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Paper Citation Record · LEDGER

KAT-V1: Kwai-AutoThink Technical Report

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2507.08297.

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

pith.paper-citation-record.v1
2507.08297 v3

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:28:31.763652Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:42:21.114924Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:42:21.257284Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df6293d8-b8ad-48bb-a257-d401ef5144ee · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

KAT-V1: Kwai-AutoThink Technical Report DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:28:27.498496Z digest=sha256:62d25cda3a1718de71ea41fe9d06a979d7de29fc9e81f53a9571852e677269a7

Observation 5af7d74f-259a-4352-b98f-b94de0aac534 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

KAT-V1: Kwai-AutoThink Technical Report DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 2

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no resolver link, observed 2026-08-06T18:28:27.557422Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:28:27.557422Z digest=sha256:ac8ca9aac1a1e76f89c554135038e6b2e2e497f38e5afdb75dfe15c02d2440b4

Observation 39082e59-7523-46ae-90ca-fc0b662bcf0c · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

KAT-V1: Kwai-AutoThink Technical Report DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 3

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source=pdf_text observed=2026-08-06T18:28:27.693211Z digest=sha256:cfbd94239fbbf2a283af7680ac9ecabec5ea56434a2339ebde8c084f9df6d86a

Observation 3dd36adf-a2a0-4851-8d98-1b0102eae9cb · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

KAT-V1: Kwai-AutoThink Technical Report DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 4

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source=pdf_text observed=2026-08-06T18:28:27.826209Z digest=sha256:14760e15e25a8414c414dfb0bdc19e597f4e932b3667a44d45a4f58d9f12f13b

Observation 37994a6b-b5a4-47d2-8e34-010cc582dcd7 · outbound

This paper cites Qwen3 Technical Report.

KAT-V1: Kwai-AutoThink Technical Report Qwen3 Technical Report

Reference 5

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source=pdf_text observed=2026-08-06T18:28:27.963650Z digest=sha256:816478f1333ebe32a82da72972eca59e3f390a69889d5e769a01d53759e679a7

Observation b68b8b0b-c2e4-4af5-a220-6f56c3de64ea · outbound

This paper cites Qwen2.5 technical report, 2025.

KAT-V1: Kwai-AutoThink Technical Report Qwen2.5 technical report, 2025

Reference 6

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:28:28.069380Z digest=sha256:21891bb0186a8069d7ac2e26ae430af6932e600c71d324b8e0f8a1a6ccab2092

Observation d3b4bb2d-6db6-43cb-b900-dc3fee507080 · outbound

This paper cites Qwen2 technical report, 2024.

KAT-V1: Kwai-AutoThink Technical Report Qwen2 technical report, 2024

Reference 7

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:28:28.146933Z digest=sha256:942016dee37d57941bc70bd22f4525009c4c01f4eb6cb8eb589dfea9cf7d81d8

Observation 653eddec-8234-4c31-924c-8790c73cd6c3 · outbound

This paper cites Qwen Technical Report.

KAT-V1: Kwai-AutoThink Technical Report Qwen Technical Report

Reference 8

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source=pdf_text observed=2026-08-06T18:28:28.212924Z digest=sha256:1525ada500644e92fa04303c9f7646f14ce0d86bb5cb9f741ddec9e4d977b3e9

Observation 206941bf-ea76-40f9-8fa2-4d75be2c54d9 · outbound

This paper cites The llama 4 herd: The beginning of a new era of natively multimodal ai inno- vation.

KAT-V1: Kwai-AutoThink Technical Report The llama 4 herd: The beginning of a new era of natively multimodal ai inno- vation

Reference 9

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raw_fallback, observed 2026-08-06T18:28:34.850544Z

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.

source=pdf_text observed=2026-08-06T18:28:28.373635Z digest=sha256:2c5f1abd1c3ee8eba82a7a81b305346af47566696df555d4e865681bceede830

Observation 742eacc5-f932-4c65-a033-6e5b3dc80190 · outbound

This paper cites The Llama 3 Herd of Models.

KAT-V1: Kwai-AutoThink Technical Report The Llama 3 Herd of Models

Reference 10

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:28:28.549505Z digest=sha256:5a5809961a2cf09da5c1a3507010bc84383a834355fcdaa1d754a420a24c692b

Observation 0464e130-857d-4970-8702-4dfea5d899f8 · outbound

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

KAT-V1: Kwai-AutoThink Technical Report Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 11

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source=pdf_text observed=2026-08-06T18:28:28.638031Z digest=sha256:9bdde6125f4609a21b24d18a2ef49eadbfb0f1bf7bd4528110f5e13081be3285

Observation e012ba48-fb23-476c-a35e-3e2fe6ccd326 · outbound

This paper cites MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models.

KAT-V1: Kwai-AutoThink Technical Report MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models

Reference 12

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source=pdf_text observed=2026-08-06T18:28:28.753626Z digest=sha256:3baf165958d78d167ba323d10253a87af5b0837f02a3f913c19a07da31197d5e

Observation 9b689633-bd08-456d-94c1-81c78a624f73 · outbound

This paper cites A comprehensive survey on long context language modeling.

KAT-V1: Kwai-AutoThink Technical Report A comprehensive survey on long context language modeling

Reference 13

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source=pdf_text observed=2026-08-06T18:28:28.901478Z digest=sha256:2d0ffb30ae9e7442aaecee34bb456b950ca34643cbbadacb77bb32087fc0708d

Observation 93c864e8-0719-4dec-8ba7-15a36f7a62d5 · outbound

This paper cites M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation.

KAT-V1: Kwai-AutoThink Technical Report M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:28:29.016177Z digest=sha256:8d59a25ec22db4e8b57a36e3647332697d91ce18bbb6f290263ccf1ab013379d

Observation 9bb3e5c8-a43e-4f28-acff-326768ede098 · outbound

This paper cites R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models.

KAT-V1: Kwai-AutoThink Technical Report R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models

Reference 15

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source=pdf_text observed=2026-08-06T18:28:29.123556Z digest=sha256:8ba1dfc6e3030e20551e420d9293f81fff42f068186aaa85a1e3f0709ae45174

Observation 78b7e412-e66e-406f-9421-544bd614a400 · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

KAT-V1: Kwai-AutoThink Technical Report Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 16

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source=pdf_text observed=2026-08-06T18:28:29.186958Z digest=sha256:16bb67224cee47e98167e34dc3a333cdaea75681762a277b55f171ea86fc20f3

Observation a811da9d-68ac-4211-96e5-92ec16c1edab · outbound

This paper cites Concise reason- ing via reinforcement learning.

KAT-V1: Kwai-AutoThink Technical Report Concise reason- ing via reinforcement learning

Reference 17

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source=pdf_text observed=2026-08-06T18:28:29.243344Z digest=sha256:70c71ce20396b028775601f41acfb2f2f10c3ce6f7f9cca78cba83a0407b3532

Observation 6d8e1119-11b2-45b6-9163-7a84a6791055 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

KAT-V1: Kwai-AutoThink Technical Report Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 18

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source=pdf_text observed=2026-08-06T18:28:29.360176Z digest=sha256:4ff79d65b48a41b1a77955d65133631312e5e1e6c747f6d011801a7a1abe0dcf

Observation 9366b63d-c32d-4685-aa52-fe7a75cf065d · outbound

This paper cites AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning.

KAT-V1: Kwai-AutoThink Technical Report AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning

Reference 19

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source=pdf_text observed=2026-08-06T18:28:29.506916Z digest=sha256:602cf5094df28131f929b949285d211fab29beb1628523644672a24869446396

Observation ee207456-fbe3-4f58-ae61-6c7c64b52dd9 · outbound

This paper cites Think Only When You Need with Large Hybrid-Reasoning Models.

KAT-V1: Kwai-AutoThink Technical Report Think Only When You Need with Large Hybrid-Reasoning Models

Reference 20

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source=pdf_text observed=2026-08-06T18:28:29.688777Z digest=sha256:da34dffb557cb077f216555e4109bf568a7bb5a4586687b646b93f9470dd0fd0

Observation abe65655-e6ef-4284-bd57-2d0231e6b8fd · outbound

This paper cites Thinkless: LLM Learns When to Think.

KAT-V1: Kwai-AutoThink Technical Report Thinkless: LLM Learns When to Think

Reference 21

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source=pdf_text observed=2026-08-06T18:28:29.779954Z digest=sha256:ffdbee71d7c0b13ab0d862b4a3af8343803873d2b80964dd9c12d8b5cd8f3692

Observation e7a4d10b-1457-4876-bc65-57fb3c990cb6 · outbound

This paper cites Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl.

KAT-V1: Kwai-AutoThink Technical Report Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl

Reference 22

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source=pdf_text observed=2026-08-06T18:28:29.855822Z digest=sha256:d14a791b8b85c86ec9f5bd456615a98bb14cc6dbd8acf28b6d2638a8e9a5910b

Observation 741910ae-913f-4c41-b24c-e439b6b1f449 · outbound

This paper cites AdaptThink: Reasoning Models Can Learn When to Think.

KAT-V1: Kwai-AutoThink Technical Report AdaptThink: Reasoning Models Can Learn When to Think

Reference 23

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source=pdf_text observed=2026-08-06T18:28:29.917410Z digest=sha256:481af0076dc1a921da38882d82656273112f7bd6c9e6e1e8d91939f477d7513d

Observation c1c4e177-98d1-494f-bac1-6a01397ac3b8 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

KAT-V1: Kwai-AutoThink Technical Report DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 24

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source=pdf_text observed=2026-08-06T18:28:29.991061Z digest=sha256:e2c9f2e1664d8a228bf46fa6480a2d3616c0efba24316f0d6ab221e880f3ea78

Observation c2df3dae-67e9-4e7d-817f-5258b8c4f937 · outbound

This paper cites Proximal Policy Optimization Algorithms.

KAT-V1: Kwai-AutoThink Technical Report Proximal Policy Optimization Algorithms

Reference 25

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source=pdf_text observed=2026-08-06T18:28:30.104343Z digest=sha256:5e431a66bb635fd4b3b4d70f43f780e4821e2861b5249223a6a4c7df3573cea8

Observation 16f2a635-f86a-4f5a-879b-26846a248205 · outbound

This paper cites Knowledge distillation: A survey.

KAT-V1: Kwai-AutoThink Technical Report Knowledge distillation: A survey

Reference 26

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source=pdf_text observed=2026-08-06T18:28:30.194557Z digest=sha256:681533d2760eea673ef98fe8abeeedc1d4bd2b07bfa98107886107cc00f5e3d0

Observation db88f0c5-5659-47a9-871c-2b9d1b63fe4a · outbound

This paper cites Distilling the Knowledge in a Neural Network.

KAT-V1: Kwai-AutoThink Technical Report Distilling the Knowledge in a Neural Network

Reference 27

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source=pdf_text observed=2026-08-06T18:28:30.244737Z digest=sha256:3b6f6bf0393ede0c49d08d0bbf2da4b3ae1b8e6f8ee0543bb5b5826e64022db1

Observation 2112b756-3c29-4bd6-b8c4-0305d53be76b · outbound

This paper cites Ddk: Distilling domain knowledge for efficient large language models.

KAT-V1: Kwai-AutoThink Technical Report Ddk: Distilling domain knowledge for efficient large language models

Reference 28

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raw_fallback, observed 2026-08-06T18:28:34.666846Z

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.

source=pdf_text observed=2026-08-06T18:28:30.308945Z digest=sha256:418cc77e749d0ae3111bedced418d662a72df3556b3f6f830afba0a7c0d7c710

Observation 04bad843-450f-4b5d-81a0-0b5eba50a237 · outbound

This paper cites DeepSeek-V3 Technical Report.

KAT-V1: Kwai-AutoThink Technical Report DeepSeek-V3 Technical Report

Reference 29

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source=pdf_text observed=2026-08-06T18:28:30.376076Z digest=sha256:83f3f19a53d847aaf7717661946cfedbdd9fb875fc715be982ed3f249d82ca7a

Observation e16bb927-3e76-40a4-91cc-875e83af6f8a · outbound

This paper cites SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM.

KAT-V1: Kwai-AutoThink Technical Report SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM

Reference 30

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source=pdf_text observed=2026-08-06T18:28:30.436815Z digest=sha256:560265413d1eb45aa59c5ea834224faeba027dca12e7bb11a8aac58bd8c77096

Observation b6441f73-7418-4a7f-bc11-cf2744d4ac69 · outbound

This paper cites Livecodebench pro: How do olympiad medalists judge llms in competitive programming?, 2025.

KAT-V1: Kwai-AutoThink Technical Report Livecodebench pro: How do olympiad medalists judge llms in competitive programming?, 2025

Reference 31

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raw_fallback, observed 2026-08-06T18:28:34.485559Z

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.

source=pdf_text observed=2026-08-06T18:28:30.513645Z digest=sha256:98b05ff658cbe2dc360459332c173710be471ecc54ccb01d09223024932375b5

Observation d7c6d704-8629-42bb-b7cd-587f59831ca4 · outbound

This paper cites Introduction to techniques used in seed1.6.

KAT-V1: Kwai-AutoThink Technical Report Introduction to techniques used in seed1.6

Reference 32

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raw_fallback, observed 2026-08-06T18:28:34.322767Z

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.

source=pdf_text observed=2026-08-06T18:28:30.651388Z digest=sha256:0676216fa4e3290bac93559b0dcc5573eee345efdcc8c3b40a1d89f0d240ea86

Observation 62493af4-5edc-451b-a85d-ae9f5fff9b99 · outbound

This paper cites Introducing openai o3-mini.

KAT-V1: Kwai-AutoThink Technical Report Introducing openai o3-mini

Reference 33

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raw_fallback, observed 2026-08-06T18:28:34.118900Z

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.

source=pdf_text observed=2026-08-06T18:28:30.708835Z digest=sha256:ef5e7de296253d7066a66f5f88fe471d1483f90d87978b895fb3b7fec8f3ab46

Observation c5910ea4-34db-4eab-a3c5-ae7b19d124e4 · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

KAT-V1: Kwai-AutoThink Technical Report Yi: Open Foundation Models by 01.AI

Reference 34

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source=pdf_text observed=2026-08-06T18:28:30.801945Z digest=sha256:45fbb501b35219b5f1e8a91fdf13d5a46cb3cfa48823d6e69aa8d4214e9acb55

Observation e58c0ffe-f16c-42b3-b8ac-ecef3e38d59b · outbound

This paper cites Llama-nemotron: Efficient reasoning models, 2025.

KAT-V1: Kwai-AutoThink Technical Report Llama-nemotron: Efficient reasoning models, 2025

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.990962Z

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.

source=pdf_text observed=2026-08-06T18:28:30.891863Z digest=sha256:b8818bf6bdd825c79b1d817b64f1b440292115665f39d800bbdcd151dcc41e8e

Observation e5eeaa8f-8f2b-456e-8220-84ff31c598b7 · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

KAT-V1: Kwai-AutoThink Technical Report Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 36

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source=pdf_text observed=2026-08-06T18:28:30.957011Z digest=sha256:aa9ef025b0f82474a3774df99eca999b560f811b8c0e66837c752cd0053cd412

Observation 8d212d7b-df23-4154-b05c-7424be6d2047 · outbound

This paper cites DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

KAT-V1: Kwai-AutoThink Technical Report DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.766165Z

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.

source=pdf_text observed=2026-08-06T18:28:31.055456Z digest=sha256:55f6f436c1cdf70858bb996e322093ca3d9febcfaa6d06947d8afc314082f1d7

Observation 07c72986-055d-40cc-b147-1df613ae4cbe · outbound

This paper cites Wildbench: Benchmarking llms with challenging tasks from real users in the wild, 2024.

KAT-V1: Kwai-AutoThink Technical Report Wildbench: Benchmarking llms with challenging tasks from real users in the wild, 2024

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T18:28:31.104895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:28:31.104895Z digest=sha256:cff4d6e6f90ae3924c6004765350f0b811fb44c4fe96f48fc6b9b38f05b02bc8

Observation cd5858b0-7c73-45e9-aab4-9b4ad52aa01e · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

KAT-V1: Kwai-AutoThink Technical Report Gpqa: A graduate-level google-proof q&a benchmark

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T18:28:31.185978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:28:31.185978Z digest=sha256:686128cd60b724af4a5de635cbc290ad70c69b86233e0dbea90c79c0d2a72f11

Observation 6833591c-79f6-4ef3-bddf-ca7503074d35 · outbound

This paper cites Math-500 dataset.

