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Paper Citation Record · LEDGER

One STEP at a time: Language Agents are Stepwise Planners

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2411.08432.

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

pith.paper-citation-record.v1
2411.08432 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:40:44.397579Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved47
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e81531c2-2b50-472b-937b-d204faa0bcb0 · outbound

This paper cites online" 'onlinestring :=.

One STEP at a time: Language Agents are Stepwise Planners online" 'onlinestring :=

Reference 1

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source=arxiv_source observed=2026-08-12T21:40:44.156468Z digest=sha256:7c73f7ca3858da2ac2fd8030f8ac383be42836a16e3603599d9d581d41ac615a

Observation dc0be3d3-2062-4653-9f6b-5b5dfa6b1d71 · outbound

This paper cites write newline.

One STEP at a time: Language Agents are Stepwise Planners write newline

Reference 2

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source=arxiv_source observed=2026-08-12T21:40:44.162197Z digest=sha256:4356b393c0d9b826081090b364ce4ef910563b7e0bc9c5b9e0e81e202950f118

Observation 3b4364ad-029d-4e37-9b8c-a39c7a437977 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

One STEP at a time: Language Agents are Stepwise Planners Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 3

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source=arxiv_source observed=2026-08-12T21:40:44.168045Z digest=sha256:10c909e504dfdd493f034c499daff809e372248b4ef506222b5c9a0019a5d343

Observation 8db16dd8-b764-479a-b564-667bb2b11afd · outbound

This paper cites Graph Constrained Reinforcement Learning for Natural Language Action Spaces.

One STEP at a time: Language Agents are Stepwise Planners Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Reference 4

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source=arxiv_source observed=2026-08-12T21:40:44.173789Z digest=sha256:82a53e9ea5dd5953575c7444e413b587932171eddb435e9aa84a4a59312bd21e

Observation 9e3148ff-8b87-4a44-9778-69c3d5757be0 · outbound

This paper cites an unresolved cited work.

One STEP at a time: Language Agents are Stepwise Planners Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-12T21:40:44.179422Z digest=sha256:81bbbac51a833ce88177e3ea06a7270c7b8c8121484b83a0236243b0d1adecc6

Observation e9cad111-cb98-46b8-a842-0b9f166ea841 · outbound

This paper cites Deep reinforcement learning from human preferences.

One STEP at a time: Language Agents are Stepwise Planners Deep reinforcement learning from human preferences

Reference 6

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source=arxiv_source observed=2026-08-12T21:40:44.184594Z digest=sha256:75d6a3aba5a2dd7bb2206baa379eca1e409aa917c8d8c93808afa63d14fac74e

Observation ab938670-212b-4959-89f2-8edb77f65705 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

One STEP at a time: Language Agents are Stepwise Planners Training Verifiers to Solve Math Word Problems

Reference 7

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source=arxiv_source observed=2026-08-12T21:40:44.189854Z digest=sha256:87cc45ab360e5afb152b47b58dc8ac667f1238afe402e535910852f09b7d9bfc

Observation eef30407-c33b-4e50-b113-10ce5315187b · outbound

This paper cites Dynamic Planning with a LLM.

One STEP at a time: Language Agents are Stepwise Planners Dynamic Planning with a LLM

Reference 8

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source=arxiv_source observed=2026-08-12T21:40:44.195906Z digest=sha256:f6cda3c80e08ee4b80ede30ed2c1b58ae9e6538ebe915eed9de75740f5c23535

Observation c15fcc6f-a954-4f49-be34-197003fc33d1 · outbound

This paper cites Language Models can be Logical Solvers.

One STEP at a time: Language Agents are Stepwise Planners Language Models can be Logical Solvers

Reference 9

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source=arxiv_source observed=2026-08-12T21:40:44.201822Z digest=sha256:0d8a6b4893efa1a3fa352b6c4b86f70e290fd5ba16ffc64fa810b3efd0fccd1a

Observation ede06ed7-417b-4f79-aa1d-3e0eddf9b1c8 · outbound

This paper cites AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents.

One STEP at a time: Language Agents are Stepwise Planners AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

Reference 10

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source=arxiv_source observed=2026-08-12T21:40:44.207200Z digest=sha256:7ee9377eecf25309d489e11087f799dd51e1052fff2c2382e74675d873ec61b2

Observation b1b3899a-7999-43dc-9ee4-18ecbd7a06a6 · outbound

This paper cites Introduction to Reinforcement Learning.

