The Compositional Architecture of Regret in Large Language Models | Paper Citation Record

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This paper cites Challenges and applications of large language models, 2023.

The Compositional Architecture of Regret in Large Language Models Challenges and applications of large language models, 2023

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This paper cites Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning.

The Compositional Architecture of Regret in Large Language Models Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning

Reference 2

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This paper cites Can large language models identify implicit suicidal ideation? an empirical evaluation.

The Compositional Architecture of Regret in Large Language Models Can large language models identify implicit suicidal ideation? an empirical evaluation

Reference 3

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This paper cites Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements.

The Compositional Architecture of Regret in Large Language Models Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements

Reference 4

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This paper cites DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text.

The Compositional Architecture of Regret in Large Language Models DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text

Reference 5

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This paper cites Fake News Detectors are Biased against Texts Generated by Large Language Models.

The Compositional Architecture of Regret in Large Language Models Fake News Detectors are Biased against Texts Generated by Large Language Models

Reference 6

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This paper cites Language Models Represent Space and Time.

The Compositional Architecture of Regret in Large Language Models Language Models Represent Space and Time

Reference 7

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This paper cites EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identification.

The Compositional Architecture of Regret in Large Language Models EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identification

Reference 8

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Observation 11302cd1-731a-4613-bb17-a0e0ca66af24 · outbound

This paper cites Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing.

The Compositional Architecture of Regret in Large Language Models Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing

Reference 9

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This paper cites Exploring the Personality Traits of LLMs through Latent Features Steering.

The Compositional Architecture of Regret in Large Language Models Exploring the Personality Traits of LLMs through Latent Features Steering

Reference 10

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This paper cites Understanding Reasoning in Chain-of-Thought from the Hopfieldian View.

The Compositional Architecture of Regret in Large Language Models Understanding Reasoning in Chain-of-Thought from the Hopfieldian View

Reference 11

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This paper cites Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning.

The Compositional Architecture of Regret in Large Language Models Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning

Reference 12

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This paper cites Improving interpretation faithfulness for vision transformers.

The Compositional Architecture of Regret in Large Language Models Improving interpretation faithfulness for vision transformers

Reference 13

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This paper cites Seat: stable and explainable attention.

The Compositional Architecture of Regret in Large Language Models Seat: stable and explainable attention

Reference 14

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

The Compositional Architecture of Regret in Large Language Models Physics of language models: Part 2.1, grade-school math and the hidden reasoning process

Reference 15

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This paper cites How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study.

The Compositional Architecture of Regret in Large Language Models How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study

Reference 16

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This paper cites COMPKE: Complex Question Answering under Knowledge Editing.

The Compositional Architecture of Regret in Large Language Models COMPKE: Complex Question Answering under Knowledge Editing

Reference 17

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This paper cites CODEMENV: Benchmarking Large Language Models on Code Migration.

The Compositional Architecture of Regret in Large Language Models CODEMENV: Benchmarking Large Language Models on Code Migration

Reference 18

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Observation f06f1f11-8f03-47a5-bd34-8af2c714ef87 · outbound

This paper cites MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language.

The Compositional Architecture of Regret in Large Language Models MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language

Reference 19

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Observation 2ca81bb1-25e9-40f7-8f13-b735e8d269a5 · outbound

This paper cites Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top.

The Compositional Architecture of Regret in Large Language Models Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top

Reference 20

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This paper cites Multi-hop Question Answering under Temporal Knowledge Editing.

The Compositional Architecture of Regret in Large Language Models Multi-hop Question Answering under Temporal Knowledge Editing

Reference 21

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This paper cites Model autophagy analysis to explicate self-consumption within human-ai interactions.

The Compositional Architecture of Regret in Large Language Models Model autophagy analysis to explicate self-consumption within human-ai interactions

Reference 22

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This paper cites Understanding Aha Moments: from External Observations to Internal Mechanisms.

The Compositional Architecture of Regret in Large Language Models Understanding Aha Moments: from External Observations to Internal Mechanisms

Reference 23

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This paper cites Regret: A theoretical and conceptual analysis.

The Compositional Architecture of Regret in Large Language Models Regret: A theoretical and conceptual analysis

Reference 24

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Observation 19999e91-2e7e-48ea-a943-c2aea77e4b52 · outbound

This paper cites The experience of regret: what, when, and why.

The Compositional Architecture of Regret in Large Language Models The experience of regret: what, when, and why

Reference 25

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Observation 7bcdb442-88a7-4200-bf71-2cc1a236878e · outbound

This paper cites Memory and decision processes: The impact of cognitive loads on decision regret.

The Compositional Architecture of Regret in Large Language Models Memory and decision processes: The impact of cognitive loads on decision regret

Reference 26

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This paper cites A Comprehensive Study of Knowledge Editing for Large Language Models.

The Compositional Architecture of Regret in Large Language Models A Comprehensive Study of Knowledge Editing for Large Language Models

Reference 27

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This paper cites Locating and editing factual associations in gpt.

The Compositional Architecture of Regret in Large Language Models Locating and editing factual associations in gpt

Reference 28

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This paper cites Mass-Editing Memory in a Transformer.

The Compositional Architecture of Regret in Large Language Models Mass-Editing Memory in a Transformer

Reference 29

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This paper cites Pmet: Precise model editing in a transformer.

The Compositional Architecture of Regret in Large Language Models Pmet: Precise model editing in a transformer

Reference 30

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This paper cites Massive Editing for Large Language Models via Meta Learning.

The Compositional Architecture of Regret in Large Language Models Massive Editing for Large Language Models via Meta Learning

Reference 31

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This paper cites Attention heads of large language models.

The Compositional Architecture of Regret in Large Language Models Attention heads of large language models

Reference 32

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This paper cites Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models.

The Compositional Architecture of Regret in Large Language Models Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models

Reference 33

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This paper cites Knowledge editing for large language models: A survey.

The Compositional Architecture of Regret in Large Language Models Knowledge editing for large language models: A survey

Reference 34

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This paper cites Get my drift? catching llm task drift with activation deltas, 2025.

The Compositional Architecture of Regret in Large Language Models Get my drift? catching llm task drift with activation deltas, 2025

Reference 35

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This paper cites Understanding How Value Neurons Shape the Generation of Specified Values in LLMs.

The Compositional Architecture of Regret in Large Language Models Understanding How Value Neurons Shape the Generation of Specified Values in LLMs

Reference 36

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This paper cites Knowledge editing for large language model with knowledge neuronal ensemble, 2024.

The Compositional Architecture of Regret in Large Language Models Knowledge editing for large language model with knowledge neuronal ensemble, 2024

Reference 37

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This paper cites The geometry of concepts: Sparse autoencoder feature structure.

The Compositional Architecture of Regret in Large Language Models The geometry of concepts: Sparse autoencoder feature structure

Reference 38

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This paper cites Large language models (llms) and the institutionalization of misinformation.

The Compositional Architecture of Regret in Large Language Models Large language models (llms) and the institutionalization of misinformation

Reference 39

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This paper cites DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection.

The Compositional Architecture of Regret in Large Language Models DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection

Reference 40

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This paper cites Combating misinformation in the age of llms: Opportunities and challenges.

The Compositional Architecture of Regret in Large Language Models Combating misinformation in the age of llms: Opportunities and challenges

Reference 41

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This paper cites Hallucination as disinformation: The role of llms in amplifying conspir- acy theories and fake news.

The Compositional Architecture of Regret in Large Language Models Hallucination as disinformation: The role of llms in amplifying conspir- acy theories and fake news

Reference 42

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This paper cites Can LLM-Generated Misinformation Be Detected?.

The Compositional Architecture of Regret in Large Language Models Can LLM-Generated Misinformation Be Detected?

Reference 43

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This paper cites Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies.

The Compositional Architecture of Regret in Large Language Models Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies

Reference 44

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This paper cites Preventing and detecting misinformation generated by large language models.

The Compositional Architecture of Regret in Large Language Models Preventing and detecting misinformation generated by large language models

Reference 45

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Observation 963b3c51-9391-4af9-a143-a4d7f4c13581 · outbound

This paper cites Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges.

The Compositional Architecture of Regret in Large Language Models Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges

Reference 46

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This paper cites Toward mitigating misinformation and social media manipulation in llm era.

The Compositional Architecture of Regret in Large Language Models Toward mitigating misinformation and social media manipulation in llm era

Reference 47

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Observation 0328fbb1-e290-4d6e-8af1-1892a5ef78f3 · outbound

This paper cites The dark side of language models: Exploring the potential of llms in multimedia disinformation generation and dissemination.Machine Learning with Applications, page 100545, 2024.

The Compositional Architecture of Regret in Large Language Models The dark side of language models: Exploring the potential of llms in multimedia disinformation generation and dissemination.Machine Learning with Applications, page 100545, 2024

Reference 48

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Observation d48ebb88-411c-42ab-a0a2-ff44961b822f · outbound

This paper cites Probing for Constituency Structure in Neural Language Models.

The Compositional Architecture of Regret in Large Language Models Probing for Constituency Structure in Neural Language Models

Reference 49

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This paper cites Probing the Category of Verbal Aspect in Transformer Language Models.

The Compositional Architecture of Regret in Large Language Models Probing the Category of Verbal Aspect in Transformer Language Models

Reference 50

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Observation ff034b2e-5bd5-4677-b461-2db6cee4c229 · outbound

This paper cites Understanding the repeat curse in large language models from a feature perspective.

The Compositional Architecture of Regret in Large Language Models Understanding the repeat curse in large language models from a feature perspective

Reference 51

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This paper cites Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models.

The Compositional Architecture of Regret in Large Language Models Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models

Reference 52

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Observation 4380ad10-071a-4292-b557-a320edec9322 · outbound

This paper cites Probing llms for logical reasoning.

The Compositional Architecture of Regret in Large Language Models Probing llms for logical reasoning

Reference 53

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Observation 9c868fdd-780a-424f-9611-77d0ed17dac4 · outbound

This paper cites Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis.

The Compositional Architecture of Regret in Large Language Models Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis

Reference 54

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This paper cites Probing conceptual understanding of large visual- language models.

The Compositional Architecture of Regret in Large Language Models Probing conceptual understanding of large visual- language models

Reference 55

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Observation 3cd0502c-3827-428c-a7b8-4020d0fc49f5 · outbound

This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

The Compositional Architecture of Regret in Large Language Models Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 56

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Observation 92b73684-4a92-4a76-90e4-e999959bff52 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

The Compositional Architecture of Regret in Large Language Models Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 57

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This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

The Compositional Architecture of Regret in Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:50.905197Z

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This paper cites Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts.

The Compositional Architecture of Regret in Large Language Models Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:58.320258Z

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Observation 249da9bd-b85d-4d4e-a98d-43a5a4b80b38 · outbound

This paper cites When corrections fail: The persistence of political misper- ceptions.

The Compositional Architecture of Regret in Large Language Models When corrections fail: The persistence of political misper- ceptions

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:58.007747Z

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Observation ae392059-e6ec-4954-89c6-471f5d8fdfe3 · outbound

This paper cites Vlasceanu and A.

The Compositional Architecture of Regret in Large Language Models Vlasceanu and A

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:57.801824Z

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Observation ca0824e3-f198-4031-9162-8ff915c44750 · outbound

This paper cites Attention is all you need.

The Compositional Architecture of Regret in Large Language Models Attention is all you need

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:57.614451Z

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Observation 816db756-8a9a-495b-9d14-f59cb7829643 · outbound

This paper cites Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes.

The Compositional Architecture of Regret in Large Language Models Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:57.416334Z

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Observation a7ac63d7-c4fa-420a-8aea-aa7d922c6bf3 · outbound

This paper cites Compressing neural networks using the variational information bottleneck.

The Compositional Architecture of Regret in Large Language Models Compressing neural networks using the variational information bottleneck

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:57.259266Z

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Observation fc80c617-f45b-4718-a8ca-afdc90341340 · outbound

This paper cites Deep learning and the information bottleneck principle.

The Compositional Architecture of Regret in Large Language Models Deep learning and the information bottleneck principle

Reference 65

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The Compositional Architecture of Regret in Large Language Models Decoupled networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:57.021985Z

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Observation 1b41e8a5-23ef-42a8-83b6-4cc0f48029f3 · outbound

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

The Compositional Architecture of Regret in Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 67

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This paper cites Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers.

