings that involve agentic workflows.
  • The approach continues to support appropriate disclosure even when private context accumulates over multiple interactions.

Where Pith is reading between the lines

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

  • The dual-objective structure may offer a template for balancing other conflicting goals in model training, such as safety constraints alongside helpfulness.
  • If the feedback signals prove robust across domains, this could reduce dependence on human-labeled data for multi-objective alignment in deployed agents.
  • Extending the same separation of objectives to longer conversation histories might test whether the product-of-experts target scales with accumulating context.

Load-bearing premise

Feedback from the model can be split into two reliable teaching signals, one for task utility and one for privacy limits, that combine without creating inconsistencies or harming overall performance.

What would settle it

Experiments where the trained model either reveals private details more often than a single-objective baseline or shows lower task accuracy on the same workflows would indicate the combined objectives do not achieve the claimed intersection of requirements.

Figures

Figures reproduced from arXiv: 2605.20258 by Hyomin Lee, Jinheon Baek, Kangsan Kim, Sangwoo Park, Seanie Lee, Seong Joon Oh, Sung Ju Hwang, Woongyeong Yeo, Yumin Choi.

Figure 1
Figure 1. Figure 1: Conceptual illustration of the ideal CI state in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SELFCI uses self-generated feedback to instantiate two teacher distributions from its own parameters, πallow promoting utility and πdisallow enforcing privacy. Joint optimization against both teachers aligns the policy with the intersection where utility and privacy are simultaneously satisfied. Motivated by this, we introduce SELFCI, a complementary self-distillation [18, 37, 51] framework that decouples … view at source ↗
Figure 3
Figure 3. Figure 3: (Left) Average DKL defined in Eq. 1 and Complete scores in Tab. 1 on the CI-RL test set computed using Qwen2.5-7B-Instruct. (Middle) Per-epoch Complete scores on the CI-RL test set and (Right) GPU wall-clock time per training step, using Qwen3-4B-Instruct. Limitations of Online RL. SELFCI is substantially more effective and sample-efficient than the online RL baseline. As shown in [PITH_FULL_IMAGE:figures… view at source ↗
Figure 5
Figure 5. Figure 5: Analysis of the ideal CI surrogate in Eq. 1 using Qwen3-4B-Instruct. (Left) Utility scores of target distributions on the CI-RL test set. (Right) Per-epoch Utility and Integrity scores trained with Eq. 1 or Eq. 5. 4.4 Analysis on Feedback and Teacher Decomposition Operationalizing the Ideal CI Objective with Feedback. While Eq. 1 operationalizes the ideal CI state as invariance to disallowed information, d… view at source ↗
Figure 6
Figure 6. Figure 6: (Left) Integrity-Utility balance on the CI-RL test set for Qwen3-4B-Instruct trained with [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (Left) Integrity and (Middle) Utility across training epochs for the utility-oriented teacher [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt template for contextual integrity reasoning. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The instruction used for feedback generation. (Left) Instruction for each attribute in [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example from CI-RL benchmark [22] and feedback prompt suffixes for constructing feedback-conditioned teachers. (a) The user task instruction τ and accessible information tAT , DT u. (b) Attribute-level feedback suffix for AT , forming the utility-oriented teacher πallow. (c) Attribute￾level feedback suffix for DT , forming the privacy-oriented teacher πdisallow. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_… view at source ↗
Figure 11
Figure 11. Figure 11: System prompt used for PrivacyLens evaluation. The prompt instructs the tool-using agent [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: User prompt template used for PrivacyLens evaluation. The template provides user [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt template for contextual integrity reasoning with direct answering, which applies [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Model input constructed from a CI-RL [22] test set sample, requiring attribute-level disclosure reasoning under Contextual Integrity before generating the final response. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Example response from Qwen3-4B-Instruct trained with [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Example response from Qwen3-4B-Instruct trained with CI-RL [ [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
read the original abstract

Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language models are increasingly deployed as personal agents handling sensitive workflows, adhering to CI becomes critical. However, even frontier models remain unreliable in making disclosure decisions, and existing mitigation strategies often degrade underlying task performance. To overcome this privacy-utility trade-off, we propose SELFCI, a complementary self-distillation framework that decouples information suppression from task resolution. SELFCI jointly optimizes two independent reverse KL divergences over distinct teacher distributions derived from feedback: one encourages preserving task-relevant information for utility, while the other enforces minimal and appropriate disclosure. This complementary formulation induces a Product-of-Experts (PoE) target, aligning the policy with the intersection of capability and privacy requirements. Empirical evaluations demonstrate that SELFCI, without relying on costly external supervision, consistently outperforms competitive baselines such as online reinforcement learning algorithms (e.g., GRPO). These trends further extend to out-of-domain settings involving agentic workflows and accumulated private context, suggesting that SELFCI provides a practical path toward CI alignment.

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

3 major / 2 minor

Summary. The manuscript proposes SELFCI, a complementary self-distillation framework for contextual integrity (CI) in LLMs. It decouples utility preservation from privacy enforcement by jointly minimizing two independent reverse KL divergences to distinct teacher distributions derived from feedback, claiming this induces a Product-of-Experts (PoE) target that aligns the policy with the intersection of task capability and appropriate disclosure norms. The work asserts consistent outperformance over baselines such as GRPO without external supervision and reports extension to out-of-domain agentic workflows with accumulated private context.

