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Paper Citation Record · LEDGER

Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2305.12295.

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

pith.paper-citation-record.v1
2305.12295 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:33:37.114620Z

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

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External citation measurements

7
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 91b4beb1-1673-45ba-b079-166aa623a10c · inbound

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency cites this paper.

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:33:37.114620Z digest=sha256:26f89181fa5cf4f538c28b62302350889786c8d570a26d19557e7c3ed712fe78

Observation bf513ac5-1bbb-484e-a12e-17cb30a23d72 · inbound

Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation cites this paper.

Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 2024

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source=pdf_text observed=2026-08-08T15:08:25.805554Z digest=sha256:56a348306c833decae0edb3dee7d70b7f1bbac0578ac643bcf04d9d63e0af3cd

Observation 0df097c1-4e23-4e60-b534-3b39a5ec619a · inbound

Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis cites this paper.

Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 15

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source=pdf_text observed=2026-08-08T06:01:39.767453Z digest=sha256:f6a55f514ed460f01c2841929a7be7d132bf75843bc247026abb2181563122ff

Observation 3bd2cf32-83f4-4865-bb23-7e76c137be11 · inbound

CRANE: Reasoning with constrained LLM generation cites this paper.

CRANE: Reasoning with constrained LLM generation Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 313

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no resolver link, observed 2026-08-07T22:51:12.993196Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T22:51:12.993196Z digest=sha256:bb7a20f6fa30845b2d83dc0934f2c5f2ae6bfb79c7e0d9f96e20c232ae2f92a4

Observation 60d2efc8-9aab-4783-a575-2e06bb0da4e8 · inbound

Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search cites this paper.

Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:38.207557Z digest=sha256:c05722fa637d45f226b7b2552151e6a2287408506ff09ccd781e48b8612d3ff9

Observation 9ac60a4f-5e51-47fe-a5f0-be8a60e805c2 · inbound

Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models cites this paper.

Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 2013

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no resolver link, observed 2026-08-07T15:17:16.104722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:17:16.104722Z digest=sha256:045305d0286ace62d4f455677c4fb57e281f0f02809efa7d9513ee80e0408ae1

Observation 9edd47b4-ac57-4155-9936-1d2149c622fa · inbound

Learning to Reason via Mixture-of-Thought for Logical Reasoning cites this paper.

Learning to Reason via Mixture-of-Thought for Logical Reasoning Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:40.488713Z digest=sha256:5dd42ec671a3c38ba14c98af1a649426b2ee764ac05d0c0ee6d4d9135b93ff84

Observation 1ae764c6-652a-4509-bbce-8d1d52f0252d · inbound

DINGO: Constrained Inference for Diffusion LLMs cites this paper.

DINGO: Constrained Inference for Diffusion LLMs Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 19

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no resolver link, observed 2026-08-07T12:58:37.353432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:58:37.353432Z digest=sha256:583d1deea87f1b795843c517c37547478d70f1ff7cdb52635aaba0ee768c543c

Observation 399a9281-5066-4780-a858-9d62920a3259 · inbound

ORFS-agent: Tool-Using Agents for Chip Design Optimization cites this paper.

ORFS-agent: Tool-Using Agents for Chip Design Optimization Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 42

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verified exact
arxiv_id, observed 2026-05-19T11:27:16.038681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T11:23:29.010807Z digest=sha256:1267f4975c31f4297eb590bab5625837f815c73b99791c4f0b1353f0f9d71616

Observation 36030903-62ee-480d-a24d-521272b167a5 · inbound

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training cites this paper.

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 12

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no resolver link, observed 2026-08-07T04:55:42.284502Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:55:42.284502Z digest=sha256:9bdf13c308023d1b71276888f4f9fd2ec7d54beb75131a9383a7c14a975c5950

Observation beb63caa-ce67-4e6e-ad26-f5af4ee87a93 · inbound

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools cites this paper.

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 14

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no resolver link, observed 2026-08-07T04:53:43.487935Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:53:43.487935Z digest=sha256:61235e68ab114228b47c2f13168248cad2070403cb7aa16a86c6c67c635ce0d3

Observation efca936b-7996-4a94-ae34-160008f0d7a2 · inbound

DipSVD: Dual-importance Protected SVD for Efficient LLM Compression cites this paper.

DipSVD: Dual-importance Protected SVD for Efficient LLM Compression Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 31

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:58:09.398339Z digest=sha256:296c3b1828ff00ef1af18d46d6a3f33e7d1b719629b9d3e448638ce729069e1e

Observation 591f1166-531e-4e12-a0a5-c7c7ed1f2d9b · inbound

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law cites this paper.

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 23

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:41.105915Z digest=sha256:384dd064ea274d00e30918b27a346e8d43fa058acb229b09066b23f34c173a1d

Observation 2aae2ec5-df0f-48e4-9102-e11e01bcec42 · inbound

Integrating External Tools with Large Language Models to Improve Accuracy cites this paper.

Integrating External Tools with Large Language Models to Improve Accuracy Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 15

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source=pdf_text observed=2026-08-06T19:05:02.548607Z digest=sha256:6789c6efe7b085b115ccd4a7bad99ec248dc5e9120e8606e96dc2d4085977489

Observation e78e6b8f-fd5a-44c5-bc81-f07f93c39478 · inbound

Beyond Isolated Capabilities: Bridging Long CoT Reasoning and Long-Context Understanding cites this paper.

Beyond Isolated Capabilities: Bridging Long CoT Reasoning and Long-Context Understanding Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 32

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no resolver link, observed 2026-08-06T15:50:51.197770Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:50:51.197770Z digest=sha256:c2b9a1badb60f13bf841fe14c486199c23870999eaf9e8c37f828f9710ac08bc

Observation 521c1797-7c53-4feb-b9b6-8ca5cbd30f3a · inbound

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems cites this paper.

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 35

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no resolver link, observed 2026-08-06T14:58:26.406230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:58:26.406230Z digest=sha256:40b06ace904b19320fcc2c9d17d46354407905c72dab87ea90f19c8e57f67214

Observation 9c82c850-dc4a-4eed-b7c4-a33328f3eecf · inbound

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms cites this paper.

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 26

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no resolver link, observed 2026-08-06T15:33:30.736315Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:33:30.736315Z digest=sha256:69f1cbed9ab2687429435c44f78051e5ab03126bc86e6353435532a29333e281

Observation 1460bfc4-a498-4ffa-9ffe-5eff8e4a1d4c · 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 Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 9

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no resolver link, observed 2026-08-05T12:56:33.038472Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:56:33.038472Z digest=sha256:de2041e18aba3eabff86b40b3a522375d102061d62475ac48f60fc7ecea0c47d

Observation 51e80c11-e038-4c17-b879-15a02ae88354 · inbound

Throttling Web Agents Using Reasoning Gates cites this paper.

Throttling Web Agents Using Reasoning Gates Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 73

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no resolver link, observed 2026-08-05T12:28:04.507275Z

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source=pdf_text observed=2026-08-05T12:28:04.507275Z digest=sha256:434449594c515998f85b16e5b9f71c7bdff66e34ad46186d101e5e13a9dd66d2

Observation 532a7a83-8582-48f3-abb6-eae8bd688b7c · inbound

From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs cites this paper.

From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 9

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no resolver link, observed 2026-08-04T23:56:34.889878Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:56:34.889878Z digest=sha256:a430b74493a19861419b1a166c7b57b9446114a1f5e708700236f691a2ca61d5

Observation 04a2d3e3-f656-4f71-acbc-e94e3bc7b019 · inbound

Semantic-Aware Logical Reasoning via a Semiotic Framework cites this paper.

Semantic-Aware Logical Reasoning via a Semiotic Framework Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 27

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arxiv_id, observed 2026-05-18T13:01:23.904534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-18T12:57:45.584017Z digest=sha256:419d0b21786ab9d9c8ed493fdefe75d32b1c5b8263b278fe9b78a6d29db4ecf0

Observation abcb1a15-4888-4094-a1bc-44b6651f736d · inbound

LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations cites this paper.

LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 8

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arxiv_id, observed 2026-05-18T02:00:38.471090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T01:56:50.023943Z digest=sha256:607e74761e0d55804415e39763a9b8d1e535b186b5cf5d1a4932523dc105d6c3

Observation 132dfeb9-69fe-4ade-a705-1e26e229f018 · inbound

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning cites this paper.

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 4

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arxiv_id, observed 2026-05-16T10:20:49.936365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T10:20:43.601411Z digest=sha256:1d90739b1f8a7bbc1d0f848ce42f687032801679c83ec3aa506bd3c7e9d3de2a

Observation 2593c52c-99ca-4d05-8e58-d6f9a62a7393 · 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 Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 28

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verified exact
arxiv_id, observed 2026-05-10T23:00:47.733310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T19:26:23.660206Z digest=sha256:76345c1a59cc183c38f6d2a3ed47805c777faae39f96af6f26e2f97d1319d623

Observation 6d6fdff5-98d6-48ef-a483-7dae4bf5cbe0 · inbound

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline cites this paper.

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 17

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verified exact
arxiv_id, observed 2026-05-11T10:31:01.299222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T15:27:41.993499Z digest=sha256:d7b432c00249600a313ca59d80e07db59f8c8418c1e64134f297f4d46a177af7

Observation 4ba27694-447d-425d-a2dd-41af68703675 · inbound

LLM Reasoning Is Latent, Not the Chain of Thought cites this paper.

LLM Reasoning Is Latent, Not the Chain of Thought Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 14

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arxiv_id, observed 2026-05-10T08:53:04.715478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T08:49:05.178087Z digest=sha256:0a3f713ccdbaac7e9c486e677e4fcb254a70a15ecbff9ee07aa335c77fa79da6

Observation 25bc98b3-cc0f-40cc-8ad3-f69aa0b53d1b · inbound

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS cites this paper.

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 8

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metadata mismatch
arxiv_id, observed 2026-05-11T12:06:05.008200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T04:08:38.327402Z digest=sha256:214a6af63104bd987f301bf5debf9d62ec78a45a78de8c8df3be1ab3404bf6a8

Observation cd840e3f-15e3-4f10-b97c-6980fd262956 · inbound

NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise cites this paper.

NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 11

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metadata mismatch
arxiv_id, observed 2026-05-11T17:56:07.199741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T16:54:17.663989Z digest=sha256:2739a0583f3d877f91c732e5f503628cfdda1ee498e6139adc9ea477d34e2716

Observation d346097c-3317-4549-bd2d-80a2a9d1715f · inbound

CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents cites this paper.

CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 29

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metadata mismatch
arxiv_id, observed 2026-05-12T07:41:46.209339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T02:17:41.650948Z digest=sha256:af1ebed1d0219d2b447516733f08d95e453efe1a905cd0a233cd9352b59ab169

Observation d9692fc1-02e5-4c21-8add-1c4365148bd4 · inbound

Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes cites this paper.

Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 17

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no resolver link, observed 2026-08-01T15:40:36.356091Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:40:36.356091Z digest=sha256:c3fe0b684def47a2efbf740d2563198a9018b99f7cb3161fd104061cb6c72fa5

Observation 102e3e67-6bcd-419a-aad1-ab70891e1ef6 · inbound

Training Large Language Models for Self-Explanation Faithfulness cites this paper.

Training Large Language Models for Self-Explanation Faithfulness Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 136

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no resolver link, observed 2026-08-01T08:36:29.810585Z

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source=arxiv_source observed=2026-08-01T08:36:29.810585Z digest=sha256:c0a99cb7a0f7973e40ca5b85c469f6829b46409d9846d2fe95065588d79343dd

Observation f70611ae-384b-4130-bd4d-001d53695152 · inbound

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation cites this paper.

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 10

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no resolver link, observed 2026-07-30T23:41:20.535489Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T23:41:20.535489Z digest=sha256:401f9ca3478010bf488b53aeebc1342d1922f3abfe37d06d49ef658067e3aa6f

Observation 4825c95e-1578-4122-850c-14ffbe4df3b6 · inbound

Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning? cites this paper.

Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning? Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Reference 171

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no resolver link, observed 2026-07-30T16:01:42.518723Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T16:01:42.518723Z digest=sha256:1dde80d08856193fcbd434f0c7a0529fdd6ad80c2dd7410df062ca2bceaad9fd