Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments | Paper Citation Record

Observation 713e2198-bc20-4571-ba04-6ad493b5de81 · outbound

This paper cites Introducing gpt-5.2.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Introducing gpt-5.2

Reference 1

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This paper cites Gemini 3 pro model card.https://storage.googleapis.com/deepmind-media/Model- Cards/Gemini-3-Pro-Model-Card.pdf, 2025.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Gemini 3 pro model card.https://storage.googleapis.com/deepmind-media/Model- Cards/Gemini-3-Pro-Model-Card.pdf, 2025

Reference 2

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This paper cites Introducing LongCat-flash-thinking: A technical report.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Introducing LongCat-flash-thinking: A technical report

Reference 3

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Observation 6945d13c-f20e-4658-a1e2-4c6c5031ff8c · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Kimi K2: Open Agentic Intelligence

Reference 4

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 5

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This paper cites Longcat-flash technical report.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Longcat-flash technical report

Reference 6

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 7

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Observation 5db102f9-01ef-4e8c-b681-d52a25a652a9 · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Reference 8

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Observation 3673533f-c22d-4334-bd58-d7853dca0dda · outbound

This paper cites Vitabench: Benchmarking llm agents with versatile interactive tasks in real-world applications.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Vitabench: Benchmarking llm agents with versatile interactive tasks in real-world applications

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.522389Z

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Observation 33d84901-61ff-4cac-b5bc-655b3ef7ba5c · outbound

This paper cites Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114, 2023.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114, 2023

Reference 10

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Observation dd81adb2-6535-4e0f-be3a-f86a48702946 · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 11

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Observation 7a0d0305-1d87-4abc-8ddd-f92200527cce · outbound

This paper cites An illusion of progress? assessing the current state of web agents.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments An illusion of progress? assessing the current state of web agents

Reference 12

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Observation 6b7c2918-a3c4-4cc8-a229-296ae267deec · outbound

This paper cites Agenttuning: Enabling generalized agent abilities for llms.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Agenttuning: Enabling generalized agent abilities for llms

Reference 13

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This paper cites WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Reference 14

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This paper cites Communication accommodation theory.Theo- rizing about intercultural communication, pages 121–148, 2005.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Communication accommodation theory.Theo- rizing about intercultural communication, pages 121–148, 2005

Reference 16

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Observation e272e8ed-3f32-4595-9711-38fc902b6e5d · outbound

This paper cites What do users really ask large language models? an initial log analysis of google bard interactions in the wild.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments What do users really ask large language models? an initial log analysis of google bard interactions in the wild

Reference 17

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Observation e1bafd98-451d-46b0-b152-80b648d93340 · outbound

This paper cites PALADIN: Self-correcting language model agents to cure tool-failure cases.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments PALADIN: Self-correcting language model agents to cure tool-failure cases

Reference 19

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Observation 87714e8d-ba27-49b3-94c8-09466e150bbe · outbound

This paper cites Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems

Reference 20

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Observation 11c90cc8-67e5-426a-939f-5e5cd3777ae8 · outbound

This paper cites AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.585685Z

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Observation 6fcc4d92-9867-45d3-82a3-eb6be7fff78d · outbound

This paper cites Gui-xplore: Empowering generalizable gui agents with one exploration.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Gui-xplore: Empowering generalizable gui agents with one exploration

Reference 22

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Observation 38590bdf-db02-4e16-9400-a3af4308d3b4 · outbound

This paper cites Out-of-distribution segmentation in autonomous driving: Problems and state of the art.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Out-of-distribution segmentation in autonomous driving: Problems and state of the art

Reference 23

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Observation 2de9696a-7a3f-4c7e-8e7c-736369a64c9c · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Domain randomization for transferring deep neural networks from simulation to the real world

Reference 24

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Observation f6a77746-e317-46aa-99b3-8e25a5e20796 · outbound

This paper cites Cad2rl: Real single-image flight without a single real image.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Cad2rl: Real single-image flight without a single real image

Reference 25

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Observation 4b06a032-97bd-4703-874d-4e9d2d116afb · outbound

This paper cites Robust reinforcement learning as a stackelberg game.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Robust reinforcement learning as a stackelberg game

Reference 26

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Observation 5a28c917-b334-4e04-b1b9-1c306c65e98e · outbound

This paper cites The Landscape of Agentic Reinforcement Learning for LLMs: A Survey.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.551897Z

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Observation 23cb580c-76b7-4ba2-92c4-0e4b53232f5c · outbound

This paper cites Verltool: Towards holistic agentic reinforcement learning with tool use.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Verltool: Towards holistic agentic reinforcement learning with tool use

