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Paper Citation Record · LEDGER

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.14180.

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

pith.paper-citation-record.v1
2607.14180 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:34:08.140575Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81902e09-5352-4574-85ba-cd3d46dc33d1 · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 3

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no resolver link, observed 2026-08-02T03:34:08.069750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c23ed939-7c91-4a28-bad8-be69926009aa · outbound

This paper cites Offline Learning from Demonstrations and Unlabeled Experience.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Offline Learning from Demonstrations and Unlabeled Experience

Reference 6

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Observation c7a23a83-34a6-433a-8cc7-5c5bd8df2a24 · outbound

This paper cites B Related Work Learning dynamics from preferences.Several recent papers have detailed methods for improv- ing world model realism with human preferences.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences B Related Work Learning dynamics from preferences.Several recent papers have detailed methods for improv- ing world model realism with human preferences

Reference 8

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no resolver link, observed 2026-08-02T03:34:07.564105Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.564105Z digest=sha256:9da83d4f4fd5b99c90289e0e3e7fdb7652da86c233f1b988e599290b3ab4cad3

Observation 13d2aa79-ed5d-4d6d-9e15-7819d2346b4d · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.621247Z digest=sha256:ea9334840cbd21991d7e77332bd00ea7d0cb03226a4167d1bc55a395990565fe

Observation 1d43dec6-693c-489f-ac45-2fa0bf2a0691 · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-02T03:34:07.691390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.691390Z digest=sha256:086300ffc9e5b00b8fd23ea7bc9d940896bf6a6e1c4a860e3ebfda1d64aeeb21

Observation 3bbd50fe-6b46-47e1-b7b5-740e21459c6f · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 11

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no resolver link, observed 2026-08-02T03:34:07.760846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.760846Z digest=sha256:ac3f74704831ec6b8f262e01c8fc9a5a4a93577edc9d626fb7115bf61cac2303

Observation 033f6775-e28b-462c-b54a-301043b8db97 · outbound

This paper cites Each block applies a depthwise3×3convolution, LayerNorm, a two-layer MLP with4×channel expansion and GELU activation, and a residual connection.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Each block applies a depthwise3×3convolution, LayerNorm, a two-layer MLP with4×channel expansion and GELU activation, and a residual connection

Reference 12

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no resolver link, observed 2026-08-02T03:34:07.830742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.830742Z digest=sha256:da9a526bdcc94d13a3070db4e388eaa00ae44b0f5c63716395a6402d460284f5

Observation a7372a0a-d3d4-4db0-bd9b-d7b2e658303f · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.906979Z digest=sha256:07e6e7465ad5b4a5d09e156bef2c9de87db8674377bf60921a3d3f6c7ba10520

Observation a5f4839e-bdbf-4cad-b1b3-0f301a7f1d3e · outbound

This paper cites an unresolved cited work.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Unresolved cited work

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:08.140575Z digest=sha256:42e41a2a53e454c7c53e640dc1b15e104a6aab6a3650b357945943f8b246c217

Observation 11ded2c5-6edd-4bb3-bca8-1fe989368908 · outbound

This paper cites Imperfect World Models are Exploitable.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Imperfect World Models are Exploitable

Reference 2004

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.153034Z digest=sha256:e618de45e79392708723361e4c719cebcc7223c441c4132dfb3389c3f058df7a

Observation be5ffce1-f1c9-4881-a715-217acad4f859 · outbound

This paper cites Anatomy of a robotaxi crash: Lessons from the Cruise pedestrian dragging mishap.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Anatomy of a robotaxi crash: Lessons from the Cruise pedestrian dragging mishap

Reference 2014

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.225485Z digest=sha256:98a078f4edf48d332cdfd59efe800762e1e370eb4bd31f67ffab924419cf2a03

Observation eed79e46-0b8f-46a1-b0b9-c0c0f5239bae · outbound

This paper cites UCB/EECS-2019-98.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences UCB/EECS-2019-98

Reference 2019

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source=pdf_text observed=2026-08-02T03:34:07.047631Z digest=sha256:8d7b4fc356e015a4b6f201fda16682bf79a7a8bd4a11dae0a057c992bc4b1196

Observation e890f298-edd6-4fe6-b626-b361672a8d97 · outbound

This paper cites A Preliminaries We briefly review the necessary background to understand DLHF and RENEW.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences A Preliminaries We briefly review the necessary background to understand DLHF and RENEW

Reference 2020

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.497880Z digest=sha256:947fc204bb1c2fcd990391cfb4b7c4c1ea6e30c48188075c8c73bdaac3c888db

Observation b48c5f8a-842e-4a3e-baa4-33838d3e9472 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 2022

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source=pdf_text observed=2026-08-02T03:34:07.294572Z digest=sha256:68773fc88689016a9001d110903f8de3bed49c2a2e1aa65fa973d035f4e7130a

Observation 0b9a4e69-7a96-433e-b3be-b680ac711614 · outbound

This paper cites •Maze10×10.A grid world where an agent navigates corridors to reach a goal position.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences •Maze10×10.A grid world where an agent navigates corridors to reach a goal position

Reference 2024

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no resolver link, observed 2026-08-02T03:34:07.979332Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:34:07.979332Z digest=sha256:f6a57b9612e28810ac4ec42b44f919668de615de8b105dc52007e74ccf258499

Observation 128e33b3-63fa-4113-8434-8cfe7ac66e63 · outbound

This paper cites IntPhys 2019: A benchmark for visual intuitive physics under- standing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5016–5025,.

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences IntPhys 2019: A benchmark for visual intuitive physics under- standing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5016–5025,

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.