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Paper Citation Record · LEDGER

Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2306.14050.

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

pith.paper-citation-record.v1
2306.14050 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

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

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:54.134182Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T20:50:09.440304Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1d763c53-8a2b-41aa-bedb-a815a758bcc6 · inbound

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes cites this paper.

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:50:09.442757Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-21T20:50:09.265838Z digest=sha256:a8e98233519118903bcd5c585897944701d09bf32847af377fb4519806be15e3

Observation ab791f6a-c35d-4938-a510-adb2c7f83aa4 · inbound

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security cites this paper.

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 259

Resolution
verified exact
arxiv_id, observed 2026-05-17T00:57:26.752897Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-17T00:57:26.303195Z digest=sha256:342b5a77935074d5be9e4d119dfacce9b4e6690e4fb1ef8590ceed5ec59f42ee

Observation d4f07132-6cf0-4c44-8697-cd8c4c7fd116 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 121

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:39:33.436240Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-15T02:39:33.007894Z digest=sha256:04c97139a9e648997c5c06ecb7ff90142ca5b0024665a97dc94e4ae3a2de3bc7

Observation 724af7c0-7b19-4e21-8b5f-b3d201b443e6 · inbound

AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing cites this paper.

AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 116

Resolution
unresolved
no resolver link, observed 2026-08-07T14:52:54.134182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:52:54.134182Z digest=sha256:d63463dd8201adb018a3768a2296816d5e89490c70a66bff728f8524487bc5d4

Observation 03f60794-e42d-4c6d-bb29-e70ccfec69bb · inbound

Multi-MLLM Knowledge Distillation for Out-of-Context News Detection cites this paper.

Multi-MLLM Knowledge Distillation for Out-of-Context News Detection Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T13:09:50.561934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:09:50.561934Z digest=sha256:6a1311ff55ada6b4f4a08ef73654003a2498c2e08e82b3f10fa45485617b8abc

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

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability cites this paper.

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:22.199178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:22.199178Z digest=sha256:6c0210bd0ed7f09e5c1d5f0e6ae118938f204440fdce4510c963188c9c1b27c8

Observation 55fbda0d-6636-4dc9-97e0-9359a8b58afd · inbound

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes cites this paper.

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T00:13:35.505844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:13:35.505844Z digest=sha256:a9dd44e858a0bed7b3624721ef27772465493c324f8b4a86746852594dd4cd38

Observation b5e4e869-66c6-4dfc-86c1-ebaebe1e5d2f · inbound

iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development cites this paper.

iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T16:36:26.214424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:36:26.214424Z digest=sha256:12c08fcf6b8c74046bfd463cf65a90e496d1f795ebc81e4b591ff6465eb016ec

Observation e4e3cf4b-5877-4baf-aafe-cefbdcde2017 · inbound

Robust pid sliding mode control for dc servo motor speed control cites this paper.

Robust pid sliding mode control for dc servo motor speed control Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-05T23:42:04.171296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:42:04.171296Z digest=sha256:bf23e4ec023937823074af6fb94c21fcf3c921cf2e435acc1e4279052062a534

Observation 3866e492-b30f-4acd-b759-f84197b1437b · inbound

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium cites this paper.

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-18T02:52:21.843951Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-18T02:51:19.275111Z digest=sha256:37006b07030475c89ec9eb286a1504bb345bc06c2f7088b2fdd2c68049f905ff

Observation c379e78b-11fd-4c69-85d0-5a89c8ea454b · inbound

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts cites this paper.

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T03:50:57.400428Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-11T02:11:19.295354Z digest=sha256:79cf7236badc3b52408287ccad495b9ad624e3ee20fd06dd079855cd9c12db5a