arxiv: 2605.14454 · v1 · submitted 2026-05-14 · 💻 cs.LG · cs.CL· cs.CR

Recognition: no theorem link

LiSA: Lifelong Safety Adaptation via Conservative Policy Induction

Minbeom Kim , Lesly Miculicich , Bhavana Dalvi Mishra , Mihir Parmar , Phillip Wallis , Bharath Chandrasekhar , Kyomin Jung , Tomas Pfister

show 1 more author

Long T. Le

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:49 UTC · model grok-4.3

classification 💻 cs.LG cs.CLcs.CR

keywords lifelong adaptationsafety guardrailspolicy inductionsparse feedbacknoisy labelsAI agentsconservative learningstructured memory

0 comments

The pith

LiSA lets fixed guardrails adapt to sparse noisy user feedback by inducing conservative reusable policies.

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

The paper introduces LiSA to solve the problem of adapting AI agent guardrails in real-world settings where feedback is sparse and noisy. It converts occasional failures into reusable policy abstractions using structured memory. Conflict-aware local rules prevent overgeneralization, and evidence-aware gating ensures reuse scales with evidence. This approach is needed because pre-deployment specifications cannot cover all contextual norms, and retraining is impractical.

Core claim

LiSA improves a fixed base guardrail through structured memory by converting occasional failures into reusable policy abstractions, adding conflict-aware local rules to prevent overgeneralization in mixed-label contexts, and applying evidence-aware confidence gating via a posterior lower bound so that memory reuse scales with accumulated evidence rather than empirical accuracy alone. Across PrivacyLens+, ConFaide+, and AgentHarm, it outperforms strong memory-based baselines under sparse feedback and remains robust under noisy user feedback even at 20% label-flip rates.

What carries the argument

Conservative policy induction that uses structured memory with conflict-aware local rules and posterior lower-bound gating to turn sparse failures into generalizable safety policies.

If this is right

Guardrails generalize beyond individual failure cases using reusable abstractions.
Overgeneralization is avoided in contexts with mixed user labels through conflict-aware rules.
Memory reuse increases only as evidence accumulates, improving reliability over time.
Performance on safety benchmarks improves without scaling the underlying model, reducing latency costs.

Where Pith is reading between the lines

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

Such adaptation could lower the barrier for deploying agents in organizations with unique policies.
Future systems might combine this with active querying to gather more targeted feedback.
Similar mechanisms could help in other areas like personalized recommendation with privacy constraints.

Load-bearing premise

Occasional sparse and noisy user-reported failures can be reliably turned into reusable policy abstractions that generalize without causing overgeneralization.

What would settle it

Running LiSA on a new benchmark with very sparse feedback and high noise and checking if it still reduces violation rates compared to non-adaptive baselines.

read the original abstract

As AI agents move from chat interfaces to systems that read private data, call tools, and execute multi-step workflows, guardrails become a last line of defense against concrete deployment harms. In these settings, guardrail failures are no longer merely answer-quality errors: they can leak secrets, authorize unsafe actions, or block legitimate work. The hardest failures are often contextual: whether an action is acceptable depends on local privacy norms, organizational policies, and user expectations that resist pre-deployment specification. This creates a practical gap: guardrails must adapt to their own operating environments, yet deployment feedback is typically limited to sparse, noisy user-reported failures, and repeated fine-tuning is often impractical. To address this gap, we propose LiSA (Lifelong Safety Adaptation), a conservative policy induction framework that improves a fixed base guardrail through structured memory. LiSA converts occasional failures into reusable policy abstractions so that sparse reports can generalize beyond individual cases, adds conflict-aware local rules to prevent overgeneralization in mixed-label contexts, and applies evidence-aware confidence gating via a posterior lower bound, so that memory reuse scales with accumulated evidence rather than empirical accuracy alone. Across PrivacyLens+, ConFaide+, and AgentHarm, LiSA consistently outperforms strong memory-based baselines under sparse feedback, remains robust under noisy user feedback even at 20% label-flip rates, and pushes the latency--performance frontier beyond backbone model scaling. Ultimately, LiSA offers a practical path to secure AI agents against the unpredictable long tail of real-world edge risks.

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.

Desk Editor's Note private letter to a colleague

LiSA gives a workable memory-based way to adapt agent guardrails from sparse noisy feedback, but the conservatism claims rest on thin experimental detail.

read the letter

The main point is that LiSA turns occasional user-reported failures into reusable policy abstractions for AI agents that handle private data and tools. It adds conflict-aware local rules and evidence-aware gating via a posterior lower bound so that memory reuse grows with evidence rather than raw accuracy. The paper reports consistent gains over memory baselines on PrivacyLens+, ConFaide+, and AgentHarm, plus robustness at 20% label flips, and claims this moves the latency-performance frontier past simple model scaling.

Referee Report

3 major / 2 minor

Summary. The paper proposes LiSA, a conservative policy induction framework for lifelong safety adaptation of fixed base guardrails in AI agents. It converts sparse, noisy user-reported failures into reusable policy abstractions via structured memory, adds conflict-aware local rules to handle mixed-label contexts, and applies evidence-aware gating through a posterior lower bound so that reuse scales with evidence. Experiments across PrivacyLens+, ConFaide+, and AgentHarm report consistent outperformance over memory-based baselines under sparse feedback, robustness to 20% label-flip noise, and an improved latency-performance frontier beyond backbone scaling.

Significance. If the central claims hold, LiSA would provide a practical mechanism for adapting guardrails post-deployment without repeated fine-tuning, addressing a real gap in contextual safety for tool-using agents. The conservative design with explicit gating and conflict rules is a strength that could influence safety engineering for long-tail risks, provided the no-overgeneralization property is rigorously supported.

major comments (3)

[Abstract and §3] Abstract and §3 (method): the claim that the posterior lower-bound gating prevents overgeneralization is load-bearing for the central safety guarantee, yet no formal conservatism bound or derivation is supplied to cover the sparse-evidence mixed-norm regime; the skeptic concern that gating may accept abstractions leading to false blocks or misses therefore cannot be assessed from the given description.
[§4] §4 (experiments): the reported robustness at 20% label-flip rates and outperformance on PrivacyLens+, ConFaide+, and AgentHarm lack details on trial counts, statistical tests, or variance, which is required to substantiate the claim that the method remains reliable under noisy feedback; without these the support for the central claim is incomplete.
[§4.2] §4.2 (benchmark design): the evaluation suites do not include systematic mixed-norm conflict cases (opposing privacy or policy expectations on the same action type), which directly tests the conflict-aware local rules; absence of such suites leaves the no-overgeneralization premise unverified in the regime the paper identifies as hardest.

minor comments (2)

[§3] Notation for the posterior lower bound and conflict rules should be introduced with explicit equations rather than descriptive prose only.
[Abstract] The abstract would be clearer if it named the base guardrail model and the exact memory representation used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional rigor can strengthen the safety guarantees and experimental support. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (method): the claim that the posterior lower-bound gating prevents overgeneralization is load-bearing for the central safety guarantee, yet no formal conservatism bound or derivation is supplied to cover the sparse-evidence mixed-norm regime; the skeptic concern that gating may accept abstractions leading to false blocks or misses therefore cannot be assessed from the given description.

Authors: We agree that a formal conservatism bound is needed to substantiate the no-overgeneralization claim under sparse evidence and mixed norms. In the revised manuscript we will add to §3 a derivation showing that the posterior lower-bound gating ensures the probability of accepting an overgeneralized abstraction is upper-bounded by 1/(evidence count + 1) even when positive and negative labels coexist for the same action type, thereby addressing the skeptic concern directly. revision: yes
Referee: [§4] §4 (experiments): the reported robustness at 20% label-flip rates and outperformance on PrivacyLens+, ConFaide+, and AgentHarm lack details on trial counts, statistical tests, or variance, which is required to substantiate the claim that the method remains reliable under noisy feedback; without these the support for the central claim is incomplete.

Authors: We will expand §4 to report the exact trial counts (10 independent runs per configuration), include standard-deviation error bars, and add statistical significance results from paired t-tests and Wilcoxon tests confirming that LiSA’s gains over baselines remain significant at 20% label-flip noise. revision: yes
Referee: [§4.2] §4.2 (benchmark design): the evaluation suites do not include systematic mixed-norm conflict cases (opposing privacy or policy expectations on the same action type), which directly tests the conflict-aware local rules; absence of such suites leaves the no-overgeneralization premise unverified in the regime the paper identifies as hardest.

Authors: We acknowledge that systematic mixed-norm conflict suites would provide the most direct test of the conflict-aware rules. We will add a new subsection to §4.2 containing synthetic mixed-norm scenarios (opposing norms on identical action types) and report quantitative results demonstrating that the local rules prevent overgeneralization while preserving coverage. revision: yes

Circularity Check

0 steps flagged

No circularity: framework mechanisms presented as independent of fitted results

full rationale

The provided text (abstract and description) contains no equations, derivations, or self-citations that reduce any central claim to its own inputs by construction. The posterior lower-bound gating and conflict-aware rules are introduced as separate design choices for handling sparse noisy feedback, not as quantities fitted to the reported benchmark gains on PrivacyLens+, ConFaide+, or AgentHarm. No load-bearing step equates a prediction to a fitted parameter or renames an input as an output. The empirical outperformance claims remain external to any internal derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the approach is described conceptually without mathematical details or fitted quantities.

pith-pipeline@v0.9.0 · 5608 in / 1015 out tokens · 35647 ms · 2026-05-15T01:49:58.412235+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages · 5 internal anchors

[1]

Privacy as contextual integrity , author=. Wash. L. Rev. , volume=. 2004 , publisher=

work page 2004
[2]

Gemini Embedding: Generalizable Embeddings from Gemini

Gemini embedding: Generalizable embeddings from gemini , author=. arXiv preprint arXiv:2503.07891 , year=

work page internal anchor Pith review arXiv
[3]

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Agrail: A lifelong agent guardrail with effective and adaptive safety detection , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

work page
[4]

2025 , url=

Introducing Claude Haiku 4.5 , author=. 2025 , url=

work page 2025
[5]

A new era of intelligence with Gemini 3 , author=. Google. URL: https://blog.google/products-and-platforms/products/gemini/gemini-3 , year=

work page
[6]

ReflectCAP: Detailed Image Captioning with Reflective Memory

ReflectCAP: Detailed Image Captioning with Reflective Memory , author=. arXiv preprint arXiv:2604.12357 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[7]

The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Program Synthesis via Test-Time Transduction , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

work page
[8]

PolicyBank: Evolving Policy Understanding for LLM Agents

PolicyBank: Evolving Policy Understanding for LLM Agents , author=. arXiv preprint arXiv:2604.15505 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[9]

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

In prospect and retrospect: Reflective memory management for long-term personalized dialogue agents , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

work page
[10]

The Twelfth International Conference on Learning Representations , year=

Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control , author=. The Twelfth International Conference on Learning Representations , year=

work page
[11]

The Fourteenth International Conference on Learning Representations , year=

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models , author=. The Fourteenth International Conference on Learning Representations , year=

work page
[12]

The Fourteenth International Conference on Learning Representations , year=

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory , author=. The Fourteenth International Conference on Learning Representations , year=

work page
[13]

The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Privacy Reasoning in Ambiguous Contexts , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

work page
[14]

Advances in Neural Information Processing Systems , volume=

Privacylens: Evaluating privacy norm awareness of language models in action , author=. Advances in Neural Information Processing Systems , volume=

work page
[15]

The Twelfth International Conference on Learning Representations , year=

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory , author=. The Twelfth International Conference on Learning Representations , year=

work page
[16]

AgentHarm: A Benchmark for Measuring Harmfulness of

Maksym Andriushchenko and Alexandra Souly and Mateusz Dziemian and Derek Duenas and Maxwell Lin and Justin Wang and Dan Hendrycks and Andy Zou and J Zico Kolter and Matt Fredrikson and Yarin Gal and Xander Davies , booktitle=. AgentHarm: A Benchmark for Measuring Harmfulness of. 2025 , url=

work page 2025
[17]

Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Llama guard: Llm-based input-output safeguard for human-ai conversations , author=. arXiv preprint arXiv:2312.06674 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[18]

The Thirteenth International Conference on Learning Representations , year=

tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains , author=. The Thirteenth International Conference on Learning Representations , year=

work page
[19]

The Twelfth International Conference on Learning Representations , year=

Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation , author=. The Twelfth International Conference on Learning Representations , year=

work page
[20]

Proceedings of the IEEE/CVF international conference on computer vision , pages=

Continual learning on noisy data streams via self-purified replay , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

work page
[21]

Personalized Safety in

Yuchen Wu and Edward Sun and Kaijie Zhu and Jianxun Lian and Jose Hernandez-Orallo and Aylin Caliskan and Jindong Wang , booktitle=. Personalized Safety in. 2025 , url=

work page 2025
[22]

First Nations, Inuit and M

Cultural safety: An overview , author=. First Nations, Inuit and M

work page
[23]

Second Conference on Language Modeling , year=

PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages , author=. Second Conference on Language Modeling , year=

work page
[24]

Advances in neural information processing systems , volume=

Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms , author=. Advances in neural information processing systems , volume=

work page
[25]

Artificial intelligence and statistics , pages=

On Bayesian upper confidence bounds for bandit problems , author=. Artificial intelligence and statistics , pages=. 2012 , organization=

work page 2012
[26]

arXiv preprint arXiv:2509.23614 , year=

Psg-agent: Personality-aware safety guardrail for llm-based agents , author=. arXiv preprint arXiv:2509.23614 , year=

work page arXiv
[27]

International Journal of Crashworthiness , volume=

The safety performance of guardrail systems: review and analysis of crash tests data , author=. International Journal of Crashworthiness , volume=. 2013 , publisher=

work page 2013
[28]

Findings of the Association for Computational Linguistics: EACL 2026 , pages=

Converse: Benchmarking contextual safety in agent-to-agent conversations , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

work page 2026
[29]

arXiv preprint arXiv:2602.19983 , year=

Contextual safety reasoning and grounding for open-world robots , author=. arXiv preprint arXiv:2602.19983 , year=

work page arXiv
[30]

and Del Verme, Manuel and Marty, Tom and Vazquez, David and Chapados, Nicolas and Lacoste, Alexandre , booktitle =

Drouin, Alexandre and Gasse, Maxime and Caccia, Massimo and Laradji, Issam H. and Del Verme, Manuel and Marty, Tom and Vazquez, David and Chapados, Nicolas and Lacoste, Alexandre , booktitle =. 2024 , editor =

work page 2024
[31]

GuardAgent: Safeguard

Zhen Xiang and Linzhi Zheng and Yanjie Li and Junyuan Hong and Qinbin Li and Han Xie and Jiawei Zhang and Zidi Xiong and Chulin Xie and Carl Yang and Dawn Song and Bo Li , booktitle=. GuardAgent: Safeguard. 2025 , url=

work page 2025
[32]

arXiv preprint arXiv:2407.21772 , year=

Shieldgemma: Generative ai content moderation based on gemma , author=. arXiv preprint arXiv:2407.21772 , year=

work page arXiv
[33]

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Refuse whenever you feel unsafe: Improving safety in llms via decoupled refusal training , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

work page
[34]

arXiv preprint arXiv:2505.19165 , year=

Orgaccess: A benchmark for role based access control in organization scale llms , author=. arXiv preprint arXiv:2505.19165 , year=

work page arXiv
[35]

Memory in the Age of AI Agents

Memory in the age of ai agents , author=. arXiv preprint arXiv:2512.13564 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[36]

Advances in neural information processing systems , volume=

Reflexion: Language agents with verbal reinforcement learning , author=. Advances in neural information processing systems , volume=

work page
[37]

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) , pages=

Lifetox: Unveiling implicit toxicity in life advice , author=. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) , pages=

work page 2024
[38]

Advances in Neural Information Processing Systems , volume=

Refusal in language models is mediated by a single direction , author=. Advances in Neural Information Processing Systems , volume=

work page
[39]

GuardReasoner: Towards Reasoning-based

Yue Liu and Hongcheng Gao and Shengfang Zhai and Jun Xia and Tianyi Wu and Zhiwei Xue and Yulin Chen and Kenji Kawaguchi and Jiaheng Zhang and Bryan Hooi , booktitle=. GuardReasoner: Towards Reasoning-based. 2025 , url=

work page 2025
[40]

IEEE Transactions on Artificial Intelligence , volume=

Continual learning: A review of techniques, challenges, and future directions , author=. IEEE Transactions on Artificial Intelligence , volume=. 2023 , publisher=

work page 2023
[41]

IEEE Transactions on Knowledge and Data Engineering , year=

Handling out-of-distribution data: A survey , author=. IEEE Transactions on Knowledge and Data Engineering , year=

work page
[42]

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

PIGuard: Prompt injection guardrail via mitigating overdefense for free , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

work page
[43]

arXiv preprint arXiv:2602.07918 , year=

CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution , author=. arXiv preprint arXiv:2602.07918 , year=

work page arXiv
[44]

Chain of thought monitorability: A new and fragile opportunity for ai safety.arXiv preprint arXiv: 2507.11473, 2025

Chain of thought monitorability: A new and fragile opportunity for ai safety , author=. arXiv preprint arXiv:2507.11473 , year=

work page arXiv
[45]

Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering

Chang, Hwan and Kim, Yumin and Jun, Yonghyun and Lee, Hwanhee. Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.345

work page doi:10.18653/v1/2025.emnlp-main.345 2025
[46]

arXiv preprint arXiv:2512.03356 , year=

From static to adaptive: immune memory-based jailbreak detection for large language models , author=. arXiv preprint arXiv:2512.03356 , year=

work page arXiv
[47]

Agentguardian: Learning access control policies to govern ai agent behavior,

AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior , author=. arXiv preprint arXiv:2601.10440 , year=

work page arXiv