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Paper Citation Record · LEDGER

Debate Helps Weak-to-Strong Generalization

As of 22 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 6 inbound Pith citation observations for arXiv:2501.13124.

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

pith.paper-citation-record.v1
2501.13124 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:50:56.511839Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:26:53.123046Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:36:08.609301Z

Reference resolution

56 of 56 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved53
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88b986f3-1102-4090-9bdd-a7015835e1fb · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Debate Helps Weak-to-Strong Generalization , " * write output.state after.block = add.period write newline

Reference 1

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no resolver link, observed 2026-08-10T17:50:56.241891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.241891Z digest=sha256:305b99d60be3f837aeb14a8e9ab0c757647909b3902ec5cb03d4895fc3b3c7f1

Observation 0896924d-094c-4f74-9f09-5644fc8d2e4c · outbound

This paper cites write newline.

Debate Helps Weak-to-Strong Generalization write newline

Reference 2

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source=arxiv_source observed=2026-08-10T17:50:56.247736Z digest=sha256:77960cdabc1636888abb25db5563f442831d969b235ef352345a3358244493db

Observation e0e29708-bb08-40d8-867e-54c22da7cd08 · outbound

This paper cites Concrete Problems in AI Safety.

Debate Helps Weak-to-Strong Generalization Concrete Problems in AI Safety

Reference 3

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source=arxiv_source observed=2026-08-10T17:50:56.253475Z digest=sha256:5267c25ed5702384f7ea68aaeffdd103851b8892a2a3081d942152134c2918c2

Observation 6438ba96-8daa-4a1c-8151-488fe0751362 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.258993Z digest=sha256:ef96bf1487130c2d9b1a55f0e64d1b18cc7b2efb78625580f1246926e2005d11

Observation 0d0c80e0-51ce-44dd-a8d8-a90e988019b6 · outbound

This paper cites G.; and Schaal, S.

Debate Helps Weak-to-Strong Generalization G.; and Schaal, S

Reference 5

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raw_fallback, observed 2026-08-10T17:50:57.330473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.265371Z digest=sha256:fef21c01b298f7ae90857d1d7bebfdf4be86ce2acec7aebbea4e32523eb848cc

Observation 33da70ba-b46a-4be9-babf-5ecccb1a3f4f · outbound

This paper cites Qwen Technical Report.

Debate Helps Weak-to-Strong Generalization Qwen Technical Report

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.270603Z digest=sha256:e98adda46ada47d61be7883e32bed4543108c4dea2fe90817547e1c176f7df09

Observation 8a75b038-91a8-47db-8bb4-0a6c34baaed0 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Debate Helps Weak-to-Strong Generalization Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 7

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T17:50:56.276561Z digest=sha256:f10fe35dc555ccde2087513bbb9acdf039a38daa9b448279d173b02b173e7371

Observation fe41f8f8-0c98-4f12-9592-d8eb22b0b3e5 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 8

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raw_fallback, observed 2026-08-10T17:50:57.314866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.281867Z digest=sha256:9c11c5e2c8f45ab4e4a39f44fcebd4824fa25b8b038d1b047f48c45aa9993183

Observation 75af3184-376c-4a08-a8e3-6b1f7060ada3 · outbound

This paper cites Measuring Progress on Scalable Oversight for Large Language Models.

Debate Helps Weak-to-Strong Generalization Measuring Progress on Scalable Oversight for Large Language Models

Reference 9

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source=arxiv_source observed=2026-08-10T17:50:56.287233Z digest=sha256:fafa54c90c7ccdef03c909ec56eb124afde45cc4bb65837e6a48192a83cc10a9

Observation 0a5ed603-6ac4-478f-bded-fa4183eeff75 · outbound

This paper cites D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al.

Debate Helps Weak-to-Strong Generalization D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al

Reference 10

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source=arxiv_source observed=2026-08-10T17:50:56.292184Z digest=sha256:636d14752c06f587dfff7fa4a59322dad48be75d202ff9e5d826a18e0cd80936

Observation aef1fb42-359c-4b0b-869a-6d07ff42c162 · outbound

This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Debate Helps Weak-to-Strong Generalization Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Reference 11

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source=arxiv_source observed=2026-08-10T17:50:56.296653Z digest=sha256:77e812fa092636fbe1ef973e599dc918a79732d20e0284fdddf348d1567f07b1

Observation 35d465cd-9187-466e-b0d1-6a5209d0a8b6 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.301563Z digest=sha256:508fbd0868bb598ee8b4879a1c40e600fa05ef8eedb6b67cad73152659c86cb1

Observation a2973a96-1e78-4e54-8240-458dcbcedce9 · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Debate Helps Weak-to-Strong Generalization Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.305623Z digest=sha256:7bf1a6d4cbd04cccaae5bf8433a65b2420ecf7be18c0c3eb153ece5aa54b4fb7

Observation b5d89875-5b60-46e3-a9cc-059d0bb8bd10 · outbound

This paper cites ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.

Debate Helps Weak-to-Strong Generalization ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.310347Z digest=sha256:807fbdeb09e1850855265a4e4b72b49e41c7f2624aea143b0813501f492a18c5

Observation 4b84875e-5b83-4572-ad32-945a2bab70ae · outbound

This paper cites Quantifying the Gain in Weak-to-Strong Generalization.

Debate Helps Weak-to-Strong Generalization Quantifying the Gain in Weak-to-Strong Generalization

Reference 15

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source=arxiv_source observed=2026-08-10T17:50:56.315391Z digest=sha256:5045e33f94d01e2a96085a8cddd0ea4fdcbb9907ef6a30336d690cc450d299c0

Observation 3087cdbc-1675-4558-b382-6f15d4118138 · outbound

This paper cites Supervising strong learners by amplifying weak experts.

Debate Helps Weak-to-Strong Generalization Supervising strong learners by amplifying weak experts

Reference 16

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source=arxiv_source observed=2026-08-10T17:50:56.320383Z digest=sha256:752225ee9edfea729dc57a68f8377e364650d270f7b8a1cc8d209e489377dcc1

Observation c7c3a6cd-be19-47e4-9483-0a0096892f1e · outbound

This paper cites F.; Leike, J.; Brown, T.; Martic, M.; Legg, S.; and Amodei, D.

Debate Helps Weak-to-Strong Generalization F.; Leike, J.; Brown, T.; Martic, M.; Legg, S.; and Amodei, D

Reference 17

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.325992Z digest=sha256:3dbca36e2794b81a482d64579c119ec88b060f645c3748ea37c053b2ed3906d8

Observation 6f7464fa-b19d-4103-84d5-6aa0eff06bee · outbound

This paper cites W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, Y.; Wang, X.; Dehghani, M.; Brahma, S.; et al.

Debate Helps Weak-to-Strong Generalization W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, Y.; Wang, X.; Dehghani, M.; Brahma, S.; et al

Reference 18

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Observation ddb29646-ebf6-494b-a045-3227d2e77203 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Debate Helps Weak-to-Strong Generalization BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 19

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source=arxiv_source observed=2026-08-10T17:50:56.335016Z digest=sha256:938a2eb591301f31187cc5d8cc5856792014d437a1a0c3fd4bdb068f095a6462

Observation a093c96a-6bff-4f54-83cc-876ca28f9af7 · outbound

This paper cites Reward Model Ensembles Help Mitigate Overoptimization.

Debate Helps Weak-to-Strong Generalization Reward Model Ensembles Help Mitigate Overoptimization

Reference 20

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source=arxiv_source observed=2026-08-10T17:50:56.339928Z digest=sha256:ee13eee234e7f8d0a733cc3930997d639469bd080d002eda74955ecad6a2b679

Observation 0794e0c3-b5b8-48c6-8d7f-9c0204b0f856 · outbound

This paper cites Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.

Debate Helps Weak-to-Strong Generalization Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Reference 21

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source=arxiv_source observed=2026-08-10T17:50:56.344879Z digest=sha256:006a30bde420649470a095b04d06d462a52e2599fd3410a1360a66f58069c726

Observation 25122465-b429-4bd7-bc85-5d0d11ca0659 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Debate Helps Weak-to-Strong Generalization Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:50:56.349760Z digest=sha256:5deef8346705f642f2647f06279f92bc05d3916de1a09e2149b9c057dc0b148d

Observation b63362f9-d06b-42c4-8664-7137633d3a03 · outbound

This paper cites Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.

Debate Helps Weak-to-Strong Generalization Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

Reference 23

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source=arxiv_source observed=2026-08-10T17:50:56.354654Z digest=sha256:07c2a10452a7b055d082e2c36654863af9206b81e8d5765e312be4217502b81e

Observation 2cce18f7-1911-4376-87b8-d7d0a366370b · outbound

This paper cites A.; Hu, M.; Malik, A.

Debate Helps Weak-to-Strong Generalization A.; Hu, M.; Malik, A

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation bf5b522b-0b4c-4cc9-a149-eb003bf29f00 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-10T17:50:56.364007Z digest=sha256:23da85bb2593416a3c2c805198b291db6d118224c989687f452eca67596cdd97

Observation 2d13f164-7a67-43e0-a340-da875cb48cb7 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-10T17:50:56.368700Z digest=sha256:2c0620599934440a46074bdc98945245a903790ed6929c346c733780ae0b1a92

Observation dabe00a3-a172-4d0a-8830-908925e0a2e0 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Debate Helps Weak-to-Strong Generalization Distilling the Knowledge in a Neural Network

Reference 27

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source=arxiv_source observed=2026-08-10T17:50:56.373631Z digest=sha256:6a70c5d4a3fb352364d55881496167da6a5219ecba6c57392329c6dc5a9c3d2a

Observation 08e8d225-db2e-460a-8f78-9d4e6913bcd1 · outbound

This paper cites Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning.

Debate Helps Weak-to-Strong Generalization Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Reference 28

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Observation 040d30dd-6701-4b76-b8af-00846b06dfac · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 29

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raw_fallback, observed 2026-08-10T17:50:57.218240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f5dda201-36fc-476f-a6de-1eff9b9f5276 · outbound

This paper cites AI safety via debate.

Debate Helps Weak-to-Strong Generalization AI safety via debate

Reference 30

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source=arxiv_source observed=2026-08-10T17:50:56.386019Z digest=sha256:1b9acdc7778d49267edf5a5bf9e98b1d7735dfca197cf54b3c2e39e9a875efb5

Observation 4db34dd8-bf07-41ee-b3d7-53e64e0fb2e1 · outbound

This paper cites AI Alignment: A Comprehensive Survey.

Debate Helps Weak-to-Strong Generalization AI Alignment: A Comprehensive Survey

Reference 31

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Observation c87603ab-fa8c-4f99-b622-7cf576f11694 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-10T17:50:57.202494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 613cbe95-0e87-4cbc-b7ef-9e45c271b683 · outbound

This paper cites On scalable oversight with weak LLMs judging strong LLMs.

Debate Helps Weak-to-Strong Generalization On scalable oversight with weak LLMs judging strong LLMs

Reference 33

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Observation a4e71ad5-91af-49df-b316-1699893a3401 · outbound

This paper cites Debating with More Persuasive LLMs Leads to More Truthful Answers.

Debate Helps Weak-to-Strong Generalization Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 34

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source=arxiv_source observed=2026-08-10T17:50:56.406071Z digest=sha256:d7bcf9fef064468420556ae7cf3047d1c55ed93cbe00f0d82293e98974d04449

Observation 7840a6b4-be4f-4701-b4fe-263728911ab2 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 35

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source=arxiv_source observed=2026-08-10T17:50:56.411203Z digest=sha256:13d20de32ace5f1e33500d95961fcd4f1cf871095428df9329df0804ecfe6736

Observation 8b016a23-a8ed-4301-90e2-19a6d26b8e72 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 36

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raw_fallback, observed 2026-08-10T17:50:57.177433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.415740Z digest=sha256:e84bedccd3128e6ff2165d1e80fb3d2703f555a981b7138abc08660769e7b85f

Observation 18412e5e-0a41-4f6d-a3dc-2769650d39ec · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Debate Helps Weak-to-Strong Generalization Scalable agent alignment via reward modeling: a research direction

Reference 37

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source=arxiv_source observed=2026-08-10T17:50:56.420130Z digest=sha256:fdce933476d7c4b4b2deecc663d8a124feafcaf5de19188f3f459636e281efa0

Observation 79b8e75c-c33b-4b7b-bd74-18a8bf21a44e · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Debate Helps Weak-to-Strong Generalization Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 38

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no resolver link, observed 2026-08-10T17:50:56.425127Z

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Observation dac2c284-3502-4851-9e37-3aba43fec5e9 · outbound

This paper cites Let's Verify Step by Step.

Debate Helps Weak-to-Strong Generalization Let's Verify Step by Step

Reference 39

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source=arxiv_source observed=2026-08-10T17:50:56.429589Z digest=sha256:9102cbf3df7b2ee89e42e48c62089fc0b8da46a46a78418a3b6d850a2029ee30

Observation 7443a61d-e455-470d-b184-eff7485e55fc · outbound

This paper cites Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts.

Debate Helps Weak-to-Strong Generalization Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts

Reference 40

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source=arxiv_source observed=2026-08-10T17:50:56.434566Z digest=sha256:544b1f9c6d46e0fe92a728d502ed28b8870f71ab14e7242e11275b08f3c998bf

Observation d77895bb-706e-4f44-a70e-a052df001dbc · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

Debate Helps Weak-to-Strong Generalization LLM Critics Help Catch LLM Bugs

Reference 41

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source=arxiv_source observed=2026-08-10T17:50:56.439642Z digest=sha256:9544876edb24efd71c747240e3a9939b71905b0e93aff60957eb30dad558a200

Observation 6cdb109c-7d78-4794-bde5-6323b7bb6463 · outbound

This paper cites Debate Helps Supervise Unreliable Experts.

Debate Helps Weak-to-Strong Generalization Debate Helps Supervise Unreliable Experts

Reference 42

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source=arxiv_source observed=2026-08-10T17:50:56.444550Z digest=sha256:7266250348fc5ca44e9a16448575707b053b8e5c43b8ae20fac6d747f73f4876

Observation cf8676e2-59bd-4a3b-b4a1-fff30f0cd943 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-10T17:50:56.449710Z digest=sha256:614009fb4b349a40c1243918f9071aa96b258296d280a2e39c30f711e5f15037

Observation a80c717a-d2e8-4c5a-8106-a948f7fe55a6 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-10T17:50:56.454179Z digest=sha256:f7c8e35dbb9d5143df26513b71a44583ab4dcf5c2789ab631b9c2600e23ea11e

Observation fe9162d3-da8c-458b-ac74-76a21ff33b47 · outbound

This paper cites Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions.

Debate Helps Weak-to-Strong Generalization Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions

Reference 45

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source=arxiv_source observed=2026-08-10T17:50:56.458365Z digest=sha256:6ee1b68509bd84b983a39ceeefd98b0485535a9f29bf7502e7e061fb58f622e6

Observation f8883fcd-2bd7-492a-a5c4-680a02b466ca · outbound

This paper cites Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions.

Debate Helps Weak-to-Strong Generalization Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions

Reference 46

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source=arxiv_source observed=2026-08-10T17:50:56.463195Z digest=sha256:f5589f56cb8711a3fa7ddfcc42ee48e78df3b276d7fdffb7e653d7a72364c7e7

Observation 9797acda-9252-48c1-bcea-0dd230db113c · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-10T17:50:56.468545Z digest=sha256:28ad68411ec3d642f5e6be7722d99230f20cf4024839bb39ccb6b196612ef5eb

Observation aa73abb2-54c2-4e48-a89f-a33772d9189b · outbound

This paper cites Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms.

Debate Helps Weak-to-Strong Generalization Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

Reference 48

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source=arxiv_source observed=2026-08-10T17:50:56.473929Z digest=sha256:4aafeb34e5392fcb3548dff45c2db1aa71ec6818301591d27623a9e1bd5b371d

Observation bc923df2-6a27-47d9-a441-174a7123af19 · outbound

This paper cites D.; Ermon, S.; and Finn, C.

Debate Helps Weak-to-Strong Generalization D.; Ermon, S.; and Finn, C

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-10T17:50:57.124393Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.478450Z digest=sha256:5e260ca6edef33a23e260f8fa0a2138dcf0296db587e2633397148934367a815

Observation 82daee7e-957e-457b-9af5-ea6479137dbb · outbound

This paper cites Self-critiquing models for assisting human evaluators.

Debate Helps Weak-to-Strong Generalization Self-critiquing models for assisting human evaluators

Reference 50

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source=arxiv_source observed=2026-08-10T17:50:56.483151Z digest=sha256:e0f29ee861154358ce69bba2b87a204cfbf0ac9756a58b45105c889decfc597e

Observation f8aaced8-6619-4b7b-981b-1fa955669cd2 · outbound

This paper cites an unresolved cited work.

Debate Helps Weak-to-Strong Generalization Unresolved cited work

Reference 51

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T17:50:56.488166Z digest=sha256:9e1ac292d69e21569217001855e315e60f07b585cf7597c0086bb4ea2aafa36c

Observation c16f1a93-db46-4c6e-b855-2fcb8010beeb · outbound

This paper cites Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision.

