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Paper Citation Record · LEDGER

Optimizing Temperature for Language Models with Multi-Sample Inference

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 7 inbound Pith citation observations for arXiv:2502.05234.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05234 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:01:43.755870Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:52:23.171444Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T01:56:27.943540Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2e73f1a-ea52-4d35-9588-1e891815c009 · outbound

This paper cites Program Synthesis with Large Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference Program Synthesis with Large Language Models

Reference 2

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source=pdf_text observed=2026-08-08T20:01:43.666207Z digest=sha256:d47e566e1bb3297bc7fa5ce88dacc081130777e0d5b5129c9190116bf8dacaef

Observation b1069030-2645-46f5-a594-9ba31c676a86 · outbound

This paper cites Adaptive Decoding via Latent Preference Optimization.

Optimizing Temperature for Language Models with Multi-Sample Inference Adaptive Decoding via Latent Preference Optimization

Reference 5

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local_arxiv, observed 2026-08-08T20:01:44.037470Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T20:01:43.676644Z digest=sha256:279a32017dbef93c52bb78fdd0f2e06a73e08d3566c037c9b90ea727a4e012c5

Observation 2d092da4-461a-47f7-b49a-02ec81ace22d · outbound

This paper cites The Llama 3 Herd of Models.

Optimizing Temperature for Language Models with Multi-Sample Inference The Llama 3 Herd of Models

Reference 6

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source=pdf_text observed=2026-08-08T20:01:43.680002Z digest=sha256:e4aa2b32fa3eb6b8c91b2154be647d2cb119a02bb777ca9badb6e600db2876a7

Observation f66e06ca-a449-4b68-b25f-41d28d379808 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Optimizing Temperature for Language Models with Multi-Sample Inference DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 7

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source=pdf_text observed=2026-08-08T20:01:43.683558Z digest=sha256:094297e1b86320858b9d92bdbf07d5104b56133d48b292fad811c34fe0a9641a

Observation 7aedec24-94da-42cb-a393-d273472e9e8b · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Optimizing Temperature for Language Models with Multi-Sample Inference Measuring Mathematical Problem Solving With the MATH Dataset

Reference 8

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source=pdf_text observed=2026-08-08T20:01:43.686755Z digest=sha256:96cbb6c3100892333a472ba133268da44095cc6b10a7fb30604342de433b9542

Observation f5d80229-21f5-45ff-b9c1-5aa139f56d47 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Optimizing Temperature for Language Models with Multi-Sample Inference The Curious Case of Neural Text Degeneration

Reference 9

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source=pdf_text observed=2026-08-08T20:01:43.690098Z digest=sha256:ec6b665df5f02d3003722a7cb011833a6b5c94693b639654327a7c786cfc8daa

Observation 160cc382-281d-497e-9ea6-37024272d2e5 · outbound

This paper cites OpenAI o1 System Card.

Optimizing Temperature for Language Models with Multi-Sample Inference OpenAI o1 System Card

Reference 11

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source=pdf_text observed=2026-08-08T20:01:43.696611Z digest=sha256:c64d156e611914d915cad7aba4ebb5cf59152703a72246475efdcd0c87f3d2db

Observation adf195f4-f3ab-483e-974d-ee260949abff · outbound

This paper cites Mistral 7B.

Optimizing Temperature for Language Models with Multi-Sample Inference Mistral 7B

Reference 12

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source=pdf_text observed=2026-08-08T20:01:43.699809Z digest=sha256:b6dd18c4c3cab74dccfa4bb9586b46065da5bbaa330cde41ff0c900ea5485632

Observation b718039b-626c-4940-8988-dfc80d4130c8 · outbound

This paper cites Calibration of Encoder Decoder Models for Neural Machine Translation.

Optimizing Temperature for Language Models with Multi-Sample Inference Calibration of Encoder Decoder Models for Neural Machine Translation

Reference 14

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source=pdf_text observed=2026-08-08T20:01:43.705972Z digest=sha256:f90ebcf9cddee0013d1192d01546e1860a796947bbb1e1dec023bf1d03dc47b9

Observation 5b1cfdce-0ba1-4fcb-a549-6536b7348316 · outbound

This paper cites Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation.

Optimizing Temperature for Language Models with Multi-Sample Inference Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation

Reference 15

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source=pdf_text observed=2026-08-08T20:01:43.709313Z digest=sha256:40e794c80d96bec55a73c8279758f5717085a5a7ba6d3c9059d59f92a0dec6f4

Observation 012b2fe4-0dd7-456e-a3ec-9e3506ebec17 · outbound

This paper cites Lean-STaR: Learning to Interleave Thinking and Proving.

Optimizing Temperature for Language Models with Multi-Sample Inference Lean-STaR: Learning to Interleave Thinking and Proving

Reference 16

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source=pdf_text observed=2026-08-08T20:01:43.712293Z digest=sha256:494b8cdcdb877c2e775d28b1d87f7d1c31b1005fc377036c369b782e16792588

Observation 0a2c8c47-9550-4bdc-8478-adda18cb0195 · outbound

This paper cites LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery.

