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Paper Citation Record · LEDGER

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2605.29687.

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

pith.paper-citation-record.v1
2605.29687 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T07:07:49.523646Z

measured 38 of 38 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

  • verified exact6
  • verified fuzzy0
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 648edcf6-3cd0-41d8-8af7-2d8e29beb377 · outbound

This paper cites Maximum Satisfiability , volume =.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Maximum Satisfiability , volume =

Reference 1

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doi, observed 2026-06-29T07:13:15.770145Z

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Observation 4a2f8dbc-7d51-4781-a74e-de1f44b03c45 · outbound

This paper cites IOS Press, 2009.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability IOS Press, 2009

Reference 2

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Observation f24b8ae5-de9c-4d95-81a8-535efe6d6c4c · outbound

This paper cites Logically consistent language models via neuro-symbolic integration.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Logically consistent language models via neuro-symbolic integration

Reference 3

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Observation f973620d-569d-4753-b71a-efd0042ceaf3 · outbound

This paper cites an unresolved cited work.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Unresolved cited work

Reference 4

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Observation 1bc459ba-18ec-4399-a46c-bcc97a92fb0b · outbound

This paper cites Pal: Program-aided language models.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Pal: Program-aided language models

Reference 5

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Observation 4d36cd05-d849-493e-9075-b2fb3fe27ab2 · outbound

This paper cites Pddl—the planning domain definition language.Technical Report, Tech.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Pddl—the planning domain definition language.Technical Report, Tech

Reference 6

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Observation 5626906d-1c49-43ca-b21a-e86be817a0dd · outbound

This paper cites Gemini-3.1-Pro https://blog.google/innovation-and-ai/ models-and-research/gemini-models/gemini-3-1-pro/ , 2026.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Gemini-3.1-Pro https://blog.google/innovation-and-ai/ models-and-research/gemini-models/gemini-3-1-pro/ , 2026

Reference 7

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Observation c2ff6b75-88e2-4fa9-b31f-8bf36b055b9b · outbound

This paper cites an unresolved cited work.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Unresolved cited work

Reference 8

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source=pdf_text observed=2026-06-29T07:07:49.523646Z digest=sha256:db34559a97de760f48725726aa8262922853233f5ba4516e917933bca0ed5154

Observation 60198141-d37c-40ea-84f4-3cfe43d9f5d0 · outbound

This paper cites Large language models can solve real-world planning rigorously with formal verification tools.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Large language models can solve real-world planning rigorously with formal verification tools

Reference 9

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Observation c9db2046-d8f2-4cee-8052-28f5b41c0435 · outbound

This paper cites Planning anything with rigor: General-purpose zero-shot planning with llm-based formalized programming.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Planning anything with rigor: General-purpose zero-shot planning with llm-based formalized programming

Reference 10

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Observation 48aedac5-27b1-4536-a782-b5fac85351c6 · outbound

This paper cites https://huggingface.co, 2026.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability https://huggingface.co, 2026

Reference 11

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Observation ff6c3539-4d82-43ef-a0b6-cbee3f699453 · outbound

This paper cites PySAT: A Python toolkit for prototyping with SAT oracles.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability PySAT: A Python toolkit for prototyping with SAT oracles

Reference 12

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Observation 90a25fe4-eab6-4ccf-8602-5ff11532619e · outbound

This paper cites RC2: an efficient MaxSAT solver.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability RC2: an efficient MaxSAT solver

Reference 13

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Observation b11faad7-3e38-4975-bac9-2d2ae0d8adcb · outbound

This paper cites Exploiting maxsat for preference-based planning.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Exploiting maxsat for preference-based planning

Reference 14

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Observation ca7f06e4-91e1-4431-87c8-d1496f7b117b · outbound

This paper cites Hsu, and Sheila A.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Hsu, and Sheila A

Reference 15

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source=pdf_text observed=2026-06-29T07:07:49.523646Z digest=sha256:fc6f81747c639f1f86fec27be16fa5acb10be36d6a539e193b49c7f230c23b72

Observation 93096a01-aadb-441d-819d-69ab1d4d4d08 · outbound

This paper cites Position: Llms can’t plan, but can help planning in llm-modulo frameworks.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Position: Llms can’t plan, but can help planning in llm-modulo frameworks

Reference 16

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Observation de6aaedc-7223-44b3-bb27-49a4b418b462 · outbound

This paper cites Maxsat, hard and soft constraints.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Maxsat, hard and soft constraints

Reference 17

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source=pdf_text observed=2026-06-29T07:07:49.523646Z digest=sha256:0f63da76a52d1ece662d7318a3dc496bcf588cd2d7a76e19f075a952d75cd5a7

Observation a593ab20-e80c-4a2d-8ee0-b3f9d371647d · outbound

This paper cites Liang, Chenyang Yang, and Brad A.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Liang, Chenyang Yang, and Brad A

Reference 18

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source=pdf_text observed=2026-06-29T07:07:49.523646Z digest=sha256:dc36fd535665746f78ab89cc3405b722b4b353788a6b9e0bcaf399b1900a03e4

Observation 2dfdf022-7c55-48d6-b53c-821f1337c60c · outbound

This paper cites Liang, Chenyang Yang, and Brad A.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Liang, Chenyang Yang, and Brad A

Reference 19

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Observation 15f0201d-0aaf-400b-b6ca-83bfb61dd667 · outbound

This paper cites Zebralogic: On the scaling limits of llms for logical reasoning.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Zebralogic: On the scaling limits of llms for logical reasoning

Reference 20

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Observation 8fcd5ec7-36d3-40b7-9f7f-ad28c77db77c · outbound

This paper cites Truthfulqa: Measuring how models mimic hu- man falsehoods.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Truthfulqa: Measuring how models mimic hu- man falsehoods

Reference 21

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Observation d7725120-7da1-4b13-8c5f-e2c2b8229d42 · outbound

This paper cites Llama-3.3 https://www.llama.com/docs/model-cards-and-prompt-formats/ llama3_3/, 2024.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Llama-3.3 https://www.llama.com/docs/model-cards-and-prompt-formats/ llama3_3/, 2024

Reference 22

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Observation f39d1919-0863-46c5-9e0f-1e42cf5a8b39 · outbound

This paper cites GPT5.5 https://openai.com/index/introducing-gpt-5-5/ , 2026.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability GPT5.5 https://openai.com/index/introducing-gpt-5-5/ , 2026

Reference 23

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Observation f5238dbf-aab0-46d8-806c-dcde3d3a37b1 · outbound

This paper cites Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning

Reference 24

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Observation b39bd469-89f6-4791-a4c1-f1df46b6ff6d · outbound

This paper cites PhD thesis, University of Cambridge, UK, 2012.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability PhD thesis, University of Cambridge, UK, 2012

Reference 25

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Observation e236c64a-48ad-4f99-a051-0afc7cf53928 · outbound

This paper cites Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

Reference 26

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Observation d5676976-1990-451d-bda1-1710bc0ceac9 · outbound

This paper cites Constraintllm: A neuro-symbolic framework for industrial-level constraint programming.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Constraintllm: A neuro-symbolic framework for industrial-level constraint programming

Reference 27

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Observation 20af1086-65e9-4cf7-b0aa-c3154a14baf3 · outbound

This paper cites The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity

Reference 28

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Observation 35c36428-be48-4284-989d-a734c8f78f20 · outbound

This paper cites MCP-Solver: Integrating Language Models with Constraint Programming Systems.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability MCP-Solver: Integrating Language Models with Constraint Programming Systems

Reference 29

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Observation 735c897c-badb-472f-926a-d0dc6b8ebbee · outbound

This paper cites On the planning abilities of large language models-a critical investigation.Advances in Neural Information Processing Systems, 36:75993–76005, 2023.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability On the planning abilities of large language models-a critical investigation.Advances in Neural Information Processing Systems, 36:75993–76005, 2023

Reference 30

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Observation 48a14836-1020-44ef-9e05-db49d1b5d26c · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Chain-of-thought prompting elicits reasoning in large language models

Reference 31

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Observation c69d6fa1-6630-4296-bc36-21607e0d14a8 · outbound

This paper cites Aristotle: Mastering logical reasoning with a logic- complete decompose-search-resolve framework.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Aristotle: Mastering logical reasoning with a logic- complete decompose-search-resolve framework

Reference 32

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Observation 3df14b06-7854-40b2-89fa-33ae03505220 · outbound

This paper cites Qwen3 Technical Report.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Qwen3 Technical Report

Reference 33

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Observation 480ab3a5-62d0-4c70-82d0-5889abe7fac2 · outbound

This paper cites Satlm: Satisfiability-aided language models using declarative prompting.Advances in Neural Information Processing Systems, 36: 45548–45580, 2023.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Satlm: Satisfiability-aided language models using declarative prompting.Advances in Neural Information Processing Systems, 36: 45548–45580, 2023

Reference 34

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Observation b811325d-63a4-4c31-9c8e-02cdd557d297 · outbound

This paper cites Multilingual machine translation with large language models: Empirical results and analysis.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Multilingual machine translation with large language models: Empirical results and analysis

Reference 35

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Observation af5dcfaa-b5cc-498e-b732-b1a608af3bed · outbound

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Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Unresolved cited work

Reference 36

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Observation 1eb872c8-46ea-473c-85f8-05c4163e656a · outbound

This paper cites an unresolved cited work.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability Unresolved cited work

Reference 37

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Observation 08c12ee5-65f7-48db-9936-57e065454fe8 · outbound

This paper cites objective_cost.

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability objective_cost

Reference 38

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source=pdf_text observed=2026-06-29T07:07:49.523646Z digest=sha256:9fa0a805dcec761099a4e5c6905ea33358d53d793ee230e501f0d4ea15a04c5a

Pith citing papers

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