ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning | Paper Citation Record

Observation 74d05ffa-e3b2-4d56-a0f8-cf93403eb520 · outbound

This paper cites Simulated Annealing: an Introduction.Statistica Neerlandica, 43(1):31–52, 1989.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Simulated Annealing: an Introduction.Statistica Neerlandica, 43(1):31–52, 1989

Reference 1

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Observation f6f69ae1-994f-49eb-bd96-9ce7e2f62deb · outbound

This paper cites ACPBench: Reasoning about Action, Change, and Planning.arXiv preprint arXiv:2410.05669, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning ACPBench: Reasoning about Action, Change, and Planning.arXiv preprint arXiv:2410.05669, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.215078Z

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Observation 42499826-cc8e-4ff4-bd4b-c9d6da00c25e · outbound

This paper cites Starjob: Dataset for LLM-Driven Job Shop Scheduling.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Starjob: Dataset for LLM-Driven Job Shop Scheduling

Reference 3

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Observation 2b9eb877-0639-415a-b64b-65e9c1f32135 · outbound

This paper cites LLMs can Schedule.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning LLMs can Schedule

Reference 4

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Observation f5d4d812-5189-4d79-8330-880092dde168 · outbound

This paper cites The shifting bottleneck procedure for job shop scheduling.Management Science, 34(3):391–401, 1988.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning The shifting bottleneck procedure for job shop scheduling.Management Science, 34(3):391–401, 1988

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.227529Z

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Observation 6e772eb0-5914-4f63-b0aa-8b271f59d4c9 · outbound

This paper cites Claude Technical Report, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Claude Technical Report, 2024

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.231436Z

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Observation 14f26119-750e-438a-9125-f84d29b92296 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Graph of thoughts: Solving elaborate problems with large language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.235615Z

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Observation f3a9f3e6-3fc6-4c17-a51a-0699064eeba4 · outbound

This paper cites A generalized permutation approach to job shop scheduling with genetic algorithms.OR-Spektrum, 17(2-3):87–92, 1995.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning A generalized permutation approach to job shop scheduling with genetic algorithms.OR-Spektrum, 17(2-3):87–92, 1995

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.239290Z

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Observation 70b8e0ca-0c43-4f0e-a122-763dda87e772 · outbound

This paper cites Hudson, Ehsan Adeli, Russ Altman, and more.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Hudson, Ehsan Adeli, Russ Altman, and more

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.243116Z

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Observation ecf2da15-1430-4672-b732-a17913d79e4d · outbound

This paper cites PLASMA: Making small language models better procedural knowledge models for (counterfactual) planning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning PLASMA: Making small language models better procedural knowledge models for (counterfactual) planning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.248965Z

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Observation 008613ee-5421-4cb2-a83b-e1d11a7cc6f5 · outbound

This paper cites Language Models are Few-Shot Learners.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Language Models are Few-Shot Learners

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.252910Z

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Observation 2b27bdb4-38b0-4bcb-b23b-07f27c73387c · outbound

This paper cites Why Do Multi-Agent LLM Systems Fail?.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Why Do Multi-Agent LLM Systems Fail?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.256667Z

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Observation dfc93004-4526-4255-9197-6499d84a8e9f · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 13

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Observation 50f9674f-c0a6-4069-8d3e-a34e83784db3 · outbound

This paper cites Examining GPT-4’s Capabilities and Enhancement with SocraSynth.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Examining GPT-4’s Capabilities and Enhancement with SocraSynth

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.263642Z

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Observation 57b4f44c-2f1d-4fc3-a7d2-ea4405c296b2 · outbound

This paper cites EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.267542Z

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Observation 8f6254ec-2828-4c05-846e-bebe607e9476 · outbound

This paper cites LLMArena: Assessing capabilities of large language models in dynamic multi-agent environments.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning LLMArena: Assessing capabilities of large language models in dynamic multi-agent environments

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.271862Z

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Observation 99c00300-63d1-47ca-adb2-d471af878bc4 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 17

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Observation 34950972-75fd-4196-8ce1-397741a3c2a4 · outbound

This paper cites Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.280577Z

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Observation f97b287e-e644-473f-930c-e8b0ae22ed8c · outbound

This paper cites TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.284558Z

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Observation f0d4b564-8900-4e4d-a03e-c8502341d0b4 · outbound

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

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.288454Z

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Observation 1376761f-f9bc-4cd9-ae19-f6e76a9a2a7e · outbound

This paper cites Demirkol, S.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Demirkol, S

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.292277Z

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Observation d7eaaaad-6893-49af-b613-b54f8095ed1e · outbound

This paper cites Benchmarks for shop scheduling problems.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Benchmarks for shop scheduling problems

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.296064Z

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Observation b837de36-d83d-4e8e-a567-6d62bf4b7c51 · outbound

This paper cites Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement.J.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement.J

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.299687Z

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Observation 46d606cb-68e1-4af0-96b7-6ccf84fc1faf · outbound

This paper cites Towards revealing the mystery behind chain of thought: A theoretical perspective.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Towards revealing the mystery behind chain of thought: A theoretical perspective

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.303350Z

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Observation 6b3e4846-121a-43cd-ab21-6dee3fd08b44 · outbound

This paper cites ToRA: A tool-integrated reasoning agent for mathematical problem solving.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning ToRA: A tool-integrated reasoning agent for mathematical problem solving

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.307099Z

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Observation 171f4895-5685-436c-a19d-37fc27c08526 · outbound

This paper cites On formally undecidable propositions ofPrincipia Mathematicaand related systems i.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning On formally undecidable propositions ofPrincipia Mathematicaand related systems i

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.310928Z

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Observation b73fa7a4-ce42-44cd-b458-194cce4ab68b · outbound

This paper cites The curious case of neural text degeneration.International Conference on Learning Representations (ICLR), 2020.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning The curious case of neural text degeneration.International Conference on Learning Representations (ICLR), 2020

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.314785Z

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Observation f6ad3526-7c53-4149-b9be-089879f0cc69 · outbound

This paper cites A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.318480Z

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Observation 280ee010-037f-45c7-a8e3-53f6c0d6109c · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.322596Z

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Observation ead9892e-92c7-47c4-9034-28d9b6cdcfe7 · outbound

This paper cites Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.326624Z

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Observation 33f4048d-8990-4261-a750-37ee41b3a463 · outbound

This paper cites Automated Design of Agentic Systems.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Automated Design of Agentic Systems

Reference 31

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Observation db302550-1610-4b37-829b-328c46e3f41d · outbound

This paper cites Large language models cannot self-correct reasoning yet.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Large language models cannot self-correct reasoning yet

Reference 32

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Observation 5571dac3-b6c7-4884-8018-fab50fde25c6 · outbound

This paper cites Automatic programming via large language models with population self-evolution for dynamic job shop scheduling problem.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Automatic programming via large language models with population self-evolution for dynamic job shop scheduling problem

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.338799Z

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Observation f5b6d66b-fc4a-48c1-8378-839e58577329 · outbound

This paper cites Understanding the planning of LLM agents: A survey.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Understanding the planning of LLM agents: A survey

Reference 34

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Observation c6b1f4aa-ad43-4d0f-8462-06087d5f38ad · outbound

This paper cites Evaluating Creative Short Story Generation in Humans and Large Language Models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Evaluating Creative Short Story Generation in Humans and Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.346551Z

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Observation 1a501680-46d6-44b6-9a5f-80aca76cef79 · outbound

This paper cites Self-[in]correct: Llms struggle with refining self-generated responses.CoRR, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Self-[in]correct: Llms struggle with refining self-generated responses.CoRR, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.678478Z

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Observation a74c0fa6-8c51-4d7f-84f5-4ca7f6ce5c9b · outbound

This paper cites Gemini 2.5.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Gemini 2.5

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.663927Z

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Observation 7905bf73-d8e0-4054-af2f-8de092118c1f · outbound

This paper cites Daniel Gelatt, and Mario P.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Daniel Gelatt, and Mario P

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.650567Z

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Observation d2ec3491-147d-4a80-9b34-67ef4dcad7c6 · outbound

This paper cites Langgraph: Building structured applications with llms.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Langgraph: Building structured applications with llms

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.637299Z

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Observation 9689eb03-f7cf-4d44-ab4b-c25ac5953557 · outbound

This paper cites Lawler, Jan Karel Lenstra, Alexander H.G.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Lawler, Jan Karel Lenstra, Alexander H.G

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.624282Z

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Observation 1eade335-8a9c-4b23-8e66-cf7fff1585fe · outbound

This paper cites Pathways to autonomous machine intelligence.https://openreview.net/ forum?id=BZ5a1r-kVsf, 2022.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Pathways to autonomous machine intelligence.https://openreview.net/ forum?id=BZ5a1r-kVsf, 2022

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.608278Z

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Observation 16d38c7f-e1aa-4c7a-8a80-dcfbb7f9fd59 · outbound

This paper cites CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

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Observation e2c49235-e624-46eb-bec6-859dc8b4b3ca · outbound

This paper cites Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:37:37.820196Z

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Observation 63102a20-5e39-43b8-9116-eea3b38a21fc · outbound

This paper cites Dynamic job-shop scheduling via graph attention networks and deep reinforcement learning.IEEE Transactions on Industrial Informatics, 20(6):8662–8672, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Dynamic job-shop scheduling via graph attention networks and deep reinforcement learning.IEEE Transactions on Industrial Informatics, 20(6):8662–8672, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.596210Z

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Observation 385ab6ee-147e-48f9-88d5-5f2e854060d6 · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.385189Z

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Observation 62e0ed26-961a-4331-ab5d-60a6992c4c37 · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Large Language Model Agent: A Survey on Methodology, Applications and Challenges

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Observation d886d3a8-8908-41cf-b50c-9e70ad4729c6 · outbound

This paper cites Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango

Reference 47

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Observation dd172ab8-de66-4b84-8b91-0a8d6911b828 · outbound

This paper cites A Survey on Large Language Models with some Insights on their Capabilities and Limitations.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning A Survey on Large Language Models with some Insights on their Capabilities and Limitations

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.396647Z

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Observation 77f9e3fe-290f-4a18-8737-2ee546b965db · outbound

This paper cites Large Language Models: A Survey.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Large Language Models: A Survey

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.400790Z

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Observation dbd964e4-cd04-4bed-8848-0877121bf970 · outbound

This paper cites Reactive Scheduling in a Job Shop Where Jobs Arrive Over Time.Computers & Industrial Engineering, 66(2):389–405, 2013.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Reactive Scheduling in a Job Shop Where Jobs Arrive Over Time.Computers & Industrial Engineering, 66(2):389–405, 2013

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.575315Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce96b407-7e5e-4f16-8520-b3fe2aef9477 · outbound

This paper cites A fast taboo search algorithm for the job shop problem.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning A fast taboo search algorithm for the job shop problem

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.563397Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 90e9cd7c-b4d0-4516-89cd-dffeff4f9252 · outbound

This paper cites Hello GPT-4o, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Hello GPT-4o, 2024

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.551472Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e121104-5994-422a-9b5a-3d0835ddb590 · outbound

This paper cites Context-faithful Prompting for Large Language Models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Context-faithful Prompting for Large Language Models

Reference 53

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Observation 6a1a912d-e0cc-43de-b472-3dce54265012 · outbound

This paper cites Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.420595Z

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Observation af3ca3a5-dda2-49eb-bedf-db88868c9b0f · outbound

This paper cites HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.424367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70e40620-91f0-4a29-bc30-0e27cce72fe8 · outbound

