Paper Citation Record · LEDGER

ReQuestNet: A Foundational Learning model for Channel Estimation

As of 9 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2508.08790.

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

pith.paper-citation-record.v1
2508.08790 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:24:07.341611Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:22:49.391997Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:22:49.500664Z

Reference resolution

58 of 58 outbound references displayed

verified exact0
verified fuzzy5
unresolved49
parse uncertain0
malformed identifier3
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72d3ced4-0f45-42fd-8a55-ade0ee28a5f2 · outbound

This paper cites Program Synthesis with Large Language Models.

ReQuestNet: A Foundational Learning model for Channel Estimation Program Synthesis with Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:01.901584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 062bc058-e97d-4ed3-9ccb-cc354ed61aa0 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

ReQuestNet: A Foundational Learning model for Channel Estimation Constitutional AI: Harmlessness from AI Feedback

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:01.969959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff7d31e0-23c7-4a4f-912f-3951c5959e17 · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.086338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38bf9f33-7847-49bc-9c65-8abb91851c49 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ReQuestNet: A Foundational Learning model for Channel Estimation Evaluating Large Language Models Trained on Code

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.182323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f22bd7da-51aa-44d2-8908-8efb394927fc · outbound

This paper cites MUC-4 evaluation metrics.

ReQuestNet: A Foundational Learning model for Channel Estimation MUC-4 evaluation metrics

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T21:24:08.196398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ea09e766-600f-4926-bc16-badec024167f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ReQuestNet: A Foundational Learning model for Channel Estimation Training Verifiers to Solve Math Word Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.384760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0820f5d6-a825-456d-9cf2-903dc8a90b39 · outbound

This paper cites Gemini 2.5: Pushing the fron- tier with advanced reasoning, multimodality, long context, and next generation agentic capabilities,.

ReQuestNet: A Foundational Learning model for Channel Estimation Gemini 2.5: Pushing the fron- tier with advanced reasoning, multimodality, long context, and next generation agentic capabilities,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.525500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 836087a9-5729-4087-bb77-95e523827e90 · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.694100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ef1ae86-1d30-4ee8-ab60-37b841645fb2 · outbound

This paper cites Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.799543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7db0457-94eb-49fa-b07c-c9e984a2fcd5 · outbound

This paper cites Measuring massive multitask language understanding.

ReQuestNet: A Foundational Learning model for Channel Estimation Measuring massive multitask language understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.898038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fef0d2a2-db35-46cf-a4e7-b2d825879882 · outbound

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

ReQuestNet: A Foundational Learning model for Channel Estimation Measuring mathematical problem solving with the MATH dataset

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.102924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bdaea96-8c3d-40f9-9a73-9a3cfd1a0c6f · outbound

This paper cites Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models.

ReQuestNet: A Foundational Learning model for Channel Estimation Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.314790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f133bc7-575b-4fed-a0e6-b1a12f04dfe3 · outbound

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

ReQuestNet: A Foundational Learning model for Channel Estimation REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.511531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed57e453-7c2f-45db-bdf3-66519d3fd69f · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:24:09.309724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cdfab46a-e097-4042-84eb-e7a3d43780cf · outbound

This paper cites Controlllm: Augment language models with tools by searching on graphs.

ReQuestNet: A Foundational Learning model for Channel Estimation Controlllm: Augment language models with tools by searching on graphs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.690912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8627508d-fe59-4ed2-8c97-46fd33e59fb7 · outbound

This paper cites Inference-time scaling for generalist reward modeling.

ReQuestNet: A Foundational Learning model for Channel Estimation Inference-time scaling for generalist reward modeling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.802206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 881ac3d2-2750-4bcb-b5ac-a6569ef52883 · outbound

This paper cites GPT-4 Technical Report.

ReQuestNet: A Foundational Learning model for Channel Estimation GPT-4 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.876859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549b8745-db0b-4490-9861-5200df48661a · outbound

This paper cites Metatool benchmark for large language models: Deciding whether to use tools and which to use.

ReQuestNet: A Foundational Learning model for Channel Estimation Metatool benchmark for large language models: Deciding whether to use tools and which to use

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.585687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58fe3dc1-fb91-4afd-bad5-8e033f2d8778 · outbound

This paper cites ToolRL: Reward is All Tool Learning Needs.

ReQuestNet: A Foundational Learning model for Channel Estimation ToolRL: Reward is All Tool Learning Needs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.063105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e06b5b2-a030-43d0-87f9-580dfc2ad26f · outbound

This paper cites Toolllm: Facilitating large language models to master 16000+ real-world apis.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolllm: Facilitating large language models to master 16000+ real-world apis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.152966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3a315b6-b585-4312-b13b-84f168e0ae0a · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 21

Resolution: malformed identifier; doi_truncated, observed 2026-08-05T21:24:09.127693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc6bf703-bbb0-4ed9-b952-4d270b70d2b8 · outbound

This paper cites ART: Automatic multi-step reasoning and tool-use for large language models.

ReQuestNet: A Foundational Learning model for Channel Estimation ART: Automatic multi-step reasoning and tool-use for large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.969546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de04b99c-1237-4c7b-8b84-d70ea5ad57b8 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

ReQuestNet: A Foundational Learning model for Channel Estimation Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.467861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c102afb-d2ba-4e25-801e-19dae8520919 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolformer: Language models can teach themselves to use tools

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:24:08.937583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a22e02bd-49cd-447f-99bd-2c323ce060ed · outbound

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

ReQuestNet: A Foundational Learning model for Channel Estimation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.817779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5a9a784-712c-4c2f-9a72-cd4a7d233b3c · outbound

This paper cites Tool learning with large language models: a survey.

ReQuestNet: A Foundational Learning model for Channel Estimation Tool learning with large language models: a survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.382545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e4f44cb-40d4-40cc-86d7-1a36ce47484c · outbound

This paper cites RestGPT: Connecting Large Language Models with Real-World RESTful APIs.

ReQuestNet: A Foundational Learning model for Channel Estimation RestGPT: Connecting Large Language Models with Real-World RESTful APIs

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.949517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b1fefc4-487d-4452-bd8c-7354f6621ba5 · outbound

This paper cites Le, Ed H.

ReQuestNet: A Foundational Learning model for Channel Estimation Le, Ed H

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.080821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45fc2b2c-2822-434d-afdf-f36a38321946 · outbound

This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

ReQuestNet: A Foundational Learning model for Channel Estimation ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.172507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ecfd76b-3e60-4447-8f15-56fc16c42836 · outbound

This paper cites Introducing claude 4, 2025.

ReQuestNet: A Foundational Learning model for Channel Estimation Introducing claude 4, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.266233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df744409-35a8-44c9-9e8a-2179072b60a3 · outbound

This paper cites Appworld: A controllable world of apps and people for benchmarking interactive coding agents.

ReQuestNet: A Foundational Learning model for Channel Estimation Appworld: A controllable world of apps and people for benchmarking interactive coding agents

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.373010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5baa48e-f71b-4548-866c-a7ea449f56b2 · outbound

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

ReQuestNet: A Foundational Learning model for Channel Estimation Hybridflow: A flexible and efficient RLHF framework

Reference 33

Resolution: malformed identifier; no resolver link, observed 2026-08-05T21:24:04.881423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ab65ae6-fdb6-403b-b831-028de67d4659 · outbound

This paper cites The rise and potential of large language model based agents: a survey.

ReQuestNet: A Foundational Learning model for Channel Estimation The rise and potential of large language model based agents: a survey

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.568922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cdd817c-44cb-42a3-a65f-2e5d34e417f6 · outbound

This paper cites RestGPT: Connecting Large Language Models with Real-World RESTful APIs.

ReQuestNet: A Foundational Learning model for Channel Estimation RestGPT: Connecting Large Language Models with Real-World RESTful APIs

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.021467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd69682-cfa9-4249-9239-8d326e2e8e48 · outbound

This paper cites GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction.

ReQuestNet: A Foundational Learning model for Channel Estimation GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.966887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a50ae62a-68af-4a3f-8fbd-55fea55ee34c · outbound

This paper cites Narasimhan, and Yuan Cao.

ReQuestNet: A Foundational Learning model for Channel Estimation Narasimhan, and Yuan Cao

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:06.078479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26c992d7-5137-42a1-85a5-18cc165d771e · outbound

This paper cites Narasimhan.

ReQuestNet: A Foundational Learning model for Channel Estimation Narasimhan

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:24:08.774265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f7e59fd-933a-4276-80b3-b24283abc30f · outbound

This paper cites Toolgen: Unified tool retrieval and calling via generation.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolgen: Unified tool retrieval and calling via generation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.462304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9ac1b26-6679-4255-9f72-8c825430be0d · outbound

This paper cites Rotbench: A multi- level benchmark for evaluating the robustness of large language models in tool learning.

ReQuestNet: A Foundational Learning model for Channel Estimation Rotbench: A multi- level benchmark for evaluating the robustness of large language models in tool learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:06.490447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c33443-dde3-4632-a97c-7d83818fbab5 · outbound

This paper cites Qwen2.5 Technical Report.

ReQuestNet: A Foundational Learning model for Channel Estimation Qwen2.5 Technical Report

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.664096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9d3738a-5913-4138-8af2-16fb713cfa63 · outbound

This paper cites MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models.

ReQuestNet: A Foundational Learning model for Channel Estimation MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:06.756813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68343f4f-6211-4529-a9ba-6b21c96e61dd · outbound

This paper cites Qwen3 Technical Report.

ReQuestNet: A Foundational Learning model for Channel Estimation Qwen3 Technical Report

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:05.836107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d9fefae-a3a1-4bea-a36d-5a326b8e7de5 · outbound

This paper cites Tl-training: A task-feature-based framework for training large language models in tool use.

ReQuestNet: A Foundational Learning model for Channel Estimation Tl-training: A task-feature-based framework for training large language models in tool use

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:24:08.494692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 11918eaa-85da-4812-8af5-86bb51210827 · outbound

This paper cites StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning.

ReQuestNet: A Foundational Learning model for Channel Estimation StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:07.034670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6063efe-068b-4e87-b5ff-a213379dd495 · outbound

This paper cites Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training.

ReQuestNet: A Foundational Learning model for Channel Estimation Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:07.124905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8569707e-cfdd-4764-b55d-a9d671be3832 · outbound

This paper cites Toolsword: Unveiling safety issues of large language models in tool learning across three stages.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolsword: Unveiling safety issues of large language models in tool learning across three stages

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:24:08.668369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b6036e3e-6469-433f-976b-2c1c4ed7bb79 · outbound

This paper cites Toolqa: A dataset for LLM question answering with external tools.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolqa: A dataset for LLM question answering with external tools

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:24:08.328349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 72f010fe-0a2e-48e6-aaca-2458802cf55b · outbound

This paper cites political_figure.

ReQuestNet: A Foundational Learning model for Channel Estimation political_figure

Reference 50

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Source-reported events for the cited work

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Observation 4c92f98d-820e-4d04-bbda-ebeef4bd28f2 · outbound

This paper cites Toolhop: A query-driven benchmark for evaluating large language models in multi-hop tool use.

ReQuestNet: A Foundational Learning model for Channel Estimation Toolhop: A query-driven benchmark for evaluating large language models in multi-hop tool use

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:06.619988Z

Source-reported events for the cited work

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Observation eaa52a73-253e-4482-a0ee-d02d72c6178f · outbound

This paper cites Tooleyes: Fine-grained evaluation for tool learning capabilities of large language models in real-world scenarios.

ReQuestNet: A Foundational Learning model for Channel Estimation Tooleyes: Fine-grained evaluation for tool learning capabilities of large language models in real-world scenarios

Reference 54

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Observation 6c151bfb-0482-4346-9966-4d2a8438773b · outbound

This paper cites Opennovelty: An llm-powered agentic system for verifiable scholarly novelty assessment.

ReQuestNet: A Foundational Learning model for Channel Estimation Opennovelty: An llm-powered agentic system for verifiable scholarly novelty assessment

Reference 58

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Observation 9201c40f-ebfe-4f69-963c-c1f9072a8151 · outbound

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ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 435

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Source-reported events for the cited work

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Observation 46e8c7d6-48b7-4fb8-bce2-c580fc9d192b · outbound

This paper cites Proximal Policy Optimization Algorithms.

ReQuestNet: A Foundational Learning model for Channel Estimation Proximal Policy Optimization Algorithms

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:04.758855Z

Source-reported events for the cited work

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Observation 288a4bcf-8125-4f53-bb72-6686a34efeca · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 2021

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Observation 55687e44-0335-4f2a-8f4a-b8aba726815b · outbound

This paper cites Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models.

ReQuestNet: A Foundational Learning model for Channel Estimation Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:03.386564Z

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Observation a7052a55-1951-47fc-98e7-b3fe8b157a3e · outbound

This paper cites URL https://doi.org/10.18653/v1/2024.acl-l ong.119.

ReQuestNet: A Foundational Learning model for Channel Estimation URL https://doi.org/10.18653/v1/2024.acl-l ong.119

Reference 2024

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Observation f053f76c-84ca-4c06-8704-21dd8668d5a3 · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:02.630135Z

Source-reported events for the cited work

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Observation f28b68bc-7401-43b3-b219-b19c126b9dcc · outbound

This paper cites an unresolved cited work.

ReQuestNet: A Foundational Learning model for Channel Estimation Unresolved cited work

Reference 2211

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Pith citing papers

Observation 85d3a807-2b0a-46e4-b805-acc3608969c6 · inbound

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance cites this paper.

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance ReQuestNet: A Foundational Learning model for Channel Estimation

Reference 1

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Observation 3e3dbe03-7d82-4a21-a7df-5adc94d94cfa · inbound

Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems cites this paper.

Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems ReQuestNet: A Foundational Learning model for Channel Estimation

Reference 42

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