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Paper Citation Record · LEDGER

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2504.20946.

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

pith.paper-citation-record.v1
2504.20946 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:19:57.613688Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved44
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db34fa8f-57f1-42f9-b935-1c41244d5131 · outbound

This paper cites Can LLMs perform structured graph reasoning?.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Can LLMs perform structured graph reasoning?

Reference 1

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source=arxiv_source observed=2026-08-16T05:19:57.437866Z digest=sha256:1e2d71a505ba161cd28d3b2a40f792de9ca923e1efe82c42decc857eeeb000d1

Observation 689231ce-03b6-4a97-ac0e-521ca67b196f · outbound

This paper cites Language Models are Few-Shot Learners.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Language Models are Few-Shot Learners

Reference 2

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source=arxiv_source observed=2026-08-16T05:19:57.442659Z digest=sha256:248f251a4c9af7537cd0b6f13218e8dbae2236cdf15d845abe11cebfd5b7f0eb

Observation 0899e5c3-7718-4c8c-ba49-65bba25aae15 · outbound

This paper cites SocraSynth: Multi-LLM Reasoning with Conditional Statistics.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 3

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source=arxiv_source observed=2026-08-16T05:19:57.446872Z digest=sha256:02f17cfee63df676edf7e3208174aeae35a0ad9706f2b7a508ab28a0d1e40b67

Observation 7506e141-6003-4a13-84a1-362ca612649e · outbound

This paper cites Unleashing the potential of prompt engineering for large language models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Unleashing the potential of prompt engineering for large language models

Reference 4

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source=arxiv_source observed=2026-08-16T05:19:57.450868Z digest=sha256:76fb73bde44bcd7ac77957f9f76397ebf18709ceeb4024df31407a4a0fcfd114

Observation e2b55c18-f68d-42c8-938c-7422a8dfb35f · outbound

This paper cites CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving

Reference 5

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source=arxiv_source observed=2026-08-16T05:19:57.455125Z digest=sha256:35935790fd8d446aa9fe8cc6330167b559f76428c5504d3193e4656fcede190f

Observation ee905b54-88c6-4249-a495-081a48b29b70 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 6

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source=arxiv_source observed=2026-08-16T05:19:57.459531Z digest=sha256:fe8b905fa44e8d0f4a076fcb274247b08ead2726b6b6350a61e63cbbbb8f0b65

Observation 0e58d9e0-9e46-4688-9da6-f30e4d216de6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Training Verifiers to Solve Math Word Problems

Reference 7

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source=arxiv_source observed=2026-08-16T05:19:57.464366Z digest=sha256:ec39287f6e329be4f9a1f353a9cb8fb241396109d22f869e04c58fa96a1c2b5f

Observation 69b9990c-c9ac-495b-a0dc-17d486c6b9a9 · outbound

This paper cites Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving

Reference 8

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source=arxiv_source observed=2026-08-16T05:19:57.467973Z digest=sha256:1895f7fea9a7ce5dc39362f5406edc131a85c001f83cf99bbabc2a3c9fbe25b3

Observation f3fba6ed-2c05-4c19-996c-dc596cc22f2c · outbound

This paper cites Successive Prompting for Decomposing Complex Questions.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Successive Prompting for Decomposing Complex Questions

Reference 9

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source=arxiv_source observed=2026-08-16T05:19:57.471940Z digest=sha256:6a8fba5b8e6e89e99d5a452a269cd0185495f1c43b3cc3aaf8949fda4eb5605d

Observation 43d4e24a-cbb3-4851-88dd-50292cb6d62f · outbound

This paper cites Socratic Reasoning Improves Positive Text Rewriting.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Socratic Reasoning Improves Positive Text Rewriting

Reference 10

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source=arxiv_source observed=2026-08-16T05:19:57.475167Z digest=sha256:3405d39cec0355cce0053996de960207bfb88d0241cde804a6293c35f20a687e

Observation f021245d-6c6b-4e8e-ab5a-5e21c4f82b63 · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition MiniLLM: On-Policy Distillation of Large Language Models

Reference 11

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source=arxiv_source observed=2026-08-16T05:19:57.478932Z digest=sha256:a4e4408fda0fe4a15bc812e464ee7b527c2bb068b075d33276face2dd744a358

Observation be738131-80a1-42c0-a49f-8d4b66328b42 · outbound

This paper cites Textbooks Are All You Need.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Textbooks Are All You Need

Reference 12

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source=arxiv_source observed=2026-08-16T05:19:57.482207Z digest=sha256:79797588b9d6f059fbf26d1545dbad946f9a997ca90e1f1fb55ab427badb1a31

Observation a9d5048f-6545-4db7-82d1-37f983a3b033 · outbound

This paper cites Embodied LLM Agents Learn to Cooperate in Organized Teams.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Embodied LLM Agents Learn to Cooperate in Organized Teams

Reference 13

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source=arxiv_source observed=2026-08-16T05:19:57.485522Z digest=sha256:76b92fdad73b36bc8614ee58a349c3ce9caa1d74afa7c1e0a614d4ad09299df6

Observation 5b2483e6-c862-41e7-930b-bcbf8a3278e4 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Distilling the Knowledge in a Neural Network

Reference 14

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source=arxiv_source observed=2026-08-16T05:19:57.488810Z digest=sha256:1f1b631d2889efbfa7b027b97d10f780a470f7ee34e1338655dc48746fc8de16

Observation 22c5e922-e9da-45b7-99ab-77e545bab535 · outbound

This paper cites $\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition $\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning

Reference 15

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source=arxiv_source observed=2026-08-16T05:19:57.492497Z digest=sha256:fe93e90aa48deb1e9d69cac4dec9772d7b21e60ba16847b08ca257f093753e14

Observation d989cdf2-9a5d-4a6b-a824-903deec75c5e · outbound

This paper cites Decomposed Prompting: A Modular Approach for Solving Complex Tasks.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Reference 16

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source=arxiv_source observed=2026-08-16T05:19:57.496521Z digest=sha256:8d7bf74d59e5b4cac7fc8f4382908cd32371c28ab0d057dbb67b94c91da84cba

Observation 5427d922-4fd1-45a2-978b-09b446af9d96 · outbound

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

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Large Language Models are Zero-Shot Reasoners

Reference 17

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source=arxiv_source observed=2026-08-16T05:19:57.501389Z digest=sha256:0ccdc886aa784200315f694a1adfcbbc1b2fc5aeb351c9158edb45548fad666d

Observation d8f2374a-453d-43a4-aaec-97bd96b71c36 · outbound

This paper cites an unresolved cited work.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Unresolved cited work

Reference 18

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source=arxiv_source observed=2026-08-16T05:19:57.506066Z digest=sha256:842beb2fad8d970ba6763968619961573b8c27d47d15e16608072d170839e370

Observation fda2a4a9-7891-44a2-b98b-2c307cbc5b1d · outbound

This paper cites CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Reference 19

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source=arxiv_source observed=2026-08-16T05:19:57.510353Z digest=sha256:80a2850baebb2766b587b96070491f2cab484be16c856206b99582d964c585b0

Observation 3a5d76f4-913a-4699-bf7e-3938f11d9687 · outbound

This paper cites Long-context LLMs Struggle with Long In-context Learning.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Long-context LLMs Struggle with Long In-context Learning

Reference 20

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source=arxiv_source observed=2026-08-16T05:19:57.514586Z digest=sha256:75cd4008cffa9aaa543b4fe787500a131954614acdee141968e24737f1c1c0af

Observation ae821507-cc28-45e5-a1bf-4c9bf27736f5 · outbound

This paper cites Evolving Knowledge Distillation with Large Language Models and Active Learning.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Evolving Knowledge Distillation with Large Language Models and Active Learning

