Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 11 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 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:15:04.627801Z

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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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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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 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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

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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

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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

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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.

Observation d46331dc-b1ae-4f63-97d0-e5f686348cc2 · inbound

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL cites this paper.

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T00:15:04.627801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.