Paper Citation Record · LEDGER

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

As of 13 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 18 inbound Pith citation observations for arXiv:2412.02674.

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

pith.paper-citation-record.v1
2412.02674 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:15:28.793471Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:54:29.270448Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:56:13.476979Z

Reference resolution

62 of 62 outbound references displayed

verified exact0
verified fuzzy1
unresolved61
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a582408f-2d25-4c20-9cc5-d3096012ef66 · outbound

This paper cites GPT-4 Technical Report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.483490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1006b770-934f-41e5-9ed3-7cad476dce90 · outbound

This paper cites Critique-out-Loud Reward Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Critique-out-Loud Reward Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.498365Z

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Observation 3e86cb86-5877-4f2d-a9bc-0c27bb0838af · outbound

This paper cites Qwen Technical Report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Qwen Technical Report

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.503131Z

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Observation 4e21ec20-274a-43bf-9623-e75a19af96ee · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Constitutional AI: Harmlessness from AI Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.508973Z

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Observation 8cecfc31-dcfe-4552-9b88-b57bf309ce46 · outbound

This paper cites On the Stability of Iterative Retraining of Generative Models on their own Data.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.525342Z

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Observation d2c8edaf-45f1-49d0-9a5a-abaa4a59eb3e · outbound

This paper cites Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.530737Z

Source-reported events for the cited work

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Observation 7a626e7b-1941-48ca-aa31-42cb9b82f236 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.535758Z

Source-reported events for the cited work

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Observation 2eabb5c7-befa-4308-9e34-e7a3f7b09e84 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.539819Z

Source-reported events for the cited work

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Observation 4b86df85-5d4a-4114-bf8b-29e86fa1edcd · outbound

This paper cites Learning to Generate Better Than Your LLM.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Learning to Generate Better Than Your LLM

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.551006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d31c9d90-01d5-4efa-a92b-d52e383a60f7 · outbound

This paper cites Teaching Large Language Models to Self-Debug.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Teaching Large Language Models to Self-Debug

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.554478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49dffea3-7aed-42a6-aa7f-9ed251e93a59 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.559268Z

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Observation e7f030f7-35dc-4570-97ee-63008b05c85a · outbound

This paper cites Can Large Language Models Be an Alternative to Human Evaluations?.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Can Large Language Models Be an Alternative to Human Evaluations?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.563407Z

Source-reported events for the cited work

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Observation 97831683-ac83-4a52-96f8-61f4818844d7 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.567793Z

Source-reported events for the cited work

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Observation 9d8a2194-e9ad-46f9-9fa4-ba7564fe7149 · outbound

This paper cites Model Collapse Demystified: The Case of Regression.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Model Collapse Demystified: The Case of Regression

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.579987Z

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Observation bb192b01-f8f8-47b4-831f-9bd212ebdeaf · outbound

This paper cites Training on the Test Task Confounds Evaluation and Emergence.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Training on the Test Task Confounds Evaluation and Emergence

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.585346Z

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Unavailable: canonical work link unavailable.

Observation 28e71f36-6cbb-4fcd-998d-7ad8038de913 · outbound

This paper cites RLHF Workflow: From Reward Modeling to Online RLHF.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models RLHF Workflow: From Reward Modeling to Online RLHF

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.589443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20974d57-4de3-4937-bda5-070ac236be73 · outbound

This paper cites The Llama 3 Herd of Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models The Llama 3 Herd of Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.593584Z

Source-reported events for the cited work

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Observation b14dd521-d354-43a2-8a13-bd9516519e42 · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.598804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed77794f-ce2e-484c-8aff-76c5130c7452 · outbound

This paper cites Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, et al.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, et al

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.603090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95e0dace-5d23-469c-a1dd-ff4d14cc32ae · outbound

This paper cites Self-Correcting Self-Consuming Loops for Generative Model Training.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Self-Correcting Self-Consuming Loops for Generative Model Training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.607337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7419b19-0da9-4ea2-9723-92b4175c42b8 · outbound

This paper cites Textbooks Are All You Need.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Textbooks Are All You Need

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.611461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71f6d78d-5621-4854-a1ab-cd6d43502736 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Teaching Large Language Models to Reason with Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.615413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99c1ad99-4373-41cc-84e8-58185689d9f1 · outbound

This paper cites Scaling laws for downstream task performance of large language models.arXiv preprint arXiv:2402.04177,.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Scaling laws for downstream task performance of large language models.arXiv preprint arXiv:2402.04177,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.637902Z

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Unavailable: canonical work link unavailable.

