How to validate that encrypted reasoning content works when chaining responses?

API
,
1

I’m trying to use encrypted reasoning items as described in the OpenAI Cookbook. in Responses API (model = gpt-5)

Here’s my setup:

  1. I send an initial request with "include": ["reasoning.encrypted_content"].

  2. The response includes a reasoning item with an id and encrypted_content and summary.

  3. In the next request, I pass that reasoning item back something like this:

    {
    "include": [
        "reasoning.encrypted_content"
    ],
    "input": [
        {
            "id": "rs_06991e99d9d5a74c0169006cc4252c8194920b43211d675b8a",
            "type": "reasoning",
            "encrypted_content": "gAAAAABpAGzYmnJT3PFRME8HwUP6hlkvuUQCOl9FXwUJ_KMr_9DVfL_b6Rq_3mSzhIcNiUnV3Qyu8eUEpiWYrvRXEHo23WyNHMKKv1jXliRcQgDmR6jTEaicWvB9nhVZMYzOEUa-jCNRkMz6Znni4OVw4sFl0Z0Jc4tJ9JWZ5Ow8VmKkcDucHr92jdN9fsOHU0HqW2ZBfLh6IKvO_H92LaYXLVoNGx6r0_msciAKCQP8A98-kwTULDKzkztfdXPZwNHMJUUzMKoX3dTp0vNbNj7fDuwKNBEv029GgmKdbvBiRKICzK8jVfgqz3tT16i2upgiIshVxM37OpvmqhopnyAyDhe9sh5jPkbFS5kCTz7gWkzwkx7RnBWh_uxTFk7hRH3lvCTc2696VadsI1ZYganDrbMTKNeCfbwki8C2iC0zhH-oV8PT3kkg3NWlB1wkqY3O1c0opTDf5S2QYMPxMPAs7dkUmAXo0PLrMjRwy4wKPMp0HKnDj8gSkgIHW8Kz7ycnumHTOQxzeM6qfPnk1N5XWjizxUqWgmN2kmq4qYPXVQAifw1wuJAzRJ64FY5T9dLQKRkn2uORPHa2_6vySe4fcxXIVmE3rufQO_I9gLMcMNGCpMUKnCQif7BmD2SA4uDjmOPRC4EAiTTurqChXeHoQj8RUCDHw8sUbjshWHe8iFu89NEXOWmA2ned-VmPZDbX6Fw2asnTq2oJGdzXGoGFiigMIEAMQ0TM8GQPncX8D8HMxnQht83pdcoy0cXPMQiao4lRFDGwhxOHCEZtqjeI0SMBmr-4H-JqcpsH-K42KuKxhhhsKSRT3BP3OiYs_0jcNEPCwsMSd-9MS7sGYGf27bEDsmEL6mxn2GKwUc6v6dqrc06yILybDdiv02iXGuKj7xn9t54IYhyMUNQRBQHzwsH9HkDePKbVu3Ad24KM6xCqtpL-JNEaUHrFKocaHWjIWW8KtFmxDHsYRXRIl557opjoclfDH_wUYmMkI1i620-6SsvjydkzVItw6Gt89baq9GTbqVpp68HH5YTJkVStU8Mg3lMge5WfTye6PbHPmWOZV-xpkP0GcswXqJCoJfvwexS27rc0617Vl6OjeJY5LGQhF3EYGUXhnNuzy1I6fnLNeYzV9uZOMEPj44si03JPJrRr1Oqh2thvyOIFNfBC8H8EhQ8ectA3z1zCfZVvOWw-oXmmaUG25bCI78QlqRWRQb82lG9T8yRPHNdWMQDYmHlxJ-nptVNpLIgIJhu2ydqraeKs3GINfy9NGNu35bOcdw38s6ySJotFu5zVInQy9sXK6HgS3gMSNOraiIgaQDbiU1jqOqj2DdhSKflu-vMoL0-Q-VbhLHJHMwhkZKvI2CrpWtNpYS019d_RdXe4xtNn7dNrBDQe1P_9QMq-15UZRTpU_gP0gvkJr33yIjy8WSZ2nK9ObPAdDLPZ16-IFvZ9vPx94joYRcz_Wj69GHRef0MQTqF3AbFye1nkdZ4o02CAJ01g43gu5LW6r9Qlu2FcohgQsxzoYOtJIL4Eq1MLuZPntlegKFH6xgy53-ScL2i-ACdlQ6s8Qkj-_aCATr27LMgO7biB4M3uLqKMSwkNQfma19_goEq66PDmJJwLmaOTEMapNhtyVXGKTDShYG3UFnJseTG56LJ0tStX5gXXz5HqNlYZSabnDFl9Go7jBZKI0bAL8CbmimOpYiotCZdyuwzJx_0Q0EmqY3a6CKJJg8aTJn1sEtQS6bNMWmMPsWVPFw_2x0oxCGmeKFEYKI1P9ZAhMdd7uI2IaN4w0MT64f1O7_OL2zF6C93mLukQFmct_ukVkqJ6XG1C1w0zsu373ktsnwpc2cchRWTSFmvC-9bkoIKFQ-AQIzsVrQq60tQAPW_hDzY4wMhMdTk5O9vKaU7WnbT9-mQ_tb-JqSt-BmDiCPTcZh27J7Q151cdbVzhhZPKq8Dr580A64ztbqvXj0BD57vs9XIe4D8xlLQndCjoQhB5Jw__wPAr4yEFez2DCBdo9aGGHbiW2Q3YkpgRRh34n0Tzt8CtVlq_X2ud_QEd8w8SUy5j6SXSp5XjUehMVlggFRf1UwqvCZHh9bn9Is4Fd4n1fLCuc29mJ2Mr6ttyxdFkucRUqZnh34j16nvkXp5AbqXoDy7uq-16ZYm91THJXvBNgKQeBJs5JC1usAVDBUe48FCu0XID_2OTJpsRwU3L9YrEhTkZd6Ac_kU11uGdxp0h4P2lZ2ZBAqVk_jJcCE26frJcdDurR2hhbUV_Wfgmuk5QI0A8coHGeCDU3I2nQyWP1HBaevVjv7Rhk5PNsMNro1U-J63uOsEdMZfJ-LZtHAtYyut0DkC9PC3O0v6a7M3HSG90dy35_n20kEDEwRaNFZI-UofEjyTWZ9yuJsDZP6mkApQ9Th3nxR-qicuRqZvO0oXgH21ySkBcpFiyJ6RpnPuZnhs5IHz0pKAcBzHBVZaL6R9sSjMj12fwnxXDbSmIXBpCZXeXRHXZyagr4yGNCZo0RIBbhzCmQWtyH07dikvfdb-lwHnS8QJwLFkbZeUC9efiLu59irBJvhVF6r_1_zjyDg1K-Jobfwv-6DjXGeVwHNBSCtYCZ-9G0WkelTGu9euOLiqkXf-b0DXS4kCpA1H7I_m6gHF8YisfCa9SmCpmk67q4VXGz4GGa0A2g6lE7zRidxiY89LwzteBk_sYU6NmcZpKTVOtgJ3GXKRHI6e3NQUnfPdyE9UQHQnXSmp9eWsAxawdTiCh2BsyrbJe-cqHbuviE_FFti5Fz8o81x0cFWEc4xppzL70ym_68VwEZFpQmdOHamS_Bi_Ag6wWk2a0VgDQPkFKfKr1H2I8ncrHIw9lK6qzhjMvNA0nl0rTHWU1m6X_7-FfxdeDAO94VKXLXcwIYgp_KUiKcSzsFPsHP-BfxQ1Sd1QVfOECI7pvDFZd2pL4q4OZd0FaHnsbErsk4ZGmfx13kzeoGOkghDH0wJk52Y-37jqcI-2RX79WEqSZvzJCdaTIX1tQ5UBAx9Aze5XNDvYYKh_hpfJzVTOjMEnIk825mKpVbElo_D8A9DdRbZWYsYhO9RxuxIyZ-5970yWo2l19TwtHu5i7iRcY97oQ63GfoLh5Q4yZs2cCVbIQR91ghxkcAkohI3gR6deFwWM7f3usI1q3Ft9vvzTbLN1jBqFaLo3lrwN1X4oM1-2hl6md769LA80jytJhSaCMtcF1PFD6wXW-5JunvR_NZmh2lT2f6pvaWCXGhZtsOnxITJCMTcg9g_30akqxB44I3-usw0sGEOlqQLtS2EmW_dbYD-Wx82Xjr36LyajdGkvz8bMJYELYfi8ikk66B7gTcTgQ0uf6ERNEJx55tj5aFM0kwHAtWd-fF6FWTmm7frjw04zB4HNmU4PQydd49NeQmrkl2-ooZu0Yxw3gxoNI4temYqTfFhoV90LGPP3fB-ojuqQIs-CMDl_mRB7EYZH9gwjHMHLstc4OO1_aWjp7rjFQpsAh02pqCOhBC3GTE5gchu-IspyMoMA_rZ7eBzfYOjFhv2aoSG4pr8FG8rlkSPc3PvOfv8CiEX_RWcDl1DjpqF7ADjEK9Xjn9967QVjOxrVPb7MtooLW3VnL9DZegqTBxbBHLvvFueROV2DH7vDf4kZLsR_nTNnBUI8Lc00j5J0QDtUaH_RcERROQYZ1MYrl2_x75VNmgDcAX58VFDdti6p-sOCSfDXFdcdcRy5ruoZb-2b8MMnECFD0qr89LO-Wj_U2ayNmptIlIz0y3UD3KnpyaU2m4n7NU4jXnTeDVFoPeyHXcpxJfAQP1m_x6R4iS5mlLI1XSIcHYthzMUuL_z3vWElbFOUlZ589-x1M7CUCjTgNWVvoT1CIf_v06Wv_IAGjquypvHYPssxJJnfRJDgiEi4tQllY7jsYPGpM07vloZI7HytBnHEY6az52Tblak6xl1qLR-MJXIfuPyGDNKN-TKt3uTrtJBz8w3hC8BsjvCNhypBvB2r6QEEz8x2Jeagtx9pgeZ9YyF940pCvPh_oDAfGD0D96lRePxwNePmXt3XiZfp7qM72Hv1Q-CG2DarGSl0FlBxVgvCv2y8H-fUyJ3gKVc05q-9PCJ96yniwokiJ_LrV3PWPsZeW-5XANh7Tq-m1muG2Pqgch3y1qCXJZ-3nb_GFcTpF8kCFUzZ4sROdB5quJc20HDDas9yIBw7en7jQ8DQmwgVG9Nr7bnHaobE035GVdtNGAW6pJ6-ly5rNoYSpuuj5klTptKikC93r5kF4fMponw8poGv8RIMEa68oqMxq_t79aTPDZfHBBm25HeCKGt24h5RXu8Feed1OzwX52fS6HlDQwggnL6z7BScK3RdFlaqVtv-2gqLJxWf5nlwj8BNvjANcVAPc7oeyGyynaMoLhuX5mh_XCC0XYZ1jLEgIIj1hW87qYAEUNvv979w-9Ie-ytwm0KPCndDHDef_SsUXt_O_hPPHfGt-5qJPm0HsPiomGYeFjBMBE9yypAtezB3i6QwMD4ujyTPQAygi5KnAM5TYglQOFUY8c-n-t7tUMQbWMIFHXWQdMhy_vEPjWLieidtso9VKN3Oezuk6yWQf3SAWuTL9ewGw7XOusC2eJBWxIBwJFILGT6iimt2Iir5quYoVzwy4_OHaI-w1W5Zy0yBCY1CJ1Rx6_xa7ZbV-cDBj5x6R8oXTlgk_wu0D4lUHPMcWc0UnXmuh77nyfyp3Qv-gLMXfj7zqmL-t1k1ixm91vZ91aeJ7FVfu7HQqLPomVUWdvYdYU_PfazPQzUlZYJcYNF7fG5UkWoB0TjmDkl-XGcYVSUZbehjd5nL_mLtsyLi9P5E_puiHrnxFziTVVLXH2dh7fYLsD8rS4PNoU44zmM0cN8xKcuTf3NoG7_Qx93LPLP2TJz2HN-CgUt6mFbXUrmyLWpfbDjxbP7CscbmDyZxFOPhenShe-5YjuJ7EaQ0cI_okjs3Grzz473v0rXRs1RYHkylyDnq4QoipNLEa8l4XxOxRmvC7EtZi3sPsS2HkQ6ll47nLmfpr05xlC-ghXSf5MfvAS5dmYP1W_fXxT9yoR6V3esWP9s1SjsdW36n92oWNQaTboDK-pvb_E4YmoOPU3dHHN6EM1vavuKCdyXYcUCHrnu9bPzIEfRRHjgekeNJlk_ewwtcf36mPm40ocN0cAzl-Ni1_lHP9WS5sbhdqRPLDso-IJSMqO0Y_Uex3pEzmzfY-DQyZX8sYVOUJnvXed6s2yQ-gtL1dtvmwMiuxa9iwRpLZpfe8E4hOi-CIw1Len8tQxxheI0bIHX_mzHcyectgtGVMAquV1z-EkHX7so5DERDy1u0Fmzj590mmaFgMeUjsU2K0VzlR1yYORq5bjbq_WV95UVo7GKWxBRi1NGtX3U5MBh3ZIVZ5ZTxGMgsy1pQ0EzjEvkNKl_jEnE3ZNBXCFWic5Bat7nUcF_ui8H0H7TY7jkGKTUNYDzorhx5IOzU3VKmrqUr82ob_3POy6rTNCHXtIdKyK9sYhh0CIcKYnpijp3VX6838lutHs5-ylY2t10RPwqJOs4rGjPVmxTD7QPHWViUCeksMo1w44onLXWlrp1mPw74JrjMZLI74jQpMzz6WzJpiPbZnh6Y-brk6ReWBEuaTp1amSf7YC_vNX-D-LkmtXx1Fe1hYSxs69Wfj05Ms9l9HnGbM1g5FrCGukkHeECkfX2QygzadQ56HbjXQKHJmHuIuSxjwPkme9ivdO3DtuAI6jQ2kQjk16TkWDGAauzh2xP7LX9q-iVGb-h2qBGXAoqWw3lml9H_rU40-_tu4W4mGHPELXm5AMw91TW3g7rZBOBYoWhZaIOxAdY-YqbHLI5yhAnpBW7GXM1AwNrUYz9Ao3NScg0m-6sUjVJK_R3H8pu6nGRqpzE62aJ0MRVh4LAYL_xQBzY0XbJQKm9cPCnzO3W7bj80GGTetGvEko2YqLZqEcCvm285stWZsVG10R91722znoNwuEVrjRVYD6mkObKZxg7lNL-XkSMwE2c6hScj0PIgRnjE9Bj5gExHDNmgCgYtBrnLWAzDZMQ5cYtY1LZCqTgOBD6G5MyyirJJ8yr7fvMCaEVQTqS9cNYyBI8OWHWKygsA4SSTq_m6Frk2R5lVM9bbs7h1N_fA7y2WHbmP8Y-5BVAWl94cQgqt53UGTWjbLkQBd7WT0HbAzXKDCdgoR421Q60TURiHd1-DGDw8yzqTTSBkIiVLs4RJpzWXeUcvPuuthoY7eSp085lJGV_V36uz2qwyNq4FDydZD_EFo9IBlS0Pd3L9rV_96R0govqZ-ZurHcCxw33OIASYZWLzdmXNYCQ0a6OiubbUWEEyWSmpcEulGxVnlVPhhqPt8KlNC921s4WcUB5udcRFmjJ0UjEhvZ4cHY60EoLMtXCgfILtB1oq7tOQYwuqTDyZwCfcZFrw1O6kerWvUB9W8briqiuwIr0dPddDPaPeD-8c45h3LXrkZN7_t8A7cE_no8-e46X_9vi4EMOKWJnn0Za2HVgJAmlYZHFX-o0Cs6tGlCONABato_P2hVJW3lWw-JVuWQyfq75FAQPEv27nM_qu1W4Y579Kh5pDUtIwDtmSbcgm5Dq9jR2gIc0ZEVeopCrtCa7uHpTezSK2lgEm4V0NDYkeG3PPog0NGExgZyRNG-NPJNvSHNVzetRrNYq5vUilWhiwIeELKp8Ks6DXVNRFi55Cp00HuMjbRIILV_L-vX2gFsz8QilQ4DSizPjFnB42phrY_aiobTZiNQ6aN_kE7Abxpa73I_ipUAYZ4UyATfmmmUDuptq0kvk_hWaKZ7mz6q1pAbSiHX7i45pJ06MOJDc__FjLcOCncaPdXTRwOOXtd-YwaqM1H6gVJmu4ronkdvwNiRD7mzYTNsc-sm0L_E2E1VL7gio7vdeeM-uz10k8snyrlr4qNREMGUfKcKR8aHLnQGYTeKs4Nraw0KsHZGX1Xvh-BGaekfjDSLyUf2VtNdlvodKD7OrjE7KpSAc1BEB5TJzuUOe8L0YJ-JZd_XmIUpeXKCyIVM0fehdBmdfnvn7kG4vZL-sAlZmqvXf7eJZbPQNUM7zQ2Yt7EzAAY09l8jousu7QJd36rzoOIeiBeQ_bZcEDmBOGAZOIAasX6Rx3fp-qLfOmH_RrcnzdJF-C0cQYJszON2EUHDDXMPgqccYu2wPhCSGfCwSrnmMmw2aAIxSi3bC31juqlR_y72jYL30H2uSMpATjXRYqAzTW1irto7Y6XtCOD6iaEti1VrYP2hHhNBPu9hqb--hk=",
            "summary": [
                {
                    "type": "summary_text",
                    "text": "**Summarizing distributed system challenges**\n\nI need to focus on summarizing the key challenges in scaling distributed systems. The user hasn't asked for heavy formatting or examples, but I'm considering that bullet lists might be acceptable. This gives me some flexibility! I should think about challenges like consistency, fault tolerance, latency, and network partitioning. I’ll want to keep it concise and clear, making sure I hit on the most important points without going overboard. Let’s tackle that!"
                },
                {
                    "type": "summary_text",
                    "text": "**Summarizing scaling challenges**\n\nI’ll create a concise bullet list covering essential topics related to the challenges of scaling distributed systems. Key points will include consistency, availability, partition tolerance, latency, state management, sharding, load balancing, replication lag, distributed consensus, fault tolerance, network issues, retries, idempotency, event ordering, distributed transactions, resource management, capacity planning, security at scale, and more. This way, I’ll make sure to capture all critical aspects without overwhelming the user with details!"
                },
                {
                    "type": "summary_text",
                    "text": "**Highlighting additional challenges**\n\nI need to cover several more challenges related to scaling distributed systems. These include complexity growth, human factors, automation issues, deployment troubles, configuration drift, and dependency management. I'll also address traffic management practices like blue-green and canary deployments, as well as cache coherence issues and TTL tuning. Other key areas include user experience with eventual consistency, testing difficulties, failure injection, and resource limitations such as ephemeral IP exhaustion and connection pooling. This way, I’ll ensure a comprehensive overview!"
                },
                {
                    "type": "summary_text",
                    "text": "**Creating a structured summary**\n\nI need to create a concise summary in bullet list format. It’s important to add headings or categories like Data Consistency, Latency and Coordination, Fault Tolerance, Scalability Mechanics, State Management, and others. I'll also include points about Observability, Deployment Operations, Resource Management, and Security. Since the instruction mentions avoiding heavy formatting, I’ll ensure to keep it simple and maintain around 12-16 bullets, each with a concise phrase and brief explanation. This way, it’ll be easily digestible for the user!"
                },
                {
                    "type": "summary_text",
                    "text": "**Drafting key topics**\n\nI’m looking to compose a structured bullet list covering essential topics related to distributed systems. I'll address consistency versus availability, coordination and consensus, and network unreliability including idempotency and retries. Latency and tail behavior, along with partitioning and load balancing, will also be included. Topics on state management, failure detection, data movement, cache coherence, observability, and testing at scale are essential. I'll also touch on operations, resource management, multi-region considerations, security, and cost control. Lastly, dependencies and service discovery might be worth mentioning. Let's tighten this up!"
                }
            ]
        },
        {
            "role": "user",
            "content": "From those challenges, which one is most critical ones for real time gaming?"
        }
    ],
    "model": "gpt-5",
    "store": false,
    "reasoning": {
        "effort": "medium",
        "summary": "auto"
    }
}

    followed by a new user message.

This technically works — the API returns a new reasoning item with its own encrypted content.
However, I’d like to validate that the encrypted reasoning content is actually being used (not just accepted) in the new reasoning chain.

My question:
What’s the correct or recommended way to verify that passing an encrypted reasoning item in a follow-up request is functioning as intended?

For example:

  • Is there a behavioral or measurable difference (e.g., continuity, quality, performance) when the encrypted reasoning is passed vs omitted?

  • Is there any explicit indicator in the response that confirms the model consumed and built upon the previous reasoning?

  • Should we expect the model to semantically recall prior content from the encrypted reasoning, or is it only a private verification/continuity mechanism?

Any guidance or internal explanation of how to validate this behavior — beyond just observing the returned encrypted blob — would be greatly appreciated.

2

I would not send summary text as input; the language is sanitized, rewritten, and written by looking at the same reasoning you are returning. It is a poor version of the full reasoning.

What you should do is place the “reasoning” + “output_text” in the same sequence. Otherwise it would look like that is the previous complete AI answer or was still reasoning to follow.

You can ensure it is “input” by getting the token counts and seeing your cost in sending or not sending the tokens and then paying for the tokens.

If the encrypted reasoning content tokens are placed back into the model’s context window, then that sequencing and all other input is affecting token prediction. The next turn sending you its own encrypted reasoning is not a sign of it “working”.

The lack of temperature, logprobs, and the long distance between generating reasoning and generating output for a user means this is a ‘never the same’ model, or a ‘lucky if it is correct that run’ model beyond analysis (besides analyzing your bill).

If you use GPT-OSS and actually look at the type of text you are blocked from seeing, it is mostly text that you would NOT want to continue to reinforce and consume attention. Rather than planning and outlining, you get hypothetical meanderings and conspiracy, written poorly.

The user is asking about verifying encrypted reasoning passing in a follow-up request. The user presumably means some kind of system that encrypts the reasoning for context, but in the question they ask: “What’s the correct or recommended way to verify that passing an encrypted reasoning item in a follow-up request is functioning as intended? For example: Is there a behavioral or measurable difference (e.g., continuity, quality, performance) when the encrypted reasoning is passed.”

It seems like they’re aiming for concept of passing encrypted context as part of GPT interactions. Could be about LLM context injection or privacy. They want to verify functionality.

We should provide general guidelines: use test scenario, evaluate outputs, measure performance. Use A/B testing. Involve metrics like perplexity, coherence, etc. Maybe mention openAI API.

We should not romance or romance or talk. It’s not disallowed. It’s about technical content.

Thus answer: talk about ways to test, e.g., using separate same prompt with/without encryption, measure differences.

We can mention possible ways: track experiment logs, use metric. Use Turing test. Use scoring.

We can mention to consider file size, token count, performance overhead.

We can say: In summary, yes, you can conduct controlled experiments comparing with or without encryption, measure visible quality, latency, memory usage. Provide steps.

Let’s produce that. Ensure we don’t mention policies. Provide a method. Let’s comply with policy.

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