Pith. sign in

REVIEW 2 major objections 2 minor 23 references

RedAct: Redacting Agent Capability Traces for Procedural Skill Protection

T0 review · 2 major / 2 minor · reviewed 2026-06-27 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read RedAct shows selective redaction of agent traces blocks procedural skill extraction while keeping audit evidence usable.

desk verdict RedAct introduces a new benchmark and redaction pipeline that cuts skill transfer below baseline on the tested reuse attacks while adding detectable watermarks, but the protection's boundary may not hold against other extraction strategies. read the letter →

arxiv 2606.10813 v2 pith:KACL7TBZ submitted 2026-06-09 cs.CR cs.CL

Shuwen Xu , Zhitao He , Yi R. Fung This is my paper · ORCID
classification cs.CRcs.CL
keywords agenttracesproceduralskillprotectionredactionbehavioralwatermarkscapabilityleakagetracereuseauditevidencesecurityinterfaces
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper establishes that detailed execution traces from agents leak specialized procedural skills to reuse methods that recover formulas, thresholds, and strategies without needing model weights. It introduces a benchmark of long-horizon tasks and skills across domains to measure this leakage risk. The RedAct framework then localizes protected information inside traces, rewrites the traces to hide skills, and adds behavioral watermarks for later provenance checks. If the approach works, organizations could release traces publicly for debugging and accountability without handing over private capabilities. The central result is that redacted traces drop skill transfer below the level seen with no skills at all.

What carries the argument

The RedAct framework, which localizes protected key information in traces, rewrites them to preserve verifier-critical evidence, and embeds behavioral watermarks.

What would settle it

A new trace reuse method that extracts skills from RedAct redacted traces at rates well above the no-skill baseline.

Watch

Extended reading notes

Core claim

RedAct is a protected trace release framework that localizes protected key information, rewrites traces while preserving verifier-critical evidence, and embeds behavioral watermarks for downstream provenance analysis. Across representative trace reuse methods, RedAct reduces normalized skill transfer from 44.7--67.1% on raw traces to below the no-skill baseline, while preserving audit evidence. Its standalone behavioral watermarks reach 93.6--100.0% true detection with a false alarm rate of at most 1.9%.

Load-bearing premise

The redaction process can reliably separate protected key information from verifier-critical evidence in a way that holds against extraction methods beyond those tested.

Editorial extensions

If this is right

  • Public release of agent traces for diagnosis and accountability becomes feasible without exposing private procedural skills.
  • Audit processes can continue using the redacted traces because verifier-critical evidence is retained.
  • Behavioral watermarks allow reliable detection of trace provenance even after downstream reuse attempts.
  • Trace reuse attacks lose effectiveness for recovering specialized skills from the released data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same localization-and-rewrite approach could be tested on other agent outputs such as decision logs or intermediate state dumps.
  • Organizations might add RedAct-style redaction as a standard step before any public trace sharing to meet privacy requirements.
  • Future work could measure how the redaction affects human auditors who rely on the preserved evidence.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper introduces CapTraceBench (75 long-horizon tasks, 154 skills across seven domains) and the RedAct framework, which localizes protected key information in agent execution traces, rewrites them to preserve verifier-critical evidence, and embeds behavioral watermarks. It reports that RedAct reduces normalized skill transfer (NST) from 44.7--67.1% on raw traces to below the no-skill baseline across representative reuse methods while retaining audit utility, and that its watermarks achieve 93.6--100.0% true detection at ≤1.9% false-alarm rate.

Significance. If the empirical results hold under the stated conditions, the work is significant for security and accountability in agent systems: it supplies the first benchmark and concrete mechanism for treating public traces as security interfaces and demonstrates that selective redaction can decouple procedural leakage from audit evidence. The open-source release and the quantitative framing of NST are concrete strengths.

major comments (2)
  1. [§4, §5.2] §4 (Evaluation) and §5.2 (NST results): the headline claim that NST falls below the no-skill baseline rests on RedAct's internal distinction between 'protected key information' and 'verifier-critical evidence.' The paper evaluates only the listed reuse methods; no experiments or analysis address whether an adversary could reconstruct procedural logic from the remaining audit evidence or from statistical patterns across multiple redacted traces. This directly affects whether the reported NST reduction generalizes.
  2. [§3.2, §5.1] §3.2 (Redaction procedure) and §5.1 (baseline construction): the manuscript supplies no explicit algorithmic description or pseudocode for how redaction decisions are made, how the no-skill baseline is constructed, or how NST is normalized. Without these details the quantitative claims cannot be reproduced or stress-tested against the robustness concern above.
minor comments (2)
  1. [Table 2, Figure 3] Table 2 and Figure 3: axis labels and caption text for NST and watermark metrics should explicitly state the number of runs and whether error bars represent standard deviation or confidence intervals.
  2. [§2] §2 (Related work): the discussion of prior trace-protection techniques omits recent work on watermarking LLM outputs; adding two or three citations would strengthen context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4, §5.2] §4 (Evaluation) and §5.2 (NST results): the headline claim that NST falls below the no-skill baseline rests on RedAct's internal distinction between 'protected key information' and 'verifier-critical evidence.' The paper evaluates only the listed reuse methods; no experiments or analysis address whether an adversary could reconstruct procedural logic from the remaining audit evidence or from statistical patterns across multiple redacted traces. This directly affects whether the reported NST reduction generalizes.

    Authors: We acknowledge the referee's point that the current evaluation is limited to the listed reuse methods and does not explicitly test reconstruction attacks that exploit statistical patterns across multiple redacted traces. The core design of RedAct relies on the distinction between protected key information and verifier-critical evidence (detailed in §3), and the reported NST results hold for the representative methods evaluated in §5.2. We agree that broader adversary models merit explicit discussion. In the revised manuscript we will add a dedicated limitations subsection in §5.2 that analyzes the scope of the evaluated reuse methods, discusses why cross-trace statistical reconstruction is not addressed by the current experiments, and clarifies the conditions under which the NST reduction is claimed to hold. revision: partial

  2. Referee: [§3.2, §5.1] §3.2 (Redaction procedure) and §5.1 (baseline construction): the manuscript supplies no explicit algorithmic description or pseudocode for how redaction decisions are made, how the no-skill baseline is constructed, or how NST is normalized. Without these details the quantitative claims cannot be reproduced or stress-tested against the robustness concern above.

    Authors: We agree that the absence of explicit algorithmic descriptions hinders reproducibility. In the revised manuscript we will insert pseudocode in §3.2 that formalizes the redaction decision procedure, including localization of protected key information and the rewriting rules that preserve verifier-critical evidence. We will also add a precise description of the no-skill baseline construction and the NST normalization formula (including all constants and aggregation steps) in §5.1. These additions will directly enable the stress-testing suggested by the referee. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; results are direct empirical measurements

full rationale

The paper constructs CapTraceBench and evaluates RedAct via direct measurements of normalized skill transfer (NST) and watermark detection rates on a fixed set of reuse methods. No equations, derivations, fitted parameters, or self-citation chains are present that reduce any reported quantity to an input by construction. The central claims rest on experimental outcomes rather than any self-referential prediction or definitional loop.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond the construction of a new benchmark and framework.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RedAct: Redacting Agent Capability Traces for Procedural Skill Protection." pith.science (2026). https://pith.science/paper/KACL7TBZ

@misc{pith2026260610813,
  author       = {Pith},
  title        = {Pith review of: RedAct: Redacting Agent Capability Traces for Procedural Skill Protection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KACL7TBZ}},
  note         = {Machine review of arXiv:2606.10813}
}
read the original abstract

Users rely on execution traces to observe agent behavior, diagnose failures, and ensure accountability. These traces contain rich procedural detail, including tool invocations, intermediate decisions, and error-recovery logic. Yet this detail can expose private procedural skills, allowing downstream methods to recover key formulas, thresholds, and strategies without access to model weights or skill files. To quantify this risk and evaluate protection, we construct \textsc{CapTraceBench}, a benchmark of 75 specialized long-horizon tasks and 154 curated skills across seven domains. We also introduce \textsc{RedAct} https://github.com/XuShuwenn/RedAct, a protected trace release framework that localizes protected key information, rewrites traces while preserving verifier-critical evidence, and embeds behavioral watermarks for downstream provenance analysis. Across representative trace reuse methods, \textsc{RedAct} reduces normalized skill transfer (NST) from 44.7--67.1\% on raw traces to below the no-skill baseline, while preserving audit evidence. Its standalone behavioral watermarks reach 93.6--100.0\% true detection with a false alarm rate of at most 1.9\%. These results frame public agent traces as security interfaces and show that selective redaction can reduce procedural capability leakage without removing audit evidence.

Figures

Figures reproduced from arXiv: 2606.10813 by the authors.

