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Paper Citation Record · LEDGER

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2305.14825.

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

pith.paper-citation-record.v1
2305.14825 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:40.621844Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

15
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ac56103f-2833-4bf0-88fb-919aa3d21f56 · inbound

ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection cites this paper.

ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T15:04:40.621844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:04:40.621844Z digest=sha256:91af1f7d1d819ec6d9b317075c7def8a44bf8efea2497ecc012982c30ce6f578

Observation bf09d5e3-9b9a-4115-8648-ff3120664943 · inbound

CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building cites this paper.

CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T13:46:09.578801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:46:09.578801Z digest=sha256:c8823a20ef150bde46c97a4225b519fc9719f69bd3728880203168460b7482e4

Observation 065c93eb-75d1-4fd3-b75f-094b352369ae · inbound

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs cites this paper.

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-07T12:44:08.172414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:44:08.172414Z digest=sha256:9784c542039402cc68d80a12d732b7f6ad9a59267e7fdca03566206d1fc29aa1

Observation fe89a0c4-47dc-49ee-a142-9668930bf8ef · inbound

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities cites this paper.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:16.402380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:16.402380Z digest=sha256:ce806fc1e002832a9f79190cf4630056488185bc5fad6590e43f3848ae86d1c3

Observation 7d356364-cca8-44a5-952a-e662e2c37094 · inbound

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks cites this paper.

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T11:06:06.741394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:06:06.741394Z digest=sha256:52d382c319598542b08db4befb7e5afb5f0fbca5fec8331c4e4a01d3ae5e7f16

Observation 0f3e9d90-9b56-4273-8936-73e2d17fa88e · inbound

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens cites this paper.

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-19T01:21:58.063928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T01:18:31.661827Z digest=sha256:a6bde9710d17beb90efe4c893093b3f84cd9a0db6f2a5f7f63d5041e854df404

Observation 73172593-dd7c-48d0-a5fc-2629512352e6 · inbound

Deep Learning in Classical and Quantum Physics cites this paper.

Deep Learning in Classical and Quantum Physics Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-05T20:23:57.854063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:23:57.854063Z digest=sha256:e6a78b3faa4750393f2784b578b13be90bbafca5842247d7b8585140116bf487

Observation 62dbf80c-ad67-481e-a897-8acb0f994243 · inbound

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction cites this paper.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T19:12:38.869952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T19:12:38.869952Z digest=sha256:7c98808baaab6b56d366819da15bbf10e1d22a6be9f33f556f86bb5767db0488

Observation 3ed8a10c-dc87-4bb9-9a20-c1303e3e29b7 · inbound

AlignCultura: Towards Culturally Aligned Large Language Models? cites this paper.

AlignCultura: Towards Culturally Aligned Large Language Models? Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:56:05.104332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T02:36:36.854805Z digest=sha256:8f52fa5e347fb3f0565db67ed2679ed4863fb1104b8d2432d1ccc47d3696e810

Observation 2e48a36e-2374-4eaf-84ea-05d44207ae3c · inbound

EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair cites this paper.

EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-12T01:41:15.003474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T01:39:32.316491Z digest=sha256:c325678a2c62ca4bf463733de5c0c9513d08af9416115f88960ea1b58996437f

Observation 2b640598-2165-407b-bd19-67974ec84b39 · inbound

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective cites this paper.

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:13:58.696496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T05:09:37.588841Z digest=sha256:eb86dcc83bb5924d5903196b38d96f5d997555934abfcb8bc6912692fa60c480

Observation 380dc6c9-b71e-45c8-904b-b3b625128adf · inbound

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation cites this paper.

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:33:45.492953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T17:24:32.401230Z digest=sha256:1748e6ab76e0316b9ccfa724a5a684c632534e0b545b920f193b309a0cabd82a

Observation 3d9b9819-028d-47fe-a6da-05af11d4d9e6 · inbound

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation cites this paper.

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-02T13:08:41.379698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T13:08:41.379698Z digest=sha256:7c698f0073739a703fc331724f4a02315b76b9bf8fb677bd877b691e52483905

Observation c4515456-710c-4bc7-9ace-4211c0f8bc11 · inbound

A Systematic Evaluation of Traditional Privacy Policy Analysis Tools Against LLMs cites this paper.

A Systematic Evaluation of Traditional Privacy Policy Analysis Tools Against LLMs Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-01T19:09:53.148943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:09:53.148943Z digest=sha256:0f4a92105579f7cbf523535cd8c73445e67bfd1e3d2024fbfb82ba21aff9108d