Paper Citation Record · LEDGER

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

As of 15 August 2026, this Paper Citation Record lists 100 of 110 outbound references and 9 inbound Pith citation observations for arXiv:2505.11581.

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

pith.paper-citation-record.v1
2505.11581 v1

Coverage vector

measured 100 of 110 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:58:16.968686Z

measured 109 of 109 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:33:27.973683Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:29.828647Z

Reference resolution

100 of 110 outbound references displayed

verified exact0
verified fuzzy12
unresolved88
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5484eea-9bd3-4849-bf25-a53c6610371d · outbound

This paper cites GPT-4 Technical Report.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis GPT-4 Technical Report

Reference 1

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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This paper cites A., Snyder, E., and Packard, N.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis A., Snyder, E., and Packard, N

Reference 3

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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This paper cites Mechanistic Interpretability for AI Safety -- A Review.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Mechanistic Interpretability for AI Safety -- A Review

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis a is b" fail to learn

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis T., Li, Y., Lundberg, S., et al

Reference 11

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Understanding disentangling in $\beta$-VAE

Reference 12

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis O., Pennock, R

Reference 18

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Training Verifiers to Solve Math Word Problems

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis L., Jiang, L., Lin, B

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis O., and Clune, J

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Toy Models of Superposition

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis H., and Hutter, F

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis and Arak, A

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis General Intelligence Requires Reward-based Pretraining

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Training Compute-Optimal Large Language Models

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Open-Endedness is Essential for Artificial Superhuman Intelligence

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis The Platonic Representation Hypothesis

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis O., and Clune, J

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis A Theory of Universal Artificial Intelligence based on Algorithmic Complexity

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis A., Jordan, M

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis On the "steerability" of generative adversarial networks

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Mixtral of Experts

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Othellogpt learned a bag of heuristics

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Scaling Laws for Neural Language Models

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis and Gerhart, J

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis S., Henderson, D., Howard, R

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis B., and M \"u ller, K.-R

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Evolution and The Knightian Blindspot of Machine Learning

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis and Stanley, K

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis and Stanley, K

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis K., Bau, D., Vi \'e gas, F., Pfister, H., and Wattenberg, M

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis DeepSeek-V3 Technical Report

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis S., Michaud, E., Tegmark, M., and Williams, M

Reference 59

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Omnigrok: Grokking Beyond Algorithmic Data

Reference 60

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Reference 61

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This paper cites Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

Reference 62

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Observation 59cbdaf8-6959-4b72-87ee-85f026cccb09 · outbound

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Reference 63

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 64

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

Reference 65

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This paper cites Illuminating search spaces by mapping elites.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Illuminating search spaces by mapping elites

Reference 66

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

Reference 67

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Progress measures for grokking via mechanistic interpretability

Reference 68

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Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.873511Z

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This paper cites M., Yosinski, J., and Clune, J.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis M., Yosinski, J., and Clune, J

Reference 70

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics

Reference 71

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Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.839683Z

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Observation f316b73c-8e0a-4255-8e54-7e38527f5d75 · outbound

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Reference 73

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis In-context Learning and Induction Heads

Reference 74

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:16.858816Z

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 76

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 77

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Reference 78

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Reference 79

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Unresolved cited work

Reference 80

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Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.777034Z

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Observation b912a6e0-260e-43a4-a2f8-740f178da63b · outbound

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis B., Rodriguez, A., Campbell, A., and Stanley, K

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:58:17.762705Z

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Observation 40b81599-6bde-491d-bb7d-b21820c591be · outbound

This paper cites B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:58:17.747296Z

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Observation f7dd1ccd-e0db-4a2d-9fe5-d1b7002ff715 · outbound

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Open Problems in Mechanistic Interpretability

Reference 84

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 85

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Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.732326Z

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Observation 21e5ebb5-c4f6-481d-88e1-1f53c8d5ad82 · outbound

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis T., Rota, P., and Sebe, N

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:16.911445Z

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Observation c774f219-dcb6-4d97-9afe-8017639d4338 · outbound

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Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.710628Z

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Observation 23509014-9179-492a-a6d6-ba9aad22e9b9 · outbound

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Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.692582Z

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Observation 78e36588-5cde-4ada-ba4d-e763334554da · outbound

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Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.678579Z

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis O., Lehman, J., and Soros, L

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:58:17.654630Z

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Observation 5be79065-8809-4d1b-b262-deb48bd19f75 · outbound

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Reference 92

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Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.630389Z

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Observation dd03cb61-f9b8-4b0f-9346-7e2f3c3ef994 · outbound

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Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.615836Z

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Observation bac83665-e75b-4067-b02a-0a6887b81baf · outbound

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Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:58:17.598361Z

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Observation 22f14a03-ae2d-4030-b299-da01aacf60c0 · outbound

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis O., Stanton, A., Timperley, C., Vostinar, A., and Wiser, M

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:58:17.584207Z

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Observation 6d564829-fff9-4eaf-9155-6a6fd29bd033 · outbound

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Human-Timescale Adaptation in an Open-Ended Task Space

Reference 97

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis L., McDougall, C., MacDiarmid, M., Freeman, C

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:16.960572Z

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Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:16.964564Z

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Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis Explaining grokking through circuit efficiency

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:16.968686Z

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Pith citing papers

Observation 0a910644-6faf-4fc2-b8be-474089d42788 · inbound

The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference cites this paper.

The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 26

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Procedural Pretraining: Warming Up Language Models with Abstract Data cites this paper.

Procedural Pretraining: Warming Up Language Models with Abstract Data Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 14

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Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale cites this paper.

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 50

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Observation fd523d6a-18e8-4a32-b67c-7c6747edb740 · inbound

Gradient-Based Program Synthesis with Neurally Interpreted Languages cites this paper.

Gradient-Based Program Synthesis with Neurally Interpreted Languages Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 116

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Observation e1d843e8-05cd-4f04-b329-594901567873 · inbound

Characterizing Universal Object Representations Across Vision Models cites this paper.

Characterizing Universal Object Representations Across Vision Models Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 19

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Observation 37eec982-d563-4bd1-8176-b6504be4ab61 · inbound

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models cites this paper.

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 13

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Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds cites this paper.

Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:09:29.831879Z

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Observation 16753a84-2521-4a75-84ac-a6c6d4762863 · inbound

CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse cites this paper.

CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:28:55.186728Z

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Observation beac19f4-b222-40b6-9e7e-69cf97e0fcbe · inbound

Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models cites this paper.

Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T15:33:27.973683Z

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Unavailable: canonical work link unavailable.