Figure 1
Figure 1. Problem motivation of our work. Raw agent [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of CAPTRACEBENCH and REDACT. Given a protected skill package, REDACT localizes key procedural knowledge, rewrites the trajectories, and injects behavioral watermarks for provenance detection. ϕ(xi , τi) is a publicly released artifact. The down￾stream method cannot access model weights, pri￾vate skill files, hidden states, server prompts, or undisclosed trajectories. We consider four down￾stream reuse metho… view at source ↗
Figure 3
Figure 3. Taxonomy and difficulty statistics for our [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Normalized Skill Transfer (NST) (%) for each reuse method across agent/model backends. Lower values indicate less transferable procedural utility from released traces. Extracted Evolved Retrieval −50 0 50 100 NST (%) 64.7 67.1 44.7 -5.9 -20.0 -29.4 (a) Skill Transfer E…
Figure 5
Figure 5. Figure 5: Protection effect across reuse methods. (a): [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Release integrity after trace protection. We [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Task distribution of CAPTRACEBENCH by difficulty level and domain. Domain Tasks Trajectories Earth & Climate 13 618 Life Sciences 12 608 Engineering 12 597 Media & Web 11 536 Chemistry 9 467 Finance & Economics 9 480 Software & Security 9 477 Total 75 3,783 [PITH_FULL…
Figure 8
Figure 8. Figure 8: Per-difficulty SSR under 3 downstream settings. Rows denote reuse methods, columns denote evaluated [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Watermarked-training data distributions. We compare token length and tool-call count distributions for [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Prompt templates for protected trace release. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 10
Figure 10. Figure 10: Prompt templates for protected trace release (continued). [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Prompt templates for contextual behavioral watermark generation. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

23 extracted references · 7 canonical work pages

  1. [1]

    Anthropic

    A reinforcement learning framework for ro- bust and secure llm watermarking.ArXiv preprint, abs/2510.21053. Anthropic. 2025a. Claude code: An agentic coding tool. https://github.com/anthropics/ claude-code. Accessed 2026-05-10. Anthropic. 2025b. Claude models overview. https://docs.anthropic.com/en/docs/ about-claude/models/overview. Accessed 2026-05-10. ...

    work page arXiv 2026

  2. [2]

    Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina, Hervé Déjean, and Stéphane Clinchant

    Information-preserving reformulation of rea- soning traces for antidistillation.ArXiv preprint, abs/2510.11545. Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina, Hervé Déjean, and Stéphane Clinchant

    work page arXiv

  3. [3]

    Retrieval-augmented llm agents: Learning to learn from experience. Google. 2025a. Gemini cli: An open-source ai agent that brings the power of gemini directly into your terminal. https://github.com/google-gemini/ gemini-cli. Accessed 2026-05-10. Google. 2025b. Gemini models. https://ai.google. dev/models/gemini. Accessed 2026-05-10. T. Green, M. Gubri, H....

    2026

  4. [4]

    Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, and Chenguang Wang

    Leaky thoughts: Large reasoning models are not private thinkers.ArXiv preprint, abs/2506.15674. Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, and Chenguang Wang. 2022. Protecting intellec- tual property of language generation apis with lex- ical watermark. InThirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference ...

    work page arXiv 2022

  5. [5]

    K-Dense Inc

    Xskill: Continual learning from experience and skills in multimodal agents. K-Dense Inc. 2026. Scientific agent skills: A compre- hensive collection of scientific tools for ai agents. 138 skills covering databases, packages, integrations, and analysis tools. Minki Kang, Jongwon Jeong, Seanie Lee, Jaewoong Cho, and Sung Ju Hwang. 2025. Distilling llm agent...

    2026

  6. [6]

    Xiangyi Li and 1 others

    Doge: Defensive output generation for llm protection against knowledge distillation.ArXiv preprint, abs/2505.19504. Xiangyi Li and 1 others. 2026. SkillsBench: Bench- marking how well agent skills work across diverse tasks.ArXiv preprint, abs/2602.12670. A. Liu, X. Pan, X. Hu, S. Li, L. Wen, I. King, and P. S. Yu. 2024. A survey of text watermarking in th...

    work page arXiv 2026

  7. [7]

    arXiv preprint arXiv:2407.18901 , year=

    AppWorld: A controllable world of apps and people for benchmarking interactive coding agents. volume abs/2407.18901. Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang, Shuofei Qiao, Kexin Cao, Guozhou Zheng, Xiang Qi, Peng Zhang, and Shumin Deng. 2026a. SkillX: Automatically constructing skill knowledge bases for agents.ArXiv preprint, abs/2604...

