Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning | Paper Citation Record

Observation c3e53cb1-ebd0-4c7c-a552-6bda95b151f5 · outbound

This paper cites GPT-4 Technical Report.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning GPT-4 Technical Report

Reference 1

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 2

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

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Observation aa9543ae-9ede-448c-9588-cd8323be6273 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 4

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

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Observation a5eb2931-e5ad-4230-b801-0a29a7ff016a · outbound

This paper cites InThe Twelfth International Conference on Learning Representations.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning InThe Twelfth International Conference on Learning Representations

Reference 6

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Observation ec28b629-adad-41dc-8d0f-e5676fc14723 · outbound

This paper cites OpenAI Gym.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning OpenAI Gym

Reference 7

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 8

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Observation f570b6a2-2d4e-4191-b922-26e98f6ac7be · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 9

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Observation 32f1a6da-88fd-4f65-860d-6eda1a50d85c · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 10

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Observation 1bba08f2-eb2a-4215-8203-97923389cd04 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 11

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Observation 76164afb-cf1e-4624-97a1-f7e57cfb2728 · outbound

This paper cites InThe Thirty-eighth Annual Conference on Neural Information Processing Systems.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning InThe Thirty-eighth Annual Conference on Neural Information Processing Systems

Reference 12

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Observation 456cff23-58e2-4a7a-8438-ba2ca4ebc2d5 · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 13

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Observation faf063fb-933e-4f29-9580-ed20e2ca9024 · outbound

This paper cites Dated Data: Tracing Knowledge Cutoffs in Large Language Models.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Dated Data: Tracing Knowledge Cutoffs in Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.057256Z

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Observation fff636c9-9793-4b52-a103-0b4dfef09d6c · outbound

This paper cites arXiv preprint arXiv:2504.02546 , year=.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning arXiv preprint arXiv:2504.02546 , year=

Reference 15

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 16

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Observation c39c1b8c-2f08-45ac-adf2-f8a89e69adc1 · outbound

This paper cites A Survey on In-context Learning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning A Survey on In-context Learning

Reference 17

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Observation eaec61d6-1d0f-46a3-98ab-27af44397d12 · outbound

This paper cites KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.184917Z

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 19

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Observation b500ae7e-ec51-4adb-b5c3-057a6578b8ca · outbound

This paper cites InThe Thirty-eighth Annual Conference on Neural Information Processing Systems.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning InThe Thirty-eighth Annual Conference on Neural Information Processing Systems

Reference 20

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Observation d6ea4b75-c922-4497-96ea-316c910e668f · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 21

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This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 22

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Observation 2307f248-44f0-46bb-bef2-5198833b060a · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 23

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 24

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 25

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This paper cites Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.200359Z

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 27

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This paper cites Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.191442Z

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 29

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 30

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This paper cites Query graph generation for answering multi-hop complex questions from knowledge bases.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Query graph generation for answering multi-hop complex questions from knowledge bases

Reference 31

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 32

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 33

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 34

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This paper cites Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains

Reference 35

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 36

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 37

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 38

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This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 39

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 40

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 41

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 42

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Observation f215d781-bf47-443b-9beb-a8b2cf9787a5 · outbound

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

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Reference 43

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 44

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Observation 75832a51-976c-45a1-ae3d-dddaa45c694a · outbound

This paper cites A Decade's Battle on Dataset Bias: Are We There Yet?.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning A Decade's Battle on Dataset Bias: Are We There Yet?

Reference 45

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Observation 6670b300-d590-483f-9b0c-cabfa0b639a6 · outbound

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 46

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 47

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 48

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 49

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This paper cites https://openreview.net/forum?id=6embY8aclt.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning https://openreview.net/forum?id=6embY8aclt

Reference 50

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 51

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Observation c9369a94-b6f2-4b77-b73b-37ea4795e6f7 · outbound

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 52

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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 53

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Observation 69df683a-c45e-4a71-a3a2-bb69d6605ed1 · outbound

This paper cites GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.142856Z

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Observation 14250196-9e63-422f-a59a-6d6c32e24ec9 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.840951Z

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.

Observation 81cfd157-a3ca-4b9d-8f9a-01bfec9f6305 · outbound

This paper cites Liang, Y.-C.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Liang, Y.-C

Reference 56

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T13:34:52.903307Z

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.

Observation b8620f26-cf82-4662-bc82-f76be9a04c4e · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.840325Z

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.

Observation e0d20a82-8765-4136-831f-f8377869dd0a · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.852460Z

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.

Observation 5eb9df8d-c109-41d5-9be3-7aa126110235 · outbound

This paper cites Robertson and Hugo Zaragoza , title =.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Robertson and Hugo Zaragoza , title =

Reference 59

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.931638Z

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.

Observation e2ee5bfa-4dfa-4cf6-8980-92c1b92c6d78 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.941039Z

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.

Observation 228b8b47-96c9-436f-860e-615fc6d554b9 · outbound

This paper cites InInternational conference on machine learning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning InInternational conference on machine learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:53.867969Z

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.

Observation 12167cee-1ac8-482f-811a-d6d5e41c3157 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 62

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T13:34:53.181982Z

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.

Observation 797c5836-3941-4e07-a42f-de4606297130 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.938468Z

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.

Observation 2706d255-9682-4cc4-ba20-b831588acbf5 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.179900Z

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.

Observation 2d4a8903-2457-42b3-8a14-0e123a2c1a51 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.880175Z

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.

Observation 2bac2fb5-2872-48f9-9b5a-7fc6c15102d9 · outbound

This paper cites Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy

Reference 66

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.955461Z

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.

Observation 7c20d264-2b2b-4b7e-b31e-3fac4fc8d913 · outbound

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

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.121735Z

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.

Observation ca894031-08bc-42f6-8360-3416e12ea9eb · outbound

This paper cites 37 Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning 37 Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang

Reference 68

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.942199Z

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.

Observation fd15051a-ee70-40cf-b320-c3c937fe93b6 · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.126608Z

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.

Observation 889a08a8-7a6e-4973-ba6a-b0f9fe017e5a · outbound

This paper cites FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.175061Z

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.

Observation 79501cbb-1579-41cb-a840-1559fde82470 · outbound

This paper cites ZeroSearch: Incentivize the Search Capability of LLMs without Searching.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.194692Z

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.

Observation 71256b7f-e2f4-4600-b20d-a03f10b55eab · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.816985Z

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.

Observation b0e5bb44-0db6-4927-bedc-d5343411dff7 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.870897Z

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.

Observation 964319b7-0fdb-42e6-b593-2559a1415f93 · outbound

This paper cites Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.201008Z

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.

Observation 34518477-89fe-4da5-abbc-e97a0e2281e9 · outbound

This paper cites Understanding the performance gap between online and offline alignment algorithms.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Understanding the performance gap between online and offline alignment algorithms

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.134007Z

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.

Observation 33f444d3-985b-4343-b106-162249ed29ba · outbound

This paper cites Transactions of the Association for Computational Linguistics(2022).

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Transactions of the Association for Computational Linguistics(2022)

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:53.860458Z

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.

Observation d8dfacda-bfec-4f13-bf01-362c0cab971e · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.810470Z

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.

Observation 116dad29-f9cb-42b0-b061-23f89c84286d · outbound

This paper cites Diverse demonstrations improve in-context compositional generalization.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Diverse demonstrations improve in-context compositional generalization

Reference 79

Resolution: malformed identifier; doi_truncated, observed 2026-05-22T13:34:52.946232Z

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.

Observation 2a118e1e-8340-4c57-990a-720af121c6dd · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 80

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.912149Z

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.

Observation 0c3093e0-ab25-4f99-a493-68928a426006 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.873564Z

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.

Observation 83848d29-549d-4c99-869b-e010215a0e85 · outbound

This paper cites Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.127980Z

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.

Observation 73d52bed-eb41-4079-8ce1-ed541ba811ab · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.892178Z

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.

Observation 85f14fd2-7ca6-4689-ae6b-8f56afd6a595 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.850984Z

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.

Observation 5635aa0e-60d7-438b-829f-58327abdc44a · outbound

This paper cites Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T13:34:53.099678Z

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.

Observation 55773bad-37ca-41b8-a1eb-2acc1865b435 · outbound

This paper cites A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.196041Z

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.

Observation 744025e2-7327-4f65-9798-649308419808 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.857388Z

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.

Observation 26d1666a-6f4c-4031-8025-cbbd37736ba2 · outbound

This paper cites LLM-based Discriminative Reasoning for Knowledge Graph Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning LLM-based Discriminative Reasoning for Knowledge Graph Question Answering

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.189924Z

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.

Observation c34a6781-781d-4b64-a7a0-0c0878f5ffc8 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 89

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.953701Z

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.

Observation 96cb2585-a28b-469f-906f-1e56ba80d457 · outbound

This paper cites Qwen2 Technical Report.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Qwen2 Technical Report

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.154410Z

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.

Observation bbf6eed6-121f-4527-9d61-d8102f12107a · outbound

This paper cites InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:52.938704Z

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.

Observation f1ca34e9-8d19-4c81-8015-61a9d5580aea · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 92

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:34:53.159287Z

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.

Observation d2b11e15-31da-40f1-93d4-652075c327c3 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.815042Z

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.

Observation 553067e3-c901-4d4e-8ae6-15ec35ae4f9b · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.826203Z

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.

Observation 114f9d0c-204a-4d3b-b4f3-de7ccea98f83 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.974645Z

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.

Observation 89b8b538-6fd2-4371-83d3-5370d9340ba5 · outbound

This paper cites Inference Scaling for Long-Context Retrieval Augmented Generation.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Inference Scaling for Long-Context Retrieval Augmented Generation

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.100003Z

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.

Observation 6f642769-e39f-4326-b254-a2ea2e2b33a4 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.883215Z

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.

Observation e701eb92-8fa7-4964-8b84-e16ccf2652f2 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.837971Z

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.

Observation d60bc1d6-5595-4421-9139-991e946b924c · outbound

This paper cites End-to-End Beam Retrieval for Multi-Hop Question Answering.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning End-to-End Beam Retrieval for Multi-Hop Question Answering

Reference 99

Resolution: verified exact; doi, observed 2026-05-22T13:34:52.926586Z

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.

Observation ea46880a-be4d-4622-b18f-ec5f87dea494 · outbound

This paper cites an unresolved cited work.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:53.854075Z

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.

Observation 54048b82-c392-4f35-874b-a38894ada681 · outbound

This paper cites RAFT: Adapting Language Model to Domain Specific RAG.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning RAFT: Adapting Language Model to Domain Specific RAG

Reference 101

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T13:34:53.170772Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers