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Paper Citation Record · LEDGER

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 6 inbound Pith citation observations for arXiv:2505.00234.

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

pith.paper-citation-record.v1
2505.00234 v3

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:55:41.598963Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T06:52:17.232759Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact1
  • verified fuzzy4
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 70911698-d103-4939-9b0f-65a39cc979bf · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Chain-of-thought prompting elicits reasoning in large language models

Reference 1

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source=pdf_text observed=2026-08-16T04:55:41.363684Z digest=sha256:3f9ac1bcca4edb0ca0b746893a9ec2866451f29c8f9025ba27ce36fc646135d8

Observation 510cc097-290f-4b17-a963-1ca1cbd26337 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 2

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source=pdf_text observed=2026-08-16T04:55:41.368942Z digest=sha256:359a53481126da239cdedc5691b7d0cdb04154fc0c9c881578877ed399f36c1d

Observation 0804d742-43fb-47e9-8167-64e32c3d59ba · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Larger language models do in-context learning differently

Reference 3

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source=pdf_text observed=2026-08-16T04:55:41.374562Z digest=sha256:9caff58451e4d3ff263f4039a1e4c6e5d022826a7d04662189baf85f0879993b

Observation 87be1895-d168-4b9b-b280-af610e1b21ef · outbound

This paper cites AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning

Reference 4

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source=pdf_text observed=2026-08-16T04:55:41.380614Z digest=sha256:08da9470fd3df90d082a9fa80a2ac52ac21b7d40fee4b17223cff976bceafc2c

Observation 4b88d6c2-9f9f-44b7-8afe-c6ac7f9e805e · outbound

This paper cites AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents

Reference 5

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source=pdf_text observed=2026-08-16T04:55:41.387716Z digest=sha256:6f6abb15f0293ba131542b95173396e54e8f64b24c4b7640099a845d7a931e49

Observation 253a9481-1799-4abf-b366-9f637795d9e7 · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks What learning algorithm is in-context learning? Investigations with linear models

Reference 6

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source=pdf_text observed=2026-08-16T04:55:41.393615Z digest=sha256:524a5c81424edde435034bd9e365b1498c08eb8b9ed3eb1b982fd29149bc6594

Observation dbb408cf-2dd4-4233-bbad-4b1246ceef2a · outbound

This paper cites Transformers learn in-context by gradient descent.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Transformers learn in-context by gradient descent

Reference 7

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source=pdf_text observed=2026-08-16T04:55:41.399589Z digest=sha256:697af493f2474b8bd3fbb2fd0750432883a34333f3b79ded28f08377faff870a

Observation 2c5c8edb-dba9-4c8d-9ffd-8f8db7eda2fd · outbound

This paper cites Many-shot in-context learning.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Many-shot in-context learning

Reference 8

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source=pdf_text observed=2026-08-16T04:55:41.404150Z digest=sha256:1e73c7e71e14bc9eb74f573a241e9e1ae3146d710ca40fb50534a9ee65ff5e0d

Observation b02541ed-1d18-4b4d-83a7-9a7cf556873c · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks React: Synergizing reasoning and acting in language models

Reference 9

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Observation 44660e9d-9e64-406e-84dd-6cb5d602dc3d · outbound

This paper cites RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents

Reference 10

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source=pdf_text observed=2026-08-16T04:55:41.413489Z digest=sha256:11d79590443adb2cd58a52d7468c963625f46c6105a97aeb76640d755b3ec325

Observation 9f9c9de9-7574-42f3-acd2-1284870905e1 · outbound

This paper cites Trad: Enhancing llm agents with step-wise thought retrieval and aligned decision.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Trad: Enhancing llm agents with step-wise thought retrieval and aligned decision

Reference 11

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source=pdf_text observed=2026-08-16T04:55:41.419157Z digest=sha256:cdd798aaad600e9dcf28a71660be9dfbf93e6c1d291a3b962a98ca2419444e99

Observation 4cee4501-61e7-40f6-875e-fa137f4a3fdc · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

Reference 12

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source=pdf_text observed=2026-08-16T04:55:41.423712Z digest=sha256:a640f2eec3b6b21bc94a37f2a9160fe6f2c39fb6cf11ab270b06a62871f5a634

Observation 84384bd3-8157-4170-9212-843a2eddeabb · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Measuring Mathematical Problem Solving With the MATH Dataset

Reference 13

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source=pdf_text observed=2026-08-16T04:55:41.428916Z digest=sha256:e2aea38aaea033aea80a0cd7bf8ae00f6b03570f7368d2ac7d076742a423d993

