Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory GPT-4 Technical Report

Reference 1

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This paper cites Qwen2.5 Technical Report.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Qwen2.5 Technical Report

Reference 2

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Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 3

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This paper cites Scaling Laws for Neural Language Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Scaling Laws for Neural Language Models

Reference 4

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 5

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This paper cites Continual Pre-training of Language Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Continual Pre-training of Language Models

Reference 6

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This paper cites Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue,

Reference 7

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Observation e939edfd-1ec4-466c-9dcc-90c064543209 · outbound

This paper cites Efficient continual pre-training for building domain specific large language models,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Efficient continual pre-training for building domain specific large language models,

Reference 8

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Observation 5fb9434e-4eaf-44e1-b05a-a47940bab41d · outbound

This paper cites Lora: Low-rank adaptation of large language models.,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Lora: Low-rank adaptation of large language models.,

Reference 9

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Observation d9e539b3-bd1b-47d5-9c70-241490bc248c · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 10

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This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 11

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Observation 89c50e5e-01f5-4e3c-a2fe-83c76281bae6 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 12

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Observation 0ebb33eb-7b35-445a-9634-714a94640624 · outbound

This paper cites MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations

Reference 13

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Observation 00189679-22c9-4f2b-9dc4-0e04eddb6b23 · outbound

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

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Chain-of- thought prompting elicits reasoning in large language models,

Reference 14

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Observation 6b0617cf-0a31-44d7-85bd-a1eb4d1fcb06 · outbound

This paper cites Learning to reason with large language models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Learning to reason with large language models

Reference 15

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Observation d730a4a7-c698-48cf-9285-c0fd4eb7a813 · outbound

This paper cites O1 Replication Journey: A Strategic Progress Report -- Part 1.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory O1 Replication Journey: A Strategic Progress Report -- Part 1

Reference 16

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This paper cites O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

Reference 17

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This paper cites O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning

Reference 18

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Observation 74d83cb4-fcf8-4b0a-b14e-cc2228118f65 · outbound

This paper cites Co-occurrence is not Factual Association in Language Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Co-occurrence is not Factual Association in Language Models

Reference 19

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This paper cites Locating and editing factual associations in gpt,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Locating and editing factual associations in gpt,

Reference 20

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This paper cites Mass-Editing Memory in a Transformer.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Mass-Editing Memory in a Transformer

Reference 21

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This paper cites Small models struggle to learn from strong reasoners,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Small models struggle to learn from strong reasoners,

Reference 22

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Observation 6b0ab4da-7e25-4cb1-bea6-267e7c612f0b · outbound

This paper cites Kahneman, Thinking, Fast and Slow.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Kahneman, Thinking, Fast and Slow

Reference 23

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This paper cites Understanding Chain-of-Thought in LLMs through Information Theory.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Understanding Chain-of-Thought in LLMs through Information Theory

Reference 24

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This paper cites Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning

Reference 25

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This paper cites Measuring Massive Multitask Language Understanding.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Measuring Massive Multitask Language Understanding

Reference 26

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This paper cites Medical exam question answering with large- scale reading comprehension,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Medical exam question answering with large- scale reading comprehension,

Reference 27

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This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 28

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This paper cites The Llama 3 Herd of Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory The Llama 3 Herd of Models

Reference 29

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This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Gemma 2: Improving Open Language Models at a Practical Size

Reference 30

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Observation c8d7f939-577e-4c0f-9355-1d8eb779f62b · outbound

This paper cites Phi-4 Technical Report.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Phi-4 Technical Report

Reference 31

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This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 32

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Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.324286Z

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This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.328995Z

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Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Algorithms for learning kernels based on centered alignment,

Reference 35

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This paper cites Similarity of neural network representations revisited,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Similarity of neural network representations revisited,

Reference 36

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This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Tree of thoughts: Deliberate problem solving with large language models,

Reference 37

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This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 38

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Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Graph of thoughts: Solving elaborate problems with large language models,

Reference 39

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Observation 270a20ba-bf66-4566-ba0c-4c5e14212c71 · outbound

This paper cites Large language models are zero-shot reasoners,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Large language models are zero-shot reasoners,

Reference 40

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Observation 4b830e7b-16ba-4c2a-9c32-faff9e2a2a92 · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Automatic Chain of Thought Prompting in Large Language Models

Reference 41

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Observation 7dc2161d-0ff3-4fec-ad33-95fdaf8c48d5 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Training Verifiers to Solve Math Word Problems

Reference 42

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Observation add75416-41ef-4c9c-a2c5-2f6c68e25f50 · outbound

This paper cites Rest-mcts*: Llm self-training via process reward guided tree search,.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Rest-mcts*: Llm self-training via process reward guided tree search,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.278367Z

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Observation a2f948b1-a27e-4588-bd59-b5b5343340d5 · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.370761Z

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Observation 584f3c12-ceca-4bef-98aa-47e5b7b2a949 · outbound

This paper cites Enhancing LLM Reasoning with Reward-guided Tree Search.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Enhancing LLM Reasoning with Reward-guided Tree Search

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.374617Z

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Observation 1c6475b5-2cb3-4e6c-8cb9-5c14b197d638 · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 46

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Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Autopsv: Automated process- supervised verifier,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.266041Z

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Observation d3c1c249-9a83-426f-8c7b-ebb1a2462b7b · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.384969Z

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This paper cites Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.389024Z

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:22:13.392968Z

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Observation 5c53a152-b397-4537-b0cb-8d508fd2b167 · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Qwq-32b: Embracing the power of reinforcement learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.253604Z

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Observation 16ce41fb-f83b-42df-a434-9f9a6208b91f · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 52

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Observation 9d196490-ef43-4e63-a898-0d73fb8eb094 · outbound

This paper cites - The key is to understand the combinatorial nature of the problem, which involves selecting and assigning courses to professors.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory - The key is to understand the combinatorial nature of the problem, which involves selecting and assigning courses to professors

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.241703Z

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Observation 724a12fc-f9e4-4946-b086-a922573e222a · outbound

This paper cites - The total number of courses is 20, and each course is assigned to exactly one professor.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory - The total number of courses is 20, and each course is assigned to exactly one professor

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.229451Z

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

Observation 370b766a-e3a3-4ef3-90e5-148a016ee339 · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.217309Z

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.

Observation d2c4b5fb-6bce-4a72-97f1-3c9f3ea38498 · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.205355Z

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.

Observation da167241-1d3f-44d1-b2f1-dd7c740c8ed5 · outbound

This paper cites - The second approach is more accurate as it accounts for the indistinguishability of the order of courses for each professor.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory - The second approach is more accurate as it accounts for the indistinguishability of the order of courses for each professor

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.192382Z

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.

Observation 0859e811-6dee-40cd-b151-93cc64c59234 · outbound

This paper cites ### Solution.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory ### Solution

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.179333Z

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.

Observation 4faa497e-2109-4a9b-833d-5fcc4c621b2f · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.166800Z

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.

Observation 25ed7e8a-de63-451c-ae7d-230d51595231 · outbound

This paper cites - There are 10 professors, so we divide by (2^(10)).

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory - There are 10 professors, so we divide by (2^(10))

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.154430Z

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.

Observation 3f96620e-3551-4a61-abe7-bef2bf39bd6f · outbound

This paper cites Correct",.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Correct",

Reference 61

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T18:22:14.141835Z

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.

Observation 591d6792-4aee-4bd9-98b5-c9addd2b1d56 · outbound

This paper cites Given two matricesX andY representing the activations of two models or layers, the centering operation involves subtracting the mean of each column from the respective elements.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Given two matricesX andY representing the activations of two models or layers, the centering operation involves subtracting the mean of each column from the respective elements

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.128242Z

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.

Observation 7a9acdbb-f07b-4cd1-ac80-b77fd14bb449 · outbound

This paper cites The Gram matrix represents the inner product between the centered data points.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory The Gram matrix represents the inner product between the centered data points

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:14.116020Z

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.

Observation 94ea85be-1a75-49d6-8014-f078e6e8318d · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.101413Z

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.

Observation 599da960-275d-45dd-9bba-191cdeea47c4 · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.088671Z

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.

Observation c77d3f1b-6251-4001-95c0-3ea413e89269 · outbound

This paper cites an unresolved cited work.

Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:22:14.075860Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers