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Paper Citation Record · LEDGER

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2507.18305.

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

pith.paper-citation-record.v1
2507.18305 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:20:49.663929Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T19:52:11.018335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:53.685679Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e9db89c-04c3-4fca-84fd-ce7c031249f8 · outbound

This paper cites Stealthy and persistent unalignment on large language models via backdoor injections.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Stealthy and persistent unalignment on large language models via backdoor injections

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.542676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.423120Z digest=sha256:59ca7d4cda96b8533b776a94f9f1db829b11221f5aff0305c43e434dffd138fd

Observation 05476a2f-4644-4145-8486-8777fc9e4210 · outbound

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

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.431194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.431194Z digest=sha256:469041c1f8c4807fe3bd9e2b72b2a82a1eda235d81d380d5ba0a8470f6500b08

Observation 6a14410f-8e26-443e-9e39-321e2b9fc0a7 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Training Verifiers to Solve Math Word Problems

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.437459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.437459Z digest=sha256:0ea5331ae7ee44f59b39c15bedd82d7d59a00beee50a654b12ed050a2c2ca67a

Observation bc8a09ea-4929-4fbb-b902-dc53d57c9605 · outbound

This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.442782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.442782Z digest=sha256:56ec982874ac44d331080679fc61209672e7c5ef1ca3b71ec68933f269ac42d5

Observation 88a98d13-ff2a-4a13-b9ed-92d9cb10a856 · outbound

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

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.447759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.447759Z digest=sha256:89cb7a777ce2255bffcd84add13b52b7e6fd0b652a59be9fa56ead1a87f1cb8e

Observation cea1374a-4435-4553-b2a7-4e123213753f · outbound

This paper cites An Engorgio Prompt Makes Large Language Model Babble on.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit An Engorgio Prompt Makes Large Language Model Babble on

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.453101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.453101Z digest=sha256:f5d8f36d7910e84aff8ceb5ed4db617bf0a2f7c84f8017a5f3c61adb58d8e0ee

Observation 2235fb81-823b-40a7-a1b5-5953287965aa · outbound

This paper cites Towards revealing the mystery behind chain of thought: A theoretical perspective.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Towards revealing the mystery behind chain of thought: A theoretical perspective

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.528869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.459317Z digest=sha256:e9a472bcd1e33b994bf3ef85dc2e0ab49b91c5dffc2a256ef0b1f2c63e2dae5b

Observation c1e1610d-5587-4038-9dd7-70158ce52b39 · outbound

This paper cites Denial-of-Service Poisoning Attacks against Large Language Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Denial-of-Service Poisoning Attacks against Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.463977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.463977Z digest=sha256:221ccc7fd84637e18fe4322799a2ff8afb32f84641caf41c3b23e279e2a7ade6

Observation 850ee62a-b512-42e6-adc0-e3c89b2f3dff · outbound

This paper cites Coercing LLMs to do and reveal (almost) anything.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Coercing LLMs to do and reveal (almost) anything

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.469492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.469492Z digest=sha256:5edcbf6f676d0624ae5d846ffa1a022a6aead8a1968d85917aaa94af393b0b05

Observation 71332089-ec13-4514-8671-cbafae29be19 · outbound

This paper cites Exploring Backdoor Vulnerabilities of Chat Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Exploring Backdoor Vulnerabilities of Chat Models

Reference 10

Resolution
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no resolver link, observed 2026-08-15T18:20:49.474056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.474056Z digest=sha256:f85560e4c337e33071643f80b91611ce9b07312faafe12868dec95934c4fee21

Observation 29a178d2-4c44-4ab4-9d9a-14e37bc5287e · outbound

This paper cites OpenAI o1 System Card.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit OpenAI o1 System Card

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.479378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.479378Z digest=sha256:0631d6458ee3f1b8c3adf07a2865b6462c04624bd9e3ca04c67e23a3573bee2a

Observation b5bf3fd7-1785-4157-aefc-552a07118c2d · outbound

This paper cites Overthink: Slowdown attacks on reasoning llms.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Overthink: Slowdown attacks on reasoning llms

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.485425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.485425Z digest=sha256:811251b7d35115de15b495ea7e3c6ddbca9b08a1fa02b54aa4f7d55f89ec47e9

Observation de4f0b73-234c-4d4f-9d23-eb456cb562a6 · outbound

This paper cites Weight poisoning attacks on pretrained models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Weight poisoning attacks on pretrained models

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.514430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.490150Z digest=sha256:71c42e3b8185d6e7db35c1be308c4268721a73f6235f416977a918e8291968f2

Observation 6d85ab41-edc1-4926-aad2-adf69e31d024 · outbound

This paper cites BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.495320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.495320Z digest=sha256:b15c318f5d17fb031c7bd1b5a197cb33e4b74db1ea92125806c2abd64fcf3900

Observation 9deca424-1994-4363-829d-107b2ae1d246 · outbound

This paper cites Backdoor learning: A survey.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Backdoor learning: A survey

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.500821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.500149Z digest=sha256:30255a5d49fc9d3d3d86b4874e018b9eaef1ed749d8665d96be5de65dba38076

Observation 83df5e0f-cd74-48b4-8c20-d1a3fe2cf019 · outbound

This paper cites Let's verify step by step.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Let's verify step by step

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.487332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.505494Z digest=sha256:eca1d63137f1c0676c4b871c5dd6ad38fb0925a792f50be234cd5d38bfe82648

Observation ffbb9464-34be-4c31-8aa4-85017f8dd4a9 · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Fine-pruning: Defending against backdooring attacks on deep neural networks

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.473134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.510294Z digest=sha256:ffd6aa08291472c9d9b804f7249ffd757442090dde35c90f86f34362aac0999a

Observation 596ab527-3771-40e3-9f56-550348c959b8 · outbound

This paper cites Trojtext: Test-time invisible textual trojan insertion.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Trojtext: Test-time invisible textual trojan insertion

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.458671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.515544Z digest=sha256:31e45e8a9d83e9fb742a6edbd219479508af4caf6a6e027d48543d940914a98d

Observation 3a302ec1-bfc6-461d-b731-a2d9a1f29fec · outbound

This paper cites Hidden trigger backdoor attack on NLP models via linguistic style manipulation.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Hidden trigger backdoor attack on NLP models via linguistic style manipulation

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.443856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.521367Z digest=sha256:4fb59536e6c1c0e960bdbce2f43e814105e57a50e2ff0c7e8c330cfca4dc6386

Observation 19f36903-bca8-4b29-86ba-9ddc63c9d3ac · outbound

This paper cites Mind the style of text! adversarial and backdoor attacks based on text style transfer.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Mind the style of text! adversarial and backdoor attacks based on text style transfer

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.429979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.527177Z digest=sha256:97ff1a593bfe93acdd6d470c6110baf78034bda189fa94d88b7d63ce0a6fba43

Observation b83080ef-a1b3-461d-9355-93ad4d46edc4 · outbound

This paper cites Hidden killer: Invisible textual backdoor attacks with syntactic trigger.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Hidden killer: Invisible textual backdoor attacks with syntactic trigger

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.415606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.532014Z digest=sha256:0c4b32b9b1df983fb098753589d074b81f1e6aff29ac9b7d8abda349472e4d17

Observation 5fceee3e-6d24-40a9-af70-36ea43b310dd · outbound

This paper cites Fine-tuning aligned language models compromises safety, even when users do not intend to! In ICLR, 2024.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Fine-tuning aligned language models compromises safety, even when users do not intend to! In ICLR, 2024

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.401404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.536661Z digest=sha256:f1c75998f2784770b526c2b9f2733eb38307e01329e5d84625bc4bf02dd5aee4

Observation 9e988b3f-8c7f-42ce-a4a2-6187d0df488f · outbound

This paper cites Hello qwen2.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Hello qwen2

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.385345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.540840Z digest=sha256:bfab02765071d9493b10173c213eae5091a4fd3112bda8553a65fa2634bbb462

Observation 6261e850-aea0-4bc2-bf6a-d00574ed861f · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Qwq-32b: Embracing the power of reinforcement learning

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.371514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.545878Z digest=sha256:561373d0493e1889511bf55ff300770a2c00948a739aa865d4510a0eed5a05c4

