Reasoning Can Hurt the Inductive Abilities of Large Language Models | Paper Citation Record

Observation 1f4de856-41fc-4d83-937d-ec0f4cd16e9c · outbound

This paper cites GPT-4 Technical Report.

Reasoning Can Hurt the Inductive Abilities of Large Language Models GPT-4 Technical Report

Reference 1

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Observation f9049580-350a-445d-aea6-3eb260ea781f · outbound

This paper cites The Role of Deductive and Inductive Reasoning in Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The Role of Deductive and Inductive Reasoning in Large Language Models

Reference 3

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Observation c8171f44-53e0-487f-84ba-34d2825c0188 · outbound

This paper cites The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

Reference 4

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Observation e0b86c66-c7b7-48fd-a51d-a2a0725b8b4a · outbound

This paper cites Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

Reference 5

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Observation d1ef4ad5-f34e-4b86-8965-ae1bd8716100 · outbound

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

Reasoning Can Hurt the Inductive Abilities of Large Language Models Chain-of-thought prompting elicits reasoning in large language models

Reference 6

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Observation 8bc0058c-6a20-4eca-a9b1-c4b5207960bd · outbound

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

Reasoning Can Hurt the Inductive Abilities of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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Observation 2d57be7f-7ff6-4350-9fa5-d31407dee828 · outbound

This paper cites OpenAI o1 System Card.

Reasoning Can Hurt the Inductive Abilities of Large Language Models OpenAI o1 System Card

Reference 8

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Observation 074f449a-f6d4-468d-888b-12b3b58882c1 · outbound

This paper cites Advancing Reasoning in Large Language Models: Promising Methods and Approaches.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Advancing Reasoning in Large Language Models: Promising Methods and Approaches

Reference 9

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Observation 630ce156-d8db-41de-8125-ef206de922d0 · outbound

This paper cites Unveiling the impact of coding data instruction fine-tuning on large language models reasoning.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Unveiling the impact of coding data instruction fine-tuning on large language models reasoning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:21.680424Z

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Observation aef65e81-4938-435d-9d39-fb3adcb2dc34 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:12.010583Z

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Observation 09886fe7-2acf-420e-92df-615a3f03878d · outbound

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

Reasoning Can Hurt the Inductive Abilities of Large Language Models Tree of thoughts: Deliberate problem solving with large language models

Reference 12

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Observation c9474d78-679f-4bbd-9f69-7c6ca67707fa · outbound

This paper cites DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs.

Reasoning Can Hurt the Inductive Abilities of Large Language Models DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:36:18.521315Z

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Observation 66308417-6aa6-402e-bc3c-fc4f90ad0020 · outbound

This paper cites When More is Less: Understanding Chain-of-Thought Length in LLMs.

Reasoning Can Hurt the Inductive Abilities of Large Language Models When More is Less: Understanding Chain-of-Thought Length in LLMs

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:12.394128Z

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Observation e6956d87-1322-4ffa-9ffd-f396439dc520 · outbound

This paper cites An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models

Reference 15

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Observation 2b7779f6-1b55-43b2-bb7e-8805c75235c3 · outbound

This paper cites Towards revealing the mystery behind chain of thought: a theoretical perspective.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Towards revealing the mystery behind chain of thought: a theoretical perspective

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:21.555267Z

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Observation e8c5c150-1777-486b-8c92-bd88c35cf2b6 · outbound

This paper cites Chain of Thought Empowers Transformers to Solve Inherently Serial Problems.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Reference 17

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Observation 63dbfc06-a3e7-4239-80d3-ec1a3802fc95 · outbound

This paper cites A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration.

Reasoning Can Hurt the Inductive Abilities of Large Language Models A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:12.976141Z

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Observation fa822113-bd35-4bbf-8f98-053a71479fbb · outbound

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

Reasoning Can Hurt the Inductive Abilities of Large Language Models Understanding Chain-of-Thought in LLMs through Information Theory

Reference 19

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Observation f8148d11-4e9b-4667-b687-3b5f65736822 · outbound

This paper cites What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective.

Reasoning Can Hurt the Inductive Abilities of Large Language Models What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:13.280627Z

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Observation 24472878-277f-4902-a98a-d7502dcc949d · outbound

This paper cites Physics of language models: Part 2.1, grade-school math and the hidden reasoning process.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Physics of language models: Part 2.1, grade-school math and the hidden reasoning process

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:21.417092Z

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Observation 7ab12307-3281-450a-bf09-64a2fb8b1d62 · outbound

This paper cites Wiley interdisciplinary reviews: cognitive science.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Wiley interdisciplinary reviews: cognitive science

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:21.317581Z

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Observation 162a73e5-2ce0-4874-a738-23117b8a5b3e · outbound

This paper cites Properties of inductive reasoning.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Properties of inductive reasoning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:21.187884Z

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Observation 1cff7c1b-815e-406f-8749-3cef187f2ddd · outbound

This paper cites WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs.

Reasoning Can Hurt the Inductive Abilities of Large Language Models WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs

Reference 24

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Observation 4563356d-65fd-42e1-96db-203ae3aca637 · outbound

This paper cites MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:14.039304Z

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Observation b0197460-5caf-46f5-b45f-b96f47b795bf · outbound

This paper cites KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks.

Reasoning Can Hurt the Inductive Abilities of Large Language Models KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:14.175558Z

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Observation 326b00ba-2a17-4e5b-b649-2cd2cc3d3e54 · outbound

This paper cites LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts.

Reasoning Can Hurt the Inductive Abilities of Large Language Models LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:14.280759Z

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Observation b6a64769-747e-4a1b-941d-68284a465387 · outbound

This paper cites LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations.

Reasoning Can Hurt the Inductive Abilities of Large Language Models LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations

Reference 28

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Observation edfdc99d-cb9d-4dcd-9a42-d6059ca480d9 · outbound

This paper cites Mir-bench: Benchmarking llm’s long-context intelligence via many-shot in-context inductive reasoning.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Mir-bench: Benchmarking llm’s long-context intelligence via many-shot in-context inductive reasoning

Reference 29

Resolution: verified exact; raw_fallback, observed 2026-08-07T12:36:18.300125Z

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Observation 1aef8c7d-0e28-43d8-8ecb-ee48599d428d · outbound

This paper cites DeepSeek-V3 Technical Report.

Reasoning Can Hurt the Inductive Abilities of Large Language Models DeepSeek-V3 Technical Report

Reference 30

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Observation 3a607135-b79f-4a59-9e45-7e971a2a4df6 · outbound

This paper cites Qwen2.5 Technical Report.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Qwen2.5 Technical Report

Reference 31

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Observation c6b568c0-00cc-4377-b960-991ef0e73f7b · outbound

This paper cites Grok-2 beta release, 2024.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Grok-2 beta release, 2024

Reference 32

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Observation 3bd03979-083d-43b9-ac6a-bd8c5fed864d · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Qwq-32b: Embracing the power of reinforcement learning, March 2025

Reference 33

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Observation 88435ffb-a43b-4056-ac26-47ed7b00bfc9 · outbound

This paper cites Grok 3 beta — the age of reasoning agents, 2025.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Grok 3 beta — the age of reasoning agents, 2025

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:20.868561Z

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Observation 1f3fb8fc-4efe-4dd0-bd40-826ac123cadc · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 35

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Observation 5414fb89-c95b-4c87-abe9-a8bbd2484a16 · outbound

This paper cites Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?

Reference 36

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Observation 11ffdafa-a40f-4fed-a7ec-2ff260d6f832 · outbound

This paper cites Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries

Reference 37

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Observation 5b5973be-1cfa-466c-a7b4-db1ea8c84e36 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Self-refine: Iterative refinement with self-feedback

Reference 38

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Observation db507701-fa5f-4662-9a42-50b97d3975f8 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 39

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Observation cbbddb4b-508f-4240-aed3-c1b83eff6caf · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting

Reference 40

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Observation b9bb9319-88cc-4637-8039-8780c4cb8c56 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The Impact of Reasoning Step Length on Large Language Models

Reference 41

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Observation 63477325-4766-4eca-b331-141ebbd93ce4 · outbound

This paper cites H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking.

Reasoning Can Hurt the Inductive Abilities of Large Language Models H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking

Reference 42

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Observation 5a4baa69-bb22-401c-9f99-c91340d0725c · outbound

This paper cites Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Reference 43

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Observation d685371d-f7d9-42ad-a2d7-f27d67bbfa11 · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Token-Budget-Aware LLM Reasoning

Reference 44

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Observation 650dc704-a7bb-4524-9db6-a726f523189a · outbound

This paper cites an unresolved cited work.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Unresolved cited work

Reference 45

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Observation 78272be9-936e-404f-8df8-020334321d25 · outbound

This paper cites Evidence model.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Evidence model

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:20.519923Z

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Observation 107624f0-ca4c-484b-adff-08ffe4993493 · outbound

This paper cites The deterministic component αk(y⋆ − mk−1) is collinear with the current error vector.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The deterministic component αk(y⋆ − mk−1) is collinear with the current error vector

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:20.317679Z

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Observation c8ab49e3-6b13-4a48-bf1c-034a73ef5235 · outbound

This paper cites The random component εk is isotropic and unbiased, reflecting that answer noise does not systematically drift the belief in any preferred direction.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The random component εk is isotropic and unbiased, reflecting that answer noise does not systematically drift the belief in any preferred direction

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:20.190958Z

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Observation 1aa68dae-2b12-493e-9cca-2e8858edd4e7 · outbound

This paper cites an unresolved cited work.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:36:20.034174Z

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Observation 9a6481ce-9f45-4620-ac4f-5c01d04af344 · outbound

This paper cites Consequently, E(N ) is U-shaped when plotted against reason- ing depth N.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Consequently, E(N ) is U-shaped when plotted against reason- ing depth N

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:19.820782Z

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Observation 60204cce-00ce-4ccc-ad94-5bf4a0357e88 · outbound

This paper cites an unresolved cited work.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:36:19.638919Z

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Observation d81ee458-5dee-40b6-989f-ecc577469fac · outbound

This paper cites 19 Proof.

Reasoning Can Hurt the Inductive Abilities of Large Language Models 19 Proof

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:19.454910Z

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Observation beba0fe5-9f32-4c53-8be9-9b466d321319 · outbound

This paper cites The partial derivative of Eα,γ(N ) with respect to α is strictly negative: ∂ ∂α Eα,γ(N ) < 0, ∀ (α, γ) ∈ (0, 1)2, N ≥ 1.

Reasoning Can Hurt the Inductive Abilities of Large Language Models The partial derivative of Eα,γ(N ) with respect to α is strictly negative: ∂ ∂α Eα,γ(N ) < 0, ∀ (α, γ) ∈ (0, 1)2, N ≥ 1

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:19.278773Z

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Observation 4bd19153-42f8-486b-bca4-239f8d491706 · outbound

This paper cites an unresolved cited work.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:36:19.125411Z

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Observation 6bac2480-48d0-49a0-96e4-75fb23527851 · outbound

This paper cites Here 0 < ρ(α) < 1 and ∂ ∂α Eα(N ) < 0, ∀N ≥ 1.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Here 0 < ρ(α) < 1 and ∂ ∂α Eα(N ) < 0, ∀N ≥ 1

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:19.034522Z

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Observation 573f14c1-646a-4bf2-98df-2461e6471724 · outbound

This paper cites Then ρ = 1 and E0(N ) = b0 + N σ2γ2.

Reasoning Can Hurt the Inductive Abilities of Large Language Models Then ρ = 1 and E0(N ) = b0 + N σ2γ2

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:18.900283Z

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.

Observation 35ed043a-a264-469a-8ae9-e30ee2b020e2 · outbound

This paper cites angular displace- ment.

Reasoning Can Hurt the Inductive Abilities of Large Language Models angular displace- ment

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:36:18.739769Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers