Paper Citation Record · LEDGER

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 53 inbound Pith citation observations for arXiv:2502.07374.

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

pith.paper-citation-record.v1
2502.07374 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:59:59.530790Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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

measured 53 of 53 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:02:43.290663Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy9
unresolved3
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 09c7f4fd-4b35-42c1-8e89-72f2abad0a5e · outbound

This paper cites For the fraction to be nonnegative, we need log n − 3 > 0.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! For the fraction to be nonnegative, we need log n − 3 > 0

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.733979Z

Source-reported events for the cited work

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

Observation 5884ceb0-9ff9-4d7c-8e59-b0083a95f0fd · outbound

This paper cites Using logarithmic properties, we can rewrite log(n2) as 2 logn.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Using logarithmic properties, we can rewrite log(n2) as 2 logn

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.721589Z

Source-reported events for the cited work

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

Observation 8aa68321-20d7-49b2-bad3-e943d3f2e4d0 · outbound

This paper cites This leads to the inequality: 2 logn − (log n)2 ≥ 0.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! This leads to the inequality: 2 logn − (log n)2 ≥ 0

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.707881Z

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

Observation 6ed19d2e-70f4-4739-a753-191605f8c7a1 · outbound

This paper cites Factoring, log n(log n − 2) ≤ 0.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Factoring, log n(log n − 2) ≤ 0

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.694027Z

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

Observation a14a7214-c8d0-40fb-b756-cf28375eef32 · outbound

This paper cites However, these two conditions are contradictory.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! However, these two conditions are contradictory

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.680291Z

Source-reported events for the cited work

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

Observation 73b30d78-1ffb-49e4-9bbc-cd6a5769add8 · outbound

This paper cites Counting these integers, we find there are 100 such integers.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Counting these integers, we find there are 100 such integers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.661952Z

Source-reported events for the cited work

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

Observation f59cd030-f4ac-4503-bbe0-153aba4f5b88 · outbound

This paper cites an unresolved cited work.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Unresolved cited work

Reference 7

Resolution: parse uncertain; raw_fallback, observed 2026-08-08T12:59:59.647257Z

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

Observation 57acaa00-eb63-4c4c-8912-8e2e8f707014 · outbound

This paper cites an unresolved cited work.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:59:59.631911Z

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

Observation 97d85104-ea6c-4efe-b7f4-e2a47cfb2b45 · outbound

This paper cites an unresolved cited work.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:59:59.617955Z

Source-reported events for the cited work

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

Observation c8727bef-ac50-476a-93f9-3df72759b6fe · outbound

This paper cites Then, the expression simplifies to: x(2 − x) x − 3 16 Submission and Formatting Instructions for ICML 2025 Long CoT answer (4/4).

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Then, the expression simplifies to: x(2 − x) x − 3 16 Submission and Formatting Instructions for ICML 2025 Long CoT answer (4/4)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.603899Z

Source-reported events for the cited work

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

Observation a54d8de9-abf7-454f-bad8-28601e30425d · outbound

This paper cites an unresolved cited work.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:59:59.590279Z

Source-reported events for the cited work

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

Observation be283119-792b-4945-b716-11a3d4ef5fd8 · outbound

This paper cites - For 1 < n <100: The expression is negative, not acceptable.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! - For 1 < n <100: The expression is negative, not acceptable

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.576872Z

Source-reported events for the cited work

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

Observation 8b056724-3a0c-46f0-97a2-2d602ff3d564 · outbound

This paper cites Alternatively.

LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Alternatively

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:59:59.562501Z

Source-reported events for the cited work

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

Pith citing papers

Observation 7878528f-d02e-4c75-b080-5e90d4d5c33f · inbound

OpenCodeReasoning: Advancing Data Distillation for Competitive Coding cites this paper.

OpenCodeReasoning: Advancing Data Distillation for Competitive Coding LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:21:42.140525Z

Source-reported events for the cited work

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

Observation 839545db-9d96-4ab3-827c-700d5ed32573 · inbound

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 156

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:11.034235Z

Source-reported events for the cited work

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

Observation bc71d366-6050-4a8f-a69f-abf7ccfbd837 · inbound

Generative AI Act II: Test Time Scaling Drives Cognition Engineering cites this paper.

Generative AI Act II: Test Time Scaling Drives Cognition Engineering LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 173

Resolution: unresolved; no resolver link, observed 2026-08-16T12:02:43.290663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a283155b-0541-4ef4-93a6-f3f45cf6a0bf · inbound

Generative to Agentic AI: Survey, Conceptualization, and Challenges cites this paper.

Generative to Agentic AI: Survey, Conceptualization, and Challenges LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.972709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e9a4cf-b829-46c6-99b4-54ffbc0a3d60 · inbound

Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning cites this paper.

Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T10:54:52.554396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d23b799-4476-4a5f-a1a6-d79e274fdce3 · inbound

Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective cites this paper.

Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:28:19.005717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d9c2716-6faa-4785-8080-07a32931bec1 · inbound

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens cites this paper.

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:15:17.943970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64fa7b70-3768-4fbd-a505-d29328ce1ac0 · inbound

Multilingual Test-Time Scaling via Initial Thought Transfer cites this paper.

Multilingual Test-Time Scaling via Initial Thought Transfer LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:21.383942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c85a73ea-ced4-4ece-9674-fc6ba7d93cf9 · inbound

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants cites this paper.

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:29.125682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34f5e305-0b0e-4771-9a15-bea1284fcc89 · inbound

Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFN cites this paper.

Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFN LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:17.071531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd34cbf1-6d0f-44d7-ada5-35e0c1095c0b · inbound

Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models cites this paper.

Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:46:14.997716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51932b9f-35f8-4c96-a928-a98cdc01a828 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 230

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:13.329443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38defa1-fe24-4b5c-a4d9-92b76c4a108d · inbound

Beyond path selection: Better LLMs for Scientific Information Extraction with MimicSFT and Relevance and Rule-induced(R$^2$)GRPO cites this paper.

Beyond path selection: Better LLMs for Scientific Information Extraction with MimicSFT and Relevance and Rule-induced(R$^2$)GRPO LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:19.506483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 037b8c57-1954-4814-a83f-344cccb655ee · inbound

Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start cites this paper.

Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:54.424523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ecf08bf-18a9-4dd2-b6f0-ef893a4eecfc · inbound

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning cites this paper.

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:36.043828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bc10ce7-793c-453d-86b4-ccc15d19268d · inbound

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

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T12:12:08.884185Z

Source-reported events for the cited work

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

Observation a6807fc2-30f5-4543-adcc-fee13ca3928d · inbound

StreamBP: Memory-Efficient Exact Backpropagation for Long Sequence Training of LLMs cites this paper.

StreamBP: Memory-Efficient Exact Backpropagation for Long Sequence Training of LLMs LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:36.286634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8a60fb3-f2bf-4cd4-bc87-a02141441e58 · inbound

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity cites this paper.

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:10:31.494010Z

Source-reported events for the cited work

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

Observation b5856e66-e3bf-4112-b2c0-a838138b03f0 · inbound

From Emergence to Control: Probing and Modulating Self-Reflection in Language Models cites this paper.

From Emergence to Control: Probing and Modulating Self-Reflection in Language Models LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:43.321333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 731500e5-d9e6-4222-b1f7-6c1765cda600 · inbound

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning cites this paper.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:13:19.473338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08a250c5-131e-43bf-af69-a3042d743b4c · inbound

LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning cites this paper.

LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:52:09.359833Z

Source-reported events for the cited work

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

Observation 92365ba1-0d84-471e-a4c7-1cf627cf7658 · inbound

ReasonBridge: Efficient Reasoning Transfer from Closed to Open-Source Language Models cites this paper.

ReasonBridge: Efficient Reasoning Transfer from Closed to Open-Source Language Models LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:01:31.971045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62e9ecb1-74b4-4f61-99e8-d61a795408f7 · inbound

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence cites this paper.

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:14.969998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efa7cf45-bf9a-4a17-9283-81967d2642f4 · inbound

ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context cites this paper.

ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:21:41.873922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12671650-3fc0-4ac1-a651-65143299ec16 · inbound

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess cites this paper.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:13:14.885294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3edadb06-f346-4820-9dce-cb30ecbc8ec1 · inbound

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique cites this paper.

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:11:17.687092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afb432f3-93a1-4898-82d6-2917b06861ec · inbound

The Challenge of Teaching Reasoning to LLMs Without RL or Distillation cites this paper.

The Challenge of Teaching Reasoning to LLMs Without RL or Distillation LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:51:17.298387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11949cf2-eac1-4506-805d-9f8473787bbd · inbound

KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning? cites this paper.

KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning? LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:17:05.905312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c1abac-b546-4a5d-91d8-a572085d039e · inbound

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training cites this paper.

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:57.928076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 068f0e3c-37e3-4a5e-a2fd-c4098039a091 · inbound

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR cites this paper.

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:24:26.265938Z

Source-reported events for the cited work

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

Observation c93e9254-7bd2-4d41-9e02-2dead679d57a · inbound

Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors cites this paper.

Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T12:49:08.607462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d845bad-5b4d-47ef-974d-c38ae4dc6692 · inbound

Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity cites this paper.

Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T22:25:54.854382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912b9c90-5531-44c3-9cf4-38cceca727eb · inbound

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization cites this paper.

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T09:48:06.546638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0887370a-54b9-4fdf-a4ac-577d9376f2f7 · inbound

MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation cites this paper.

MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:58.659542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81d80dd5-ebc7-4474-9fa3-e166410f3b80 · inbound

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning cites this paper.

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:12:23.629162Z

Source-reported events for the cited work

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

Observation dc72c22f-d0aa-42ae-91cc-7f24d66c35dc · inbound

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning cites this paper.

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:10:34.922966Z

Source-reported events for the cited work

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

Observation 836a0a5e-7ca5-4377-ac97-b2b72f0e58b8 · inbound

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning cites this paper.

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T08:53:06.449889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18c9e2e0-8918-4471-be1e-4b453b330f95 · inbound

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment cites this paper.

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:27:52.351973Z

Source-reported events for the cited work

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

Observation 346a92a1-4247-4788-804d-f29d0c0e8dff · inbound

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment cites this paper.

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T09:22:44.434969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 283e0bbd-c7c7-429a-abf5-93fe92b74de1 · inbound

Embarrassingly Simple Self-Distillation Improves Code Generation cites this paper.

Embarrassingly Simple Self-Distillation Improves Code Generation LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T14:33:35.834383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3176639f-22bd-466a-9764-758ef4cdcf88 · inbound

Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning cites this paper.

Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 81

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:41:01.883900Z

Source-reported events for the cited work

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

Observation 707eccf1-7783-4abc-a4de-0d26a3499389 · inbound

How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem cites this paper.

How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.972528Z

Source-reported events for the cited work

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

Observation 63741be1-881d-4f3c-8ffa-fea451ec484f · inbound

Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities cites this paper.

Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:46:26.053222Z

Source-reported events for the cited work

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

Observation 5b610586-5d45-4cfa-beec-e4174f2d53dc · inbound

LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories cites this paper.

LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:46:10.711009Z

Source-reported events for the cited work

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

Observation 5438f4ea-6941-46ff-ad78-23663f3d5e2a · inbound

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not cites this paper.

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:16:00.088155Z

Source-reported events for the cited work

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

Observation 02350108-7574-4912-9c72-600ea3b666aa · inbound

The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models cites this paper.

The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:26:56.903491Z

Source-reported events for the cited work

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

Observation f2eb1c00-3a8b-4e6a-8d8e-8c8a268d1435 · inbound

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces cites this paper.

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T22:31:21.537868Z

Source-reported events for the cited work

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

Observation 7fbaed2a-9d8e-407b-a931-5c74684c2b8f · inbound

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes cites this paper.

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 134

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T13:00:56.056098Z

Source-reported events for the cited work

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

Observation f99a7479-b36e-4f5a-b24b-c61b62af4fe9 · inbound

ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models cites this paper.

ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:39:45.235157Z

Source-reported events for the cited work

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

Observation 53759b41-b2c1-45d8-b0e1-9ee234500d9d · inbound

Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use cites this paper.

Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:16:56.314752Z

Source-reported events for the cited work

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

Observation a4eb6803-f3a0-41bc-94eb-54041dfef263 · inbound

LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction cites this paper.

LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 169

Resolution: unresolved; no resolver link, observed 2026-07-30T22:49:43.474347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceb3355d-fcfc-4944-bffa-2fe1284b8ce4 · inbound

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility cites this paper.

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 179

Resolution: unresolved; no resolver link, observed 2026-08-15T14:49:05.498048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eb90253-3686-45b2-80c8-d5fe74a795fa · inbound

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair cites this paper.

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-14T14:32:16.657008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.