The Compositional Architecture of Regret in Large Language Models Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:51.822383Z

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Observation 2ac6e882-ba30-421c-a92b-dc34f3e5557a · outbound

This paper cites Scaling Laws for Neural Language Models.

The Compositional Architecture of Regret in Large Language Models Scaling Laws for Neural Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:51.918270Z

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This paper cites Visualizing and understanding convolutional networks.

The Compositional Architecture of Regret in Large Language Models Visualizing and understanding convolutional networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:52.037473Z

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This paper cites Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.

The Compositional Architecture of Regret in Large Language Models Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:56.806808Z

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Observation 13eacf8b-dfdc-4fe8-8196-9c25a64f7aee · outbound

This paper cites Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid.

The Compositional Architecture of Regret in Large Language Models Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:56.539773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6bbce877-68fe-4860-853e-e0b0482ea56c · outbound

This paper cites A comparison of optimal and suboptimal rna secondary structures predicted by free energy minimization with structures determined by phylogenetic comparison.

The Compositional Architecture of Regret in Large Language Models A comparison of optimal and suboptimal rna secondary structures predicted by free energy minimization with structures determined by phylogenetic comparison

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:56.454954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbd77bc4-7ba0-4b97-bb10-e83b84420326 · outbound

This paper cites Oscillatory dynamics and information processing in olfactory systems.Journal of Experimental Biology, 202(14):1855–1864, 1999.

The Compositional Architecture of Regret in Large Language Models Oscillatory dynamics and information processing in olfactory systems.Journal of Experimental Biology, 202(14):1855–1864, 1999

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:56.323473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 578c7d74-bfc8-4849-b488-551bdde368f7 · outbound

This paper cites Distributed representations in memory: insights from functional brain imaging.

The Compositional Architecture of Regret in Large Language Models Distributed representations in memory: insights from functional brain imaging

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:56.106574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8628bfb8-3427-4dd4-adef-d5fe69b96531 · outbound

This paper cites A combinatorial neural code for long-term motor memory.

The Compositional Architecture of Regret in Large Language Models A combinatorial neural code for long-term motor memory

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:55.936015Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec222c43-a639-4d02-8dd6-95fc150919c6 · outbound

This paper cites Default mode network scaffolds immature frontoparietal network in cognitive development.

The Compositional Architecture of Regret in Large Language Models Default mode network scaffolds immature frontoparietal network in cognitive development

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:55.802349Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08f12d4d-699b-48dc-b252-4590248162b3 · outbound

This paper cites Hippocampal-neocortical functional reorganization underlies children’s cognitive development.

The Compositional Architecture of Regret in Large Language Models Hippocampal-neocortical functional reorganization underlies children’s cognitive development

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:55.689874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a96e4142-6e53-477c-a51e-cd6ad03801d5 · outbound

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The Compositional Architecture of Regret in Large Language Models Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:59:55.523396Z

Source-reported events for the cited work

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Observation 541be3c3-1edb-42c1-ba89-23587c8fdeca · outbound

This paper cites an unresolved cited work.

The Compositional Architecture of Regret in Large Language Models Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:59:55.423517Z

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Observation da344741-016c-4082-bcba-41a2d455bf2d · outbound

This paper cites an unresolved cited work.

The Compositional Architecture of Regret in Large Language Models Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:59:55.285500Z

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Observation c4dfc3cb-e83f-44bb-a99d-dbd0833bd6dc · outbound

This paper cites Provide only the weak hint, without any additional explanations or introductions.

The Compositional Architecture of Regret in Large Language Models Provide only the weak hint, without any additional explanations or introductions

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:55.166622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e6e28192-1ef5-4196-a5ef-9a52377d38b8 · outbound

This paper cites This approach captures both the hint-induced regret in a2 and the evidence- induced regret in a3, providing a more comprehensive view of regret’s neural representation.

The Compositional Architecture of Regret in Large Language Models This approach captures both the hint-induced regret in a2 and the evidence- induced regret in a3, providing a more comprehensive view of regret’s neural representation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:55.036089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1538825-d497-45da-b4fb-ee0236904162 · outbound

This paper cites enlarging model sizes almost could not automatically impart additional knowledge.

The Compositional Architecture of Regret in Large Language Models enlarging model sizes almost could not automatically impart additional knowledge

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:54.855509Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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