This paper cites Pinedo.Scheduling: Theory, Algorithms, and Systems.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Pinedo.Scheduling: Theory, Algorithms, and Systems

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.539278Z

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 9267a61a-2b7e-473b-a938-fe463ba6df26 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.525852Z

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 6c9d8683-6da0-4362-89f9-51a3a6590b98 · outbound

This paper cites ChatDev: Communicative Agents for Software Development.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning ChatDev: Communicative Agents for Software Development

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.436180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f63b16cd-15d7-418a-a31e-7351842a4d63 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI Blog, 1(8):9, 2019.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Language models are unsupervised multitask learners.OpenAI Blog, 1(8):9, 2019

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.440298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7c5d487-bbe9-486e-a912-2c61ca49d3fd · outbound

This paper cites Alfworld: Aligning text and embodied environments for interactive learning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Alfworld: Aligning text and embodied environments for interactive learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.506367Z

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 d40145a7-9e46-49db-aaea-466942040b52 · outbound

This paper cites Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T20:37:37.713012Z

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 b2ff3e5c-c78b-4951-b702-a2963ff8db4f · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.451330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e91f7298-4201-4073-b77d-2347275f4a13 · outbound

This paper cites Chain of thoughtlessness? an analysis of cot in planning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Chain of thoughtlessness? an analysis of cot in planning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.494843Z

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 a0f8a4cb-912f-41a2-abb6-bb9435f3b4f7 · outbound

This paper cites AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.458977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a83c8518-c425-4bdc-bf8d-260d694f077c · outbound

This paper cites Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change.NeurIPS, 36, 2023.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change.NeurIPS, 36, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.482488Z

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 94581e88-e6c8-43e7-803d-a174fc1b5456 · outbound

This paper cites Attention is all you need.NeurIPS, 30:5998–6008, 2017.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Attention is all you need.NeurIPS, 30:5998–6008, 2017

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.469252Z

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 2a362933-e51e-4044-b084-1b51b3b6e696 · outbound

This paper cites Efficient large language models: A survey.Transactions of Machine Learning, 2023.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Efficient large language models: A survey.Transactions of Machine Learning, 2023

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.456568Z

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 f8aa1593-2702-45ac-a2c3-1b4309ae879c · outbound

This paper cites Larger language models do in-context learning differently.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Larger language models do in-context learning differently

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.474228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46b350c6-2577-4bd2-bfaf-e907e790099a · outbound

This paper cites Chi, Quoc V.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Chi, Quoc V

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.443835Z

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 0d556f23-6d87-4d12-8c19-65f0fa5395f1 · outbound

This paper cites John Wiley & Sons, 2009.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning John Wiley & Sons, 2009

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.432339Z

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 138b6479-d4d6-4b33-8f33-bab51b27e82b · outbound

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

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.420335Z

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 ff0225e2-d3cc-4c97-ac2f-8029c2caf7a7 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Efficient Streaming Language Models with Attention Sinks

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.489388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a23c3702-f652-4d3a-a1ed-2f07118af7f1 · outbound

This paper cites A survey of job shop scheduling problem: The types and models.Computers & Operations Research, 142:105731, 2022.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning A survey of job shop scheduling problem: The types and models.Computers & Operations Research, 142:105731, 2022

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.493440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dbac8ce-b90a-4836-901f-3dcff46942aa · outbound

This paper cites Large Language Models Can Learn Temporal Reasoning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Large Language Models Can Learn Temporal Reasoning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.497340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddc61fc3-74c2-4109-ac21-ddd8d866b57e · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.400821Z

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 8e9452e6-3081-4fa3-af1c-96301927e024 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Tree of thoughts: Deliberate problem solving with large language models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.388959Z

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 173bc647-8836-4b16-847a-b1581ad9d47c · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning ReAct: Synergizing Reasoning and Acting in Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.508808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd4a2bab-d389-4f0c-8eae-15c93908c338 · outbound

This paper cites Learning to dispatch for job shop scheduling via deep reinforcement learning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Learning to dispatch for job shop scheduling via deep reinforcement learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.377379Z

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 a64b2c58-ae18-499d-8dde-b58905769152 · outbound

This paper cites Genetic Programming with Multi-tree Repre- sentation for Dynamic Flexible Job Shop Scheduling.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Genetic Programming with Multi-tree Repre- sentation for Dynamic Flexible Job Shop Scheduling

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.366215Z

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 0ffa2492-ed3b-4f28-a55b-7d10b09899cf · outbound

This paper cites Aflow: Automating agentic workflow generation, 2024.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Aflow: Automating agentic workflow generation, 2024

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.519687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b9414c-203d-41c3-ace7-0ecb4d7a0efd · outbound

This paper cites Llemma: An Open Language Model For Mathematics.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Llemma: An Open Language Model For Mathematics

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.523033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40576e5f-253a-489f-b26e-d6d5b996ff6d · outbound

This paper cites Large language models as commonsense knowledge for large-scale task planning.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Large language models as commonsense knowledge for large-scale task planning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.348450Z

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 393e8404-8201-452b-93c4-ca90e2a9b810 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.335263Z

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 c4f8437a-53b8-4c1b-b8d4-e8497531ca2b · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.322724Z

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 030e5be3-40a8-4af8-bb69-37636a3e57fe · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.311403Z

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 36ff60c7-a396-4003-9f66-b1a307332d48 · outbound

This paper cites When a suitable implementation is identified, it undergoes verification testing to confirm operational compatibility with the workflow requirements.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning When a suitable implementation is identified, it undergoes verification testing to confirm operational compatibility with the workflow requirements

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.298331Z

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 67c53c8b-1262-4f96-a9ea-a041f41dec8f · outbound

This paper cites This translation preserves all critical requirements while expressing them in a form that maximizes LLM comprehension.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning This translation preserves all critical requirements while expressing them in a form that maximizes LLM comprehension

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.286936Z

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 486c96e5-6132-4ffc-905d-6a8be4263759 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.275840Z

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 fca6de3a-5352-4866-9360-b20cbc6c6167 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.264834Z

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 aebc065e-4cdc-47d0-a323-4c48f123ab42 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.253411Z

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 cabff739-dfed-4e1d-aee2-fe9ef8a6260b · outbound

This paper cites 17 A.4.5 Deployment Artifact Production The output of the Agent Factory is a deployable artifact that encapsulates the agent’s logic and inter- action patterns.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning 17 A.4.5 Deployment Artifact Production The output of the Agent Factory is a deployable artifact that encapsulates the agent’s logic and inter- action patterns

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.240878Z

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 8c31d645-04ab-4e44-8763-132a567a3f7b · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.229047Z

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 b1823712-9ad8-4f85-9fdc-1f0ba6b030a3 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.217570Z

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 74d594ed-f39d-4c0f-acef-73610bdff5a8 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:37:38.206271Z

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 4f004093-d9ce-4a32-9310-916c0b50e156 · outbound

This paper cites Driver John departs from location A.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Driver John departs from location A

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.195165Z

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 4e732e1d-3c4a-4eaf-915a-9953ba228157 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.582459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f1685de-9e90-4264-9d57-6ef849ccdaa2 · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.586097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d2a6c1-0890-4bb0-b5b7-1b552e666bda · outbound

This paper cites an unresolved cited work.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning Unresolved cited work

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T20:37:37.590081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63691913-260e-4c59-831f-2bebf45f20e8 · outbound

This paper cites At noon, James’ flight is delayed until 4:00 PM. Update the schedule to meet the deadline at 6:00 pm while meeting all constraints.

ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning At noon, James’ flight is delayed until 4:00 PM. Update the schedule to meet the deadline at 6:00 pm while meeting all constraints

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:37:38.159029Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers