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Paper Citation Record · LEDGER

Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.10276.

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

pith.paper-citation-record.v1
2305.10276 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:25:04.298071Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:12:20.771011Z

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 1e5a9122-8dc9-4819-a64f-a4d17898a199 · inbound

Disentangling Memory and Reasoning Ability in Large Language Models cites this paper.

Disentangling Memory and Reasoning Ability in Large Language Models Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T16:25:36.874829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T16:25:36.874829Z digest=sha256:dd2395c4473036ce3e04c39d9b220f8c2510dd0066a2a8907a2bf154caae6e63

Observation 0c191b15-c134-44c5-9481-af3df04b919c · inbound

PDDLFuse: A Tool for Generating Diverse Planning Domains cites this paper.

PDDLFuse: A Tool for Generating Diverse Planning Domains Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T05:56:42.697718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:56:42.697718Z digest=sha256:e8977d4dcdc45f802a2285cd211166ddd2966065d7cf4a8cc01a2a9301888be0

Observation db79845e-fa4e-4903-8371-91f52f694dae · inbound

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems cites this paper.

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-10T22:51:52.346563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:51:52.346563Z digest=sha256:17019c4f2e048cccf000ef9072b9c87c9bce82ae53012d8874cfc9e6975a44bf

Observation 4ea05107-40b0-4e9b-8155-8262db635be1 · inbound

Bridging Language Models and Financial Analysis cites this paper.

Bridging Language Models and Financial Analysis Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:12:20.774380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-23T01:08:58.528533Z digest=sha256:aa791241fe7cbd8f97190c41cb7be1332ffe076ccdde1f6246f738ce4024dc1f

Observation ec92d587-6448-4f75-9e82-2878c912b550 · inbound

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers cites this paper.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-16T04:25:04.298071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:25:04.298071Z digest=sha256:3f7e467ffbf47260d620b1148c64c8a8c218e4883fce08149a7acf2f7118fa90

Observation a241cba3-ce08-431e-90ed-5525cdbb95cc · inbound

Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning cites this paper.

Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:48.118975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:48.118975Z digest=sha256:0338950cdc0ded9b5024894ffc0228cabdb6484b7517fd25b592026be95d17a3

Observation eb561803-bab5-415d-a9e3-6d9f27f9f182 · inbound

From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles cites this paper.

From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-21T15:30:17.854286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T15:28:09.702566Z digest=sha256:ce13672ccc8e7913ab2eb0c491e1343be0302cdb7858aa08dd21c830e1d1e409

Observation 98506d08-5f72-45a9-bfe5-a9b0d426396d · inbound

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision cites this paper.

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:13:13.307817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-13T20:11:58.423612Z digest=sha256:a8daa208ec03afbf990f9aca3378b471e93ac023b20d835113b3d4af2ad7e474

Observation 5aab1eff-2bce-4e79-937f-b0145172d945 · inbound

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision cites this paper.

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:32:41.686291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-19T17:30:08.755545Z digest=sha256:97f95e0a2ab5ce533f84f9e1cee26ad45d9b9938e759b12e9b6806c456900574