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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 2026-08-20T06:33:59.587034+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:f0caf704ac4f40ec12d2ee5cf47a1970825af608aa524b30d4deac802b2f29c8

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=sha256:8ce531d95d1053c92b0c7fb82385840c099bff311bb8ce9c918a96ba160bd84f

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=sha256:10ae0131ea46556e8fce461433f5efa7e2e93c327bd711aaacc5aa5870d590cb

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=sha256:9be87bbdb451e2426f7a5a729219a37408decc671285433751c68e3ba44efa1f

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=sha256:4c591a9f7aa09c319244525ceda536a643d7e96da4985e90809b5d8314ad382e

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:a5b9bce62509fc6cec8b46293817c50ceedbe47522d13f063bcade5e51732718

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=sha256:49ac52ec21ddf6881c9deca690ea66d99d705ac41a66f64f6e8ad8f997642b71

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=sha256:2ee677e0ad618f01fea657b485d1432e8b67d860b66dee2789e17be637f14b56

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=sha256:7a01286ba3cb2cb9bce06e395921603845d6c2ddb5773ad48462907d7625727a

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=sha256:9a757caa862fc4ecd6fa96860e9dc1678beab23099c9ad879e59aa5b126cef45

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:dc61ef8bc7a4599481d7b43240fd6c425e6e375945c1c919da9ab43f8aeb24a4

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=sha256:3b7e3758c00f4e7f12036e2e2fc75827cf55da37e3755c392f48864e7675a496

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:b7d5a282effa3fac97f7cb34030dd78b15f31429717c99a0e06457744c4289e8

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=sha256:2d224b20f4cc8cc0c4b1279a1f0cbe96065d87e01b5cab8a5cf3fa99fb76d0a0

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:fea94325f54cb912c6a2934d939f0f8fccfe0fd28a6be1dcb634c4d25843ae87

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:cf327cb50cf7de817e74d0c5b1cd8953f790813dc7a62fbaf07d7751ffe97227

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=sha256:18d455515e47ca556bc9edc75eaac4a811e427cf57e15463ed83d15cc5a00f9f

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:fe20fae12f9f95b5e6e36f8b7b8c7db358294b737c61a250de71bd8eb58c6deb

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:c19b9bc2f472de99b4d32d94974bf58672da93d69f2b3f05d09658a0c1eadac7

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:d1203fecde6616fce2563a9bf7b809798d41169a6ad583b40550cf962180dcd0

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:edb96ea4dd422b73823469ab99e82815276d8d0b1b0970e8852144ac9a9a93b0

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:aa7d8f387f6fa6617b21a6ded8e059bc581145895196a47a1ef756050f44f340

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=sha256:8f34584d223751ea15d4455e73e6db0b246f9d250648214bc5c11c2cc1423032

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=sha256:2812ddffc96728a7b30d61cc549f14a1274ab6a64a840eafd3f3bb3a068bdec9

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:d72479e8ed3f4b4805e75ed7954507fcf198366ef4bc0efc3f5be71d31294236

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=sha256:598d8090f9f9a055fb636baea68d444890759bdf820509d76ca99f067faa0893

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:ccdae19f468465783af85b43a429100459d88da4d5f22c9945825ba230dd0cde

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:fea00ec86a5dc6b0518193053503ed14deb006d3594bdf6dd58974fb05671572

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=sha256:81413d037559891590481d4f6b588c8eb7a27c69a30c8f8234e1f3e854aea06e

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:fd16505d13cdb4eaae67a5981ba9c5f0d8496ad23e593514589fa4b72670ba04

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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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-16T05:19:57.558809Z digest=sha256:5e5e0269744d697c0a15b15df6453364e36382f0886c5a6e687e2d31a41aa0bc

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:e91f8245bc460728be38002bd3e0ff542fdaf94c3adb2310c3ecd6022f8518d6

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:bff4c970c8b72489b748f8044b2598a4dfecb75aade58491456892b0b3142f2e

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=sha256:1e9ff377fb910f4c9e85f3a38e4f79bdba9c6ab78b73c8e5db515a5766196fdb

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:e4e997f474230ca03bf186608d5ae6e6d42e5fb4e5592fa060e4306720e0b18b

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:19:57.577019Z digest=sha256:096d988fb39c8755060320717fdf2d226036552539c62a8a162bc186c3e19fcb

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=sha256:097c6f8e2d85f8a4d6966f4ede78d1ede944a81df0c7c022fc6a35266ecf811e

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

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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=sha256:27b1076ee0b1585c88f498635a4228092ceac411c0bd49a9f3c3bc0e6aff26d7

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

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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=sha256:63e286dcf2170e0da34f0c338119b1d2412e9adafba334ba0a971a2147fac6ac

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

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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=sha256:7873791c8a44893e6a258cc9a229a89062b17a6534f04897c998be9c455f0ab8

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=sha256:067813a6aef20aa81306cd27b0c5e2a5e170ce0c07010a05eb3b72798902f91d

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

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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=sha256:8ec500957c5493564c077d3e66ea1309cc7ed7d5251da50b029c8372d5201637

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:ce62fc2e14ca3c6739938f78218fa174bcda5ca87d737ca8dc082777b19172d0

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=sha256:94b9e3a25ab6f60b1255cb51608903c111f0be72efcfe919eb98c9978958b950

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=sha256:451ead38d4a600ba051ccd3f9db9701460200fa5aa3badbf3d64e67129f6a93c

Pith citing papers

No inbound Pith citation observations are available.