One STEP at a time: Language Agents are Stepwise Planners Introduction to Reinforcement Learning

Reference 11

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source=arxiv_source observed=2026-08-12T21:40:44.213014Z digest=sha256:19059cc9f619143d4348a6936dc1408232e6f5fef640424c710bdc0c706e81f4

Observation 0c47e3b1-09c6-4d56-973b-b7eae6e3e709 · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

One STEP at a time: Language Agents are Stepwise Planners CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 12

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source=arxiv_source observed=2026-08-12T21:40:44.218766Z digest=sha256:87217a6dd71aea4691958b9ad02b0cd354d93b4cf3be62f14d814d38ca243d48

Observation dc86d269-64e0-4022-948e-c17f395a0fc7 · outbound

This paper cites ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving.

One STEP at a time: Language Agents are Stepwise Planners ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Reference 13

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source=arxiv_source observed=2026-08-12T21:40:44.223782Z digest=sha256:8e2d28adf3dfd77c00920e384bb097a88d5683bcb2b0cf85e832379819904a0f

Observation 0e179922-b4aa-4198-bf43-b886f663d2d9 · outbound

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

One STEP at a time: Language Agents are Stepwise Planners Reasoning with Language Model is Planning with World Model

Reference 14

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source=arxiv_source observed=2026-08-12T21:40:44.229112Z digest=sha256:e1983732eaa7cd80bda20dcc66b550ef7b08fd0702dbb9f5b80002b8d115f89d

Observation c8a51292-2cc4-4d1f-b344-e87a0b9d9c0a · outbound

This paper cites Deep Reinforcement Learning with a Natural Language Action Space.

One STEP at a time: Language Agents are Stepwise Planners Deep Reinforcement Learning with a Natural Language Action Space

Reference 15

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source=arxiv_source observed=2026-08-12T21:40:44.234285Z digest=sha256:aaee27224a7884899ace4ad8465258f63274ab50cce3ad0053fbcf83335bfc4b

Observation 4e6b679c-9bf2-4237-91cb-0458a6ff6149 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

One STEP at a time: Language Agents are Stepwise Planners Measuring Mathematical Problem Solving With the MATH Dataset

Reference 16

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source=arxiv_source observed=2026-08-12T21:40:44.239363Z digest=sha256:9494702f8fdac7625c7a1165fa397d114230209d7782439fd9aaabc349b6e938

Observation 0b64d0f1-bb37-4b87-98f8-cd1034785263 · outbound

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

One STEP at a time: Language Agents are Stepwise Planners Understanding the planning of LLM agents: A survey

Reference 17

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source=arxiv_source observed=2026-08-12T21:40:44.244293Z digest=sha256:a1e7e32892bacdad8b74fc60c49e3b8fdd1c75add62c1cec55b030a1c7b01549

Observation 33561b0d-687e-402d-bb80-e94025a03255 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

One STEP at a time: Language Agents are Stepwise Planners SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 18

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source=arxiv_source observed=2026-08-12T21:40:44.249245Z digest=sha256:63e218a931560bb303d711a96e719e0ee60376cb7fbe8e4aac3636638b4ee044

Observation a0d68c15-a524-4118-833a-068d68515176 · outbound

This paper cites LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

One STEP at a time: Language Agents are Stepwise Planners LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Reference 19

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source=arxiv_source observed=2026-08-12T21:40:44.254422Z digest=sha256:bc13d12286e0e294ed32775ecc81af0dbdeeb3b65f80465deeba7fa8073d9b5e

Observation e0cd36ec-ddaf-4160-b8e6-8573645b592e · outbound

This paper cites Think Before You Act: Decision Transformers with Working Memory.

One STEP at a time: Language Agents are Stepwise Planners Think Before You Act: Decision Transformers with Working Memory

Reference 20

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source=arxiv_source observed=2026-08-12T21:40:44.259692Z digest=sha256:d93f6125914299bc49309bce3622c4255a20f21350b682ef0ff77b847b69e69c

Observation 23b63b18-4bd3-43c8-9cb2-902cd3b4c276 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

One STEP at a time: Language Agents are Stepwise Planners Large Language Models are Zero-Shot Reasoners

Reference 21

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source=arxiv_source observed=2026-08-12T21:40:44.264915Z digest=sha256:208181916d443f812aa0b6c5795580c3359199612d2a82386f3e7eee78166228

Observation 3efc3b9e-adfd-4b91-acb8-edce35e6364e · outbound

This paper cites SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks.

One STEP at a time: Language Agents are Stepwise Planners SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

Reference 22

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source=arxiv_source observed=2026-08-12T21:40:44.269743Z digest=sha256:94386ccf3371c0430132524b81d37a53eabb7246c09862a0bb39220097297e23

Observation 3a42bacc-ef36-4d31-9618-7a4c264d6293 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

One STEP at a time: Language Agents are Stepwise Planners LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 23

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source=arxiv_source observed=2026-08-12T21:40:44.274888Z digest=sha256:6602d11ba15b838e98b8a8417a1c37557eb8c72c6845b66847c651f0d3cdd501

Observation 0a1bd1dc-468e-4f5c-a6d3-50aef5b95ab7 · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

One STEP at a time: Language Agents are Stepwise Planners Self-Refine: Iterative Refinement with Self-Feedback

Reference 24

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source=arxiv_source observed=2026-08-12T21:40:44.279795Z digest=sha256:dca7dfdb4f7f73566ceeef490503c883d8f216b47df19aa8ce772ca7442aac50

Observation ea34afc0-8619-48e0-a12c-05ae70636973 · outbound

This paper cites CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization.

One STEP at a time: Language Agents are Stepwise Planners CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

Reference 25

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source=arxiv_source observed=2026-08-12T21:40:44.284722Z digest=sha256:eee28a9b320f6affa5b40be73976b7622351ca30de79418e3046ed4dd73a165e

Observation 9270adb3-6f02-4815-bdf5-92584b9cc7e6 · outbound

This paper cites Training language models to follow instructions with human feedback.

One STEP at a time: Language Agents are Stepwise Planners Training language models to follow instructions with human feedback

Reference 26

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source=arxiv_source observed=2026-08-12T21:40:44.289829Z digest=sha256:f3f0f045dae2a7bec32b5f171ee3137450957b3dde5f9f8b773734773c480685

Observation ff6a8a44-26e4-4c2a-9983-e857be26117b · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

One STEP at a time: Language Agents are Stepwise Planners Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 27

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source=arxiv_source observed=2026-08-12T21:40:44.295028Z digest=sha256:3e0136823c030ef5e436c89b435839bbe852c1a86f1b80a126abecb1a1d11d67

Observation 72089e18-3836-468a-bc83-891e905e3bea · outbound

This paper cites Confident Adaptive Language Modeling.

One STEP at a time: Language Agents are Stepwise Planners Confident Adaptive Language Modeling

Reference 28

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source=arxiv_source observed=2026-08-12T21:40:44.300064Z digest=sha256:9aa9039b52477ed3cfe3e3cdc167f06aaefd301c7d4115fcd1a807215163e1e5

Observation fc7407f5-5003-4383-be73-69d283a5b123 · outbound

This paper cites HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face.

One STEP at a time: Language Agents are Stepwise Planners HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Reference 29

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source=arxiv_source observed=2026-08-12T21:40:44.305297Z digest=sha256:085a962a9e6c47655a95c457e61cff90c45e255003d509e6a389339e90ac8e2d

Observation 89e10d9e-e70c-46e0-986a-9265ffb9db25 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

One STEP at a time: Language Agents are Stepwise Planners Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 30

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source=arxiv_source observed=2026-08-12T21:40:44.310154Z digest=sha256:0213edf58486b2755a07e11987cb57785842ba9a80f723d427f26883186987bd

Observation 67eac9c9-33a1-46b1-ba6c-a7dbbe59c627 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

One STEP at a time: Language Agents are Stepwise Planners ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 31

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source=arxiv_source observed=2026-08-12T21:40:44.315299Z digest=sha256:95e7c3cd79c9ff85e599325fc651fa6c7d4ec46e8ac8f0ede6ca4ba0f92c2dfb

Observation 1c33cd22-5d1c-42d1-8f1a-a7961efdb4ee · outbound

This paper cites ProgPrompt: Generating Situated Robot Task Plans using Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-12T21:40:44.320186Z digest=sha256:4a1a559d2477222237077f7c20a5cb38905e8042953359cc79bff46e1246b275

Observation 68fbad43-9b26-4e67-ab9f-94a89aa670a5 · outbound

This paper cites Cognitive Architectures for Language Agents.

One STEP at a time: Language Agents are Stepwise Planners Cognitive Architectures for Language Agents

Reference 33

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source=arxiv_source observed=2026-08-12T21:40:44.325147Z digest=sha256:586836bd18edd2a96c0fcb8c8fb7626084362b3e6fc5d24245152f6e16cbb5f1

Observation 426b9668-626a-4ffe-a8fd-26de1ae858bc · outbound

This paper cites ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language.

One STEP at a time: Language Agents are Stepwise Planners ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

Reference 34

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source=arxiv_source observed=2026-08-12T21:40:44.330129Z digest=sha256:30a4a92cb8703f8033080a9a0a10c30a93cf7bb324802af256359088d50f9842

Observation 9eb97eb7-a5de-41db-8a88-c0eb63cba666 · outbound

This paper cites Can Large Language Models Really Improve by Self-critiquing Their Own Plans?.

One STEP at a time: Language Agents are Stepwise Planners Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 35

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source=arxiv_source observed=2026-08-12T21:40:44.335667Z digest=sha256:f65518e99cc3b2fb02bb4ea080f1f4a24336c73ecfec89b8898b9dc60aa7edd5

Observation 003b80b6-acb1-4381-b73e-15cb0963c600 · outbound

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

One STEP at a time: Language Agents are Stepwise Planners Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 36

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source=arxiv_source observed=2026-08-12T21:40:44.340700Z digest=sha256:888fa4c6d60ca1f66a4d56ddaace5d1cf2305a6a54ef0168a172de1c5494f30d

Observation 64266c7d-ba6c-4812-a3a4-b53c8a8710db · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 37

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source=arxiv_source observed=2026-08-12T21:40:44.345538Z digest=sha256:10bf8f52d72fd72be5b8c19aab9ee10b196524362db58a321a3f09927d5de11a

Observation 9784c780-2fe2-49a7-9d25-5a54a8455e23 · outbound

This paper cites ScienceWorld: Is your Agent Smarter than a 5th Grader?.

One STEP at a time: Language Agents are Stepwise Planners ScienceWorld: Is your Agent Smarter than a 5th Grader?

Reference 38

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source=arxiv_source observed=2026-08-12T21:40:44.350267Z digest=sha256:9fb8300a2d4b83932de7b6223a091d83e0df9f1650a6ac1a827611f94afe654d

Observation ee0d92dd-c840-4662-b71f-a92186de6461 · outbound

This paper cites WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents.

One STEP at a time: Language Agents are Stepwise Planners WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.355321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.355321Z digest=sha256:e58a778ec79a280fe97f3c9e8fe9a045eeee48fc889a063b80a9a61abc7cc255

Observation 17b58234-acf6-422a-9019-229802c725ab · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.360049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.360049Z digest=sha256:71aa9ae068eec5ec849438b09056ceed365e698c4533ece06a9ffcb469ce2fc5

Observation 0b7b6502-e76f-42ee-aa05-2045e5811125 · outbound

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

One STEP at a time: Language Agents are Stepwise Planners ReAct: Synergizing Reasoning and Acting in Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.365078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.365078Z digest=sha256:141a98bc349dca6b5ca279f8f6cfeed150740167b022dfd72146c22c6c367e0c

Observation d04d520c-b4ed-4c4c-ad58-19fd2359d3ce · outbound

This paper cites ExpeL: LLM Agents Are Experiential Learners.

One STEP at a time: Language Agents are Stepwise Planners ExpeL: LLM Agents Are Experiential Learners

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.370287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.370287Z digest=sha256:89175711bcc4225ddcd6c58354953a9cf3793458f45b0c4d1c9005dd63ad67ad

Observation 624d1bc3-e9d0-4b40-b2aa-74a648f42db7 · outbound

This paper cites Large Language Models as Commonsense Knowledge for Large-Scale Task Planning.

One STEP at a time: Language Agents are Stepwise Planners Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.376910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.376910Z digest=sha256:0f4f0ae272e61e2e028ccd805fb9ccb62f5632de416ae106552081d5929002c7

Observation 91dd0862-997b-436f-bf5b-eacb3178b99d · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

One STEP at a time: Language Agents are Stepwise Planners Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.382136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.382136Z digest=sha256:ade53a42833fc2c17b8ebbec9bba249461921c5b621d9f74e45ce4b9ee6c7e57

Observation 4908903a-adbc-41aa-bba4-38a4237843f2 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.387517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.387517Z digest=sha256:9a9eef7438d03c3b94ab7b1019c4bf02f080d3adcc42013bd4debe59f6e938b9

Observation 6e6966ec-0555-4833-b549-8a70bf9d2193 · outbound

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

One STEP at a time: Language Agents are Stepwise Planners WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.392686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.392686Z digest=sha256:7f82cca528e9613025efb639849b4c96edc0bef6db04b4b63562158bd0010af7

Observation bb39c9d1-1a04-4101-bd52-7e2bf7203d13 · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

One STEP at a time: Language Agents are Stepwise Planners BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.397579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.397579Z digest=sha256:87a2a05da98ba634d7f37e90fd8c9b6bd298513a2852adb06e3dfe4ae0aed890

Pith citing papers

No inbound Pith citation observations are available.