Reference 21

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source=arxiv_source observed=2026-08-16T05:19:57.518285Z digest=sha256:5b4d415ddc8d2665f5ae476f0be76ad0c24f3d7fb785e099ab80c0ae228a286f

Observation 52e085ff-7099-48c7-9eaf-020fd871e4c2 · outbound

This paper cites Social Learning: Towards Collaborative Learning with Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Social Learning: Towards Collaborative Learning with Large Language Models

Reference 22

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source=arxiv_source observed=2026-08-16T05:19:57.522562Z digest=sha256:ac50e6272046be7f0970c1bf2e6ae387283ce563ec93c5fedd3267558d2ba0c6

Observation a8214e23-e340-4b16-a5d4-930f5e6b60de · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Measuring and Narrowing the Compositionality Gap in Language Models

Reference 23

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source=arxiv_source observed=2026-08-16T05:19:57.526291Z digest=sha256:1813999b65c53884f9386a2d3fb3ef0d9938daa850fdb1313c060196aa69437c

Observation 60a59085-42e8-4574-a05e-c0e00a7922d9 · outbound

This paper cites The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models

Reference 24

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source=arxiv_source observed=2026-08-16T05:19:57.529852Z digest=sha256:904b5b98379b68f997c23b468c71a28171bab4caed66f912cd1e5d17d8ab43e8

Observation 2f1c4682-7f6c-4683-a2e4-236e36a3ecd0 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 25

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source=arxiv_source observed=2026-08-16T05:19:57.534935Z digest=sha256:b721fa5cd7df076fd2449f4b4f7a63844c2ac35f26b7bbc635af773ada59d244

Observation bd65af0e-e675-4019-a823-c2eed5051e25 · outbound

This paper cites Distilling Reasoning Capabilities into Smaller Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Distilling Reasoning Capabilities into Smaller Language Models

Reference 26

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source=arxiv_source observed=2026-08-16T05:19:57.539548Z digest=sha256:d712a7b2c51315b4acb7b3eb56995b5522893dccf1c45a20732f88c21925a6b4

Observation ee3e4ac0-a9ba-4180-bc5e-a4a1c812f708 · outbound

This paper cites PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents

Reference 27

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source=arxiv_source observed=2026-08-16T05:19:57.543865Z digest=sha256:e460be1954ea501eea38beb831fb670e60285d68f3d05d09a5a7cea3e1de4d15

Observation 29a86ea5-1aa4-44c1-8fa3-81ba8edc79bf · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Gemma: Open Models Based on Gemini Research and Technology

Reference 28

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source=arxiv_source observed=2026-08-16T05:19:57.547861Z digest=sha256:be0d53528529fe4d9a52fad81c07782f6c5f6a217d49790e17a0be7450bbf8a9

Observation 9d7e7c3f-1824-4c15-8cd0-2b1885c1d50b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition LLaMA: Open and Efficient Foundation Language Models

Reference 29

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source=arxiv_source observed=2026-08-16T05:19:57.552092Z digest=sha256:1e23206ff84e9d15978ebe7800e5396a77a9c3708d2f0d54ada421a0c7a45331

Observation c28bfb2d-cf0a-46d0-b09a-ae65c0145e8c · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Zephyr: Direct Distillation of LM Alignment

Reference 30

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source=arxiv_source observed=2026-08-16T05:19:57.555133Z digest=sha256:141d1d2c09691583e155c5d590e4a031f5aeae71e6fca1b5520d162d35bee649

Observation 686410cb-4d51-4a3e-84fd-d86c57571465 · outbound

This paper cites Adapting LLMs for Efficient Context Processing through Soft Prompt Compression.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Adapting LLMs for Efficient Context Processing through Soft Prompt Compression

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-16T05:19:57.558809Z digest=sha256:ab55c2877edc50fcc69377f8aa3df28c4c8ddc31361cffa1c8f42a48b6d7ddb1

Observation 531ace41-b313-4653-a29f-768bc67414fd · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-16T05:19:57.562116Z digest=sha256:ece6d9f5d3b26e2c891d54f00c110681420406dac8d79b81acfdaffb4730dc7d

Observation 24311791-fa28-4dec-853b-eb606db87d5d · outbound

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

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 33

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source=arxiv_source observed=2026-08-16T05:19:57.565775Z digest=sha256:34b7bc3c0f72381ddcc927383940f65e642c8e0324234a8372247ab26bde689f

Observation 5897fa4d-def6-4e69-95ab-073b3ddd05cf · outbound

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

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 34

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source=arxiv_source observed=2026-08-16T05:19:57.569130Z digest=sha256:fd058b5d1e19e6e00289abac30cffd906b89a0499d3f2fcdb4ab0f296fb87c28

Observation 98990695-119f-467a-9b03-1094f184a004 · outbound

This paper cites Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering

Reference 35

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source=arxiv_source observed=2026-08-16T05:19:57.573033Z digest=sha256:5041e481172ea5aa65b36ad69b7150205cf8973184966c7016853c4f935c8aef

Observation 4426e90b-aa31-4c26-9ce2-cbdfda44e718 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.577019Z digest=sha256:74bd042d8d9565e45c403a18cfcac266e9278a1b1d252be2a19e5895ccd3c1e6

Observation 173df668-f811-4406-868d-43dcb60ab234 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition A Survey on Knowledge Distillation of Large Language Models

Reference 37

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no resolver link, observed 2026-08-16T05:19:57.581145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.581145Z digest=sha256:d00a16c75e7f92461f8d6d5cca843eb7023006d1ea539837d321e77f8a6ed6d9

Observation 3642f93a-a756-4231-a662-40feab2f0dfc · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.585227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.585227Z digest=sha256:6a74fc410effb47845d8d87e7b7fe21211b7f495929e7155591769e6db8cdc22

Observation 5f0b03ed-ae87-45bc-bed3-936b8dbfe1de · outbound

This paper cites Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.589412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.589412Z digest=sha256:04e21ad4767e0899a560f18cf01e2e74c69d870f38705282ab3d3297ab732799

Observation 58708d3a-6ea2-4153-bf05-7c38ea260217 · outbound

This paper cites Small Language Models Need Strong Verifiers to Self-Correct Reasoning.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Small Language Models Need Strong Verifiers to Self-Correct Reasoning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.593568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.593568Z digest=sha256:41ed3c950e2dd6002a9cf009d9392ce8542cc9845fb64cd68c3b97d2a27af2d6

Observation 8e5763d6-993d-4cd3-8486-ca78aab9cbea · outbound

This paper cites Progressive-Hint Prompting Improves Reasoning in Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Progressive-Hint Prompting Improves Reasoning in Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.597661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.597661Z digest=sha256:17662f4988bf44306a5c7db86535e814bad0d1a2ee5151e01c5a50967a332139

Observation b8f30756-469e-43e2-8919-a9002612d71a · outbound

This paper cites PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.601622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.601622Z digest=sha256:325344c85e26a05f2c225ab4ea7a5a49e4fdbd4624b74246f9540c2ccc1166e5

Observation d8904698-4fb3-4241-88b2-6d4f099e0a82 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.605929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.605929Z digest=sha256:d4ed5f79f4e6be0ab0fa4c9e7099c601819edec43a86ec3491d3152e07bfb6a7

Observation 97030440-62e9-4855-bfb3-57d3f5a90ea0 · outbound

This paper cites URL: " 'urlintro :=.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition URL: " 'urlintro :=

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.609740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.609740Z digest=sha256:fbdf3fbb461a85cba3c7d004626ae2184310e7cc4ea844f9b2b65aa24a89b2d5

Observation 54a3a619-6a24-403e-93c9-1f843ee347eb · outbound

This paper cites write newline.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition write newline

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-16T05:19:57.613688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.613688Z digest=sha256:75ac674a707518d7490b61ad46439899e42465fd1f724e26f00a23472529ebf7

Pith citing papers

No inbound Pith citation observations are available.