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.614622Z

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Observation fea48980-c37e-4bb5-9839-aa850c82a677 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Proximal Policy Optimization Algorithms

Reference 31

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Observation 1b044b96-4093-4e80-a2f9-c78355cfa3f3 · outbound

This paper cites Longcat-flash-thinking-2601 technical report.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Longcat-flash-thinking-2601 technical report

Reference 32

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Observation 84e2b736-8760-4f70-b9e8-9fdf3271a84a · outbound

This paper cites DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Reference 33

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Observation fa0fe347-3360-40c4-8658-2a4d3beb4f45 · outbound

This paper cites Scaleenv: Scaling environment synthesis from scratch for generalist interactive tool-use agent training.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Scaleenv: Scaling environment synthesis from scratch for generalist interactive tool-use agent training

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.563163Z

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Observation 4b9bb57c-02f1-47ed-a722-c6027573ca75 · outbound

This paper cites tasks": [ {.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments tasks": [ {

Reference 35

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Observation f6842076-5548-44a1-9c09-0086c9f3979a · outbound

This paper cites React: Synergizing reasoning and acting in language models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments React: Synergizing reasoning and acting in language models

Reference 36

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This paper cites Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36: 68539–68551, 2023.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36: 68539–68551, 2023

Reference 37

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Observation d67cfd49-c45f-4a08-9463-de5f17d5b41d · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Reflexion: Language agents with verbal reinforcement learning

Reference 38

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Observation c021fb3c-31ce-44b0-bfe2-e8030e42188f · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 39

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Observation 33ec84d1-a8e2-4ca3-85e3-30ddf0037b2d · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 40

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Observation 93e01972-2f06-4ec6-b610-15db4c1b357b · outbound

This paper cites Metagpt: Meta programming for a multi-agent collaborative framework.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Metagpt: Meta programming for a multi-agent collaborative framework

Reference 41

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Observation c02340fd-ee98-4379-b657-8db9db2413fb · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Generative agents: Interactive simulacra of human behavior

Reference 42

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 43

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Observation 26998045-7c36-4d1e-955a-8c801dc0eb26 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 44

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Observation 882c0224-ff88-4c58-979c-70cd0fe33887 · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.501402Z

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Observation 94ecf3d6-8a1f-40a2-bdcd-05a06c64b33f · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.560597Z

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Observation b9e958a2-a17c-4a93-ba90-08115ae5904e · outbound

This paper cites Group Sequence Policy Optimization.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Group Sequence Policy Optimization

Reference 47

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Observation 625e3557-897d-4a35-b041-62190c0b70a5 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Understanding R1-Zero-Like Training: A Critical Perspective

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.482519Z

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Observation 1a170f90-bce1-40cf-8f98-67734c5206f0 · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.530817Z

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Observation f6b814b9-6cb1-4ab2-8e64-de37b78cc4e7 · outbound

This paper cites Coba-rl: Capability-oriented budget allocation for reinforcement learning in llms.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Coba-rl: Capability-oriented budget allocation for reinforcement learning in llms

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.516552Z

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Observation c7e31f8d-1aba-4ead-924d-234f4cba419b · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.603449Z

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Observation db3a0a16-5476-4024-b7b9-e90f6f1f6f9d · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.630043Z

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Observation 01d5be0e-72d3-456a-b302-19cb2d04afee · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.600912Z

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Observation 6daba692-88d3-4268-8629-83132e4cd289 · outbound

This paper cites Look back to reason forward: Revis- itable memory for long-context llm agents.arXiv preprint arXiv:2509.23040, 2025a.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Look back to reason forward: Revis- itable memory for long-context llm agents.arXiv preprint arXiv:2509.23040, 2025a

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.624121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ae4d2e8-a9b4-4fd8-b4e6-941a76c35e22 · outbound

This paper cites Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

Reference 55

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Observation f490a6e6-4bc6-4117-bc7b-c7a6467dcaaf · outbound

This paper cites Training Software Engineering Agents and Verifiers with SWE-Gym.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Training Software Engineering Agents and Verifiers with SWE-Gym

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.583201Z

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Observation a2569508-16e4-42e9-a68e-825d2831a632 · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.588646Z

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Observation daae1af8-4483-40e3-969c-48de82746a9a · outbound

This paper cites Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig

Reference 58

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Unavailable: canonical work link unavailable.

Observation f635e77c-3d91-4871-b767-ed2f97d6e75b · outbound

This paper cites OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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Observation 0ae4f2f6-21d0-405c-b86d-f918b9788295 · outbound

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

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments AppWorld: A controllable world of apps and people for benchmarking interactive coding agents

Reference 60

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Observation b9ea1352-8d00-4145-a1c0-b2fb50b1b807 · outbound

This paper cites arXiv preprint arXiv:2503.20197 , year=.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments arXiv preprint arXiv:2503.20197 , year=

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.585738Z

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Observation 18ac6053-ae45-400d-9a6f-a88958f5dad7 · outbound

This paper cites Statistical Runtime Verification for LLMs via Robustness Estimation.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Statistical Runtime Verification for LLMs via Robustness Estimation

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.595190Z

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Observation 6a0a9f1e-6e8e-4f2e-9341-2deb21de8970 · outbound

This paper cites Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study

Reference 63

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This paper cites Anghel, Emilia Pecheanu, Adina Cocu, Adrian Istrate, and Con- stantin A.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Anghel, Emilia Pecheanu, Adina Cocu, Adrian Istrate, and Con- stantin A

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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Observation d3d6b478-3e21-4027-a5fb-66fc052d7740 · outbound

This paper cites Robust LLM training infrastructure at ByteDance.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Robust LLM training infrastructure at ByteDance

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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Observation 5a19f7c8-a44e-4485-bafe-6f5e23b7ae0e · outbound

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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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This paper cites Evaluating the performance and robustness of LLMs in materials science Q&A and property predictions.Digital Discovery, 2025.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Evaluating the performance and robustness of LLMs in materials science Q&A and property predictions.Digital Discovery, 2025

Reference 67

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This paper cites Benchmarking Reasoning Robustness in Large Language Models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Benchmarking Reasoning Robustness in Large Language Models

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.546212Z

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Observation 5b9004d8-d374-4eb5-ae40-09c7731055da · outbound

This paper cites StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.565668Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c348a4e-e675-418e-b6e0-acd3d0fc1b05 · outbound

This paper cites Multichallenge: A realistic multi-turn conversation evaluation benchmark challenging to frontier llms.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Multichallenge: A realistic multi-turn conversation evaluation benchmark challenging to frontier llms

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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Observation 7d556ebd-4c18-473b-8be4-ed6554e9953e · outbound

This paper cites AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.609311Z

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Observation 3c30d5d9-f7d8-43b4-a02f-6f7c8abe691a · outbound

This paper cites Understanding User Experience in Large Language Model Interactions.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Understanding User Experience in Large Language Model Interactions

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.627476Z

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Observation f3565ad3-3a90-4818-9ead-bb7d061061c5 · outbound

This paper cites ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T16:53:40.571485Z

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Observation 5bf061a3-ae5a-49a4-a5a3-8a72b6ff1300 · outbound

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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.624562Z

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Observation ffd0a8a4-9325-42c5-a6b3-241e681feb2b · outbound

This paper cites What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.565926Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d103b0d-6cc0-4c65-b181-8adff8013200 · outbound

This paper cites Reducing Tool Hallucination via Reliability Alignment.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Reducing Tool Hallucination via Reliability Alignment

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.546847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27a4c51f-cec4-441a-9980-a3141ab48d2a · outbound

This paper cites ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.617632Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2777f43e-a485-4c3c-9fe9-c434921fa492 · outbound

This paper cites Toolscan: A benchmark for characterizing errors in tool-use llms.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Toolscan: A benchmark for characterizing errors in tool-use llms

Reference 78

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Observation dd2e087d-bb31-4f2f-8954-711561c22ca2 · outbound

This paper cites InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-06-29T16:53:40.497612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec6920e2-1edf-4ee6-b32f-4137fc337b09 · outbound

This paper cites From allies to adversaries: Manipulating LLM tool-calling through adversarial injection.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments From allies to adversaries: Manipulating LLM tool-calling through adversarial injection

Reference 80

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de86aa06-aaca-4af3-b11d-762d5192a831 · outbound

This paper cites Zhu et al.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Zhu et al

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

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Observation c280ebd1-32be-4256-a36c-38317269654f · outbound

This paper cites Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.485548Z

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Observation 571a2810-b364-4da3-985f-75420d482925 · outbound

This paper cites SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.557491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4dfcb310-b390-4f06-94f3-686ec368a30d · outbound

This paper cites an unresolved cited work.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-29T16:51:36.524194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a32282-5a01-4201-ade7-b59c79931a23 · outbound

This paper cites On Robustness and Reliability of Benchmark-Based Evaluation of LLMs.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments On Robustness and Reliability of Benchmark-Based Evaluation of LLMs

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.568253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 35858390-1ae4-4e5a-b942-a47b1507e872 · outbound

This paper cites Yes, please return the Mechanical Keyboard and the Gaming Mouse.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Yes, please return the Mechanical Keyboard and the Gaming Mouse

Reference 86

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Pith citing papers