KAT-V1: Kwai-AutoThink Technical Report Math-500 dataset

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.590441Z

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.

source=pdf_text observed=2026-08-06T18:28:31.272794Z digest=sha256:65daeb5989eda7b2984af69ebd2e16c708d3d93718032c13480083323fd2797e

Observation ce2cd7cb-d208-49ab-a0f1-a88f4c136b77 · outbound

This paper cites Aime_2024 dataset.

KAT-V1: Kwai-AutoThink Technical Report Aime_2024 dataset

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.451575Z

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.

source=pdf_text observed=2026-08-06T18:28:31.327010Z digest=sha256:fa31b36f26f97b36539a81cdc16023a98229a6143691955c4df60a749d51fc2e

Observation 53a26be4-b108-4200-8e5f-1ca7be795f44 · outbound

This paper cites Aime2025 dataset.

KAT-V1: Kwai-AutoThink Technical Report Aime2025 dataset

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.223468Z

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.

source=pdf_text observed=2026-08-06T18:28:31.415998Z digest=sha256:8db423495258111ad140e0ee9de528f445f219e1325a3e6c5f9b9b83c65c4f10

Observation d64594a5-225a-49b7-92e7-64979b6f5381 · outbound

This paper cites Autologi: Automated generation of logic puzzles for evaluating reasoning abilities of large language models, 2025.

KAT-V1: Kwai-AutoThink Technical Report Autologi: Automated generation of logic puzzles for evaluating reasoning abilities of large language models, 2025

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:33.049895Z

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.

source=pdf_text observed=2026-08-06T18:28:31.511552Z digest=sha256:176a6cbcd906dc31e8d2514fe75d7a75fda003addf62f1c5afe61baa69530126

Observation 1c6bf2d8-93ea-498e-8513-4d94ee11fd63 · outbound

This paper cites an unresolved cited work.

KAT-V1: Kwai-AutoThink Technical Report Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:28:32.844954Z

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.

source=pdf_text observed=2026-08-06T18:28:31.570384Z digest=sha256:49d6602bba3d127a470ccec14b89cb07970266ff4b0746ec45ebfd7a68156f4a

Observation 19bd6725-d6a6-4e28-8c64-c8be83c87cf1 · outbound

This paper cites Program Synthesis with Large Language Models.

KAT-V1: Kwai-AutoThink Technical Report Program Synthesis with Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T18:28:31.650019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:28:31.650019Z digest=sha256:512b4912673291706ab83126c14938e5cb0f4c8648e74831f184482742edb216

Observation 603878f0-7d62-4007-bc62-f0a4fd641460 · outbound

This paper cites Livecodebench: Holistic and con- tamination free evaluation of large language models for code.

KAT-V1: Kwai-AutoThink Technical Report Livecodebench: Holistic and con- tamination free evaluation of large language models for code

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:32.665296Z

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.

source=pdf_text observed=2026-08-06T18:28:31.692673Z digest=sha256:72a2756c39e10d081f1fddcfd33d36862bb6e9ad3fa8cc8c8d3a89870647324c

Observation caa099e6-cd6d-488c-96b3-d61582477456 · outbound

This paper cites Patil, Huanzhi Mao, Charlie Cheng-Jie Ji, Fanjia Yan, Vishnu Suresh, Ion Stoica, and Joseph E.

KAT-V1: Kwai-AutoThink Technical Report Patil, Huanzhi Mao, Charlie Cheng-Jie Ji, Fanjia Yan, Vishnu Suresh, Ion Stoica, and Joseph E

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:28:32.552556Z

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.

source=pdf_text observed=2026-08-06T18:28:31.763652Z digest=sha256:42518401e316e72dc9381ed3fb998aa10cc7deba70351bc0844e48e88928e5c6

Pith citing papers

Observation ee06b167-3ef6-4ae8-b4a5-f26f18987766 · inbound

R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning cites this paper.

R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning KAT-V1: Kwai-AutoThink Technical Report

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:42:21.261957Z

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.

source=arxiv_source observed=2026-08-05T14:42:21.114924Z digest=sha256:9ffab724348e5a9c0abfab561357cace62cee3a69e314c33ca017b6182dfbb5f