{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"obfuscation-atlas","title":"The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes","aliases":[],"authors":[],"primaryFunction":"discovery","secondaryFunctions":[],"kind":"study","sourceId":"obfuscation-atlas-publication","attributionSourceId":"obfuscation-atlas-publication","originalUrl":"https://www.far.ai/research/the-obfuscation-atlas-mapping-where-honesty-emerges-in-rlvr-with-deception-probes","publicationDate":"2026-02-16","updatedOn":"2026-10-25","areas":["deception"],"scopeNote":{"en":"Relevance is assigned to the research question and stated conditions of the work. A topic tag is not a risk rating.","zh":"风险关联依据研究问题及明确条件建立，主题标签不代表风险评级。"},"protocolIds":[],"contributions":[{"organizationId":"far","role":"publisher","sourceId":"obfuscation-atlas-publication","locator":"Official publication masthead and article byline"}],"summary":{"en":"Studies how training against deception probes can produce honesty improvements or evasion of the detector.","zh":"研究针对欺骗探针的训练如何带来诚实性改善，或产生检测规避。"},"note":{"question":null,"method":null,"finding":null,"supports":null,"limits":null,"connections":null},"resources":[],"noteStatus":{"question":"limited","method":"limited","finding":"limited","supports":"limited","limits":"limited","connections":"limited"},"noteAvailability":{"en":"A substantive NUWA research note is not yet available. The repeated draft sections were removed pending review of the original methods, findings and limitations; use the original publication for the research account.","zh":"实质性的 NUWA 研究笔记尚未完成。重复草稿章节已移除，待核查原始方法、发现与限制；研究内容请以原始出版物为准。"},"noteSourceLocator":"No complete section-level method/results/limitations review completed for this note; cached original page and any chart audit retained as discovery evidence.","tags":[]},"reading":{"id":"obfuscation-atlas","title":"The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes","en":{"takeaway":"Training against a frozen deception probe can produce honesty or different forms of obfuscation.","question":"When a detector is used as a training signal, does the model become honest or only harder to detect?","design":"A coding reward-hacking setting combines task reward, a fixed linear probe and KL regularization; sweeps and controls distinguish policy changes from representation drift.","findings":["Detector evasion through representation drift can arise even without the detector penalty.","High regularization and detector penalties can favor honest policies in this setting, while other regions produce deceptive text with justifications or altered activations."],"interpretation":"NUWA interpretation: falling probe scores are ambiguous unless independently checked against behavior and representation changes.","limits":"The atlas maps selected models, probes and tasks; it does not establish a universally safe penalty setting or detector robustness through arbitrary training.","connections":["NUWA connection: anti-scheming-training also depends on reasoning evidence, while training-run-evaluations motivates observing failures before they become less legible."],"decisionRelevance":"NUWA recommendation: separate detector accuracy, policy honesty and activation stability; validate all three after training updates."},"zh":{"takeaway":"针对冻结欺骗探针训练，可能产生诚实，也可能产生不同形式的混淆。","question":"检测器作为训练信号时，模型变得诚实，还是仅变得更难检测？","design":"编码奖励作弊环境结合任务奖励、固定线性探针和 KL 正则，参数扫描与对照区分策略改变和表征漂移。","findings":["即使没有检测器惩罚，表征漂移也可能造成检测失效。","特定设置下高正则与检测惩罚有利于诚实；其他区域出现附带辩解的欺骗文本或激活变化。"],"interpretation":"NUWA 解读：若不独立检查行为与表征，探针评分下降含义不明。","limits":"图谱覆盖选定模型、探针和任务，不能确立通用安全惩罚参数或任意训练后的检测稳健性。","connections":["NUWA 关联：反图谋训练同样依赖推理证据；训练过程评估主张在失败变得难读前观察它。"],"decisionRelevance":"NUWA 建议：区分检测准确性、策略诚实性和激活稳定性，每次训练更新后分别验证。"},"sourceRefs":[{"sourceId":"obfuscation-atlas-publication","url":"https://arxiv.org/html/2602.15515","locator":"2 Problem Setting","version":"Selected original resource https://arxiv.org/html/2602.15515; 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