Paper Citation Record · LEDGER

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2509.24560.

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

pith.paper-citation-record.v1
2509.24560 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:51:53.735735Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T10:21:39.892271Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T10:25:26.719523Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy0
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 922ad072-4c67-42f5-8c4a-1512466e6322 · outbound

This paper cites A Survey on Data Selection for Language Models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification A Survey on Data Selection for Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:50.059178Z

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Observation 30d45229-6fff-496e-af92-2b2607f70dcb · outbound

This paper cites Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

Reference 4

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Observation fd743735-2306-447b-b3c1-165f3c32dc0f · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 5

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Observation 5266221d-fa61-4e21-82f5-9dee55eb3905 · outbound

This paper cites The Llama 3 Herd of Models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:50.482050Z

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Observation 2e8f611a-3ac5-4afd-8c0b-aa806a81f98f · outbound

This paper cites Thinkless: LLM Learns When to Think.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Thinkless: LLM Learns When to Think

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:50.649862Z

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Observation fca865c3-3576-46df-9976-e2105df64380 · outbound

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

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:50.776381Z

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Observation 33060816-e11f-426d-9e8f-069aead883ba · outbound

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

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:50.914512Z

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Observation a7d53ba7-2286-4434-ad74-83ce11f94fe8 · outbound

This paper cites m1: Unleash the po- tential of test-time scaling for medical reasoning with large language models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification m1: Unleash the po- tential of test-time scaling for medical reasoning with large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.018330Z

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Observation 8c09e99f-430d-41c8-82e7-a477716cc7bd · outbound

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

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Think Only When You Need with Large Hybrid-Reasoning Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.270386Z

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Observation a4c2c11c-1d38-4049-bf6f-06ab8a2860cd · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Reasoning Models Can Be Effective Without Thinking

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.648270Z

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Observation 0bd09e0e-b0e5-45bf-b46e-894236e74223 · outbound

This paper cites Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.711834Z

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Observation bcd48595-4c86-4765-b9f2-3cda53c2f0af · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification HybridFlow: A Flexible and Efficient RLHF Framework

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.776607Z

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Observation f0f0c9d7-f15a-49cc-9b7f-36650c6952a0 · outbound

This paper cites Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.857140Z

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Observation 783d7d7c-7c2a-461f-8d05-dcfcca2d6359 · outbound

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

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.922247Z

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Observation 8cc402e8-7787-4cd6-9349-0bb726f4555f · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.010093Z

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Observation 2f8823e5-dc40-4a55-a6c0-9dfdba1744d5 · outbound

This paper cites Qwen3 Technical Report.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Qwen3 Technical Report

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.318666Z

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Observation 116c1347-5771-4556-b7d3-49133bb87a73 · outbound

This paper cites Accessed: 2025-08-27.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Accessed: 2025-08-27

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.468526Z

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Observation f41295f0-6db1-4c41-82cb-06a6dea3396e · outbound

This paper cites Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.645620Z

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Observation 27faa2da-ce88-4f1d-a4a1-8709feb360ab · outbound

This paper cites Fast-slow thinking for large vision-language model reasoning.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Fast-slow thinking for large vision-language model reasoning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.821261Z

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Observation 75116ecc-157f-444d-b5b5-7e262081b07d · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.967619Z

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Observation 5bbc0ded-7347-4b2b-9298-8d347de07266 · outbound

This paper cites Shorterbetter: Guiding reasoning models to find optimal inference length for efficient reasoning.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Shorterbetter: Guiding reasoning models to find optimal inference length for efficient reasoning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.126651Z

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Observation c2da83b1-c134-43a4-8ab3-d97d194d2423 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.189476Z

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Observation d2718a06-1da7-4923-98ca-ca53e307e5ba · outbound

This paper cites SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.281207Z

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Observation 54401410-eca4-484e-9f6d-0e7565e33f64 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.388873Z

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Observation 00f9f1d0-c640-4be0-9a55-37921d3b8350 · outbound

This paper cites MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.456752Z

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Observation cbfecd7f-ffe6-450f-83cf-ee5b3699b4d2 · outbound

This paper cites equation 1, both critic-based reinforcement learning methods (e.g., PPO) and critic-free methods (e.g., GRPO (Guo et al., 2024)) can be applied.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification equation 1, both critic-based reinforcement learning methods (e.g., PPO) and critic-free methods (e.g., GRPO (Guo et al., 2024)) can be applied

Reference 31

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Observation a3f04f27-be85-4d8c-9fb5-5a78592fc2aa · outbound

This paper cites We adopt the Qwen2.5 (Team, 2024)-7B-Instruct model and LLama3.1-Instruct-8B as the backbone models.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification We adopt the Qwen2.5 (Team, 2024)-7B-Instruct model and LLama3.1-Instruct-8B as the backbone models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:53.643148Z

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Observation c073a16e-c6ef-46f2-9ddc-1755248b4ad8 · outbound

This paper cites yes,” “no,.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification yes,” “no,

Reference 33

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Observation f40bbd7b-2d7a-4870-af83-93f39ed0cb35 · outbound

This paper cites PubMedQA: A Dataset for Biomedical Research Question Answering.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 2021

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Observation 1edd9de9-3563-4cfd-909c-e5cc69260978 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:52.132024Z

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Observation cb402813-c124-4aaa-ad62-52804fb69d88 · outbound

This paper cites Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:51.561726Z

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Observation d5bd85dc-7ea3-422f-b2ec-095d848a1e5d · outbound

This paper cites Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding.

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding

Reference 2024

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Observation a1d07536-eda7-4e9c-b93f-4a5bbdd0edfc · outbound

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

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 2025

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Pith citing papers

Observation 44a02140-88b8-4300-9753-7e77f7822a19 · inbound

Medical Reasoning with Large Language Models: A Survey and MR-Bench cites this paper.

Medical Reasoning with Large Language Models: A Survey and MR-Bench AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:23:30.228809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.