Paper Citation Record · LEDGER

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 10 inbound Pith citation observations for arXiv:2508.18669.

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

pith.paper-citation-record.v1
2508.18669 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:22:51.681755Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:03:39.498611Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T15:35:07.151222Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy11
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd035e9a-a855-4838-9a5a-097e5365af56 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Kimi K2: Open Agentic Intelligence

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.495039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05603cab-74cd-4ddd-a45a-4f6d17706760 · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.499436Z

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Observation a0fb646b-3a1e-45cb-adb4-6f1ff32937aa · outbound

This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.503088Z

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Observation 8fe26866-1bab-4b30-bb80-e618129c16b4 · outbound

This paper cites Gonzalez, and Ion Stoica.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Gonzalez, and Ion Stoica

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.507334Z

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Observation 20ea23a1-92a8-4a08-8f9b-c2c8a16bfe37 · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.511495Z

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Observation 3e7b5f80-c70a-47ab-acb1-122be6293cfe · outbound

This paper cites Proximal Policy Optimization Algorithms.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Proximal Policy Optimization Algorithms

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.516645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1dd30f9-0879-46db-82d8-0df364935d88 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.378133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 631af179-9c0a-4ff3-8b35-4918fdcf5670 · outbound

This paper cites Buy 4 reinforce samples, get a baseline for free! Learn- ing,Learning, Mar 2019.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Buy 4 reinforce samples, get a baseline for free! Learn- ing,Learning, Mar 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.367337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 724f0b98-6405-4417-b4f9-e4fd58dc0f24 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.529177Z

Source-reported events for the cited work

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Observation e26e9302-4be4-4c86-8859-9b443d212786 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Star: Bootstrapping reasoning with reasoning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.356089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e42f05a-7af7-4101-b757-a994d78ff299 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Reasoning with Language Model is Planning with World Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.536655Z

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Observation 8eceef6f-2047-4f61-82d9-b510836faff8 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.541461Z

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Observation a0bc6eb0-5b3c-41a5-bc9e-111085d807b4 · outbound

This paper cites Code-r1: Reproducing r1 for code with reliable rewards.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Code-r1: Reproducing r1 for code with reliable rewards

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.545595Z

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Observation 57ac40bf-5d47-480f-9deb-01ad51d2eccb · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.550260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea85cc3e-a2a6-40ac-a17d-32a8c242341c · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.555224Z

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Unavailable: canonical work link unavailable.

Observation cb121056-e228-4f72-9957-c62926009f79 · outbound

This paper cites Instructerc: Reforming emotion recognition in conversation with a retrieval multi-task llms framework.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Instructerc: Reforming emotion recognition in conversation with a retrieval multi-task llms framework

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.345922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 955b1022-2129-4f30-99d0-34d7346f240c · outbound

This paper cites Hammer: Robust function-calling for on-device language models via function masking.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Hammer: Robust function-calling for on-device language models via function masking

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.563585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ee74172-ff78-4f40-9175-cbb1a1b76af0 · outbound

This paper cites xlam: A family of large action models to empower ai agent systems.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use xlam: A family of large action models to empower ai agent systems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.334954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d4b5ff1-95d3-4335-9607-07b90c82f338 · outbound

This paper cites Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.571925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 115a9a25-065f-4794-89d2-efa50f9e47bf · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.575951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4aa972d9-522e-40aa-9b7d-71a8afa19c0e · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.580729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7d18c52-045b-4390-a996-eb97c0cad2ef · outbound

This paper cites ToRL: Scaling Tool-Integrated RL.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use ToRL: Scaling Tool-Integrated RL

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.584383Z

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Observation e52003de-437d-4409-ae87-f10b1522336c · outbound

This paper cites Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.588510Z

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Unavailable: canonical work link unavailable.

Observation 6028b4a8-4a88-4342-a4a3-59af3a053aaa · outbound

This paper cites AgentInstruct: Toward Generative Teaching with Agentic Flows.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use AgentInstruct: Toward Generative Teaching with Agentic Flows

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.591964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d7c793-3654-490f-927a-e3eda35011e3 · outbound

This paper cites StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.595736Z

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Observation 116e2fdc-2134-4e72-b837-be93e9df192a · outbound

This paper cites Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.599569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fa98f77-b13e-4083-8f07-8d24fc1c566e · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.603971Z

Source-reported events for the cited work

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Observation cd6e296d-3ea8-473e-b4a2-77d08c328fd4 · outbound

This paper cites an unresolved cited work.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.608211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84ac58dc-b084-4e81-822e-dab037fe247c · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.611803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a752a56-5318-43b5-8de1-6689bd32a4e0 · outbound

This paper cites WebThinker: Empowering Large Reasoning Models with Deep Research Capability.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.615983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a9385a-cff2-4ccf-b27f-660bae9b7ddb · outbound

This paper cites Plm-based world models for text-based games.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Plm-based world models for text-based games

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.323994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d255c769-1a32-4ece-a74a-6ca7d801855c · outbound

This paper cites Generative agents: Interactive simulacra of human behavior.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Generative agents: Interactive simulacra of human behavior

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.312333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 07ad9f53-0fc0-4992-8509-da22c3a4d494 · outbound

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

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use ToolRL: Reward is All Tool Learning Needs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.627300Z

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Observation 159b4990-e5c7-4400-b315-9e5dbff73feb · outbound

This paper cites Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.631063Z

Source-reported events for the cited work

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Observation ed009504-4a98-4e4c-bb84-d7d1fe05d32d · outbound

This paper cites Reinforcing multi-turn reasoning in llm agents via turn-level credit assignment.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Reinforcing multi-turn reasoning in llm agents via turn-level credit assignment

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.301977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28acd782-d161-40c5-9e51-5a45b0b240a9 · outbound

This paper cites Qwen3 Technical Report.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Qwen3 Technical Report

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.638697Z

Source-reported events for the cited work

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Observation 29c0c48d-3474-413f-bc63-c3baff453695 · outbound

This paper cites Decoupled Weight Decay Regularization.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Decoupled Weight Decay Regularization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.642783Z

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Observation 99c154c9-34e3-4a41-9e52-733796921874 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use HybridFlow: A Flexible and Efficient RLHF Framework

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.646536Z

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Observation c329e446-20e4-4e70-a34b-45e5ac0a92eb · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.650372Z

Source-reported events for the cited work

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Observation 86f669ba-ffd7-4308-b669-38a751da45e4 · outbound

This paper cites GPT-4o System Card.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use GPT-4o System Card

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.654164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 260d555d-7799-4ba3-93ef-e9f0e6d4d55d · outbound

This paper cites $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.657952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b5a9ae-d96d-434f-aaf2-dc4ff909cb15 · outbound

This paper cites The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.291068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4a34744-4040-472f-a8f5-24a6df2c4a35 · outbound

This paper cites Acebench: Who wins the match point in tool usage? arXiv preprint arXiv:2501.12851, 2025.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use Acebench: Who wins the match point in tool usage? arXiv preprint arXiv:2501.12851, 2025

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.665618Z

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Observation d533bfd7-f531-4160-bdf8-d683f7845a16 · outbound

This paper cites DeepSeek-V3 Technical Report.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use DeepSeek-V3 Technical Report

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.669555Z

Source-reported events for the cited work

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Observation 14f7e64c-b556-46ef-bbd3-11a642a4470e · outbound

This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:51.673650Z

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Observation 7cb328aa-1ec2-4dc1-84cd-094d76a1f226 · outbound

This paper cites term":"Sakura.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use term":"Sakura

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.279618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b796e61-8568-4fab-b7ea-ccc1bd211a66 · outbound

This paper cites 02:30:00.

MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use 02:30:00

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:52.268550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 6d4fd521-4b00-4928-b9ea-5952643f3ce4 · inbound

Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory cites this paper.

Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T18:03:39.498611Z

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Unavailable: canonical work link unavailable.

Observation f10900cb-86fe-4da9-8034-b54a86907c95 · inbound

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence cites this paper.

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 134

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:25:54.646467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee64902f-ac2d-4f45-bee9-a041bc1bbb7f · inbound

Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning cites this paper.

Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 181

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:15:49.418878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a31372e8-1eb0-4ce9-b5fa-7c954d5b0eb0 · inbound

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators cites this paper.

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:24.084915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3bf48290-6246-46b3-98c8-504dd83bb253 · inbound

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators cites this paper.

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T14:37:24.484309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4812cde0-8357-4799-be70-1e70dcc89e76 · inbound

When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents cites this paper.

When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:52:22.056914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 633cafca-e809-453b-a331-369110412474 · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 141

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T20:46:14.307374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72b083e3-7392-4955-898f-74a650db6a31 · inbound

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments cites this paper.

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:36:29.692587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 73dd061a-8945-445a-86b7-ba01e5e90857 · inbound

CurateEvo: Data-Curation Evolving for Agentic Post-Training cites this paper.

CurateEvo: Data-Curation Evolving for Agentic Post-Training MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 42

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T15:35:07.152620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b1bb2712-7032-43aa-84fb-fa6502ce6bdc · inbound

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications cites this paper.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.076593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.