Observation b0dfa143-ea34-4016-90e1-eae62b4bb2e2 · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 14

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source=pdf_text observed=2026-08-16T04:55:41.433823Z digest=sha256:18a7b64b2f8f6c75f3446ea675ffa9430e293e4fd2c2b6f9d24bfa5359dccd0e

Observation a7849ee0-9b5a-4aac-9f5b-b1942bf673ee · outbound

This paper cites Llm-planner: Few-shot grounded planning for embodied agents with large language models.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Llm-planner: Few-shot grounded planning for embodied agents with large language models

Reference 15

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source=pdf_text observed=2026-08-16T04:55:41.438661Z digest=sha256:1a9f104bd2db12f6b7afe170a77586741485b6cdd944e309a1ba3f1aa7702799

Observation 1c812101-e781-4b52-9d21-2f0b68140259 · outbound

This paper cites WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models

Reference 16

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source=pdf_text observed=2026-08-16T04:55:41.443602Z digest=sha256:725c165457306ca2321944915903264164abe9e8f587e190115b94f9ecda3852

Observation 759f191b-9a6d-431e-a434-ea99d43fc35b · outbound

This paper cites Expel: Llm agents are experiential learners.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Expel: Llm agents are experiential learners

Reference 17

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source=pdf_text observed=2026-08-16T04:55:41.449173Z digest=sha256:6752cc38eadbf1ae7ce95a098259f9f432864ad5b7bd746db85c4468f917f903

Observation d8754f51-239d-474b-921d-f4b4dcd7aaeb · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 18

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source=pdf_text observed=2026-08-16T04:55:41.454897Z digest=sha256:da18fa087566f5d9a76e91c7166dceeb9d388787cc65c29d61c8e8ee0ca311f7

Observation e021bbe5-8a11-4f41-9bf7-c07312d5559d · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Direct preference optimization: Your language model is secretly a reward model

Reference 19

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source=pdf_text observed=2026-08-16T04:55:41.460374Z digest=sha256:23d8040001a6eece7c9543f22c92dbe91e6e9ca262f00e4152880bee1a9b2f6e

Observation ad1e6859-42aa-4dfb-8a8f-d0e279c47680 · outbound

This paper cites OpenAI o1 System Card.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks OpenAI o1 System Card

Reference 20

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source=pdf_text observed=2026-08-16T04:55:41.465249Z digest=sha256:8a39ba0daba32582107b573d063719a87d3043d93004dae8f0d4e988633bf819

Observation ea27f762-116d-45bc-a010-14821bcec6c1 · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 21

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source=pdf_text observed=2026-08-16T04:55:41.472711Z digest=sha256:bb3e724359c5dae3e5b630813e6ff1a2152878468304352b2f047b8a28dc7094

Observation 749b7153-70af-46cb-859d-21d5c90bb456 · outbound

This paper cites In-Context Learning with Long-Context Models: An In-Depth Exploration.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks In-Context Learning with Long-Context Models: An In-Depth Exploration

Reference 22

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source=pdf_text observed=2026-08-16T04:55:41.477799Z digest=sha256:ad0a1227efbb1bf903259c8923db48bb6c5555c07172e79db872231ad29ceaa2

Observation 30c18856-c28c-4b70-b38c-b9424e5c6394 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 23

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source=pdf_text observed=2026-08-16T04:55:41.484092Z digest=sha256:e415fe01e06e1017f5b2078a8f3dfd4239d7e680e0e2793dc4db5a75c1dd9a02

Observation a5edb61b-6d55-43b2-9919-cc36a2b4482e · outbound

This paper cites Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Reference 24

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source=pdf_text observed=2026-08-16T04:55:41.489587Z digest=sha256:e7f1bf5b4a8aae793a72cfbe5291f96348ef86c99d05d93382abb3326c0b49a5

Observation 2e6a3c8b-e18e-4b16-b4f3-052e6cfbcf70 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 25

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source=pdf_text observed=2026-08-16T04:55:41.494447Z digest=sha256:4059ce7cdf3e3f8ade93fad5b45b1b91183a9aed20600177722ee03a414300ac

Observation 2460606b-a6c4-43ea-aeec-769e0a811ab2 · outbound

This paper cites Planning In Natural Language Improves LLM Search For Code Generation.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Planning In Natural Language Improves LLM Search For Code Generation

Reference 26

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source=pdf_text observed=2026-08-16T04:55:41.499674Z digest=sha256:448517fdb274ebb54ca9a769f9c2ca106147379f380b33dbc37a4c6651e3f8c7

Observation bc65dae4-e0a4-443d-aa8b-f82f93e057c1 · outbound

This paper cites Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension

Reference 27

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source=pdf_text observed=2026-08-16T04:55:41.505813Z digest=sha256:b3c1179f3230a0bd1be769dcd6d7fa109b67d6985da3867e0daebafb0fbc83e8

Observation ac72714f-3d58-4758-ae01-bb968285465c · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Reflexion: Language agents with verbal reinforcement learning

Reference 28

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source=pdf_text observed=2026-08-16T04:55:41.511718Z digest=sha256:e12005ce6e7a86be8d16aabdd660117be1aa6367743d6c83b97e8dd66708a332

Observation 92897b7a-b172-40e4-8c1e-29fd97050f20 · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 29

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source=pdf_text observed=2026-08-16T04:55:41.517165Z digest=sha256:af8e1c05241842f9b6db60550cda749377d49cebbeb1d4d1b9cf70cffd47ae5f

Observation 01d8dfa7-7c42-43a2-94ac-5bcf4834b3a1 · outbound

This paper cites Archon: An Architecture Search Framework for Inference-Time Techniques.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Archon: An Architecture Search Framework for Inference-Time Techniques

Reference 30

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source=pdf_text observed=2026-08-16T04:55:41.523904Z digest=sha256:98ce9dc805bbfbe602669b47ed96353c57193bd8a10a7726532e214a9402ea10

Observation 5b812394-10f9-4087-87d2-59f2770ad97d · outbound

This paper cites Automated Design of Agentic Systems.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Automated Design of Agentic Systems

Reference 31

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source=pdf_text observed=2026-08-16T04:55:41.529807Z digest=sha256:e897730c1ad14cdc9b357394d9d1c58f9808eb7aaf04567509d304fc04450f91

Observation 75c7c68f-436d-4351-9ef0-a9ed89b0faa7 · outbound

This paper cites AFlow: Automating Agentic Workflow Generation.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks AFlow: Automating Agentic Workflow Generation

Reference 32

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source=pdf_text observed=2026-08-16T04:55:41.536188Z digest=sha256:ddb9d90a157e28cf18f39c56c98f476dbf217066c86d8804c509987323efbe8e

Observation 553a94dd-7261-465d-aaec-65cd946935b6 · outbound

This paper cites Reinforcement learning by reward-weighted regression for operational space control.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Reinforcement learning by reward-weighted regression for operational space control

Reference 33

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source=pdf_text observed=2026-08-16T04:55:41.541980Z digest=sha256:eacb2b9859b0c5cb5e32c5cffc23da1e225ccde2892ac3173bcdd33c5e156a5d

Observation a15f8335-5750-4905-8ec2-f08ec9821b63 · outbound

This paper cites Population Based Training of Neural Networks.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Population Based Training of Neural Networks

Reference 34

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source=pdf_text observed=2026-08-16T04:55:41.547419Z digest=sha256:a1fe557265dcd4dc6d182ffc943fe5199281088e31f8ab421961fdec7943bba9

Observation 79e3d0bd-f3c7-4dfc-af10-c762b92e3373 · outbound

This paper cites Reinforcement learning: An introduction.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Reinforcement learning: An introduction

Reference 35

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T04:55:41.553820Z digest=sha256:c06160aac33433e546466cde570dd637ef4ac5b23bdde8bdeae3d8b9881a7e48

Observation 9d5eb9df-0249-4a02-85df-309482a3c23d · outbound

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

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 36

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source=pdf_text observed=2026-08-16T04:55:41.559229Z digest=sha256:4e05ab9460f7c3ae6945c9727950df1622fcf6d956aeac60f244c8abab66944d

Observation 70e78364-9118-4fb7-b91d-ce56d748ce21 · outbound

This paper cites Intercode: Standardizing and benchmarking interactive coding with execution feedback.Advances in Neural Information Processing Systems, 36:23826–23854, 2023.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Intercode: Standardizing and benchmarking interactive coding with execution feedback.Advances in Neural Information Processing Systems, 36:23826–23854, 2023

Reference 37

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no resolver link, observed 2026-08-16T04:55:41.564832Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:55:41.564832Z digest=sha256:1b5497cee1a53f81555edbbffa13ac69bc3fff1301af5a9cba006901ff11e477

Observation f4e037e6-6df6-4668-b52f-d39f0038305e · outbound

This paper cites WordCraft: An Environment for Benchmarking Commonsense Agents.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks WordCraft: An Environment for Benchmarking Commonsense Agents