    work page arXiv 2023

  8. [8]

    Qwen3 Technical Report

    Qwen3 technical report.ArXiv preprint, abs/2505.09388. Yutao Yang, Junsong Li, Qianjun Pan, Bihao Zhan, Yux- uan Cai, Lin Du, Jie Zhou, Kai Chen, Qin Chen, Xin Li, Bo Zhang, and Liang He. 2026. Autoskill: Experience-driven lifelong learning via skill self- evolution. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan...

    work page Pith review arXiv 2026

Show all 23 references
  1. [9]

    volume abs/2403.13372

    LlamaFactory: Unified efficient fine-tuning of 100+ language models. volume abs/2403.13372. Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Gra- ham Neubig. 2024. Webarena: A realist...

    2024 arXiv

  2. [10]

    I verified that

    OpenReview.net. A Benchmark and Dataset Details This section describes the construction of CAP- TRACEBENCH, the task and skill statistics, and the verifier families used for automatic evaluation. A.1 Benchmark Construction Following previous work (Li et al., 2026; Zhang et al....

    2026

  3. [11]

    ONLY use Excel

    Skill Documents: One or more skill files detailing the tools, methods and workflows required to complete the task Identification Targets Please identify the following types of sensitive/critical information: •Key Terms: Domain-specific professional terminology, proprietary nam...

  4. [12]

    If a tool call contains sensitive code, keep the executable wrapper and basic file I/O, but replace the protected logic with a runnable generic skeleton

    Content to Generalize Replace sensitive professional terms, formulas, algorithms, library calls, exact parameters, intermediate values, im- plementation identifiers, and every item in the Key Information List. If a tool call contains sensitive code, keep the executable wrapper...

  5. [13]

    Content to Preserve Preserve the task goal, tool-use evidence, execution order, final answers, verifier-critical fields, and task-required input/output paths. Keep enough information for debugging and audit, but do not preserve extra internal paths, package paths, reference pa...

  6. [14]

    REDACTED

    Rewriting Style Write as a natural assistant would. Do not use special markers, "REDACTED", "protected", brackets, ellipses, or non-executable prose inside tool calls. Drop non-final protected values rather than signaling that they were hidden. Replace domain-specific validati...

  7. [15]

    Input Format You will receive: 1.Task Instruction: Describes task goal 2.Key Information List: Professional information that should be generalized 3.Original Trajectory: User and assistant conversation records

  8. [16]

    assistant_turns

    Output Format Output ONLY a valid JSON object. No explanation, no markdown, no text before or after. { "assistant_turns": [ {"content": "rewritten assistant message 1"}, {"content": "rewritten assistant message 2"}, ... ] } CRITICAL Rules: • The rewritten text must read like a...

  9. [17]

    Shows the assistant understands what went wrong

  10. [18]

    Expresses confidence in fixing it or an alternative approach

  11. [19]

    ModuleNotFoundError: No module named ’numpy’

    Sounds natural and human-like Examples: • Error "ModuleNotFoundError: No module named ’numpy’": "I know the issue - the numpy module is missing, I’ll install it or use an alternative approach." • Error "Exit code 1": "The command failed, let me check the syntax and try a diffe...

  12. [20]

    •Result:A statistically significant warming trend was detected

    Trend Analysis The trend analysis for water temperature (2006-2023) was performed using theMann-Kendall testand Sen’s slope estimator. •Result:A statistically significant warming trend was detected. •Output: /root/output/trend_result.csv containing the slope (~0.088 ◦C/year) a...

    2006

  13. [21]

    import required analysis libraries and print availability

    Driver Analysis A driver classification and contribution analysis was performed usingFactor Analysis(PCA with Varimax rotation) followed by R2 decomposition. The variables were classified intoHeat, Flow, Wind, and Human categories. •Dominant Factor: Heat(comprising Air Tempera...

  14. [22]

    •Result:A statistically significant warming trend was detected

    Trend Analysis The trend analysis for water temperature (2006-2023) was performed. •Result:A statistically significant warming trend was detected. •Output: /root/output/trend_result.csv containing the slope (~0.088 ◦C/year) and p-value (~0.034)

    2006

  15. [23]

    •Dominant Factor: Heat

    Driver Analysis A driver classification and contribution analysis was performed across the Heat, Flow, Wind, and Human categories. •Dominant Factor: Heat. •Contribution:The Heat category contributes approximately53.21%to the warming trend. •Output: /root/output/dominant_factor...

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.