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Paper Citation Record · LEDGER

Learning to Reason via Mixture-of-Thought for Logical Reasoning

As of 7 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 8 inbound Pith citation observations for arXiv:2505.15817.

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

pith.paper-citation-record.v1
2505.15817 v2

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:15:42.241838Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:37:24.974376Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:56:30.043811Z

Reference resolution

66 of 66 outbound references displayed

  • verified exact1
  • verified fuzzy22
  • unresolved40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60d20297-a739-47ec-bab8-075e366c0998 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Chain-of-thought prompting elicits reasoning in large language models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation eaf00f57-7b25-40a1-840f-7dfcce6eb14a · outbound

This paper cites Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation

Reference 2

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source=pdf_text observed=2026-08-07T15:15:40.009866Z digest=sha256:706035f205dff6a15a8eab9171bdc0d3b2eb63a3a8ce833a90b55dce81dece5d

Observation f0cf9a3a-ef64-4cb4-8cd8-c149da722006 · outbound

This paper cites Typedthinker: Diversify large language model reasoning with typed thinking.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Typedthinker: Diversify large language model reasoning with typed thinking

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b6347129-0bc6-4bbb-9b86-313bdbb51b44 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 4

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no resolver link, observed 2026-08-07T15:15:40.277744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:40.277744Z digest=sha256:d1224a04ced67704d3f9ffef4ee070417fa1994004c8f544dc1c8e5f6a819343

Observation d040d239-e5da-4be3-b57f-bac6fcc3f08c · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

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source=pdf_text observed=2026-08-07T15:15:40.305405Z digest=sha256:00187c85597178da431e01c3195d8194b100c744d08583924c35ccadf5b00936

Observation db18f640-dde0-4dfd-aeb5-1a51de18da63 · outbound

This paper cites Scaling LLM test- time compute optimally can be more effective than scaling parameters for reasoning.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Scaling LLM test- time compute optimally can be more effective than scaling parameters for reasoning

Reference 6

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source=pdf_text observed=2026-08-07T15:15:40.337838Z digest=sha256:d52a8c580e9f6eb334160b3313e8edc17df5531f4774509358fbefe2f4fe867c

Observation 815ae266-35e2-420e-a94d-d6c98dd83ffc · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 7

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source=pdf_text observed=2026-08-07T15:15:40.412313Z digest=sha256:a3a941d5eff77d3dd8463a2a45f07ea1bfa0be8030acd765554f89953235fd7e

Observation 9edd47b4-ac57-4155-9936-1d2149c622fa · outbound

This paper cites Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 8

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source=pdf_text observed=2026-08-07T15:15:40.488713Z digest=sha256:1bcd0176497c3935705137824f9fed4bb91ad3c27ad102fad0c10a683ca74c85

Observation 994a9c42-faef-4239-ac86-bea65cb236b3 · outbound

This paper cites Olausson, Alex Gu, Ben Lipkin, Cedegao E.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Olausson, Alex Gu, Ben Lipkin, Cedegao E

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.745664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:40.503433Z digest=sha256:09907e9e3b2028302cd98c56251a4ed6219ffab1bddf74c6fbd0828c602fd0c5

Observation 04a08381-c07a-4af6-9f47-7883a7c2b2ee · outbound

This paper cites Lee, and Eunho Yang.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Lee, and Eunho Yang

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 0a63efb7-85ad-4dd8-8af4-f657a3140ef5 · outbound

This paper cites Hybridmind: Meta selection of natural language and symbolic language for enhanced llm reasoning.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Hybridmind: Meta selection of natural language and symbolic language for enhanced llm reasoning

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:40.654879Z digest=sha256:8df17e347e9a4c5637ecbe3b22042c0b8b1e3635f619cb55e72c7b9e4da96564

Observation e010be67-be54-4f2a-99a9-7df3306f2c7f · outbound

This paper cites Faithful logical reasoning via symbolic chain-of-thought.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Faithful logical reasoning via symbolic chain-of-thought

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:40.711734Z digest=sha256:d9d49a4c78512fa6150fbfd43038d722626402b6bba10a8f57a84af7ce162a74

Observation ac1bbebe-3a49-4b67-ad86-ec20d992e5c7 · outbound

This paper cites Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Reference 13

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source=pdf_text observed=2026-08-07T15:15:40.786824Z digest=sha256:7cfe7a3f2ed27aa01e2c1db9b14b3e919a9f5b86256fe8f80347a611634a7cbf

Observation 323c9510-bb68-4a5d-bf2d-b55021dae1e3 · outbound

This paper cites Prentice- hall Englewood Cliffs, NJ, 1972.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Prentice- hall Englewood Cliffs, NJ, 1972

Reference 14

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source=pdf_text observed=2026-08-07T15:15:40.842716Z digest=sha256:0389ae3f94d6883de51eea4f9286645c971f2d8caab7a3b6a39cd4d448e2ab60

Observation faa2adaf-1ef0-4b3c-9ce9-cdca45133e63 · outbound

This paper cites Structure-mapping: A theoretical framework for analogy.Cognitive science, 7 (2):155–170, 1983.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Structure-mapping: A theoretical framework for analogy.Cognitive science, 7 (2):155–170, 1983

Reference 15

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Observation 60c15d88-eeb7-46c8-b5ea-e1a933e31adb · outbound

This paper cites Why a diagram is (sometimes) worth ten thousand words.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Why a diagram is (sometimes) worth ten thousand words

Reference 16

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 02f43de3-c5f6-4e80-b2c9-828da3eb1140 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 17

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a77f2f94-68f8-4133-b99f-8e6594fed57a · outbound

This paper cites Large language model cascades with mixture of thought representations for cost-efficient reasoning.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Large language model cascades with mixture of thought representations for cost-efficient reasoning

Reference 18

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Observation fcef8a0c-36ca-4b85-9c92-d508b054789d · outbound

This paper cites ProofWriter: Generating implications, proofs, and abductive statements over natural language.

Learning to Reason via Mixture-of-Thought for Logical Reasoning ProofWriter: Generating implications, proofs, and abductive statements over natural language

Reference 19

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Observation db6429e0-488b-48b4-bd85-ea7939e13ff9 · outbound

This paper cites FOLIO: Natural language reasoning with first-order logic.

Learning to Reason via Mixture-of-Thought for Logical Reasoning FOLIO: Natural language reasoning with first-order logic

Reference 20

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source=pdf_text observed=2026-08-07T15:15:41.250728Z digest=sha256:3f155c26205a28bdbc8f3726d17aff94411c1c5de8dd913501873dd47550d8ba

Observation be859b07-219f-4f6f-a86c-12c0b52bc610 · outbound

This paper cites Bounded model checking using satisfiability solving.Formal methods in system design, 19:7–34, 2001.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Bounded model checking using satisfiability solving.Formal methods in system design, 19:7–34, 2001

Reference 21

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raw_fallback, observed 2026-08-07T15:15:43.656744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7db8b6c7-1a8c-4743-9d74-af82582e9648 · outbound

This paper cites Grounding fo and fo (id) with bounds.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Grounding fo and fo (id) with bounds

Reference 22

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raw_fallback, observed 2026-08-07T15:15:43.642330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7befe637-0f39-42db-9903-a36171bc14b0 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.Advances in Neural Information Processing Systems, 35:15476–15488, 2022.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Star: Bootstrapping reasoning with reasoning.Advances in Neural Information Processing Systems, 35:15476–15488, 2022

Reference 23

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Observation fb93bd05-9d35-4410-9a1c-89cdf1757a79 · outbound

This paper cites Policy gradi- ent methods for reinforcement learning with function approximation.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Policy gradi- ent methods for reinforcement learning with function approximation

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 5e72854d-769c-49f3-a610-92255b2d2e42 · outbound

This paper cites Qwen2.5 Technical Report.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Qwen2.5 Technical Report

Reference 25

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Observation 63f78fa3-8436-480d-b111-209bf12dfa21 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Gemma 2: Improving Open Language Models at a Practical Size

Reference 26

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Observation 9481aa29-07d9-4097-a80b-d8f6f886566d · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Gonzalez, Hao Zhang, and Ion Stoica

Reference 27

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no resolver link, observed 2026-08-07T15:15:41.516515Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.516515Z digest=sha256:62c84d81ec2ea2babd529c11e7e5222a6ebd6077360a405ecf63df9a5cae32f9

Observation 843c0f6b-5d52-47a8-9f2e-a9ee0bd01c33 · outbound

This paper cites Large language models meet symbolic provers for logical reasoning evaluation.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Large language models meet symbolic provers for logical reasoning evaluation

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.599023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:41.554283Z digest=sha256:15f0f193836409315fa3eedf7a99e38ff3a966dfbb6976e9d7d7c266af616132

Observation 4cac93ec-e729-4ee1-b413-463b40112e55 · outbound

This paper cites Verus-lm: a versatile framework for combining llms with symbolic reasoning.arXiv preprint arXiv:2501.14540, 2025.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Verus-lm: a versatile framework for combining llms with symbolic reasoning.arXiv preprint arXiv:2501.14540, 2025

Reference 29

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.586596Z digest=sha256:1d5c1abf8529cd78316492b97cc0f7255704979f2c031830b8e27d4c81c3b801

Observation a0c89987-3345-45c4-a251-828ff7a968c4 · outbound

This paper cites Competition-level code generation with alphacode.Science, 378(6624):1092–1097, 2022.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Competition-level code generation with alphacode.Science, 378(6624):1092–1097, 2022

Reference 30

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source=pdf_text observed=2026-08-07T15:15:41.621069Z digest=sha256:3c62aa7c355462a6d177c2d48c13c232a12fb1473d8d2e7d76283ccae68562af

Observation aee10dbd-d8e3-476d-89d9-57ec934cc869 · outbound

This paper cites Exploring human-like translation strategy with large language models.Transactions of the Association for Computational Linguistics, 12:229–246, 2024.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Exploring human-like translation strategy with large language models.Transactions of the Association for Computational Linguistics, 12:229–246, 2024

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.576257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:41.666754Z digest=sha256:9e25e04d321e0308f32ec5aaea3154df95e7ae945d71844f5b74962542dcb1a7

Observation 4d4eaf10-4402-48bf-b737-2e0deddab101 · outbound

This paper cites Diversity of thought elicits stronger reasoning capabilities in multi-agent debate frameworks.ArXiv, abs/2410.12853, 2024.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Diversity of thought elicits stronger reasoning capabilities in multi-agent debate frameworks.ArXiv, abs/2410.12853, 2024

Reference 32

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Observation 9438521a-17f6-4655-bf12-70abd47bbdbc · outbound

This paper cites Self-contrast: Better reflection through inconsistent solving perspectives.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Self-contrast: Better reflection through inconsistent solving perspectives

Reference 33

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raw_fallback, observed 2026-08-07T15:15:43.563111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation acc4923a-65ee-4c49-b0f4-8bb8e22ca0ea · outbound

This paper cites Large Language Models Are Reasoning Teachers.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Large Language Models Are Reasoning Teachers

Reference 34

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source=pdf_text observed=2026-08-07T15:15:41.771366Z digest=sha256:60dd557123f5a6f264826773358484f6900b613e573dbb0a65891f1b05129b0c

Observation 1d6932b2-b9d0-4893-99f7-a7e4bdb9db6e · outbound

This paper cites Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs

Reference 35

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.816606Z digest=sha256:dd649cc45bbaabfc855c450aa85d6000aadfc4890247f97683b5525022a1b905

Observation 91e700ef-664f-4383-aa57-4d9ea0133f06 · outbound

This paper cites Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective

Reference 36

Resolution
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no resolver link, observed 2026-08-07T15:15:41.859496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.859496Z digest=sha256:018f1dc27de930669395aa28d1bb3df73e6b7ebf9f349f7e5e50bfed96632f94

Observation 656c8341-79f5-4f27-abd5-3e11036033c5 · outbound

This paper cites V-STaR: Training Verifiers for Self-Taught Reasoners.

Learning to Reason via Mixture-of-Thought for Logical Reasoning V-STaR: Training Verifiers for Self-Taught Reasoners

Reference 37

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no resolver link, observed 2026-08-07T15:15:41.894799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.894799Z digest=sha256:e3d1c52f0df148cba7bc60fe6c54e964393418a9f0ad72ab51a17188ba01edf8

Observation ede10142-2deb-481a-ae02-1cd82af73184 · outbound

This paper cites Self-taught optimizer (stop): Recursively self-improving code generation.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Self-taught optimizer (stop): Recursively self-improving code generation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.549873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:41.946675Z digest=sha256:0ed4471de8cc09f4c0dbba677d095ea780b7138f2598d3236ebe0f097639d0be

Observation f42bd0e0-562c-4f4d-b9c7-f08dab4cba0b · outbound

This paper cites Self-Taught Evaluators.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Self-Taught Evaluators

Reference 39

Resolution
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no resolver link, observed 2026-08-07T15:15:41.997675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:41.997675Z digest=sha256:f14b27d3faa3443496dbbb50186305355960d3189531e7fe6b31e839c3b2daaa

Observation 5e8cc2a8-658c-44e8-bfe7-134b5cd994fb · outbound

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

Learning to Reason via Mixture-of-Thought for Logical Reasoning Lean-STaR: Learning to Interleave Thinking and Proving

Reference 40

Resolution
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no resolver link, observed 2026-08-07T15:15:42.069890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.069890Z digest=sha256:881215cb4d7875c8ee16aebceaa3c852699e5bfbcc07d38d888224d169ddd794

Observation bb95fb40-9f6a-403e-b43b-dbd2abb40acb · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 41

Resolution
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no resolver link, observed 2026-08-07T15:15:42.079859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.079859Z digest=sha256:bc49c17c249ed39355bccc672e736a4b8c26918402d94b96c17b7df821626b17

Observation 91683d93-14f8-4657-8324-17ee5705671a · outbound

This paper cites Certified deductive reasoning with language models.Transactions on Machine Learning Research, 2024.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Certified deductive reasoning with language models.Transactions on Machine Learning Research, 2024

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.537333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.121205Z digest=sha256:a7f2a14c878290484b7580063f31f86862f50f921bc4f026e157b5952facbb69

Observation 0b1ccf3e-7388-41e7-bc96-c6e6f01be5f8 · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

Learning to Reason via Mixture-of-Thought for Logical Reasoning rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 43

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no resolver link, observed 2026-08-07T15:15:42.134427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.134427Z digest=sha256:0a0a8e405297a49dfddf5e689e749f257baefbc0903dbdeda210268adc874192

Observation cddd3fcc-cb73-4082-8e41-7f20d34ecd80 · outbound

This paper cites START: Self-taught Reasoner with Tools.

Learning to Reason via Mixture-of-Thought for Logical Reasoning START: Self-taught Reasoner with Tools

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T15:15:42.139398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.139398Z digest=sha256:805a969e4ffbda7d82ebbff7c78d17b76ff25ebf3e16e25e2cc3a6fa0ee7e042

Observation 65ca3d72-cd0d-4eea-b331-6c508dc735fc · outbound

This paper cites Regenesis: LLMs can grow into reasoning generalists via self-improvement.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Regenesis: LLMs can grow into reasoning generalists via self-improvement

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.521553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.144098Z digest=sha256:fbe86cb12b40383999b06b7ac6152fd0288b32f841ffd65d4fe9a86b0a0840be

Observation ffe13c54-1cd4-4b77-a991-54696527bae5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Learning to Reason via Mixture-of-Thought for Logical Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T15:15:42.148432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.148432Z digest=sha256:419a684bdb59de2b847cdc8f5c68a3af1ad99233b2dfd39a98716ef41c35e7df

Observation 5d081618-a6e4-42fb-9c5d-9feca81d96a1 · outbound

This paper cites Rush, and Thomas Wolf.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Rush, and Thomas Wolf

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.506064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.153350Z digest=sha256:360abd4aead05cf828dd90e4f034aee924f3eaf57b9438bf5fa450eeb60f3ff6

Observation d043562d-0d46-467d-8898-297c68c315ca · outbound

This paper cites Faithful Logical Reasoning via Symbolic Chain-of-Thought.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Faithful Logical Reasoning via Symbolic Chain-of-Thought

Reference 48

Resolution
malformed identifier
no resolver link, observed 2026-08-07T15:15:42.158555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:42.158555Z digest=sha256:62a72bfb74e3f5ab5b736132f577d2f608c4f478ec6fd8d0c3241ee5f291ea1a

Observation 48731527-4d7e-4842-abdc-b5296a17a573 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.485085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.164782Z digest=sha256:eed7303361d4b23a1a1150c6dcc5bbbc2292e847aa2d777293dfca953402888b

Observation 58308492-5524-4a26-b203-ed18c0ce1ef8 · outbound

This paper cites Only TT correct.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Only TT correct

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.471282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.169391Z digest=sha256:9161daa4857e2a13cfa20fef231ea15c55e938631525e7ed03bddf3020736fe2

Observation f27bf5db-83e6-4764-a17d-8ecf8c036108 · outbound

This paper cites <end_of_answer> Examples of Truth Table-based Reasoning generated by our models.

Learning to Reason via Mixture-of-Thought for Logical Reasoning <end_of_answer> Examples of Truth Table-based Reasoning generated by our models

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.456027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.174216Z digest=sha256:f1861932bd96e19497341b718b7b4713f1405d07d46510a84c229c00739cd90c

Observation 4de89110-8fd8-4dd1-b4b2-227fd68fed7f · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.436946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.178684Z digest=sha256:ad0cd56742d34e570141b1c58a1d6608cb1345fb07977f90c6149f663e551e9d

Observation 6c2c5f76-b28e-43bb-ab48-f7f465bbf092 · outbound

This paper cites ∃x (W(x)∧F(x)) Conclusion to Evaluate: Some evergreens are not objects of worship.

Learning to Reason via Mixture-of-Thought for Logical Reasoning ∃x (W(x)∧F(x)) Conclusion to Evaluate: Some evergreens are not objects of worship

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.422914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.183263Z digest=sha256:a6b9b03d8d82873879b817ebe484a34e4e6da81a085815009178d2782e4c97b7

Observation 142616ff-a1cc-4cc5-9aae-fecfea4dcb6d · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.409273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.188126Z digest=sha256:32c04dcfa9e74bbe50d21f0cd252a1616ce99cb0ff7e5388d6a679086039340d

Observation ce9105c9-d15c-47b4-b2f8-f58f815858fe · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.390409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.192666Z digest=sha256:1bf1a76a1747c7db4b134f8f8e42cfe35de46eb9daf9c56af197397e9df28b08

Observation b5bc6c7d-a208-4888-9919-bcf11a8961f7 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.376433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.197117Z digest=sha256:fd80ca6c9d17d086cd618d92ed556da3cfe2001d7a6a6038623909dde8526d84

Observation fbc2a016-437d-41f8-912a-cfe4975507c1 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 57

Resolution
parse uncertain
raw_fallback, observed 2026-08-07T15:15:43.362485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.201945Z digest=sha256:03628da6ed7255ed802eb02e03fbd80bc9767a57ca929f44866f84abe49d3112

Observation 0bab992f-df0a-4197-ae53-9558ffa191d8 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.348834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.206984Z digest=sha256:2d85dd432240ebec7f37c3de021e59d285134d37296848e3eeda657513d457a6

Observation 65e63cb9-b2c9-4602-a26f-60ca085e19b3 · outbound

This paper cites All employees who schedule a meeting with their customers will go to the company building today,\.

Learning to Reason via Mixture-of-Thought for Logical Reasoning All employees who schedule a meeting with their customers will go to the company building today,\

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.334164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.211242Z digest=sha256:f4b772837cc0af67a371ab9f1205720a4a55a1813cbffdcfefb85aea064a6e60

Observation d2e67450-7b63-4daa-b3f2-769a0b38bae6 · outbound

This paper cites (Not directly about Rock).

Learning to Reason via Mixture-of-Thought for Logical Reasoning (Not directly about Rock)

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.320645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.215734Z digest=sha256:ff7fe87a9ca5144878c7cab41ed309e5a9d72af1c7451736eea720414a9d1256

Observation b486c34e-7dd4-4ea8-85d9-21ccb7156a8e · outbound

This paper cites (Not directly about Rock) 35.

Learning to Reason via Mixture-of-Thought for Logical Reasoning (Not directly about Rock) 35

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.306308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.219492Z digest=sha256:5b250a3857259ca1b1d59a9ef3660fdf694b924eac430678298960c4a66e429b

Observation 0f8e66db-108c-4906-a386-b75cd6c0c214 · outbound

This paper cites (Not directly about Rock).

Learning to Reason via Mixture-of-Thought for Logical Reasoning (Not directly about Rock)

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:43.282551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.224033Z digest=sha256:4e4d3ae7b7fb50f55cbe503829d0a3ea2a68510520244a590ac69b80d1a78a4d

Observation 982701e6-7569-4e69-9a84-6609527873e0 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:43.186861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.228571Z digest=sha256:4f20033c7ae18805b1261e8e1dd07de102803d5eec878e03b487f00872cdc99c

Observation 65b21b51-a80e-421b-9e95-fb5a05651ea7 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 64

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:42.864114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.232937Z digest=sha256:9a17f254dc45560fa80a4832d4906eec91540662018aaa685efc01702f95dcce

Observation 3e605f23-3b19-45e4-893b-7389a8c06032 · outbound

This paper cites an unresolved cited work.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:15:42.799846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.237274Z digest=sha256:a1a7049dd87eb968e2d223a28f4e56baaec6a39d931c1fc3def875c0ab0f3ea3

Observation da593db9-c8a5-43d0-b17b-22ec73790273 · outbound

This paper cites Rock" and pet.is_monkey is True: return.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Rock" and pet.is_monkey is True: return

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:15:42.773257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:15:42.241838Z digest=sha256:ac30d704765c0b6c32137d9449fcb83337051cb8650896dd64c5a8de243e1e47

Pith citing papers

Observation cb2dd002-b98d-4d15-a457-1b9382094597 · inbound

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models cites this paper.

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T15:37:24.974376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:37:24.974376Z digest=sha256:e910f98c642ad9e3225aad555cdb4182305980c206296a2c8714e382c81e2b7e

Observation 2128fd9f-9219-4868-92bb-21e14ce554b4 · inbound

Parallel-R1: Towards Parallel Thinking via Reinforcement Learning cites this paper.

Parallel-R1: Towards Parallel Thinking via Reinforcement Learning Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T21:28:52.055915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:28:52.055915Z digest=sha256:e633b6fa791852545003b0bd11ec7a70f3726976dd68276e080fe1b33eb02168

Observation 832c0674-f33c-4253-8d81-84f819a15d64 · inbound

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models cites this paper.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-04T18:53:02.808040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:53:02.808040Z digest=sha256:aab2b1c916d08b507a3b00d5471a7890acf1569606fbc05ecbd24994f596051b

Observation 02dab944-003d-41d1-8cb4-4b1edec12468 · inbound

Large Lemma Miners: Can LLMs do Induction Proofs for Hardware? cites this paper.

Large Lemma Miners: Can LLMs do Induction Proofs for Hardware? Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:35:36.378825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-18T01:34:03.866227Z digest=sha256:5742bd9cb1e3879c93abef3b71682f3c8725f06ba4410f4ed7e4332e049c2c51

Observation c1264204-3ff6-4a9c-badb-78df6db48104 · inbound

Large Lemma Miners: Can LLMs do Induction Proofs for Hardware? cites this paper.

Large Lemma Miners: Can LLMs do Induction Proofs for Hardware? Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-04T00:14:48.997429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:14:48.997429Z digest=sha256:a24e34df89c0225c7fa9215e9c01cd83a54014c649cf7d49a531f273a00ea3e2

Observation 1756dd94-412c-4eb6-a0d9-dbacfea75e71 · inbound

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline cites this paper.

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T10:31:01.321076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:27:41.993499Z digest=sha256:4bd05dab92182a8fb3ecf7de338c2b5d4704e23a75b2b55d034f1c4d3fae4012

Observation 0485d328-d6f1-41f6-99ea-6222fc70d86b · inbound

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data cites this paper.

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 46

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T05:36:01.997503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T05:32:23.972335Z digest=sha256:5f449fbc57e19769aae8daef9afc324e86cb8e1ad6b93753e028c3a5d1463716

Observation 442f9b63-a889-4d2e-92d9-ab695555f6cd · inbound

Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling cites this paper.

Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 51

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T02:56:30.045707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T10:25:10.559953Z digest=sha256:cb9aa55a0d1b218faade9bb1154855b08a4e8a09ea1e85cd27408620452855db