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Paper Citation Record · LEDGER

One STEP at a time: Language Agents are Stepwise Planners

As of 13 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 2026-08-13T06:32:02.005865+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:c482929d2b75bf80ee35c088d9add38ebe58002e230b63be0fde6c820ec9311a

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=sha256:5da7d0e0be477240d936c87945a905b6654ad8c6ded63367ff0d97521271e7c8

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=sha256:0585cf69ac3f6ffec1d10c1b9be19e733286132fc2da02c1ee64882f3101020d

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=sha256:1d5c6caab8d73b121c2660b6aa7a0c701f1fb8acf60d07144035c1b341976c83

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:f867999b0628aa78d2ae501bbe262b471dad363e2459194069a5efe2be967a10

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:f609715cf332b1ae25be8292f53960d6cb20dea08461d432cd1e462f5235ecc6

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:ceb944fadd1ed146b5d964ebbe34434e2d8e8d53c0d32f69b3e7624bbbd358a0

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=sha256:03c83395e7471f0c92d4d407fcc0342cf7ed2a878f7f812e823c9547a5db7b7f

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=sha256:7bd754108d8d7a3c51a0683a21fb89ded3a47ae0330d39ba4db5b6b8eb6ebdc4

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=sha256:7fb159417d8a473a813aeb9a837f42f7d0cfafc43263493811f528e0af089a85

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=sha256:2a54e090052ab73260905b7f24af644e975bc08d6a62372d12b14dc30757c34e

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:e0327e29eac79bda321c0bbfbf9262ba381a613321d0d84113e280043473deb1

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=sha256:1e4036289f41bfaa2fe085c4a74ae8bbd2d5a4ae1f846715a802d5dc21078eee

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:fe7952b8d7600b59246f65d5b7775f701afdf4c6633bd33a68845d3decf9d9af

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=sha256:2f11c5a9c223cd48f245ad488bf32669133b9d494ef5be77710c53da7a55dba0

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=sha256:03d79770bf73a7aca953809587126480236b6f9e23e97a28b1a252e47964b51c

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=sha256:79b8bb157d2001ec1ef3e4d07aaf7d1497b453581c27d4bf5d9f9c1379f865de

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:b683f403c1bf76346c3fc0bd6331a7bba54bf2be176c28cccd932e42edbd69c5

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:a50f95c31badbb4e51a59a435b7523aa29e36271d0587457c5ef58c92f02e208

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:d8bb78b981b641383d0bb5a1aa452bde916284c14b18a4ca178c5949bae06bfa

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:ca566cd39dc78784b5399e02e24a825260d5017fbacea8ff5f6467b188146b18

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=sha256:12dca1ba759c7745248fb1379f5710873f6f729f31a4aa06953f806f9502acb4

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=sha256:8a28dc8530f2e0ac63ae8ff81c63b68e231f80cf6a07124333e325b4aaa4a524

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=sha256:87c73aac4002d9c828107ab928a1fb4dffd676961819f7a529483439c11b2b12

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=sha256:5303708f4dc003fe85f07c0cc8632bdd1bf11e9e960750585755cb40fbf489fb

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=sha256:660cecac21223b4908dee5539db9709e38deb8a3dffda25cf39701be10cd73a6

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:f7af94c7b7cb9b10b04d2375aae3358827919f65ad5a62888d8e43707851bc87

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=sha256:21bea4c89dea4ff2218fae334dd4a8f8a08f6048ce0e20d606ba497d3bf014e6

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:b399c9dffae076dd1186ff444712a19f7c42619c33a94af512798ff3229c4244

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=sha256:01f4f293603e312d08974266fd20a2bbb7c75d2bdc7c4b226d08cf98b478d3f5

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=sha256:4c59b94881d0f0d95411e57be01ee106ea66fc3b29128cb976464a1446eedf30

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:f0fa0eb67ca4ef35d2ba34ae9d7986b3cf099789e7ec6044b26cb9380017cec5

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=sha256:6f992669acca7b5cb5ee826d0ffc265d7fc8307bef09d61692fb66ede6eb80f2

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:c2a2fe8b62115eafc4f5892bd29663f2338557264c4c82fbfac5495c19a9f36d

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:b03509776f982391b8cb0fa5a02ce192bcb7c504f98c3a20ae0828c56ef552b8

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=sha256:209734ee24dcc27628184881ca586b19785ff6d0e20072846417ffce252f6e27

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=sha256:82c1c70c4a105bf25b3909a539ee4ca211c75e60b1d31f88daa950b01c7ea3cd

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.350267Z digest=sha256:d3a21f1b38bb97c85acc26cd57e03550c27b28d589f35a764e10fef80f09f2c9

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=sha256:1d394c9c849ff33dfdf45f17d910af111816ecf1c321f5d7276e991c64cd750c

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=sha256:2f957d29fbf36007b7bf826f97b94983097b916877c6ea2e5c88fad95b4f74cc

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:fc92df76e12ce6b512c467042d2aee26b1f360bb78ed32a0bd10dcb8601def74

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=sha256:5218f62899b31c211ff2781b76412dfc3f25bcad1a48f6d54d5833b1e87296ff

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=sha256:6104745d668c7ebff15edf62bbbf2aaff6038efc0091331b26f454c9fb869084

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=sha256:209f58ee87c182870351fb62563ae707512c74801853e74e256afcaace5ff9f7

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=sha256:0cb7c242bdb55fa40180ff861584670207589a6631c9d4d3cd31fb2d4da02280

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:c6dd2fcf4c9efcc3e470822858407f3a8c58b696ce183d185724b70c88965bac

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:aba0c9b6abf019c9168994e66b7239de4dd518e54057ccd875ce889b1d717faf

Pith citing papers

No inbound Pith citation observations are available.