Scalable Complexity Control Facilitates Reasoning Ability of LLMs | Paper Citation Record

Observation 5457f73d-7ac0-475d-bf02-d362e3618e9c · outbound

This paper cites https://github.com/microsoft/Megatron-DeepSpeed, 2022.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs https://github.com/microsoft/Megatron-DeepSpeed, 2022

Reference 1

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Observation c2a9b256-73be-4ca9-b7fb-a2e5e5d92217 · outbound

This paper cites GPT-4 Technical Report.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs GPT-4 Technical Report

Reference 2

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Observation b6bf9b84-1ad7-4f48-b755-584fa896a6bf · outbound

This paper cites Physics of Language Models: Part 3.2, Knowledge Manipulation.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Physics of Language Models: Part 3.2, Knowledge Manipulation

Reference 3

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Observation 5eb13650-0a32-45e3-9c7b-46d0dc03d08a · outbound

This paper cites On exact computation with an infinitely wide neural net.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs On exact computation with an infinitely wide neural net

Reference 4

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This paper cites Stronger generalization bounds for deep nets via a compression approach.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Stronger generalization bounds for deep nets via a compression approach

Reference 5

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Observation 0a433bd0-383a-47c6-bf63-5580513c8bba · outbound

This paper cites Program Synthesis with Large Language Models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Program Synthesis with Large Language Models

Reference 6

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Observation 48f30585-57a1-4125-9f61-90a465821b75 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Spectrally-normalized margin bounds for neural networks

Reference 7

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Observation af5f868e-1dc4-4f20-aafd-43bea8489f15 · outbound

This paper cites Rademacher and gaussian complexities: Risk bounds and structural results.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Rademacher and gaussian complexities: Risk bounds and structural results

Reference 8

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This paper cites Phase dia- gram of initial condensation for two-layer neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Phase dia- gram of initial condensation for two-layer neural networks

Reference 9

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Observation 95e387de-012b-4965-bbb6-7a0e72a48b2b · outbound

This paper cites On the global convergence of gradient descent for over- parameterized models using optimal transport.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs On the global convergence of gradient descent for over- parameterized models using optimal transport

Reference 10

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Observation f6a14e24-ec22-4d8f-b3c3-1fefbf5fbb0d · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 11

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Observation 7c273550-96d4-4bc5-a178-240fe84006b8 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Training Verifiers to Solve Math Word Problems

Reference 12

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Observation ade86d39-b6c9-409f-a08d-fa7154f9013a · outbound

This paper cites Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi

Reference 13

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Observation c7b4fe34-ce64-473d-acbf-0fac6dfb573d · outbound

This paper cites A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics

Reference 14

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Observation 172ebb0d-7afb-4666-a22e-f67de9f50e26 · outbound

This paper cites Machine learning from a continuous viewpoint, I.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Machine learning from a continuous viewpoint, I

Reference 15

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Observation 7b278425-63ce-4fcf-90ee-d470f3a9d9da · outbound

This paper cites The Barron space and the flow-induced function spaces for neural network models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs The Barron space and the flow-induced function spaces for neural network models

Reference 16

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This paper cites Representation formulas and pointwise properties for Barron functions.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Representation formulas and pointwise properties for Barron functions

Reference 17

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

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Towards revealing the mystery behind chain of thought: A theoretical perspective

Reference 18

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Observation 121d19d2-58df-4ee7-86e2-6d89ffd18cfb · outbound

This paper cites The language model evaluation harness, 07 2024.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs The language model evaluation harness, 07 2024

Reference 19

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Observation be5019ee-90a2-4eb5-ac33-96b7e95b27b8 · outbound

This paper cites Size-independent sample complexity of neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Size-independent sample complexity of neural networks

Reference 20

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

Scalable Complexity Control Facilitates Reasoning Ability of LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 21

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Observation 461499a4-7ddb-4d0a-a5cc-6e641eb739f7 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 22

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Observation 0bf634ed-110b-406a-8a43-fef6869be0c9 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Measuring Massive Multitask Language Understanding

Reference 23

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This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 24

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Observation 39f72398-a770-45c8-ab2f-1365577a5abb · outbound

This paper cites Improving transformer optimization through better initialization.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Improving transformer optimization through better initialization

Reference 25

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Observation 73d6da20-4525-4d4f-837c-c42a30b20276 · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 26

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Neural tangent kernel: Convergence and generalization in neural networks

Reference 27

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Scalable Complexity Control Facilitates Reasoning Ability of LLMs Unresolved cited work

Reference 28

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Observation 424f633e-088d-41e0-9a81-1f5bea2e60a4 · outbound

This paper cites A simple weight decay can improve generalization.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs A simple weight decay can improve generalization

Reference 29

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Observation e88b0141-ae3c-4763-b62a-239c05c474df · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Training Language Models to Self-Correct via Reinforcement Learning

Reference 30

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This paper cites Orr, and Klaus Robert Müller.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Orr, and Klaus Robert Müller

