Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:15:52.015644Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 1 inbound Pith citation observation for arXiv:2506.22157.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:15:52.015644Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-16T16:01:48.789986Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T16:03:04.340181Z
77 of 77 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 4a7f1b93-5de7-4269-b52a-c812af37db92 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 1
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Training Language Model to Critique for Better Refinement SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
Reference 4
Source-reported events for the cited work
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 5
Source-reported events for the cited work
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Observation 79234ca1-a02e-4b6e-8dbe-ce4a846e308e · outbound
Training Language Model to Critique for Better Refinement Offline Regularised Reinforcement Learning for Large Language Models Alignment
Reference 6
Source-reported events for the cited work
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Observation 6c5a7bbf-28cb-4268-8b9d-0e12a91e5f90 · outbound
Training Language Model to Critique for Better Refinement Self-Rewarding Language Models
Reference 7
Source-reported events for the cited work
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Observation 5092aeaa-6620-44ee-a32f-ad9a24021a44 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 11
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Training Language Model to Critique for Better Refinement Suggestions for improvement
Reference 13
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 14
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 17
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 18
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Observation 702fbb3c-fe47-4183-ad5b-60cace0f67fd · outbound
Training Language Model to Critique for Better Refinement Suggestions for improvement
Reference 19
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Training Language Model to Critique for Better Refinement Suggestions for improvement
Reference 25
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Observation eea2b43a-63b1-467d-958f-cab5c127239b · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 26
Source-reported events for the cited work
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Observation e6f3404c-9096-49e2-bb00-70b0b50460e5 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 29
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 30
Source-reported events for the cited work
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Observation 4afae6bd-1fed-49b1-a05c-e9fc1319e5d6 · outbound
Training Language Model to Critique for Better Refinement Suggestions for improvement
Reference 31
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 32
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 33
Source-reported events for the cited work
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Observation dbd36bdd-ce47-4cd9-9730-27e30ae5dc03 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 34
Source-reported events for the cited work
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Observation 4bd86db4-d0ca-41d6-bb98-1daeac6cf624 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 35
Source-reported events for the cited work
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Observation 41988e61-8da4-4fa4-98f3-feb98e84c0ed · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 36
Source-reported events for the cited work
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Training Language Model to Critique for Better Refinement Suggestions for improvement
Reference 37
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 38
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 39
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 40
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 41
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Training Language Model to Critique for Better Refinement Critique by Llama-3-8B-Instruct+RCO **Helpfulness:** 0/5 - The assistant's response is not helpful at all
Reference 42
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 43
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 44
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Observation b44d3352-cd7e-49c7-b381-707931d2eda7 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 45
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Training Language Model to Critique for Better Refinement By following these suggestions, the assistant can provide a helpful, harmless, and correct response that will assist the user in making a delicious Danish dish
Reference 46
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Observation 65612544-b4fa-472e-bfc4-37395d900b7c · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 47
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 48
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 49
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 50
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Training Language Model to Critique for Better Refinement I hope you enjoy this recipe for Koldskå l! Let me know if you have any questions or if you would like any variations on the recipe
Reference 51
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Observation 53a0d5a9-fac5-421f-bec0-168f2702fd87 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 52
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 53
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Observation 7b4a43f1-275e-4397-84df-182a0351be7a · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 54
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Observation 93c8f743-baf8-4b89-b97c-12c50c298596 · outbound
Training Language Model to Critique for Better Refinement on my team
Reference 55
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Observation 5909c8a2-6092-45ae-9abb-3f022119bae4 · outbound
Training Language Model to Critique for Better Refinement It's more of a generalization about the use of steel in construction
Reference 56
Source-reported events for the cited work
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Observation 7597d468-1f0b-4007-89a3-99c69a366163 · outbound
Training Language Model to Critique for Better Refinement This lack of credibility can undermine the answer's authority
Reference 57
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Observation 3e89f32e-5864-47a5-8a82-7e69dc8bb777 · outbound
Training Language Model to Critique for Better Refinement It doesn't mention the specific types of steel or techniques used to create the chair
Reference 58
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Observation c417cb93-e445-4ef6-92c0-eb0d0ca50b98 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 59
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Training Language Model to Critique for Better Refinement This could include links to reputable sources or citations to academic studies
Reference 60
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Training Language Model to Critique for Better Refinement This could include specific types of steel used, techniques employed, or processes involved in its creation
Reference 61
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Training Language Model to Critique for Better Refinement This could include specific measurements or technical specifications
Reference 62
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Training Language Model to Critique for Better Refinement how many grams can you eat
Reference 63
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Training Language Model to Critique for Better Refinement The question mentions the specific values of these variables, so the code should use these values to calculate the answer
Reference 64
Source-reported events for the cited work
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Observation 9c518e3a-ac7f-4185-9ca1-e9ecb4dcf212 · outbound
Training Language Model to Critique for Better Refinement The question asks for the number of grams that can be eaten, not the number of grams per calorie
Reference 65
Source-reported events for the cited work
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Observation f35d1567-0d72-4b6c-9e69-c13970643a3a · outbound
Training Language Model to Critique for Better Refinement The question asks for the specific number of grams that can be eaten, not the maximum
Reference 66
Source-reported events for the cited work
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Training Language Model to Critique for Better Refinement This can be done by subtracting the minimum of the daily calorie target and the consumed calories from the total calories in the 300g bag, not the maximum
Reference 67
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 68
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 69
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Reference 70
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Observation 56d9da64-1ede-4e52-9dd2-b6e74a8cace4 · outbound
Training Language Model to Critique for Better Refinement The code should calculate the number of grams that can be eaten while staying within the daily calorie target
Reference 71
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Training Language Model to Critique for Better Refinement The problem statement does not provide this information, and the code should not make this assumption
Reference 72
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 73
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Training Language Model to Critique for Better Refinement For example, `calories_per_serving` should be in calories, `servings_per_bag` should be in servings, and `bag_weight` should be in grams
Reference 74
Source-reported events for the cited work
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Observation 02b2000d-9a1c-4233-bd60-9e802520828e · outbound
Training Language Model to Critique for Better Refinement Instead of assuming 1 gram of the bag of chips is equivalent to 4 calories, use the information that there are 250 calories per serving and 5 servings in the bag
Reference 75
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Observation 0ad841f5-412b-4010-8da5-f59377878027 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 76
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 77
Source-reported events for the cited work
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Training Language Model to Critique for Better Refinement Figure 10: One typical case from the mathematical reasoning task in CriticEval dataset
Reference 78
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Observation 2d68980f-7673-46bd-8d5f-9aa0dd1b0d7e · outbound
Training Language Model to Critique for Better Refinement This can be done by using a more accurate approach, such as the one described in the solution
Reference 79
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Observation 48a03312-13f8-43ed-b5d8-51a6cfc3953d · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 80
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 81
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Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 82
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Training Language Model to Critique for Better Refinement Critique by Llama-3-8B-Instruct+RCO **Correctness:** The code is not entirely correct
Reference 83
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Training Language Model to Critique for Better Refinement In this case, the problem is to find the number of rotations in a circularly sorted array, not just to check if the array is sorted
Reference 84
Source-reported events for the cited work
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Observation 2af08cb1-4d8c-46da-8879-bedaaf2e3143 · outbound
Training Language Model to Critique for Better Refinement Unresolved cited work
Reference 85
Source-reported events for the cited work
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Observation bb05bfc8-464e-476b-a452-bb112ac6468c · outbound
Training Language Model to Critique for Better Refinement Then, subtract this index from the length of the array to get the number of rotations
Reference 86
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Observation e4069c3a-7689-46aa-befe-e1075826689f · outbound
Training Language Model to Critique for Better Refinement You can improve the efficiency by using a single loop to find the index where the array is no longer sorted
Reference 87
Source-reported events for the cited work
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Observation a1b8c2c7-d812-4bcc-ae3e-f3ab604a4a4f · outbound
Training Language Model to Critique for Better Refinement Figure 11: One typical case from the code generation task in CriticBench dataset
Reference 88
Source-reported events for the cited work
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Observation 8b34388a-137d-4ed3-917e-c65011822c7e · outbound
Training Language Model to Critique for Better Refinement Measuring Massive Multitask Language Understanding
Reference 2020
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Observation 52199083-eba1-4922-bd74-1e173ef0eeeb · outbound
Training Language Model to Critique for Better Refinement Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Reference 2022
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Unavailable: canonical work link unavailable.
Observation 3ab88b38-73e8-46c9-810f-701ba4d8f9f5 · outbound
Training Language Model to Critique for Better Refinement Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Reference 2023
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Observation 2ff8c770-7814-420d-9665-bbebcfbe0501 · outbound
Training Language Model to Critique for Better Refinement arXiv preprint arXiv:2409.12147
Reference 2024
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b639c69f-5680-4223-a57e-df997b34bfc2 · inbound
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning Training Language Model to Critique for Better Refinement
Reference 22
Source-reported events for the cited work
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