Observation 4ba54d39-b856-4fbb-985f-6dd250ead3cd · outbound

This paper cites SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.641881Z

Source-reported events for the cited work

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Observation f6b417b9-0794-4870-8504-6ea296da7493 · outbound

This paper cites Scaling Laws for Neural Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Scaling Laws for Neural Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.649181Z

Source-reported events for the cited work

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Observation 75b51781-c29e-4c73-9188-0be6463c098a · outbound

This paper cites Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.658554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b5a27ef-cb02-45dc-a395-dbd640ca7882 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Textbooks Are All You Need II: phi-1.5 technical report

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.662986Z

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Observation 393ae529-41ec-4015-aede-d8de3469f8a7 · outbound

This paper cites I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.667466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff6abce8-9a92-4643-ae6c-6ceb759e6950 · outbound

This paper cites Let's Verify Step by Step.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Let's Verify Step by Step

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.672176Z

Source-reported events for the cited work

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Observation 8e461d49-9e9e-40c5-a340-1ab0792071c5 · outbound

This paper cites Statistical Rejection Sampling Improves Preference Optimization.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Statistical Rejection Sampling Improves Preference Optimization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.677019Z

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Observation 11592053-4d9d-43a4-880a-94a3c6b9652f · outbound

This paper cites Improve Mathematical Reasoning in Language Models by Automated Process Supervision.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.682560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f23aae0-c249-476c-bafe-64b6086acdcb · outbound

This paper cites Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.686900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ff5c2a0-a54e-467e-a1bf-9bb59e954622 · outbound

This paper cites Iterative Reasoning Preference Optimization.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Iterative Reasoning Preference Optimization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.691595Z

Source-reported events for the cited work

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Observation 2969d4bf-fb2b-480c-aab9-9f3cccf6ecd0 · outbound

This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.695929Z

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Unavailable: canonical work link unavailable.

Observation bd27e811-fe89-4086-94dc-46179857da5f · outbound

This paper cites Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.701530Z

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Observation f2b82ff6-6639-41aa-bc6c-a542e42df54e · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.707967Z

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Observation 0e46a5ea-b307-4b1d-bed4-778114a6a13d · outbound

This paper cites SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.712803Z

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Observation 31de1885-8f17-4558-ab61-91933eaa087e · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.722455Z

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Observation 2354056d-55f4-4c98-a313-2a42c94dcf98 · outbound

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

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.726482Z

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Observation b0d9e045-3538-42cf-9d5c-60c33672daa0 · outbound

This paper cites Self-Taught Evaluators.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Self-Taught Evaluators

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.731425Z

Source-reported events for the cited work

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Observation 1fb67c28-3388-46d9-b612-cde357028e3a · outbound

This paper cites From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.742927Z

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Observation 98b2924e-d1d3-456d-bba8-9439cf7f9261 · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.747381Z

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Observation 9b3c2369-6c8b-499e-866a-88410e6e5615 · outbound

This paper cites Qwen2 Technical Report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Qwen2 Technical Report

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.751851Z

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Observation 4dc7458b-b4c8-4da7-b51e-d960c619ba71 · outbound

This paper cites Genie: Achieving Human Parity in Content-Grounded Datasets Generation.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Genie: Achieving Human Parity in Content-Grounded Datasets Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.755772Z

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Observation 629bf445-2d55-4cc5-83a5-7cabcce69417 · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Yi: Open Foundation Models by 01.AI

Reference 55

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Observation 0489c865-ea9d-4798-8599-1d8488769214 · outbound

This paper cites Self-Rewarding Language Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Self-Rewarding Language Models

Reference 56

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Observation d00dfe03-37cc-417d-b210-f49f2e73307d · outbound

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

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.769802Z

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Observation 143828bb-371f-43d0-8ba7-0e6518bb23a7 · outbound

This paper cites Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.775413Z

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Observation e47622b5-ed4e-465e-a80b-cfeee8c31b99 · outbound

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Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Unresolved cited work

Reference 60

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Observation 3c1fe777-9ef9-46c5-9fea-6da85f5d4fbb · outbound

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Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Unresolved cited work

Reference 61

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Observation 9b431a19-324a-48eb-af09-6baf75bbfade · outbound

This paper cites The answer is ANSWER.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models The answer is ANSWER

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:15:29.821481Z

Source-reported events for the cited work

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Observation 4bb79ffe-ae45-4439-87d8-7c3a80a411ff · outbound

This paper cites Learning How Hard to Think: Input-Adaptive Allocation of LM Computation.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.575938Z

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Observation 1faa3f38-df3a-46fd-b810-1c80e0fae732 · outbound

This paper cites From Lists to Emojis: How Format Bias Affects Model Alignment.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models From Lists to Emojis: How Format Bias Affects Model Alignment

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.779769Z

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Observation 63d336fa-0c4c-4805-8f21-c9204165c948 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Qwen2.5-Coder Technical Report

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.633561Z

Source-reported events for the cited work

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Observation 1b028eb2-10ef-4b77-ba70-6096fc73c12b · outbound

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

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.619469Z

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Observation 792c3e5f-31ff-46e0-944c-8e18e8bec09d · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 2017

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Observation 1064790f-6c2e-435d-a6bf-1a55ab4286f9 · outbound

This paper cites RankGen: Improving Text Generation with Large Ranking Models.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models RankGen: Improving Text Generation with Large Ranking Models

Reference 2020

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Observation 243fad25-7fbf-4e43-ad28-32c556fbd12c · outbound

This paper cites Scaling Laws for Transfer.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Scaling Laws for Transfer

Reference 2021

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Observation 96760804-8764-4cbe-b70a-66b31af334d1 · outbound

This paper cites Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Reference 2022

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Observation a423a56c-b440-4db2-9426-3ce9500910d7 · outbound

This paper cites Nemotron-4 340B Technical Report.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Nemotron-4 340B Technical Report

Reference 2023

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Observation c3c60f7e-5b8b-448f-81b5-2d195bebe09b · outbound

This paper cites Self-Consuming Generative Models Go MAD.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Self-Consuming Generative Models Go MAD

Reference 2024

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Observation 1480e59d-4c59-483d-8432-dd0882773667 · outbound

This paper cites Large Language Models Can Self-Improve.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Large Language Models Can Self-Improve

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.629098Z

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Pith citing papers

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Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges cites this paper.

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 58

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Observation e61b5bbf-b799-4ccf-ab58-29688b857d9b · inbound

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning cites this paper.

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 67

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Observation 1c0905cb-9aa6-41d5-ac77-19d69cfcfcb6 · inbound

Sample Complexity and Representation Ability of Test-time Scaling Paradigms cites this paper.

Sample Complexity and Representation Ability of Test-time Scaling Paradigms Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 78

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Observation b760d7ee-a2d3-4a3e-a999-b4747dad6391 · inbound

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs cites this paper.

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 47

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Observation ecfaabd5-011e-4fb5-8f41-d6591eece8e5 · inbound

PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier cites this paper.

PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 40

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Observation eb7f58e0-4da1-417f-8b70-2b500455401a · inbound

Because we have LLMs, we Can and Should Pursue Agentic Interpretability cites this paper.

Because we have LLMs, we Can and Should Pursue Agentic Interpretability Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 24

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Observation e2a5c216-4747-45e8-91ec-72ca80aed3cd · inbound

An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability cites this paper.

An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 18

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Observation d1eb8d0e-a29f-4d6d-8e30-6db51c75e167 · inbound

Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online cites this paper.

Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 21

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Observation 73d87d62-3600-436e-8f4c-b412c9fef2d6 · inbound

Outcome-based Exploration for LLM Reasoning cites this paper.

Outcome-based Exploration for LLM Reasoning Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 26

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Observation 88fa9f2e-85a3-466f-8bbc-a2d61ed641f9 · inbound

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula cites this paper.

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 28

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Observation 3c17bca8-58cc-4883-b5af-fff3617f1794 · inbound

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents cites this paper.

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 23

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Observation d4970fb6-29dc-4158-9ff4-388f7f142f42 · inbound

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems cites this paper.

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:11:53.591039Z

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Observation fc296c37-263e-4b37-918d-b29e828bd96c · inbound

Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains cites this paper.

Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 1

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Observation caec8726-3d6a-4f00-a7cb-0274bf57bdcc · inbound

Annotations Mitigate Post-Training Mode Collapse cites this paper.

Annotations Mitigate Post-Training Mode Collapse Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 58

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Observation f5591126-1c6c-4e21-b113-78511853ae51 · inbound

On the Generalization Gap in Self-Evolving Language Model Reasoning cites this paper.

On the Generalization Gap in Self-Evolving Language Model Reasoning Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-28T17:22:24.928244Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 95b3bb05-48ac-4514-b2bf-632c27e7fbed · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T20:56:13.478449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6d689bb3-6ea7-4f52-9251-8247dbb3f52d · inbound

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models cites this paper.

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-01T11:05:42.232371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a151dce-1fbe-4ea6-af68-2b1378e91128 · inbound

Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets cites this paper.

Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T00:42:58.750607Z

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