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Paper Citation Record · LEDGER

The pitfalls of next-token prediction

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2403.06963.

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

pith.paper-citation-record.v1
2403.06963 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:37:13.051058Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T16:32:39.688341Z

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Outbound references

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Pith citing papers

Observation 593f5c8c-8f88-4c8f-a576-f9fdf44ec515 · inbound

Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts? cites this paper.

Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts? The pitfalls of next-token prediction

Reference 4

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source=arxiv_source observed=2026-08-12T12:53:38.715882Z digest=sha256:e3c0b5e70af2c4e1320338c347a268418cb5ac3f1e978947471dafc7793cad12

Observation b603b766-1b86-41f7-990f-f439f55ed1c4 · inbound

Planning vs Reasoning: Ablations to Test Capabilities of LoRA layers cites this paper.

Planning vs Reasoning: Ablations to Test Capabilities of LoRA layers The pitfalls of next-token prediction

Reference 3

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source=pdf_text observed=2026-08-12T17:37:13.051058Z digest=sha256:db75682312f1aeb2da8db6911b8a3af8faad696a948ae57b67fb567b6c973728

Observation 7edaab7e-f345-40d9-b401-0fb1e5f3f1c7 · inbound

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation cites this paper.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation The pitfalls of next-token prediction

Reference 1

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source=arxiv_source observed=2026-08-11T21:37:19.771484Z digest=sha256:fa87de5c5ffbd96fdeba9602685953d19ab4ee72374697a0ee5cd87b7837a2e1

Observation 27ce0192-877a-4e95-9032-cefdc6c31409 · inbound

Transformers Can Navigate Mazes With Multi-Step Prediction cites this paper.

Transformers Can Navigate Mazes With Multi-Step Prediction The pitfalls of next-token prediction

Reference 2021

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source=pdf_text observed=2026-08-11T20:55:53.636817Z digest=sha256:53938d771b66e0c73243bba8ca8be4507a586033c6107fd185b0d3d81e8ff780

Observation b65cbd1a-4ec7-47a8-ba3e-774ec3dcbf54 · inbound

Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation cites this paper.

Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation The pitfalls of next-token prediction

Reference 15

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source=pdf_text observed=2026-08-11T20:53:23.652647Z digest=sha256:2c802409f27ffa5cb7be9c30c88d94928122b4d824b0aa2a44b3d8cb9217426b

Observation b7fdaa3b-1ebb-432a-9ac8-b1208bc8b1c5 · inbound

Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation cites this paper.

Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation The pitfalls of next-token prediction

Reference 4

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source=arxiv_source observed=2026-08-11T15:47:21.160944Z digest=sha256:398da23e1e5edcd9da89a1633e0de26ce777ea4bb85dff799ceb5b72b03c99cd

Observation 97561df1-5048-4243-9e12-c5be7dc2f295 · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) The pitfalls of next-token prediction

Reference 13

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arxiv_id, observed 2026-05-18T04:33:39.793820Z

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source=pdf_text observed=2026-05-18T04:33:39.076517Z digest=sha256:3cd9505fe62bb369c5861d3ff6cb1c59581816a73bbf22a2d869a3c943e1b416

Observation 425ebbd0-fd3c-4573-99d5-10b8f7e29ded · inbound

Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning cites this paper.

Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning The pitfalls of next-token prediction

Reference 2

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source=arxiv_source observed=2026-08-10T14:15:45.972771Z digest=sha256:619c09c477e74842024459e8c01e0cd2b84389a84d0eec340152504c3f0aac96

Observation cab5567d-670c-4b5e-b055-b7933a0c1642 · inbound

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 The pitfalls of next-token prediction

Reference 6

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source=arxiv_source observed=2026-08-09T14:54:29.092681Z digest=sha256:f8839f334f435b9865bb86076569c111ba02bbdd9babaa426472990e2c9fef64

Observation 8eccdc74-7ec0-406f-943e-f5ec5ff4c17b · inbound

Emergent Response Planning in LLMs cites this paper.

Emergent Response Planning in LLMs The pitfalls of next-token prediction

Reference 5

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source=arxiv_source observed=2026-08-08T16:18:47.960815Z digest=sha256:08fa47d2d39d0bbb917dd1bdff994dbabafde161fea1b432ae8ca6126d1464ca

Observation e7f9712b-a369-4230-9193-2ed4236efd4f · inbound

Large Language Models for Planning: A Comprehensive and Systematic Survey cites this paper.

Large Language Models for Planning: A Comprehensive and Systematic Survey The pitfalls of next-token prediction

Reference 6

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source=pdf_text observed=2026-08-07T14:11:50.713502Z digest=sha256:d12c414dc7a5fbbc48e7fe0b329d362dce0c57d68372f2ff3bd4b37889043c23

Observation 29109999-bdad-4cb0-9c0b-2d873aa2b929 · inbound

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities cites this paper.

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities The pitfalls of next-token prediction

Reference 26

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source=pdf_text observed=2026-08-07T14:04:55.284361Z digest=sha256:a369a47eb09ece3d4ce89e8caf011b0bc29f8f08e47a02ac1527e7eeef9c5c44

Observation 330aaa9c-2d26-4b5b-918f-12e2979f0f09 · inbound

Learning to Insert [PAUSE] Tokens for Better Reasoning cites this paper.

Learning to Insert [PAUSE] Tokens for Better Reasoning The pitfalls of next-token prediction

Reference 7

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source=arxiv_source observed=2026-08-07T11:06:16.894905Z digest=sha256:9d42a46a672e0352a4e063a56a6a6094de568b9192b83598f884f77c6abd1a2a

Observation 7bb6ceb4-7e4c-48e3-9b5c-12a1b4ca336c · inbound

Improving Large Language Models with Concept-Aware Fine-Tuning cites this paper.

Improving Large Language Models with Concept-Aware Fine-Tuning The pitfalls of next-token prediction

Reference 3

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source=arxiv_source observed=2026-08-07T05:28:23.808234Z digest=sha256:64209debc048cb8b7a02b2291cd7cdaa7ab55c2d493f9764e19c8f8f8650edc5

Observation 1c1b743d-b5eb-483e-b48c-637472cb1aaf · inbound

Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks cites this paper.

Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks The pitfalls of next-token prediction

Reference 1

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arxiv_id, observed 2026-05-19T09:52:14.138276Z

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

source=pdf_text observed=2026-05-19T09:48:56.990745Z digest=sha256:bd3eeaa138f3f5e6da8be257d33c84e2867b6c044f8eed634f4c771941e73f3e

Observation 1e615873-8bf1-4611-866f-5ac7b65f19f1 · inbound

STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner cites this paper.

STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner The pitfalls of next-token prediction

Reference 27

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source=pdf_text observed=2026-08-06T22:40:00.827394Z digest=sha256:babec58f1225eb7f1dd44ccc268b33db69e4dc253320663c3a21d107bf7f3cff

Observation 8e9b7589-c1b8-4059-a529-6b5e9fb8dab8 · inbound

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models cites this paper.

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models The pitfalls of next-token prediction

Reference 113

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source=pdf_text observed=2026-08-06T17:47:40.295137Z digest=sha256:9fcf600fc733d71a66b912ce938459ea052a919c9094b25cd1179096b84b1f61

Observation dbd17dd0-e8b6-48d2-8a91-a91f2b06f1e2 · inbound

From Reasoning to Super-Intelligence: A Search-Theoretic Perspective cites this paper.

From Reasoning to Super-Intelligence: A Search-Theoretic Perspective The pitfalls of next-token prediction

Reference 2025

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source=pdf_text observed=2026-08-06T18:02:50.296623Z digest=sha256:1136d1591f46e780f9459e35817a84b0622aec79c1356283cf47c7b2f8041fe0

Observation e31c6bf9-64e2-4f0e-88ac-8c15ed9be742 · inbound

PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models cites this paper.

PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models The pitfalls of next-token prediction

Reference 11

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source=pdf_text observed=2026-08-06T14:59:05.045852Z digest=sha256:8201f419959da808476686c13c96efe3365da346280a99f70d56b67fe034c019

Observation 90604a96-237f-4df6-bf34-a0414368a771 · inbound

What am I missing here?: Evaluating Large Language Models for Masked Sentence Prediction cites this paper.

What am I missing here?: Evaluating Large Language Models for Masked Sentence Prediction The pitfalls of next-token prediction

Reference 2

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source=arxiv_source observed=2026-08-05T21:58:16.306251Z digest=sha256:be085d754ffc0587cf460562c7331a3a3bc08bc4cce3e49ac0617363523cefda

Observation a93dc9e0-f35b-4bc3-b1e2-242761814317 · inbound

Multi-Token Prediction via Self-Distillation cites this paper.

Multi-Token Prediction via Self-Distillation The pitfalls of next-token prediction

Reference 2

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arxiv_id, observed 2026-05-16T06:47:28.355132Z

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

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Observation f3e13ea5-5a8c-45c6-8959-14dfebe2b2b9 · inbound

Noise is not always detrimental: the capacity of quantum batteries is enhanced in black holes cites this paper.

Noise is not always detrimental: the capacity of quantum batteries is enhanced in black holes The pitfalls of next-token prediction

Reference 3

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Observation fb1f1cc1-a7b0-4cfa-9481-069c09c701ab · inbound

Semantic Trimming and Auxiliary Multi-step Prediction for Generative Recommendation cites this paper.

Semantic Trimming and Auxiliary Multi-step Prediction for Generative Recommendation The pitfalls of next-token prediction

Reference 3

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arxiv_id, observed 2026-05-10T22:30:50.851078Z

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

source=pdf_text observed=2026-05-10T19:48:25.382343Z digest=sha256:8bba1de316406b64aec5db8f42053b976bf19bb30f58bd477c073fddc17f891b

Observation 8f57c136-f7bf-4062-9c43-8d19c9af8292 · inbound

Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis cites this paper.

Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis The pitfalls of next-token prediction

Reference 6

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arxiv_id, observed 2026-05-11T17:11:18.652801Z

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

source=pdf_text observed=2026-05-08T17:48:09.078064Z digest=sha256:c576d9f74b1fd296dfc9e7a5e8d204396186d552802484d101f925d6a43897d0

Observation a47ef29c-e75b-4ce3-8202-e0208ff101a4 · inbound

Continuous Latent Diffusion Language Model cites this paper.

Continuous Latent Diffusion Language Model The pitfalls of next-token prediction

Reference 3

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source=pdf_text observed=2026-05-08T10:04:09.646578Z digest=sha256:c28d1b921d5a77b42a41a488ab2cc8983839ace29efe163e8a5b47180f3cb8e8

Observation 3d523ce9-26a9-4c95-8483-43f684b5f862 · inbound

Representation Without Reward: A JEPA Audit for LLM Fine-Tuning cites this paper.

Representation Without Reward: A JEPA Audit for LLM Fine-Tuning The pitfalls of next-token prediction

Reference 28

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arxiv_id, observed 2026-05-19T16:32:39.690750Z

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

source=pdf_text observed=2026-05-19T16:27:51.512806Z digest=sha256:0f41f6e1908a6acccf9ebf6896ff62ca97a4272c431f422459179578d93f92ca

Observation 36c301e3-e3e2-4f76-b102-777b817fbdae · inbound

Hierarchical Domain Generalization cites this paper.

Hierarchical Domain Generalization The pitfalls of next-token prediction

Reference 158

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source=arxiv_source observed=2026-08-01T20:54:17.085738Z digest=sha256:af28515d9ffb0c3cdea1a2d89d4dacb8a7e1482044989ecd860acf48719d9b90

Observation 15eccb68-e8ae-4a0d-b2af-0e07de6f6266 · inbound

FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts cites this paper.

FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts The pitfalls of next-token prediction

Reference 22

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source=pdf_text observed=2026-08-02T10:39:43.469042Z digest=sha256:5dd210f8d79bf993a5f65be416f662870d9fb3c08dcff2ea6ff8a26ed3c59d67

Observation 89dffa39-1203-4507-988a-72ab5b701be1 · inbound

DeepLook: Deeper Thinking with Lookahead cites this paper.

DeepLook: Deeper Thinking with Lookahead The pitfalls of next-token prediction

Reference 37

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source=arxiv_source observed=2026-08-02T11:32:12.716308Z digest=sha256:ef94ad80d5dd1bd13ee6836fb4a0451621362751309761973142bc1f795eb774

Observation a55de02f-d124-4cbe-b45d-ea0d9dec4036 · inbound

WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA cites this paper.

WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA The pitfalls of next-token prediction

Reference 105

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source=arxiv_source observed=2026-08-06T00:41:01.894675Z digest=sha256:7ab594d7ce31e2cb14e4b763dce2cd0c524f55018b21351c14fdaf98ad382aed

Observation 732a77e5-9682-4c12-a6a6-557f96e0cbce · inbound

The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations cites this paper.

The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations The pitfalls of next-token prediction

Reference 18

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source=arxiv_source observed=2026-08-06T10:34:09.807924Z digest=sha256:0b5659e73b0b30b59954c76e96a1458f45a455b3eb0e16911d6b9e3cdfc36b1a

Observation bc540637-b016-46d0-880c-8a6348e0c045 · inbound

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning cites this paper.

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning The pitfalls of next-token prediction

Reference 23

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source=arxiv_source observed=2026-08-08T05:05:24.726104Z digest=sha256:e456cd900891018070e9eb1043ae66aff62f17901f01cc2a87b948ca5e655bb7

Observation abef9dd7-49ed-4618-92e1-8e8980398160 · inbound

Hierarchical Latent Prediction for Language Models cites this paper.

Hierarchical Latent Prediction for Language Models The pitfalls of next-token prediction

Reference 11

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source=arxiv_source observed=2026-08-07T23:13:07.281639Z digest=sha256:9b91858f7bf31968631f6dcdc7aa1dd24dc93fb6952952873a02b1bfe61d494f