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Paper Citation Record · LEDGER

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

As of 11 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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-21T20:50:09.265838Z digest=sha256:f0c7bc1759fa32ba882b1198243f4706f113c995d8d1875c09d6f5685e12ce4d

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-17T00:57:26.303195Z digest=sha256:c9f3bc2fc353b41fec74816f673619daa1e3643e14fe263d27d4a9fd6f14ca51

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-15T02:39:33.007894Z digest=sha256:3d6b03dc3622dd29a05061f11cd405bd5a0a52b1129d4d19e31f9aa0890a54c8

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:ccec65efad5afa5e0218d3ff5a89a70735eb57fa17c81c691b6392741e1e405e

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:d9f87991a568a3ea75d021995f43f1aed2103e68073b59ec0a87c9386cf29ff1

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=sha256:630856ddccaafefed4986f3f4bbc84b15e31726c5b92afaf8f26f816d7f035e4

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=sha256:98a8cb3a7fe470d3cf4fb0da3ae52340ea7652d1e597ae9d9a72ca45a0d29d58

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=sha256:953f7c7378e64b016c4942b3b0c8f2335a6cfd968740d3767e1d091c6b6b8c75

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=sha256:3a10d66af2439d640f23dfe8b3099baa93b678a25d15afd647a2188f9caac07d

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-18T02:51:19.275111Z digest=sha256:eda19c485111c43ed1926d96359dfaa48fc36117687026915a9c51f1e0b2efab

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-11T02:11:19.295354Z digest=sha256:426da45c11d8ac6615ff9a4ead9a8069bf102034ab807d0e91f0d5a84113944b