Observation e739cdbc-4858-4626-9d23-3e8d9fc18233 · outbound

This paper cites Universal jailbreak backdoors from poisoned human feedback.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Universal jailbreak backdoors from poisoned human feedback

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.357322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.550611Z digest=sha256:ca43216a5f5fbb3642f6b37d3480d8efd7689f9853e279612737888dd69bee2b

Observation 436d4c44-e35d-4e1e-903f-231f42b98d67 · outbound

This paper cites On the exploitability of instruction tuning.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit On the exploitability of instruction tuning

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.343158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.555721Z digest=sha256:42aa9737527e1de66a9aff245fa3b6791b9ca68794fc6644c0a5b22d5ff5b794

Observation eb149dfb-2d8c-4568-a9a3-65ed1dcfc025 · outbound

This paper cites Sponge examples: Energy-latency attacks on neural networks.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Sponge examples: Energy-latency attacks on neural networks

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.328871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.560647Z digest=sha256:a7c3efefbd7de172455a8a552f274c265aab2edbd1107e554f42f36a29787038

Observation 6b88ae2f-777b-4a88-8113-3dcdfde3e529 · outbound

This paper cites Qwq: Reflect deeply on the boundaries of the unknown, November 2024.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Qwq: Reflect deeply on the boundaries of the unknown, November 2024

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.565294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.565294Z digest=sha256:221f177feacca8a47e3c2d59b3cf2b646ea454942f4e17104d098168f31db976

Observation cae08903-eb78-4bf4-bd90-224d2f779b22 · outbound

This paper cites Poisoning language models during instruction tuning.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Poisoning language models during instruction tuning

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.305793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.569710Z digest=sha256:f57a4ec140d4daca437d723986def8934f950b0c9161aad8a30d34bb6bd87286

Observation 21a37586-89b4-4b74-8fb5-c7cc82d90536 · outbound

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

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Finetuned Language Models Are Zero-Shot Learners

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.574216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.574216Z digest=sha256:cd08a93bcb3de24213439e41ccff44f0fdb6ab5b92565c31b8f8fb3650a24ad3

Observation 7072eb48-4fa2-42b6-bf16-4d25f12f84a6 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Chain-of-thought prompting elicits reasoning in large language models

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.291205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.579105Z digest=sha256:3b8c5337a37052828aaad9e0555ac4f70354f3ffdc33372c57b5076698be7963

Observation 7598b979-7287-4949-9f1d-b5c6051d8f66 · outbound

This paper cites BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.583507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.583507Z digest=sha256:7bc0513971d0d1601e8766ea0f6a3eee5c6db95c075c90f0acf1d25e359a6417

Observation db743a0c-585f-473a-8abf-e34ec0f01146 · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.588169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.588169Z digest=sha256:38db72e372e0e9e77655d15415af445c62bc7e441205d9ff1fcb18ded6fed7ed

Observation ec9420a7-05cc-4cd9-8f40-da17212da17e · outbound

This paper cites Chain of Draft: Thinking Faster by Writing Less.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Chain of Draft: Thinking Faster by Writing Less

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.592495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.592495Z digest=sha256:3d8f4cac28cef194e62e836af6052423b6da603d778aefb7fb5f1a0daeec1b5b

Observation 6f3de590-9b2e-4c03-8d98-87b8a9f116e4 · outbound

This paper cites BITE: textual backdoor attacks with iterative trigger injection.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit BITE: textual backdoor attacks with iterative trigger injection

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.276326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.597671Z digest=sha256:3fd9988f1a1ca2fc8b29106bcaa9d6fa2d9f4c0d7bd080794b12d501234b50a4

Observation d06cc7f9-9462-4017-9923-7428cdd9cf94 · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Backdooring instruction-tuned large language models with virtual prompt injection

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.260384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.602338Z digest=sha256:656b9eaac584e888c4ae6bf7c625c110d6c3f178fc1da511d7d2cb7bf2f44d50

Observation 91dd9df1-80bf-4207-b76d-413407b1b99b · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Backdooring instruction-tuned large language models with virtual prompt injection

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.245379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.607653Z digest=sha256:ae98f25402598958d583f7da6fb05fc37ccad6dba6564c4393bf594c698787e1

Observation 718fec48-86f6-4531-9e8b-3674139bef97 · outbound

This paper cites Qwen2.5 Technical Report.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Qwen2.5 Technical Report

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.612101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.612101Z digest=sha256:5a60a83ab6717fd323bfc6c5da77fd1a9ba5be5011fa06a2f5717886b14ca754

Observation 1d8c7103-e43d-4af1-944e-a8f7e8b733a6 · outbound

This paper cites Be careful about poisoned word embeddings: Exploring the vulnerability of the embedding layers in NLP models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Be careful about poisoned word embeddings: Exploring the vulnerability of the embedding layers in NLP models

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.231034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.616989Z digest=sha256:1ac3b6b4f8729d3eeb3d5877cc5a68826ee2ed4c8851b8696224aa495385c3ca

Observation 575769ce-f656-4d14-a12c-0e9e2cf6b97a · outbound

This paper cites Probe before you talk: Towards black-box defense against backdoor unalignment for large language models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Probe before you talk: Towards black-box defense against backdoor unalignment for large language models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.215247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.621892Z digest=sha256:7390d595879d44568d7ebf080365500ce0e572dd015ac0ffb3f9ff2b6e602389

Observation 662347fe-9e12-4593-b3d8-38a0dea076d8 · outbound

This paper cites BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.627441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.627441Z digest=sha256:8d4d091e7ef3e343f8ca4639eb1c0cad91e4f4068241553839bbb637e79fbebf

Observation 77cf6d71-6d0d-4126-96aa-5e31e15efa1b · outbound

This paper cites Automatic chain of thought prompting in large language models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Automatic chain of thought prompting in large language models

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:20:50.200113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T18:20:49.632187Z digest=sha256:40cf92089614076c7cdd751a161780ccbbc37a8b3c2ddce84d854e302f7e6263

Observation e685d0c6-60e3-409e-8859-4f2d0d800e80 · outbound

This paper cites Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.637557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.637557Z digest=sha256:38e8eb92c1e1bfa2af1db09c5d257f6e03500030931bca858cfc8548b9aea5d3

Observation f4e22911-436d-4517-bfa7-23c32906200f · outbound

This paper cites To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.643167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.643167Z digest=sha256:f800abb61ea652f16c7ab17bab872c9fdcc14ed097d5ee316f36a94f8a0c72a2

Observation c1c16fd0-ae33-45e1-80e5-c052dbe4dbce · outbound

This paper cites write newline.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit write newline

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.648136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.648136Z digest=sha256:ea066116ae053a0fcb3ce4ed6add2296ce2f1174e69c5178c6c73144f69871e4

Observation c552472a-930c-4ec9-955d-4672982e9d1c · outbound

This paper cites @esa (Ref.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit @esa (Ref

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.653847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.653847Z digest=sha256:1e76f816fa478db373de83d3403eeab67995f2271f47bf84df9c604212590edb

Observation fa93ebb4-275e-4ecc-b4f9-b186b0963bb8 · outbound

This paper cites an unresolved cited work.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.658895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.658895Z digest=sha256:cf8f68738533734c9d55a6daa5f2475a008f5f93ad544a4b63599fda8934675f

Observation df8ca008-18a7-4a0c-80ba-efebbb4504c0 · outbound

This paper cites r 9gRam. (` : lB1.D[ٕZJ>]7O J]HDl G.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit r 9gRam. (` : lB1.D[ٕZJ>]7O J]HDl G

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T18:20:49.663929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:20:49.663929Z digest=sha256:2bbe3955474736d235f89669e1a374361636eb8da9106d16312278b62abcd427

Pith citing papers

Observation 914bc9d2-f0b4-42a4-9b2b-cdca0ed4450c · inbound

RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks cites this paper.

RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:27:24.560289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T19:45:30.671490Z digest=sha256:46bcf7e776f7f07eb9f7e9481f7257b8efc81280f04e1a34fb3241532d03cbe4

Observation c03f65bb-bcf6-43be-adef-bf82afa81112 · inbound

Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems cites this paper.

Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-03T19:58:53.687109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-07-03T19:52:11.018335Z digest=sha256:a09eae83e6eacd993fe4708ccb03e9c00471c5acc54763d3b997e944f0002835