Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective | Paper Citation Record

Observation cd7960cf-27bb-447b-9b92-a329d3c9a498 · outbound

This paper cites On sensitivity of meta-learning to support data.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective On sensitivity of meta-learning to support data

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Observation 9062d4bc-6448-4df5-88c6-5edc78f9f64c · outbound

This paper cites Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms

Reference 2

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Observation 41bcb0e9-8fef-4824-a591-e2b94b7ae97d · outbound

This paper cites What learning algorithm is in-context learning? investigations with linear models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective What learning algorithm is in-context learning? investigations with linear models

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Observation 37cd0d8e-c446-4c34-9e67-58bc0e03c546 · outbound

This paper cites Hoffman, David Pfau, Tom Schaul, and Nando de Freitas.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Hoffman, David Pfau, Tom Schaul, and Nando de Freitas

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This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Transformers as statisticians: Provable in-context learning with in-context algorithm selection

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This paper cites Numinamath 72b cot.https://huggingface.co/ AI-MO/NuminaMath-72B-CoT, 2024.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Numinamath 72b cot.https://huggingface.co/ AI-MO/NuminaMath-72B-CoT, 2024

Reference 6

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Observation 6cbcf43e-f241-4849-8d4d-3a83eb7b4a52 · outbound

This paper cites On the optimization of a synaptic learning rule.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective On the optimization of a synaptic learning rule

Reference 7

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Observation 95fbf38b-d253-4a4d-9a88-36061aad59d2 · outbound

This paper cites Llama-Nemotron: Efficient reasoning models.CoRR, abs/2505.00949, 2025.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Llama-Nemotron: Efficient reasoning models.CoRR, abs/2505.00949, 2025

Reference 8

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Observation 63ffba2c-47ae-4b84-b451-cb57d72f734b · outbound

This paper cites In-Context Learning with Long-Context Models: An In-Depth Exploration.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective In-Context Learning with Long-Context Models: An In-Depth Exploration

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Observation 492d5d87-8e50-42d7-801b-64ae44e07137 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Graph of thoughts: Solving elaborate problems with large language models

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Observation 22663e17-7e8c-4082-b7e7-5f4b984cf4e6 · outbound

This paper cites On the ability and limitations of transformers to recognize formal languages.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective On the ability and limitations of transformers to recognize formal languages

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This paper cites Application of calculus of matrices to method of least squares: with special reference to geodetic calculations.Trans.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Application of calculus of matrices to method of least squares: with special reference to geodetic calculations.Trans

Reference 12

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This paper cites an unresolved cited work.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

Reference 13

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Observation 111a5e79-786e-45ab-b28e-e508b1c541de · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 14

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Observation 1d5550d9-631f-4a81-8b12-9f17cb116a5c · outbound

This paper cites A closer look at the training strategy for modern meta-learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A closer look at the training strategy for modern meta-learning

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Observation ee5eaa55-4e10-49fc-b2d9-a4a2f9773045 · outbound

This paper cites Variational metric scaling for metric-based meta-learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Variational metric scaling for metric-based meta-learning

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Observation bd803e36-d4ad-4d2e-908b-1d81c32c6da5 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Evaluating Large Language Models Trained on Code

Reference 17

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Observation 518ca357-322d-446b-80b2-59141bdd4cc3 · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 18

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Observation e55d93cd-53dc-4e59-90ce-8d4dac890a6f · outbound

This paper cites Tighter bounds on the expressivity of transformer encoders.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Tighter bounds on the expressivity of transformer encoders

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Observation 13f9710f-010f-4439-b2c3-3413663f1975 · outbound

This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

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Observation f2bb6e67-9f19-457a-a385-9c22bdff631d · outbound

This paper cites Gpg: A simple and strong reinforcement learning baseline for model reasoning.CoRR, abs/2504.02546, 2025.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Gpg: A simple and strong reinforcement learning baseline for model reasoning.CoRR, abs/2504.02546, 2025

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Observation 873e7d08-0884-446a-af88-6381fd5c39d4 · outbound

This paper cites Task-robust model-agnostic meta-learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Task-robust model-agnostic meta-learning

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Observation 546d6461-e0ef-43e0-a34f-77188d01d39f · outbound

This paper cites How does the task landscape affect MAML performance? InCoLLAs, volume 199 ofProceedings of Machine Learning Research, pages 23–59.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective How does the task landscape affect MAML performance? InCoLLAs, volume 199 ofProceedings of Machine Learning Research, pages 23–59

Reference 23

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Observation 5d538284-4aa0-4d77-b52a-a7c81927e3b0 · outbound

This paper cites Approximations by superpositions of a sigmoidal function.MCSS, 2:183–192, 1989.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Approximations by superpositions of a sigmoidal function.MCSS, 2:183–192, 1989

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Observation b3cd39d0-bcdb-480f-9e5d-544dbf71c5a5 · outbound

This paper cites Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers

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Observation 4061d508-f39e-446a-a831-e4e9dd5c0317 · outbound

This paper cites Gemini 2.5: Our most intelligent ai model.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Gemini 2.5: Our most intelligent ai model

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Observation d825fd48-c173-4c7c-ba2c-9bc5abf04bed · outbound

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

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Observation 68cacb91-93c5-4dff-b88e-87ab20cc52c2 · outbound

This paper cites DeepSeek-V3 Technical Report.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective DeepSeek-V3 Technical Report

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Observation 8a67dbd7-0b59-4cbd-841b-f33895d00586 · outbound

This paper cites Universal transformers.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Universal transformers

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Observation 5b31e925-f738-47dd-817e-06d3ab34c297 · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective BERT: pre-training of deep bidirectional transformers for language understanding

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Observation 0785688b-7ca1-42f5-b648-87b70ce5f28c · outbound

This paper cites A survey on in-context learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A survey on in-context learning

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Observation 221e7f49-d486-4dde-8594-c117654f1f23 · outbound

This paper cites The Llama 3 Herd of Models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective The Llama 3 Herd of Models

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Observation a0b5cb80-3051-48de-847f-972378cc0d86 · outbound

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

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Towards revealing the mystery behind chain of thought: A theoretical perspective

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Observation 08087437-dede-417f-ac4c-65901acb7763 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Model-agnostic meta-learning for fast adaptation of deep networks

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Observation f3180c5d-5d3c-4818-b70f-c94029497cde · outbound

This paper cites Transformers learn to achieve second-order convergence rates for in-context linear regression.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Transformers learn to achieve second-order convergence rates for in-context linear regression

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Observation 9eff5f2d-8af6-41d4-bebd-65c8686419a8 · outbound

This paper cites Reddi, Stefanie Jegelka, and Sanjiv Kumar.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Reddi, Stefanie Jegelka, and Sanjiv Kumar

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This paper cites Lee, and Dimitris Papail- iopoulos.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Lee, and Dimitris Papail- iopoulos

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Observation 4448d8e4-a2ab-484b-89d3-7a10ae08bff6 · outbound

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

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

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Observation ffc3e076-c28e-4cfc-9980-2f1346756a49 · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Measuring mathematical problem solving with the MATH dataset

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Observation bd24f75d-82fa-4466-b347-72ef1b765fe2 · outbound

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Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

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Observation f21210e3-b3ed-4504-8e04-fc6881df648b · outbound

This paper cites Approximation capabilities of multilayer feedforward networks.Neural Networks, 4(2):251– 257, 1991.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Approximation capabilities of multilayer feedforward networks.Neural Networks, 4(2):251– 257, 1991

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This paper cites Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J

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Observation 68ed48f3-f99e-45db-a421-0b288074d9c9 · outbound

This paper cites Universal language model fine-tuning for text classification.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Universal language model fine-tuning for text classification

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Observation 49759538-da87-46cd-b2f8-683f3257d031 · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

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Observation 04a28cdf-1d77-4bec-8a79-869984e94306 · outbound

This paper cites Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

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Observation 2517825d-c34d-423a-abe8-5ff36dab6d15 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Qwen2.5-Coder Technical Report

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Observation 02f2b3f1-399c-4b62-ad26-f141f0219f78 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

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Observation 90e9767b-ae4a-4976-bae1-1253314a8f9e · outbound

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Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Xu, Jun Araki, and Graham Neubig

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Observation 9a023e01-3435-474c-ba5a-78295caafe5d · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Efficient memory management for large language model serving with pagedattention

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Observation 1e7ae17b-a6b3-48a8-a2fd-d54b5352348e · outbound

This paper cites Bespoke-stratos: The unreasonable effectiveness of reasoning distillation, 2025.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Bespoke-stratos: The unreasonable effectiveness of reasoning distillation, 2025

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Observation 89db988a-f9ec-4f09-871f-95a0574a4636 · outbound

This paper cites Rupam Mahmood, Shuicheng Yan, and Zhongwen Xu.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Rupam Mahmood, Shuicheng Yan, and Zhongwen Xu

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Observation b726312e-9fc3-4c7f-beda-8d93305874fe · outbound

This paper cites Meta-learning with differentiable convex optimization.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Meta-learning with differentiable convex optimization

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Observation 06ecbfd4-822e-4de7-869c-928c16e769ea · outbound

This paper cites Visualizing the loss landscape of neural nets.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Visualizing the loss landscape of neural nets

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Observation 76f61f26-2816-44e5-bd00-473c7774b1a9 · outbound

This paper cites Learning to optimize.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Learning to optimize

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Observation f998e8fe-af3c-4a9c-9893-69e6a9366d7d · outbound

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Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Learning to Optimize Neural Nets

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Observation 27e8b020-eb5b-4619-9341-d453c3383f62 · outbound

This paper cites A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks

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Observation 6931b8cd-e0db-4d64-a1c0-2f141e2dfed9 · outbound

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Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Let’s verify step by step

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Observation d629610e-a7cc-49a7-a1b6-d5468648bd1f · outbound

This paper cites Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.561262Z

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Observation 78b9157b-413b-488f-b7d7-5aea2ac871d9 · outbound

This paper cites an unresolved cited work.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.624447Z

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Observation 011da701-fbe3-44ef-af4e-b2ac622f280f · outbound

This paper cites Are Your LLMs Capable of Stable Reasoning?.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Are Your LLMs Capable of Stable Reasoning?

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.676651Z

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Observation fd37c512-1417-436e-851c-aa6767c0faa7 · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Reasoning Models Can Be Effective Without Thinking

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.755282Z

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Observation ee90d448-51da-4cb6-9e8a-5adcc25a3fb0 · outbound

This paper cites an unresolved cited work.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.821020Z

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Observation e53b52d6-0857-47c1-9c75-47ac5611c292 · outbound

This paper cites Academy, 1877.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Academy, 1877

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.888575Z

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Observation 7fb5527f-58c4-4ac9-b746-3f9b15ecad3a · outbound

This paper cites Metaicl: Learning to learn in context.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Metaicl: Learning to learn in context

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:07.949373Z

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Observation fa2536c2-34e6-46df-8164-eb0c6387edc8 · outbound

This paper cites Rethinking the role of demonstrations: What makes in-context learning work? InEMNLP, pages 11048–11064.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Rethinking the role of demonstrations: What makes in-context learning work? InEMNLP, pages 11048–11064

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.015925Z

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Observation 550dffcd-cfeb-4f98-970f-5d2d4c3956f4 · outbound

This paper cites Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.100248Z

Source-reported events for the cited work

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Observation 22b59dbf-ee13-4759-ad72-fd323d8caa7f · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Playing Atari with Deep Reinforcement Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.198094Z

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Observation bf72671a-9350-459f-bc18-2878a716ae4a · outbound

This paper cites On the reciprocal of the general algebraic matrix.Bull.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective On the reciprocal of the general algebraic matrix.Bull

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.267336Z

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Observation 64fec554-2a0e-4197-b806-4203055af3a3 · outbound

This paper cites s1: Simple test-time scaling.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective s1: Simple test-time scaling

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.379653Z

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Observation 0f35de02-d51b-41f4-88f5-a971b38fd0e8 · outbound

This paper cites In-context Learning and Induction Heads.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective In-context Learning and Induction Heads

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.434201Z

Source-reported events for the cited work

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Observation 9ee7c885-1c6d-4409-845d-bea84e13b2ae · outbound

This paper cites GPT-4 Technical Report.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective GPT-4 Technical Report

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.513001Z

Source-reported events for the cited work

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Observation 683dd9a5-4a8a-40ff-af3c-4ffd1827649e · outbound

This paper cites Learning to reason with llms.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Learning to reason with llms

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.575853Z

Source-reported events for the cited work

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Observation 9d3b5eb4-c868-4973-9009-be573e8d2175 · outbound

This paper cites an unresolved cited work.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.641941Z

Source-reported events for the cited work

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Observation f219487c-ed62-43de-98a5-51cc592c890f · outbound

This paper cites Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.705867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e03045f-d25d-4ae7-bc7e-351a68c5b162 · outbound

This paper cites A generalized inverse for matrices.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A generalized inverse for matrices

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.774117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5119c13-0e97-4ebe-83c0-3850e6a154b5 · outbound

This paper cites A simple guard for learned optimizers.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A simple guard for learned optimizers

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:21.684679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c47a79b1-30cf-4b43-a5c0-f515f443b4e2 · outbound

This paper cites O1 Replication Journey: A Strategic Progress Report -- Part 1.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective O1 Replication Journey: A Strategic Progress Report -- Part 1

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.930615Z

Source-reported events for the cited work

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Observation c1011af8-99ce-4177-8156-c40f5efe5df3 · outbound

This paper cites A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond.CoRR, abs/2503.21614, 2025.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond.CoRR, abs/2503.21614, 2025

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:08.992883Z

Source-reported events for the cited work

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Observation 2c8d8de0-ee38-49d5-8af1-33a0e84922af · outbound

This paper cites Improving language understand- ing by generative pre-training.OpenAI, 2018.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Improving language understand- ing by generative pre-training.OpenAI, 2018

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:21.531829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e51bd8b2-4182-498d-96cc-3b7833cdff6d · outbound

This paper cites Manning, Stefano Ermon, and Chelsea Finn.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Manning, Stefano Ermon, and Chelsea Finn

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:21.300794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a034b155-5f00-40d9-9edd-e50bca4beed3 · outbound

This paper cites Kakade, and Sergey Levine.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Kakade, and Sergey Levine

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:21.122628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a8b5061b-8020-4e77-8b7f-93b94df87b7f · outbound

This paper cites Optimization as a model for few-shot learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Optimization as a model for few-shot learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:20.922435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a65b5a9c-ba39-4bdf-aa26-e2de63b7b95d · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.318060Z

Source-reported events for the cited work

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Observation 5330e67a-6131-43f2-85f1-333ad8ba8a99 · outbound

This paper cites Learning to retrieve prompts for in-context learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Learning to retrieve prompts for in-context learning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:20.774803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93d8f48c-b70b-4b15-8978-319febdeeaaa · outbound

This paper cites One-shot Learning with Memory-Augmented Neural Networks.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective One-shot Learning with Memory-Augmented Neural Networks

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.429316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fe45818-2782-473c-abbb-d63ea510437a · outbound

This paper cites Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:20.546970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93df2217-f2c0-45f0-ab75-75ad274dafc7 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Proximal Policy Optimization Algorithms

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.625547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add5ac14-23fd-414a-84bc-edfcea2c2eae · outbound

This paper cites Rethinking Reflection in Pre-Training.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Rethinking Reflection in Pre-Training

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.701448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3388108-bb18-4d55-a6ac-eb23367b7892 · outbound

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

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.795838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52505d35-b8b5-4057-8449-f549907560a3 · outbound

This paper cites Hybridflow: A flexible and efficient RLHF framework.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Hybridflow: A flexible and efficient RLHF framework

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:20.357532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ade0988-831f-482c-85dc-b309ea41f64b · outbound

This paper cites Route Sparse Autoencoder to Interpret Large Language Models.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Route Sparse Autoencoder to Interpret Large Language Models

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:09.892396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c376dd7-ff2d-409c-8abe-555878c0ed4d · outbound

This paper cites Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:20.145364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8ad6c2e-f46e-4cf0-8cef-7ad7ff522a18 · outbound

This paper cites an unresolved cited work.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:13:19.926428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 798131f2-a93a-48e7-9826-3de2136420aa · outbound

This paper cites End-to-end memory networks.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective End-to-end memory networks

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:19.761948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9dd52e9-1da2-455e-b46e-8e90b913ea8c · outbound

This paper cites Andrew Bagnell.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Andrew Bagnell

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:10.096820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a7e9674-6b59-4031-8e4e-44638681b15f · outbound

This paper cites Blockmix: Meta regularization and self-calibrated inference for metric-based meta-learning.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Blockmix: Meta regularization and self-calibrated inference for metric-based meta-learning

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:19.584497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dc475fb-517a-4ba8-9778-89e2b58fd9fe · outbound

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

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:10.188547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b24141c-efa8-4b5f-a8bf-a283380141c8 · outbound

This paper cites Sky-t1: Train your own o1 preview model within $450.https://novasky-ai.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Sky-t1: Train your own o1 preview model within $450.https://novasky-ai

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:19.414611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d544828-9521-49e4-87a2-9fd7f3cb77b3 · outbound

This paper cites Open Thoughts.https://open-thoughts.ai, 2025.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Open Thoughts.https://open-thoughts.ai, 2025

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:19.181089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a42a299-166e-4879-9e37-ed8dfed4d730 · outbound

This paper cites Qwen3: Think deeper, act faster.https://qwenlm.github.io/blog/qwen3/, April.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Qwen3: Think deeper, act faster.https://qwenlm.github.io/blog/qwen3/, April

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:13:18.893561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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