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Paper Citation Record · LEDGER

Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2307.07696.

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

pith.paper-citation-record.v1
2307.07696 v1

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 2026-08-16T06:30:59.297886+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:39:33.773480Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:59:38.174347Z

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 03222c07-7d64-4896-9487-ce40a63789ae · inbound

Understanding the planning of LLM agents: A survey cites this paper.

Understanding the planning of LLM agents: A survey Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:12:57.633208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T18:12:57.568144Z digest=sha256:faa1bec0127d82b584b4f2ebeff0ae13d1d60cbe2ffc96f725dd9049dde1f719

Observation f771ac97-3cdd-40a6-9907-7ab19615ef34 · inbound

Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs cites this paper.

Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T11:09:17.515032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:09:17.515032Z digest=sha256:c96170a5761eaefd2d061207d912f65b5d17ab23be7712cf82b553118d4bd21a

Observation 81562e3a-26aa-443b-b894-b46e6a856a33 · inbound

Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios cites this paper.

Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T17:37:05.323779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:37:05.323779Z digest=sha256:86f4d0389c24b1484eec6f09ae97561dc9be3321a988356d817e61f55eb47d2f

Observation 85d67fe7-d0a0-4c8d-90f6-6498ea0ea9a6 · inbound

SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs cites this paper.

SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T20:11:48.658057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:11:48.658057Z digest=sha256:359be3146cb4ff5c325a369b6bab53ee525820f34418a803a0eecfbd37444f45

Observation 43a2f1b6-21ca-4f2c-9ee6-939ed2f9a0b2 · inbound

LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning cites this paper.

LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-15T20:39:33.773480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:39:33.773480Z digest=sha256:a3a5b31931253ec55f1251288cd258aeeb8df71728b4e1d4f15397485803ccf0

Observation 93b819ae-a131-401d-8f02-8eff60aba3af · inbound

Building a Stable Planner: An Extended Finite State Machine Based Planning Module for Mobile GUI Agent cites this paper.

Building a Stable Planner: An Extended Finite State Machine Based Planning Module for Mobile GUI Agent Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:36.546050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:42:36.546050Z digest=sha256:71e7a119cba08bbc724d8074819a5696dc0b198344ed49156646840a3ac38d8e

Observation df7f7fb6-2e69-4849-97e6-592633a9b22d · inbound

Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System cites this paper.

Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T20:39:06.851887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:39:06.851887Z digest=sha256:3618c7085099810feae63e29ca98ca4e7bb93a1edf763284022ad1257641d6e9

Observation 5422c0d3-2f0c-415e-bd88-9fda56bbb51e · inbound

A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning cites this paper.

A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T04:33:37.228612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:33:37.228612Z digest=sha256:5cf022f28e82b81391a01a817125dee4f4d6eaf6213a9984dafda89fa99bde39

Observation de16d5cd-2ab5-4834-84ff-b58830444bac · inbound

Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation cites this paper.

Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T12:56:33.063316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:56:33.063316Z digest=sha256:973f02aa1d27957d425f26791bcf8609b33233c61a62198f5b4c642e4dca8058

Observation 5f566de1-13ab-4ea3-8f71-71c4e1d372a8 · inbound

LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models cites this paper.

LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:00:47.708841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T19:26:23.660206Z digest=sha256:bd3de41ba9407e77403d2097cd3f58d9239527605630e822dfd2f65e332c9df7

Observation 823b7bbd-02ac-4366-b97a-a9794ff5a034 · inbound

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents cites this paper.

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T06:59:38.175626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-26T13:56:51.914966Z digest=sha256:dfdca8f35dbf18a019b199037294c9ca07854fa14073aae553e938d82b22d424