Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-08T22:25:35.119625Z
Paper Citation Record · LEDGER
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2607.05861.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T07:07:43.937980Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
27 of 27 outbound references displayed
- verified exact19
- verified fuzzy4
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f78885e2-a5d1-43c7-bcb4-82db19fde026 · outbound
This paper cites Improving text-to-image generation with input-side inference-time scaling.arXiv preprint arXiv:2510.12041, 2025a.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Improving text-to-image generation with input-side inference-time scaling.arXiv preprint arXiv:2510.12041, 2025a
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.488004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4172036c-25e3-4fa3-a7d6-3ce61867d503 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.498423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 439de9c2-ba8c-4d7a-babb-2af5d81b7bc4 · outbound
This paper cites Simpleqa verified: A reliable factuality benchmark to measure parametric knowledge.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Simpleqa verified: A reliable factuality benchmark to measure parametric knowledge
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.515791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 378ae87d-3737-4926-9fb5-67d5ee7c0ad9 · outbound
This paper cites Your models have thought enough: Training large reasoning models to stop overthinking.arXiv preprint arXiv:2509.23392,.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Your models have thought enough: Training large reasoning models to stop overthinking.arXiv preprint arXiv:2509.23392,
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.502352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5626b2da-b009-4083-a6a5-b40232e5cb46 · outbound
This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.512464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 212e8307-e81a-479c-a1aa-106cd0950245 · outbound
This paper cites T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.476832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 79c9d6de-ad71-41fa-b4ea-f910e311639d · outbound
This paper cites OpenAI o1 System Card.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization OpenAI o1 System Card
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.519029Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 97309de7-fb92-4a8f-9b92-3f424bfca6ce · outbound
This paper cites Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.525478Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ef48be6c-b42c-4735-bc2a-826ba7a60f0c · outbound
This paper cites an unresolved cited work.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-07-08T22:25:39.579426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4e574cb2-ac5b-4bed-b051-d2b58a9a6bfb · outbound
This paper cites an unresolved cited work.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-07-08T22:25:39.581612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 14bb2cb9-c0cb-4a25-8be8-8f4093ddb6df · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.521701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ae6a39f5-3cca-4c85-af88-d055964d0cfc · outbound
This paper cites Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.495002Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 11939e0f-e700-4b7a-896e-822373b2318d · outbound
This paper cites Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.516771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3131f75a-e836-4b65-9e7d-5f7bd9109e76 · outbound
This paper cites AdapThink: Adaptive Thinking Preferences for Reasoning Language Model.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.518829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e5bc2b48-05af-47df-9f1d-0dd8583bdf76 · outbound
This paper cites Unsafe by reciprocity: How generation-understanding coupling undermines safety in unified multimodal models.arXiv preprint arXiv:2603.27332,.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Unsafe by reciprocity: How generation-understanding coupling undermines safety in unified multimodal models.arXiv preprint arXiv:2603.27332,
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.484751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 26238231-4439-42e6-b9b2-6e29b052237f · outbound
This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Self-Consistency Improves Chain of Thought Reasoning in Language Models
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.508664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d1dda002-d588-4465-9f9e-8b1129edf6d2 · outbound
This paper cites Measuring short-form factuality in large language models.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Measuring short-form factuality in large language models
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.472828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3ddcd1b4-7d39-4b69-9707-b3622cd61e72 · outbound
This paper cites Qwen3 Technical Report.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Qwen3 Technical Report
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.494724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dc8f9468-bfc5-4825-9119-4b989cd0c30f · outbound
This paper cites Hotpotqa: A dataset for diverse, explainable multi-hop question answering.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T22:25:39.583408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5d2ff7b9-f1d2-465b-bcd3-d1115e9ac8f4 · outbound
This paper cites Are Reasoning Models More Prone to Hallucination?.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Are Reasoning Models More Prone to Hallucination?
Reference 20
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-08T22:25:39.514165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c6683de8-a772-4165-857f-8bd5b86c4256 · outbound
This paper cites Hallurnn: Mitigating hallucinations via recurrent cross-layer reasoning in large vision-language models.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Hallurnn: Mitigating hallucinations via recurrent cross-layer reasoning in large vision-language models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.473432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 96dae197-bff3-4abd-8b7a-dada2a6c2ff1 · outbound
This paper cites Unraveling hallucination in large reasoning models: A topological perspective.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Unraveling hallucination in large reasoning models: A topological perspective
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T22:25:39.575179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1a8b30ef-032d-4beb-99f5-a9cdfb02e038 · outbound
This paper cites Progressive-Hint Prompting Improves Reasoning in Large Language Models.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Progressive-Hint Prompting Improves Reasoning in Large Language Models
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:25:39.491495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 06880647-70f8-45c1-84dd-bd54ffdf7cbb · outbound
This paper cites Parallel-probe: Towards efficient parallel thinking via 2d probing.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Parallel-probe: Towards efficient parallel thinking via 2d probing
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-08T22:25:39.505727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 92d0ab6c-4e9d-4e3a-8c3d-ea00a32bdf90 · outbound
This paper cites A”, “B”, or “C.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization A”, “B”, or “C
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T22:25:39.583597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b13bbe99-e567-4f84-b560-4a158f8b55aa · outbound
This paper cites it is possible that.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization it is possible that
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T22:25:39.579230Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7a77d263-d036-4094-8f32-6d536bf31ae0 · outbound
This paper cites an unresolved cited work.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization Unresolved cited work
Reference 27
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-08T22:25:39.586035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation 015346d0-d947-4438-ae35-7f953e4d6a52 · inbound
Visual Contrastive Self-Distillation cites this paper.
Visual Contrastive Self-Distillation Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:07:43.937980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.