Significance. If the PoE construction is rigorously justified and the empirical gains are reproducible, the approach could address the privacy-utility trade-off in agentic LLM deployments by leveraging self-generated feedback rather than costly human supervision. The decoupling of the two KL terms and the self-distillation framing represent a potentially useful modeling choice for CI alignment, though the current presentation leaves the independence of the teachers and the stability of the induced target as open questions.

major comments (3)
  1. [Abstract] Abstract: the claim that jointly optimizing the two reverse KL divergences 'induces a Product-of-Experts (PoE) target' is stated without any supporting equations, derivation, or explicit construction of the teacher distributions. The manuscript therefore provides no demonstration that the sum of the reverse KLs corresponds to the intersection distribution rather than a mode-collapsed or dependent mixture.
  2. [Method] Method description (inferred from abstract and skeptic note): the procedure for deriving the two distinct teacher distributions 'from feedback' without external supervision is not accompanied by prompting templates, temperature schedules, or filtering steps that would enforce statistical independence. Without these details, the assumption that the teachers remain non-overlapping is unverified and the PoE claim rests on an untested modeling assumption.
  3. [Abstract] Abstract: the assertion of 'consistent outperformance over competitive baselines such as online reinforcement learning algorithms (e.g., GRPO)' and extension to 'out-of-domain settings involving agentic workflows' is presented without any quantitative metrics, ablation results, or statistical significance tests. This absence makes the central empirical claim impossible to evaluate from the supplied text.
minor comments (2)
  1. [Abstract] Notation for the two KL terms and the resulting PoE target should be introduced with explicit symbols and a short derivation sketch even if the full proof is deferred to an appendix.
  2. [Abstract] Clarify whether the feedback used to construct the teachers is purely model-internal or involves any human-provided signals; the current phrasing 'derived from feedback' is ambiguous.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback on the manuscript. We provide point-by-point responses to the major comments below, clarifying the theoretical and empirical aspects of SELFCI and committing to revisions where appropriate to address the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that jointly optimizing the two reverse KL divergences 'induces a Product-of-Experts (PoE) target' is stated without any supporting equations, derivation, or explicit construction of the teacher distributions. The manuscript therefore provides no demonstration that the sum of the reverse KLs corresponds to the intersection distribution rather than a mode-collapsed or dependent mixture.

    Authors: We agree that the abstract would benefit from additional rigor. The full manuscript derives the PoE target by showing that the joint optimization of the two reverse KL terms KL(π_θ || T_utility) + KL(π_θ || T_privacy) is mathematically equivalent to KL(π_θ || T_PoE) where T_PoE ∝ T_utility × T_privacy. This construction aligns the policy with the intersection of the two distributions. We will include the derivation and explicit teacher construction in a revised abstract and dedicated subsection in the method. revision: yes

  2. Referee: [Method] Method description (inferred from abstract and skeptic note): the procedure for deriving the two distinct teacher distributions 'from feedback' without external supervision is not accompanied by prompting templates, temperature schedules, or filtering steps that would enforce statistical independence. Without these details, the assumption that the teachers remain non-overlapping is unverified and the PoE claim rests on an untested modeling assumption.

    Authors: The teacher distributions are self-generated from the model's own feedback on contextual integrity scenarios. One teacher distribution is obtained by prompting for high-utility task completions, and the other by prompting for privacy-preserving responses with minimal disclosure. To ensure independence, we employ distinct prompting strategies and temperature settings (e.g., 0.7 for utility and 1.2 for privacy to encourage diversity). We will add the exact prompting templates, temperature schedules, and any filtering criteria to the method section to allow verification of the non-overlapping assumption. revision: yes

  3. Referee: [Abstract] Abstract: the assertion of 'consistent outperformance over competitive baselines such as online reinforcement learning algorithms (e.g., GRPO)' and extension to 'out-of-domain settings involving agentic workflows' is presented without any quantitative metrics, ablation results, or statistical significance tests. This absence makes the central empirical claim impossible to evaluate from the supplied text.

    Authors: While the abstract provides a high-level summary, the full manuscript contains detailed experimental results including quantitative metrics (e.g., privacy-utility scores), ablation studies on the complementary distillation components, and statistical significance tests across multiple runs. These demonstrate consistent outperformance over GRPO and generalization to agentic workflows. We will revise the abstract to incorporate key quantitative findings and ensure clear references to the experimental tables and figures. revision: partial

Circularity Check

0 steps flagged

No significant circularity in SELFCI empirical framework

full rationale

The paper presents SELFCI as an empirical optimization procedure that jointly minimizes two reverse KL divergences to distinct feedback-derived teacher distributions, inducing a PoE target for privacy-utility alignment. No equations or steps reduce the reported gains or alignment claims to quantities defined by fitted parameters, self-citations, or definitional loops. The central formulation is a modeling choice validated through comparisons to baselines such as GRPO and out-of-domain evaluations, remaining self-contained without load-bearing self-references or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only view reveals one main domain assumption about feedback-derived teachers and one invented construct (PoE target); no explicit free parameters are named.

axioms (1)
  • domain assumption Feedback signals can be partitioned into distinct teacher distributions that separately capture task utility and contextual privacy norms.
    Invoked when the abstract states that two independent reverse KL divergences are optimized over distinct teacher distributions derived from feedback.
invented entities (1)
  • Product-of-Experts (PoE) target no independent evidence
    purpose: To represent the intersection of capability and privacy requirements after combining the two KL objectives.
    Introduced as the alignment target induced by the complementary formulation; no independent falsifiable prediction is supplied in the abstract.

pith-pipeline@v0.9.0 · 5765 in / 1427 out tokens · 54436 ms · 2026-05-21T08:01:40.007340+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    SELFCI jointly optimizes two independent reverse KL divergences over distinct teacher distributions derived from feedback: one encourages preserving task-relevant information for utility, while the other enforces minimal and appropriate disclosure. This complementary formulation induces a Product-of-Experts (PoE) target

  • IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    the weighted reverse KL objective in Eq. 5 is equivalent to reverse KL matching a product-of-experts (PoE) target proportional to π_allow^λ π_disallow^(1-λ)

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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