Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training | Paper Citation Record

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This paper cites Scaling Laws for Neural Language Models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Scaling Laws for Neural Language Models

Reference 1

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This paper cites Emergent abilities of large language models.Transactions on Machine Learning Research, 2022.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Emergent abilities of large language models.Transactions on Machine Learning Research, 2022

Reference 2

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This paper cites Reconstructing training data from trained neural networks.Advances in Neural Information Processing Systems, 35:22911–22924, 2022.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Reconstructing training data from trained neural networks.Advances in Neural Information Processing Systems, 35:22911–22924, 2022

Reference 3

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This paper cites Extracting training data from large language models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Extracting training data from large language models

Reference 4

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This paper cites Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, and Florian Tramèr.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, and Florian Tramèr

Reference 5

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This paper cites Deep leakage from gradients.Advances in neural information processing systems, 32, 2019.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Deep leakage from gradients.Advances in neural information processing systems, 32, 2019

Reference 6

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This paper cites Deep models under the gan: Information leakage from collaborative deep learning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Deep models under the gan: Information leakage from collaborative deep learning

Reference 7

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This paper cites Poisoning language models during instruction tuning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Poisoning language models during instruction tuning

Reference 8

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This paper cites Preserving privacy in large language models: A survey on current threats and solutions.Transactions on Machine Learning Research, 2025.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Preserving privacy in large language models: A survey on current threats and solutions.Transactions on Machine Learning Research, 2025

Reference 9

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This paper cites Springer, 1st edition, 2019.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Springer, 1st edition, 2019

Reference 10

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This paper cites Hyperparameter optimization.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Hyperparameter optimization

Reference 11

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This paper cites Neural Architecture Search: Insights from 1000 Papers.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Neural Architecture Search: Insights from 1000 Papers

Reference 12

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This paper cites Hansen and A.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Hansen and A

Reference 13

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This paper cites Completely derandomized self-adaptation in evolution strategies.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Completely derandomized self-adaptation in evolution strategies

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This paper cites The CMA Evolution Strategy: A Tutorial.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training The CMA Evolution Strategy: A Tutorial

Reference 15

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This paper cites Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.Journal of Global Optimization, 11(4):341–359, 1997.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.Journal of Global Optimization, 11(4):341–359, 1997

Reference 16

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This paper cites Eberhart.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Eberhart

Reference 17

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Unresolved cited work

Reference 18

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This paper cites The bayesian approach to global optimization.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training The bayesian approach to global optimization

Reference 19

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This paper cites Cambridge University Press, 2023.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Cambridge University Press, 2023

Reference 20

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This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 21

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This paper cites On the exploitability of instruction tuning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training On the exploitability of instruction tuning

Reference 22

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This paper cites Catastrophic jailbreak of open- source LLMs via exploiting generation.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Catastrophic jailbreak of open- source LLMs via exploiting generation

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This paper cites Unveiling the generalization power of fine-tuned large language models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Unveiling the generalization power of fine-tuned large language models

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This paper cites RLHFPoison: Reward poisoning attack for reinforcement learning with human feedback in large language models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training RLHFPoison: Reward poisoning attack for reinforcement learning with human feedback in large language models

Reference 25

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This paper cites Best-of-venom: Attacking RLHF by injecting poisoned preference data.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Best-of-venom: Attacking RLHF by injecting poisoned preference data

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This paper cites Universal jailbreak backdoors from poisoned human feedback.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Universal jailbreak backdoors from poisoned human feedback

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This paper cites Is poisoning a real threat to DPO? maybe more so than you think.AAAI Conference on Artificial Intelligence, 39(26):27556–27564, 2025.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Is poisoning a real threat to DPO? maybe more so than you think.AAAI Conference on Artificial Intelligence, 39(26):27556–27564, 2025

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This paper cites Retrofitting word vectors to semantic lexicons.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Retrofitting word vectors to semantic lexicons

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This paper cites Evolutionary retrofitting.arXiv:2410.11330, 2024.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolutionary retrofitting.arXiv:2410.11330, 2024

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This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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This paper cites Rapin and O.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Rapin and O

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Campi and Simone Garatti

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Zico Kolter, and Chelsea Finn

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Unresolved cited work

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Vapnik and Alexey Y

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Bartlett and Shahar Mendelson

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Pac-bayes compression bounds so tight that they can explain generalization

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Algorithmic stability and generalization performance

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Exploiting LLM quantization

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Privacy backdoors: stealing data with corrupted pretrained models

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Certified defenses for data poisoning attacks

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolution strategies at the hyperscale.arXiv preprint arXiv:2511.16652, 2025

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Routledge, 2006

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Unresolved cited work

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Scaling up: the challenges of urban retrofit.Building research & information, 41(5):499–503, 2013

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training van der Vaart and J.A

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Vapnik.The Nature of Statistical Learning Theory

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Learning in the presence of malicious errors.SIAM Journal on Computing, 22(4):807–837, 1993

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Lemley, and Percy Liang

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Training Verifiers to Solve Math Word Problems

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Measuring mathematical problem solving with the MATH dataset

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training HellaSwag: Can a Machine Really Finish Your Sentence?

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training GSM-Plus: A comprehensive benchmark for evaluating the robustness of LLMs as mathematical problem solvers

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

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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This paper cites Random gradient-free minimization of convex functions.Founda- tions of Computational Mathematics, 17(2):527–566, 2017.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Random gradient-free minimization of convex functions.Founda- tions of Computational Mathematics, 17(2):527–566, 2017

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:48.888756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a3f3687-8b3c-45a5-9fc2-74a4f06884c8 · outbound

This paper cites Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:48.928617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0bf4c2c-809e-4775-ac23-68d8f128bf4e · outbound

This paper cites Membership inference attacks from first principles.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Membership inference attacks from first principles

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.569885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a1eb0289-6668-42a3-8051-e79fa5406964 · outbound

This paper cites Window-based membership inference attacks against fine-tuned large language models.arXiv preprint arXiv:2601.02751, 2026.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Window-based membership inference attacks against fine-tuned large language models.arXiv preprint arXiv:2601.02751, 2026

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:49.019515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7091c05d-033d-4ecf-97ee-38f6cf9c7972 · outbound

This paper cites Kearns and R.E.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Kearns and R.E

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.561874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a0647d55-fa5a-4a65-a983-462c4a1082f4 · outbound

This paper cites Uniform convergence may be unable to explain generalization in deep learning.Advances in Neural Information Processing Systems, 32, 2019.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Uniform convergence may be unable to explain generalization in deep learning.Advances in Neural Information Processing Systems, 32, 2019

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.554343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c286a80-bdb1-4a4f-9423-af9c12a99775 · outbound

This paper cites Transformers as algorithms: Generalization and stability in in-context learning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Transformers as algorithms: Generalization and stability in in-context learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.547009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c16f8c9e-3d12-479a-bb32-b4d76473fbab · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:49.216411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55e6d6a3-4cbd-4ef0-948e-c4ee992c288e · outbound

This paper cites Medical large language models are vulnerable to data-poisoning attacks.Nature Medicine, 31(2):618–626, 2025.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Medical large language models are vulnerable to data-poisoning attacks.Nature Medicine, 31(2):618–626, 2025

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.540271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa640af7-f969-4b49-ae43-342f0bf8ccad · outbound

This paper cites Christiano, Jan Leike, Tom B.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Christiano, Jan Leike, Tom B

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.532824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e9dabd5-3be4-4204-9621-b757eb2d18e5 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Direct preference optimization: Your language model is secretly a reward model

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.525669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8d8f334b-8016-4525-ae0e-99c61a7f9d77 · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Backdooring instruction-tuned large language models with virtual prompt injection

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.517389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f99c38b7-7a2e-4a96-866e-5e75d2fee624 · outbound

This paper cites Poisoning retrieval corpora by injecting adversarial passages.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Poisoning retrieval corpora by injecting adversarial passages

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.509165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f188ee29-50a0-4598-bb0e-e76b26e02eda · outbound

This paper cites How Johnny can persuade LLMs to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing LLMs.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training How Johnny can persuade LLMs to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing LLMs

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.502268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d3659f8-f26d-476a-9960-7710b6944c7b · outbound

This paper cites Aligning large language models for faithful integrity against opposing argument.AAAI Conference on Artificial Intelligence, 2025.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Aligning large language models for faithful integrity against opposing argument.AAAI Conference on Artificial Intelligence, 2025

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.494418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88793f8a-aa20-4fa3-9f70-2a3723fdd2be · outbound

This paper cites Privacy-preserving instructions for aligning large language models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Privacy-preserving instructions for aligning large language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.486811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2da1b74-2a20-4640-9010-90ce7e5883a1 · outbound

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

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:49.655351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ec7dcdf-407d-4a20-b605-2f466ed3b036 · outbound

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

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Textbooks Are All You Need II: phi-1.5 technical report

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:49.701077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9f36943-0659-4b71-b0db-7f8c2089d23e · outbound

This paper cites GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.480110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f0e597a0-5f3a-4ed3-a489-af25f2b1c409 · outbound

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

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Chain-of-thought prompting elicits reasoning in large language models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.472797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e35ffec3-2eb9-464e-b3fd-a518e09d78ff · outbound

This paper cites Complexity-based prompting for multi-step reasoning.International Conference on Learning Representations, 2022.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Complexity-based prompting for multi-step reasoning.International Conference on Learning Representations, 2022

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.464910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b326a5a-f48d-4001-a2d4-8ae947a877a7 · outbound

This paper cites Smith, and Tao Yu.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Smith, and Tao Yu

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.457581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e1e22671-2c40-4efa-aefd-e234c1f3cdeb · outbound

This paper cites Coverage-based example selection for in-context learning.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Coverage-based example selection for in-context learning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.450988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 664d3f99-0878-4ea8-b353-15b63738e0d4 · outbound

This paper cites Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.444562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c388f86e-0c54-45b5-97d5-9786a65d5c05 · outbound

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

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training LoRA: Low-rank adaptation of large language models

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.437583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71b67e42-b5ea-4bb7-aaac-7825d165184a · outbound

This paper cites LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.430591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4083f159-0b47-4063-8ac7-744fb313e390 · outbound

This paper cites Sutherland.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Sutherland

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.423188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c22cbdd3-5013-4ad5-8efa-5bfe1c24e27b · outbound

This paper cites Safety alignment should be made more than just a few tokens deep.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Safety alignment should be made more than just a few tokens deep

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.416401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 37288cdb-aac3-4a44-82fd-0103f65a90d7 · outbound

This paper cites What makes large language models reason in (multi-turn) code generation ? InThirteenth International Conference on Learning Representations, 2025.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training What makes large language models reason in (multi-turn) code generation ? InThirteenth International Conference on Learning Representations, 2025

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.409787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0cc759a8-e6ad-4de7-a790-31e039093dbc · outbound

This paper cites The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:50.208556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c06cdc14-968a-49ed-98ec-51892aabb4c7 · outbound

This paper cites Springer, 1996.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Springer, 1996

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.403110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9ae2c27-3421-4a3f-8756-76df318dc0e0 · outbound

This paper cites Probability inequalities for sums of bounded random variables.Journal of the American Statistical Association, 58:13–30, 1963.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Probability inequalities for sums of bounded random variables.Journal of the American Statistical Association, 58:13–30, 1963

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.395992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f260af76-82d0-41c4-b98e-d0c58e739050 · outbound

This paper cites Probability inequalities for the sum of independent random variables.Journal of the American Statistical Association, 57(297):33–45, 1962.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Probability inequalities for the sum of independent random variables.Journal of the American Statistical Association, 57(297):33–45, 1962

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.388443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4ca98134-9a4d-4233-9e8f-87429bdab396 · outbound

This paper cites Bonferroni.Teoria statistica delle classi e calcolo delle probabilità.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Bonferroni.Teoria statistica delle classi e calcolo delle probabilità

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.381672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8fb5d143-105e-4da7-9ca5-9741ec3ada39 · outbound

This paper cites Multiple comparisons among means.Journal of the American Statistical Association, 56(293):52–64, 1961.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Multiple comparisons among means.Journal of the American Statistical Association, 56(293):52–64, 1961

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.374625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b8e52f2-7c98-4e31-8b75-647dc4203e95 · outbound

This paper cites Evolutionary pre-prompt optimization for mathematical reasoning.arXiv:2412.04291, 2024.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolutionary pre-prompt optimization for mathematical reasoning.arXiv:2412.04291, 2024

Reference 94

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:54:52.645321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 74f147f0-d914-4ef0-a23e-3b840cf4c2fd · outbound

This paper cites Lower bounds for comparison based evolution strategies using vc-dimension and sign patterns.Algorithmica, 59(3):387–408, 2011.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Lower bounds for comparison based evolution strategies using vc-dimension and sign patterns.Algorithmica, 59(3):387–408, 2011

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.367150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7823248b-e1a9-45b4-9a43-4e9e5769e93b · outbound

This paper cites Springer, 2015.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Springer, 2015

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.359149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 762f5bf8-6308-49f7-ae6a-f9e79082ca91 · outbound

This paper cites Evolution strategies – a comprehensive introduction.Natural Computing, 1(1):3–52, May 2002.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolution strategies – a comprehensive introduction.Natural Computing, 1(1):3–52, May 2002

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.351147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c6d529d6-a3f7-4b1d-99e2-312d194c8816 · outbound

This paper cites Fromman-Holzboog Verlag, 1973.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Fromman-Holzboog Verlag, 1973

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.344212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a9e126a4-5009-4e38-9917-543a69dc1eab · outbound

This paper cites Birkhäuser Basel, 1977.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Birkhäuser Basel, 1977

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.337796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5d1ce2e3-cc7b-4095-86fc-6fe61457585b · outbound

This paper cites Schumer and K.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Schumer and K

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:54:55.331095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers