Paper Citation Record · LEDGER

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2505.24147.

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

pith.paper-citation-record.v1
2505.24147 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:26.743983Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:34:29.377709Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

63 of 63 outbound references displayed

verified exact1
verified fuzzy0
unresolved62
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a44730b-7cb5-4230-966a-77be09148183 · outbound

This paper cites e-SNLI: Natural Language Inference with Natural Language Explanations.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability e-SNLI: Natural Language Inference with Natural Language Explanations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:19.931343Z

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Observation 28d0f066-cc60-472f-9c0d-2ef3ed4c4ee9 · outbound

This paper cites What to Learn, and How: Toward Effective Learning from Rationales.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability What to Learn, and How: Toward Effective Learning from Rationales

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:20.075543Z

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Observation b611289b-cd53-4d9f-a96c-9463267bb6f7 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Scaling Instruction-Finetuned Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:20.235803Z

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Observation 161dc58f-b8ea-435b-a4cf-80d4c290baf4 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:20.376449Z

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Observation 561e22a8-88ba-42ab-b917-6760969e44fa · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Training Verifiers to Solve Math Word Problems

Reference 5

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Observation 9b199d19-f52f-4282-bdf3-034b538f339a · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80f18ef8-7ff6-4e65-a87e-96e0cadb69c5 · outbound

This paper cites Calibration of Pre-trained Transformers.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Calibration of Pre-trained Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:20.670222Z

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Observation c2a9bc4f-3040-4ad6-8cef-9b049892a534 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 8

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Observation 38c17928-dfe3-4ca3-a413-5c551fbef8a9 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 9

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Observation 32f8bb84-2b67-49b2-8150-9fde9ec8e34c · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 10

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Observation a69755ad-827a-4e21-8dd7-3121394fd948 · outbound

This paper cites Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

Reference 11

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Observation 55d64ad1-a4c2-4cb8-8a2a-f7342e66b8b9 · outbound

This paper cites Specializing Smaller Language Models towards Multi-Step Reasoning.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Specializing Smaller Language Models towards Multi-Step Reasoning

Reference 12

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Observation 5b86da8c-5c8e-4bef-aadb-2c20b1c4aaef · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 13

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Observation e4d1d33d-c773-4b3c-ba0d-be347a81914f · outbound

This paper cites On Calibration of Modern Neural Networks.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability On Calibration of Modern Neural Networks

Reference 14

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Observation f0f9e274-45f8-477e-8f7d-b3b34bb95c2b · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 15

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Observation 6a428f17-441c-4495-b920-10589e7e418f · outbound

This paper cites Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 16

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Observation ff5be32c-c1b9-40da-9360-ab046edb059c · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 17

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Observation 5c778750-e0b3-429c-859e-0ff7ba174de0 · outbound

This paper cites Program-Aided Reasoners (better) Know What They Know.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Program-Aided Reasoners (better) Know What They Know

Reference 18

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Observation 87bba128-4d09-42ba-9549-726fc78a78b1 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Language Models (Mostly) Know What They Know

Reference 19

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Observation 40c41778-a9c3-439d-a34e-ef9f44e289e7 · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 20

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Observation e9521933-7c8e-4969-844a-3c985b928b30 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Large Language Models are Zero-Shot Reasoners

Reference 21

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Observation b1324cd2-e53e-4e28-a639-d7ba67a5c294 · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 22

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Observation f999883d-599e-44fc-b4ce-a812673cec89 · outbound

This paper cites Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 23

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Observation 24ab85c0-e912-44e2-93c3-5e39193b1ccd · outbound

This paper cites Explanations from Large Language Models Make Small Reasoners Better.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Explanations from Large Language Models Make Small Reasoners Better

Reference 24

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Observation 0396449b-6fd5-4a27-b5a3-2a55086dec67 · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 25

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Observation 6d6a7199-fcda-42f4-99fb-77b23b6d8f91 · outbound

This paper cites Teaching Small Language Models to Reason.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Teaching Small Language Models to Reason

Reference 26

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Observation e86b44f5-654f-4c75-96ba-7f0641dd71f3 · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 27

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Observation c3a1828e-e532-4e12-b819-1f19d0b3e3fc · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 28

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Observation ac63deaf-e776-4267-8881-5356e894b10f · outbound

This paper cites Orca 2: Teaching Small Language Models How to Reason.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Orca 2: Teaching Small Language Models How to Reason

Reference 29

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Observation d5280896-2f0b-4ca6-afa9-c0a13888ff92 · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 30

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Observation 60471de3-a638-4f6a-b7ac-bade45924015 · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 31

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Observation 4429db8d-9897-4509-ac80-c4e8c2f14e0e · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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This paper cites Posterior calibration and exploratory analysis for natural language processing models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Posterior calibration and exploratory analysis for natural language processing models

Reference 33

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This paper cites Adversarial NLI: A New Benchmark for Natural Language Understanding.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Adversarial NLI: A New Benchmark for Natural Language Understanding

Reference 34

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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Observation 1daaaa3f-9a28-40db-8c6b-1d23294a8688 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 36

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This paper cites CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge

Reference 37

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability GPT-4 Technical Report

Reference 38

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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Observation 78315454-865e-47d5-85c2-8a506a3bdceb · outbound

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Are NLP Models really able to Solve Simple Math Word Problems?

Reference 41

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 42

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 44

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Distilling Reasoning Capabilities into Smaller Language Models

Reference 45

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 46

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Manning, Andrew Ng, and Christopher Potts

Reference 47

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

Reference 48

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 49

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This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 50

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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

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Observation 4dcbe3fe-33e6-4846-9bad-65b86dd06fa6 · outbound

This paper cites SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

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This paper cites PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales

Reference 53

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Observation dcc9c0e4-4c24-4f64-a5d2-4dd864de52b9 · outbound

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

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 54

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This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.907355Z

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Unavailable: canonical work link unavailable.

Observation 40dd2405-2a69-490a-b677-efd1ac8a51fc · outbound

This paper cites Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:27.015667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b536e245-d0fa-497e-adb3-06c316f44fdd · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.067413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6690da1f-68b7-4a73-892a-147b45a4e27e · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.195150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0dbb333-3f45-44e5-b7b9-b5790291ec42 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.350229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0fb600b-51b0-4f95-a85a-c1f343441d17 · outbound

This paper cites PAWS: Paraphrase Adversaries from Word Scrambling.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability PAWS: Paraphrase Adversaries from Word Scrambling

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.482188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab1b627-0bfa-4555-9478-4047dc0cb1a6 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.595287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0be44cce-6a99-4ae4-8fb1-142526a1010d · outbound

This paper cites online" 'onlinestring :=.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability online" 'onlinestring :=

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.667293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d084c7b1-cc74-4273-b5d3-a193c4244004 · outbound

This paper cites write newline.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability write newline

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:26.743983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b57b68c9-4876-41b3-a345-711e53062310 · inbound

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives cites this paper.

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-04T19:34:29.377709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.