Reference 31

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Observation e0f86fe0-be75-4123-a2ce-dabc152a9a38 · outbound

This paper cites Cmmlu: Measuring massive multitask language understanding in chinese, 2023.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Cmmlu: Measuring massive multitask language understanding in chinese, 2023

Reference 32

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This paper cites Chain of thought empowers transformers to solve inherently serial problems.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Chain of thought empowers transformers to solve inherently serial problems

Reference 33

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This paper cites Truthfulqa: Measuring how models mimic human falsehoods, 2021.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Truthfulqa: Measuring how models mimic human falsehoods, 2021

Reference 34

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Scalable Complexity Control Facilitates Reasoning Ability of LLMs Unresolved cited work

Reference 35

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Observation 517e97b2-521b-43d8-8be7-40fda7621f3e · outbound

This paper cites DeepSeek-V3 Technical Report.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs DeepSeek-V3 Technical Report

Reference 36

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This paper cites Crystal: Introspective reasoners reinforced with self-feedback.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Crystal: Introspective reasoners reinforced with self-feedback

Reference 37

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Observation cba16ffd-1a5f-4d76-988d-a4abbe2cb58f · outbound

This paper cites Understanding the Difficulty of Training Transformers.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Understanding the Difficulty of Training Transformers

Reference 38

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This paper cites Phase diagram for two-layer relu neural networks at infinite-width limit.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Phase diagram for two-layer relu neural networks at infinite-width limit

Reference 39

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This paper cites A mean field view of the landscape of two-layer neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs A mean field view of the landscape of two-layer neural networks

Reference 40

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This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 41

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This paper cites Norm-based capacity control in neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Norm-based capacity control in neural networks

Reference 42

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Observation 8f61ea8f-5c8f-45e5-b8d4-e9bdf6bb3f22 · outbound

This paper cites In-context learning and induction heads, 2022.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs In-context learning and induction heads, 2022

Reference 43

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This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 44

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This paper cites Measuring and narrowing the compositionality gap in language models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Measuring and narrowing the compositionality gap in language models

Reference 45

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This paper cites Li, and Noah Goodman.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Li, and Noah Goodman

Reference 46

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This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 47

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Scalable Complexity Control Facilitates Reasoning Ability of LLMs Uniform approximation of functions with random bases

Reference 48

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This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Gpqa: A graduate-level google-proof q&a benchmark

Reference 49

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This paper cites Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:20.091705Z

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This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Winogrande: An adversarial winograd schema challenge at scale

Reference 51

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This paper cites Mean field analysis of neural networks: A central limit theorem.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Mean field analysis of neural networks: A central limit theorem

Reference 52

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This paper cites SlimPajama: A 627B token cleaned and deduplicated version of RedPajama.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs SlimPajama: A 627B token cleaned and deduplicated version of RedPajama

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:19.904915Z

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Observation a49ef92b-5ad1-4ceb-b084-ca6838b02859 · outbound

This paper cites Recitation-augmented language models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Recitation-augmented language models

Reference 54

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This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 55

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This paper cites olmpics-on what language model pre-training captures.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs olmpics-on what language model pre-training captures

Reference 56

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Observation 2498b933-0398-43ec-9462-a2ccd3d76478 · outbound

This paper cites CommonsenseQA: A question answering challenge targeting commonsense knowledge.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs CommonsenseQA: A question answering challenge targeting commonsense knowledge

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:19.443133Z

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Observation ac688fde-78a6-4324-ba97-5d054b801415 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:13.742857Z

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Observation 6e9d3c00-c851-4d59-9b6f-298b726a0cc7 · outbound

This paper cites Mimetic initialization of self-attention layers.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Mimetic initialization of self-attention layers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:19.241141Z

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Observation 790fd982-3074-4413-94f2-0f2ce10c7405 · outbound

This paper cites Explaining grokking through circuit efficiency.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Explaining grokking through circuit efficiency

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:13.935188Z

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Observation d14d5aed-b5c6-4070-ab2d-f07fb1bdb810 · outbound

This paper cites Deepnet: Scaling transformers to 1,000 layers.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Deepnet: Scaling transformers to 1,000 layers

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.025808Z

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Observation 69ff8425-0302-461d-bc27-ff8c59f13279 · outbound

This paper cites Interpretability in the wild: a circuit for indirect object identification in GPT-2 small.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Interpretability in the wild: a circuit for indirect object identification in GPT-2 small

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.150052Z

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Observation 2e5f5057-f236-4bcc-8e0b-50f92b10b9a8 · outbound

This paper cites Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.215899Z

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Observation 9077a94c-a589-42cc-9935-5132c010d123 · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.319690Z

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Observation b45ce7ed-af2c-4cd7-b6bf-10cec7778aab · outbound

This paper cites Data-dependent sample complexity of deep neural networks via lipschitz augmentation.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Data-dependent sample complexity of deep neural networks via lipschitz augmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:19.021031Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 88c5ce30-60c5-4d6d-8d09-f84573b1bcdd · outbound

This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.509560Z

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Observation c70e5f60-ba96-43b7-bf19-2acbfab72c5c · outbound

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

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Chain-of-thought prompting elicits reasoning in large language models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.578489Z

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Observation ce639f83-6e4a-4c36-8b3c-90595941b8c5 · outbound

This paper cites Gradient Dynamics of Shallow Univariate ReLU Networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Gradient Dynamics of Shallow Univariate ReLU Networks

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:01:17.555137Z

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Observation 8676d420-e160-48d1-9221-7ae600eb002b · outbound

This paper cites An overview of condensation phenomenon in deep learning.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs An overview of condensation phenomenon in deep learning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.732611Z

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Observation 7ffe451a-b0ad-4059-aa26-476030934fb2 · outbound

This paper cites Qwen2.5 Technical Report.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Qwen2.5 Technical Report

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.799624Z

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Observation c5d6f6de-09fb-47ca-884a-70a91a2dfb7d · outbound

This paper cites Memory 3: Language modeling with explicit memory.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Memory 3: Language modeling with explicit memory

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.820111Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 48406109-4071-4bce-a63b-9704781068d3 · outbound

This paper cites Do Large Language Models Latently Perform Multi-Hop Reasoning?.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.988483Z

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Observation b8fc0767-8d84-497a-abfa-5756056ee175 · outbound

This paper cites An Analysis for Reasoning Bias of Language Models with Small Initialization.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs An Analysis for Reasoning Bias of Language Models with Small Initialization

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.054309Z

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Observation 1332bd4b-75a8-4d27-8c5a-4ee78f92f64f · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.150906Z

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Observation 64dcd9ba-ee72-478d-a7a1-fcc0d4adf224 · outbound

This paper cites STar: Bootstrapping reasoning with reasoning.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs STar: Bootstrapping reasoning with reasoning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.255063Z

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Observation 23b33b18-a34a-4001-a930-8ec0b9994de9 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.381133Z

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Observation 76bc0cd3-7146-40ba-8227-3fc877d7551a · outbound

This paper cites PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.459004Z

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Observation 603b62c3-90be-4501-a832-17b5326ec18a · outbound

This paper cites Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:01:17.331210Z

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Observation 47513559-7eed-4827-ac62-09b03f2b1bed · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Understanding deep learning (still) requires rethinking generalization

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.611783Z

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Observation b493c1c3-2c72-440e-8a48-caffdba9fbaa · outbound

This paper cites A type of generalization error induced by initialization in deep neural networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs A type of generalization error induced by initialization in deep neural networks

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:01:17.158113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 987b80f7-5761-4632-af58-b13023f10df8 · outbound

This paper cites Linear Stability Hypothesis and Rank Stratification for Nonlinear Models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Linear Stability Hypothesis and Rank Stratification for Nonlinear Models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:15.791549Z

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Observation 748fc06e-a645-429a-9728-05d84920d1c8 · outbound

This paper cites Stochastic Modified Equations and Dynamics of Dropout Algorithm.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Stochastic Modified Equations and Dynamics of Dropout Algorithm

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:01:17.011998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2997f494-9abe-4edd-a981-3dc1de38e6cd · outbound

This paper cites Initial- ization is critical to whether transformers fit composite functions by reasoning or memorizing.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Initial- ization is critical to whether transformers fit composite functions by reasoning or memorizing

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.589936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cedd0c3e-339a-4303-b315-50e0c1845b91 · outbound

This paper cites Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:16.053559Z

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Observation 3f4fd1f6-9a96-4f81-8ef5-72a172a9a297 · outbound

This paper cites Loss Spike in Training Neural Networks.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Loss Spike in Training Neural Networks

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:16.145489Z

Source-reported events for the cited work

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Observation eaa04008-3c79-46bc-9ec1-3459ed5cfe62 · outbound

This paper cites Implicit regularization of dropout.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Implicit regularization of dropout

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.412376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aad62654-f98b-45a0-bf83-193d3f8d7827 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:16.366510Z

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Observation 2885099e-3d38-421a-adbc-5aa65931bf0d · outbound

This paper cites Empirical phase diagram for three-layer neural networks with infinite width.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Empirical phase diagram for three-layer neural networks with infinite width

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.242964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 74d195da-2e08-4aa7-a1b5-2ed14935e9a3 · outbound

This paper cites Towards understand- ing the condensation of neural networks at initial training.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Towards understand- ing the condensation of neural networks at initial training

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.060914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4ca6c82-34c1-4b20-8b10-2c3704c4a8e9 · outbound

This paper cites Instruction-following evaluation for large language models, 2023.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Instruction-following evaluation for large language models, 2023

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:16.658014Z

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Observation 6615ec43-35b0-4841-8c9d-64592666f755 · outbound

This paper cites 0.9B Large.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs 0.9B Large

Reference 91

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:01:17.891890Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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