AGENTIC R AG-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing · CiteArk

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AGENTIC R AG-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

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Authors:Xinke Jiang, Yue Fang, Zhibang Yang, Jiaran Gao, Zhixin Zhang, Tao Feng, Rihong Qiu, Wentao Zhang, Hongxin Ding, Ruizhe Zhang, Yongxin Xu, Yuheng Huang, Xu Chu, Junfeng Zhao, Yasha Wang

arXiv 2026 Artificial Intelligence Machine Learning (cs.LG)Multiagent Systems Machine Learning (stat.ML)

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AGENTIC R AG-R1 reports higher or near-highest F1 than listed baselines across seven multi-hop, open-domain, and agentic QA benchmarks.Whole claim: Experiment blocked; see the specific reason0/3 measurements supported · 0 assessed3measurementsExplore results

No verified repository, trained checkpoint, complete curated training data, retrieval implementation, or strict evaluator is available in the fixed inputs.

Measurement results

0/3 assessed

0 supported0 disputed or conflicting3 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
datasets: ["2WikiMultiHopQA","HotpotQA","Bamboogle","FRAMES","MusiQue","NQ","TriviaQA"] · aggregation: arithmetic average · model_variant: Qwen2.5-3B	33.55 percentage_points	—	Not assessed
split: test · dataset: HotpotQA · aggregation: single reported evaluation · model_variant: Qwen2.5-3B	44 percentage_points	—	Not assessed
split: test · dataset: Bamboogle · aggregation: single reported evaluation · model_variant: Qwen2.5-7B	49.21 percentage_points	—	Not assessed

Open claim details

The paper reports increasing AGENTIC R AG-R1 F1 as the maximum reasoning-step budget grows on selected 2Wiki, Bamboogle, and TriviaQA evaluations.Whole claim: Experiment blocked; see the specific reason0/2 measurements supported · 0 assessed2measurementsExplore results

The missing checkpoint, retrieval service, and complete evaluation protocol prevent strict comparable evidence; execution resources are not the blocker.

Measurement results

0/2 assessed

0 supported0 disputed or conflicting2 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
dataset: 2WikiMultiHopQA · max_steps: 10 · aggregation: single reported evaluation · model_variant: Qwen2.5-3B	23.91 percentage_points	—	Not assessed
dataset: 2WikiMultiHopQA · max_steps: 30 · aggregation: single reported evaluation · model_variant: Qwen2.5-3B	47.45 percentage_points	—	Not assessed

Open claim details

The paper reports lower average inference time than TC-RAG and downstream results on MedQA, DeepResearch Bench, ALFWorld, and WebShop.Whole claim: Experiment blocked; see the specific reason0/3 measurements supported · 0 assessed3measurementsExplore results

The original hardware-sensitive benchmark conditions are underspecified by the fixed paper and repository, so a strict comparable verdict is not available.

Measurement results

0/3 assessed

0 supported0 disputed or conflicting3 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
hardware: NVIDIA A800 server · aggregation: average · model_variant: Qwen2.5-3B	10.15 seconds	—	Not assessed
hardware: NVIDIA A800 server · aggregation: average · model_variant: Qwen2.5-3B	11.86 seconds	—	Not assessed
judge: Qwen2.5-72B · dataset: MedQA test · model_variant: Qwen2.5-7B-Instruct	87 percentage_points	—	Not assessed

Open claim details

The paper reports that removing memory actions, rollout rejection, or both lowers average F1, and that removing both retrieval and memory rewards gives the lowest ablation average.Whole claim: Experiment blocked; see the specific reason0/2 measurements supported · 0 assessed2measurementsExplore results

The trained variants, reward model prompts, curated training set, optimizer implementation, and exact seeds are unavailable.

Measurement results

0/2 assessed

0 supported0 disputed or conflicting2 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
condition: full model · aggregation: average across seven benchmarks · model_variant: Qwen2.5-3B	33.55 percentage_points	—	Not assessed
condition: without retrieval and memory rewards · aggregation: average across seven benchmarks · model_variant: Qwen2.5-3B	19.94 percentage_points	—	Not assessed

Open claim details