Debate Helps Weak-to-Strong Generalization Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Reference 52

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source=arxiv_source observed=2026-08-10T17:50:56.492493Z digest=sha256:77b10d1bfcd6c00ccb8db1a6b1f2f394c7cbb8753d5c391066beb17687956890

Observation 0b184e7c-f9aa-4f20-9d76-4d11343e23da · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Debate Helps Weak-to-Strong Generalization Finetuned Language Models Are Zero-Shot Learners

Reference 53

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source=arxiv_source observed=2026-08-10T17:50:56.497474Z digest=sha256:099a8b2260e32e71e326378360c5fe31c4c4f526f0caac4a3ae44c6a851f6c81

Observation dc1afc4d-31ff-4cf0-8ea6-76ea9ecabc6d · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

Debate Helps Weak-to-Strong Generalization Crowdsourcing Multiple Choice Science Questions

Reference 54

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source=arxiv_source observed=2026-08-10T17:50:56.502372Z digest=sha256:faaace92afc8478f9055657048106cc78ae12b418122294ba0a2f83f1a20b933

Observation 17e391db-d66f-4f2f-9807-8fa02d80e2e5 · outbound

This paper cites Retrieval meets Long Context Large Language Models.

Debate Helps Weak-to-Strong Generalization Retrieval meets Long Context Large Language Models

Reference 55

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source=arxiv_source observed=2026-08-10T17:50:56.507015Z digest=sha256:ba93843d8ec13b0a5adf77bf38543e9f5c769426943c1b6d926422fb4a80f567

Observation 7e9772c2-cd4d-47bf-b516-1a4299b9ea11 · outbound

This paper cites Qwen2 Technical Report.

Debate Helps Weak-to-Strong Generalization Qwen2 Technical Report

Reference 56

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source=arxiv_source observed=2026-08-10T17:50:56.511839Z digest=sha256:f7848d0986fb7dc124afa6d09ac2dd3689ffb46f87b670cfe432603c1c095029

Pith citing papers

Observation 5467baa6-896a-4054-9a8a-0158afb3a8f7 · inbound

On Weak-to-Strong Generalization and f-Divergence cites this paper.

On Weak-to-Strong Generalization and f-Divergence Debate Helps Weak-to-Strong Generalization

Reference 20

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source=arxiv_source observed=2026-08-07T11:17:45.650397Z digest=sha256:a7c871108d36743b137c424e3e7a4c33f51ce0e8b0a5b9f5bc9a00a2c4da6719

Observation 482726aa-6668-4d90-841c-dc234afca742 · inbound

When to Trust Context: Self-Reflective Debates for Context Reliability cites this paper.

When to Trust Context: Self-Reflective Debates for Context Reliability Debate Helps Weak-to-Strong Generalization

Reference 19

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source=arxiv_source observed=2026-08-07T06:07:40.090180Z digest=sha256:373be2fd1d26c20f67bb502b47269e2eee7cea53939de135d76837d0949a407e

Observation 9157f07d-b02f-4640-9e92-5afabc428f72 · inbound

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications cites this paper.

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications Debate Helps Weak-to-Strong Generalization

Reference 132

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source=pdf_text observed=2026-08-07T00:48:19.218798Z digest=sha256:4cc7fa1900bdd4b3b78df119f75fa0f7ce21bc43084bef17299395932f7da500

Observation 325bd2a9-eb0f-4a67-a6c4-a13267cefb10 · inbound

Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks cites this paper.

Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks Debate Helps Weak-to-Strong Generalization

Reference 36

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source=arxiv_source observed=2026-08-06T14:44:09.346312Z digest=sha256:bdab2ea9070fc03b20d298a6e7a9846276bb5bb22a4190fa204fbe79b2a18281

Observation 6e052142-40ff-4270-a482-cb3d6643cbe3 · inbound

ArGen: Auto-Regulation of Generative AI via GRPO and Policy-as-Code cites this paper.

ArGen: Auto-Regulation of Generative AI via GRPO and Policy-as-Code Debate Helps Weak-to-Strong Generalization

Reference 49

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source=arxiv_source observed=2026-08-15T16:26:53.123046Z digest=sha256:9a84d63b02b23654ae0e9d80ad89dececbc440f8577b0e9641b3e177c19acbf1

Observation 5b5099c4-6e85-4488-b06b-625ac06b9330 · inbound

On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization Debate Helps Weak-to-Strong Generalization

Reference 117

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arxiv_id, observed 2026-05-11T18:36:08.611166Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-08T14:59:19.883399Z digest=sha256:84d305375c5f2b8e518c2373ce54790adc7b1aa550385249036354fd1391a8a0