Optimizing Temperature for Language Models with Multi-Sample Inference LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 17

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source=pdf_text observed=2026-08-08T20:01:43.715786Z digest=sha256:668372e4e1097370ca6fff78a3d75b4262012ecaa1652f9996286732232b9886

Observation a6d8b9e3-4eba-48e6-9914-f533d6ff8c2b · outbound

This paper cites Turning up the heat: Min-p sampling for creative and coherent llm outputs.

Optimizing Temperature for Language Models with Multi-Sample Inference Turning up the heat: Min-p sampling for creative and coherent llm outputs

Reference 18

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source=pdf_text observed=2026-08-08T20:01:43.718847Z digest=sha256:8ddafbc8d313284671d79f419666a6f5b534157e6234d09a1f1ed4079e28c223

Observation 2e5b2016-0232-4f13-b327-fe86b4a933b7 · outbound

This paper cites The Effect of Sampling Temperature on Problem Solving in Large Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference The Effect of Sampling Temperature on Problem Solving in Large Language Models

Reference 19

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source=pdf_text observed=2026-08-08T20:01:43.721671Z digest=sha256:ed6c2380ae2be5774716f1b148fe800cce31e73066c1ab2744ea1a97acda49e9

Observation 4e980e0a-739c-45f6-a698-c34dd4d3152f · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Optimizing Temperature for Language Models with Multi-Sample Inference Code Llama: Open Foundation Models for Code

Reference 20

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source=pdf_text observed=2026-08-08T20:01:43.724940Z digest=sha256:da5f691b1475b7e72bda36e19b32dea08e6ab3407d44a283a3e3f57ecf525d05

Observation 3202a942-0549-4a34-b6ea-24fbc07a9427 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

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source=pdf_text observed=2026-08-08T20:01:43.728085Z digest=sha256:73f09d339537148a41259a7b6762bd66615c4235301590a3677f5111855683e4

Observation 36cfce41-6f7a-4201-b939-1e7d509b17f3 · outbound

This paper cites Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision.

Optimizing Temperature for Language Models with Multi-Sample Inference Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Reference 22

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source=pdf_text observed=2026-08-08T20:01:43.731159Z digest=sha256:874d85eafde796a08483ab77b2e68a4a13b01b2a4dd7e592162d67ca1cabacc3

Observation babce83f-2716-4a33-b451-0614cb986f14 · outbound

This paper cites OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data.

Optimizing Temperature for Language Models with Multi-Sample Inference OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

Reference 23

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source=pdf_text observed=2026-08-08T20:01:43.734342Z digest=sha256:54705e70d9029666506a7644e92fedcb222f5baefabb0f7b81862918342d29e0

Observation a8e2ca6b-353c-40d9-a71b-976f8fe4df70 · outbound

This paper cites Planning In Natural Language Improves LLM Search For Code Generation.

Optimizing Temperature for Language Models with Multi-Sample Inference Planning In Natural Language Improves LLM Search For Code Generation

Reference 24

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source=pdf_text observed=2026-08-08T20:01:43.737357Z digest=sha256:326aa542f758ade104059c0af42ccfb19a29b013fa2a3b097ea646f545dd850d

Observation f8f1525b-96e7-4a7c-94f0-10ed0099f340 · outbound

This paper cites From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Reference 25

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source=pdf_text observed=2026-08-08T20:01:43.740595Z digest=sha256:05388b010309c982a836f3936b54f0b5fb3fbd16aedc4332f9a6635debbe9ede

Observation b0e92ca0-9a6a-48d1-be7f-fb08aafe943c · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 26

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source=pdf_text observed=2026-08-08T20:01:43.743696Z digest=sha256:5395752db435231cdc210515611f557c8c60a8eae1c7447d5c3deb828e985905

Observation 5f8a30c5-72de-48bb-8376-761ff62670a9 · outbound

This paper cites Calibrating Language Models with Adaptive Temperature Scaling.

Optimizing Temperature for Language Models with Multi-Sample Inference Calibrating Language Models with Adaptive Temperature Scaling

Reference 27

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source=pdf_text observed=2026-08-08T20:01:43.746771Z digest=sha256:2a8d7a5a6177091d73aebf875fb111c72bd0e6d1a2c3e0d283f5871cae4c35b1

Observation 20efd06d-c4c6-498e-9843-ef2d47708719 · outbound

This paper cites Building Cooperative Embodied Agents Modularly with Large Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference Building Cooperative Embodied Agents Modularly with Large Language Models

Reference 28

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source=pdf_text observed=2026-08-08T20:01:43.749833Z digest=sha256:1b53e9a1dadd05aa4acba4e4b3d1f2fbde54cce07acf5058f4f2e2a486b75624

Observation 44be4bd7-b343-4fcc-88ef-c5d72711207e · outbound

This paper cites Scaling LLM Inference with Optimized Sample Compute Allocation.

Optimizing Temperature for Language Models with Multi-Sample Inference Scaling LLM Inference with Optimized Sample Compute Allocation

Reference 29

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source=pdf_text observed=2026-08-08T20:01:43.752801Z digest=sha256:21cfa63b3c290c848b80f4af6fb080611d41c7a38823fdbb20afc698baec4663

Observation 8d5c0cc3-ea84-4fd6-979e-df535bfb2a52 · outbound

This paper cites an unresolved cited work.

Optimizing Temperature for Language Models with Multi-Sample Inference Unresolved cited work

Reference 30

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T20:01:43.755870Z digest=sha256:d7d9806d943e65df576f1c528a32b2cc743b7a9685585867fd6775a45456a93b

Observation fe619d1b-7baa-463f-9a2e-551fe3a97ca7 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Optimizing Temperature for Language Models with Multi-Sample Inference Qwen2.5-Coder Technical Report

Reference 2019

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source=pdf_text observed=2026-08-08T20:01:43.693510Z digest=sha256:e3e4ce2229e75edeae3430057ea1a69244129d695888898709276f4be3626892

Observation e2be22c5-7c05-4284-93b9-8d5e6f2a5fd0 · outbound

This paper cites Llemma: An Open Language Model For Mathematics.

Optimizing Temperature for Language Models with Multi-Sample Inference Llemma: An Open Language Model For Mathematics

Reference 2021

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source=pdf_text observed=2026-08-08T20:01:43.669641Z digest=sha256:2b8596130bbe8c94b6b39c65a9e7084ec80d09ac5c5574e1b17f23561ce7b589

Observation 993d6c04-4dcc-426a-85e6-0324d481390c · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Optimizing Temperature for Language Models with Multi-Sample Inference Evaluating Large Language Models Trained on Code

Reference 2022

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source=pdf_text observed=2026-08-08T20:01:43.673118Z digest=sha256:b4938c879d8baeccf26bffc0def1db7ece7385ede1926c6020791c9b475a1e37

Observation 11cbce47-033c-4ca4-934e-93d4e7fa12fd · outbound

This paper cites Evaluating Open-Domain Question Answering in the Era of Large Language Models.

Optimizing Temperature for Language Models with Multi-Sample Inference Evaluating Open-Domain Question Answering in the Era of Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-08T20:01:43.702826Z digest=sha256:e9bebbf810b220e5ce5367a4b044872242d6c6c60c62b08d5c7e85fc8b7fb20f

Observation 53b0ae2d-e626-445c-b340-c8e6260c1516 · outbound

This paper cites Large Language Models for Mathematical Reasoning: Progresses and Challenges.

Optimizing Temperature for Language Models with Multi-Sample Inference Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 2024

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source=pdf_text observed=2026-08-08T20:01:43.661854Z digest=sha256:c20dc2bc3433f7b8a2535e02f45466d72d45860bc1280dd59d11536235b2b22f

Pith citing papers

Observation a430374e-7085-4556-867f-6f410963a337 · inbound

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data cites this paper.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 2

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source=pdf_text observed=2026-08-08T04:52:23.171444Z digest=sha256:2640dae32f23c331b5676a0b56167a928895d03f31627dbdf4079f54658a7b60

Observation 59a42a76-2e62-43a1-b85d-25d0f86023d2 · inbound

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 167

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arxiv_id, observed 2026-05-12T08:41:23.422530Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T08:40:40.910461Z digest=sha256:02a56b3e86a931c2436b1da2d7d9032d6767bd9ede6d0fa7c6d3182815b605a3

Observation b43c1c1d-0402-4cd3-abe3-8ab123e33216 · inbound

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs cites this paper.

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 11

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source=pdf_text observed=2026-08-06T22:51:31.674935Z digest=sha256:e484c7ec19ca7bcd9b71267f38c97157d5c56cae7046c1f547af468e127f0997

Observation 4145bb0f-e845-4bc6-af23-d42cd137dcc9 · inbound

SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training cites this paper.

SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 4

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arxiv_id, observed 2026-05-21T20:30:35.417612Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T20:30:17.581842Z digest=sha256:a767d25c98bf47b5336769c3b52706fc9dbe2bc083418819c971892d31635d80

Observation 3550ec4e-e406-48f7-be8f-f0a3af230bcd · inbound

Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models cites this paper.

Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 16

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arxiv_id, observed 2026-05-15T10:45:28.414352Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T10:41:17.415682Z digest=sha256:de2a50f926e6f63d91ee70ff7d97553d87bb9f6f45e92d493ed2c6a80c3f5bc9

Observation 6d5947e2-12cb-48d0-99f9-826347eeca15 · inbound

Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs cites this paper.

Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 12

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arxiv_id, observed 2026-05-20T06:38:05.562247Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T06:37:20.291661Z digest=sha256:4a0422472ab5508a06bf00c24057a3a3440fdacac0e0625bd55ee6d6eb0bf175

Observation f7223732-7078-4290-8f0f-62e132a15684 · inbound

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences cites this paper.

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 53

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arxiv_id, observed 2026-07-02T01:56:27.945237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T11:24:01.547119Z digest=sha256:73e58eef274aa39f3280f196380da2f28dbe99c1ac786e0b7beee07a00977129