Reference 38

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verified exact
local_arxiv, observed 2026-08-16T04:55:41.702179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T04:55:41.569956Z digest=sha256:cd4bc9fc5a88e5cc178e085c4d0ae7c9f1bb2e0d5ccb8f01da44a713a6564b58

Observation 8097bab1-3835-4cfc-8e81-11bc864066cd · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 39

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unresolved
no resolver link, observed 2026-08-16T04:55:41.575419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:55:41.575419Z digest=sha256:c8ad0a8cde4bee8ef091313eb48a6ae9c76843a7ecebc5b5eaa4063410504e97

Observation d1d2f030-ccbf-4976-a483-d1c4d1125843 · outbound

This paper cites The Faiss library.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks The Faiss library

Reference 40

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unresolved
no resolver link, observed 2026-08-16T04:55:41.580646Z

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source=pdf_text observed=2026-08-16T04:55:41.580646Z digest=sha256:0ca28ca14bedf73f50d3cad76d51d7b868ae1d62aabed2c09742bf2112edc7df

Observation d3aed6de-f9d6-4881-a2fd-e3b44bae0579 · outbound

This paper cites Webshop: Towards scalable real-world web interaction with grounded language agents.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Webshop: Towards scalable real-world web interaction with grounded language agents

Reference 41

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unresolved
no resolver link, observed 2026-08-16T04:55:41.586272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:55:41.586272Z digest=sha256:031f506fe3cfbd25df7cf8264864d59f209fd58b15c1bf8a3c7cfeac99d38513

Observation e935c22e-252d-4c83-bad0-2cb3b99a408f · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 42

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unresolved
no resolver link, observed 2026-08-16T04:55:41.591891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:55:41.591891Z digest=sha256:59375fa9a4de57579fb80ef609fa692669894187a07fc11b666ef065253153c1

Observation 23fe2552-1198-41cf-9a81-bf8d7cf15105 · outbound

This paper cites Find the first name of a student who have both cat and dog pets.

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks Find the first name of a student who have both cat and dog pets

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-16T04:55:42.135368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T04:55:41.598963Z digest=sha256:a6534810557ab9198e083df974095234ced42353f0332f032e58eb8fcbf9d3f0

Pith citing papers

Observation 6f73a2d5-e9c5-4c58-87b8-ddcd0b107500 · inbound

Dynamic Tool Dependency Retrieval for Lightweight Function Calling cites this paper.

Dynamic Tool Dependency Retrieval for Lightweight Function Calling Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 28

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verified exact
arxiv_id, observed 2026-05-16T21:18:32.802582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-16T21:13:49.210602Z digest=sha256:3732e715536d57f195fa28ee3ba39250ac39550cf7c1fac2fb3d1120a6baf1d8

Observation d1c5373a-8336-41f0-987e-194b82e90bb4 · inbound

FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast cites this paper.

FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 14

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verified exact
arxiv_id, observed 2026-05-20T18:48:53.269342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-20T18:47:34.221197Z digest=sha256:9e2c958c19d43885a57cf3dccff99ec547ec5ce107d5ae8975f89cdd507fd677

Observation 4fbad889-1382-4ade-aac8-c3986ac99383 · inbound

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media cites this paper.

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 43

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verified exact
arxiv_id, observed 2026-05-20T14:08:20.637082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-20T14:05:14.737146Z digest=sha256:a1a588b8f65b879d956439ba6a55cc5bd8003def27cc19d9cb7d4b4660aa45e6

Observation f3822e6a-619b-4f23-9b07-9fa736463b64 · inbound

Training Language Agents to Learn from Experience cites this paper.

Training Language Agents to Learn from Experience Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 10

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verified exact
arxiv_id, observed 2026-05-21T07:14:02.632692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-21T07:11:09.642275Z digest=sha256:01f641949f1c51b199750ce40951ff25940dd416f4015f57c170b1bf26ae752e

Observation 7e13be59-e675-4c39-87d0-87265bc85e17 · inbound

IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents cites this paper.

IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 2

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verified exact
arxiv_id, observed 2026-05-22T06:24:40.511870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T06:21:56.971204Z digest=sha256:c3638fd6ac3366b88346012e01f501d80a43a658bb5709c20259234bae194050

Observation d97938c7-3bd3-4195-8a2f-115eb7cb90d8 · inbound

RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation cites this paper.

RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks

Reference 17

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verified exact
arxiv_id, observed 2026-07-02T07:36:44.957577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T06:52:17.232759Z digest=sha256:4b9e6adeb08aa630c11b6f1d4bf8f12c420ea8185e6814c73f106b4d48018132