Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-21T07:11:09.642275Z
Paper Citation Record · LEDGER
Training Language Agents to Learn from Experience
As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2605.20477.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
32 of 32 outbound references displayed
- verified exact11
- verified fuzzy16
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d0575f69-4592-4cdf-b54c-b1ac1b1a13f0 · outbound
This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.
Training Language Agents to Learn from Experience GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:14:02.609943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7335257d-2e04-41c7-8091-86fc361f1099 · outbound
This paper cites Openai gym.
Training Language Agents to Learn from Experience Openai gym
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.731454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9bc94e4c-2619-41f1-a1a8-b67f6717569e · outbound
This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.
Training Language Agents to Learn from Experience Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.733794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c464cd0b-aa18-4ada-89c9-67c9997ece16 · outbound
This paper cites System prompt optimization with meta- learning.
Training Language Agents to Learn from Experience System prompt optimization with meta- learning
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:14:02.620371Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 08f9f6e7-465d-41d0-a05e-41a2911fae6d · outbound
This paper cites Improving retrospective language agents via joint policy gradient optimization.
Training Language Agents to Learn from Experience Improving retrospective language agents via joint policy gradient optimization
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.727285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d2d0aab2-8033-4101-858f-74a3afbb3930 · outbound
This paper cites Samule: Self-learning agents enhanced by multi-level reflection.
Training Language Agents to Learn from Experience Samule: Self-learning agents enhanced by multi-level reflection
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.729416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b2792107-6ad0-473d-9d75-99635800c804 · outbound
This paper cites Meta-rl induces exploration in language agents.
Training Language Agents to Learn from Experience Meta-rl induces exploration in language agents
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.725235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation fb825063-655f-4ad6-98ce-cc7398056407 · outbound
This paper cites LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations.
Training Language Agents to Learn from Experience LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:14:02.612567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b56f7cea-0bc9-4139-8a11-4423d9966f1c · outbound
This paper cites Minihack the planet: A sandbox for open-ended reinforcement learning research.
Training Language Agents to Learn from Experience Minihack the planet: A sandbox for open-ended reinforcement learning research
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.718180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f3822e6a-619b-4f23-9b07-9fa736463b64 · outbound
This paper cites Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks.
Training Language Agents to Learn from Experience Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Reference 10
- Resolution
- 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 2026-08-06T06:34:29.942622+00:00.
Observation 3779a81b-68be-4745-beeb-8cebec9963ee · outbound
This paper cites Can foundation models actively gather information in interactive environments to test hypotheses?.
Training Language Agents to Learn from Experience Can foundation models actively gather information in interactive environments to test hypotheses?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:14:02.635780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7c08bb24-47fe-4f83-9c00-96af5b80d4cc · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Training Language Agents to Learn from Experience DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:14:02.622820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4f481cf5-6a9b-4367-b737-de41b94ac7d9 · outbound
This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.
Training Language Agents to Learn from Experience HybridFlow: A Flexible and Efficient RLHF Framework
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:14:02.625298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 82a2cfd3-3ddc-4405-aafd-be070015b8f4 · outbound
This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652.
Training Language Agents to Learn from Experience Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.720600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 61856e92-1d27-48ee-ace9-0b2603b1ddf9 · outbound
This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.
Training Language Agents to Learn from Experience ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.712234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 81080f43-7494-4373-ba7a-f76f71833c8b · outbound
This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.
Training Language Agents to Learn from Experience ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:14:02.617944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2781f0a4-ce1d-443d-8178-5212b9c6351d · outbound
This paper cites Welcome to the era of experience.Google AI, 1.
Training Language Agents to Learn from Experience Welcome to the era of experience.Google AI, 1
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.714201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 79819df9-1c0c-4542-acb8-32aeab412892 · outbound
This paper cites Cognitive architec- tures for language agents.Transactions on Machine Learning Research.
Training Language Agents to Learn from Experience Cognitive architec- tures for language agents.Transactions on Machine Learning Research
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.705714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9a165cc2-7fc3-4e0f-afde-191b8ac2a5ce · outbound
This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345.
Training Language Agents to Learn from Experience A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.708114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 26220ed4-5bac-409c-a97f-eb8cd1f98c7a · outbound
This paper cites Qwen2 Technical Report.
Training Language Agents to Learn from Experience Qwen2 Technical Report
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:14:02.630011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 044bf3c0-48eb-417c-91c5-12ad3182d313 · outbound
This paper cites Large language models as optimizers.
Training Language Agents to Learn from Experience Large language models as optimizers
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.716175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6d60c726-6090-4c48-84da-7651d43b299c · outbound
This paper cites React: Synergizing reasoning and acting in language models.
Training Language Agents to Learn from Experience React: Synergizing reasoning and acting in language models
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.723025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b931cb18-5ab5-47e2-b9d4-e2fa4fe981fe · outbound
This paper cites Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization.
Training Language Agents to Learn from Experience Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:14:02.615242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9b13a92c-9b9e-4214-9dff-29c3405f7973 · outbound
This paper cites Assessing Adaptive World Models in Machines with Novel Games.
Training Language Agents to Learn from Experience Assessing Adaptive World Models in Machines with Novel Games
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:14:02.627802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5f50fbc8-9a26-4340-87c2-cf49a37a18d3 · outbound
This paper cites Expel: Llm agents are experiential learners.
Training Language Agents to Learn from Experience Expel: Llm agents are experiential learners
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.710188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 50480d2a-a712-4630-b675-14547b666410 · outbound
This paper cites an unresolved cited work.
Training Language Agents to Learn from Experience Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T07:14:46.703743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 39e026f5-c9a7-40d2-9d95-62e76332d934 · outbound
This paper cites an unresolved cited work.
Training Language Agents to Learn from Experience Unresolved cited work
Reference 27
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T07:14:46.700145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 188e03b2-acb2-4554-b34c-9ad0f97d2d11 · outbound
This paper cites Qwen/Qwen2.5-7B-Instruct.
Training Language Agents to Learn from Experience Qwen/Qwen2.5-7B-Instruct
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.701969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1f25f9fa-c319-40ce-b8b2-b7ee59cff123 · outbound
This paper cites an unresolved cited work.
Training Language Agents to Learn from Experience Unresolved cited work
Reference 31
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T07:14:46.696464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9c149829-0e1b-4dde-b3de-8e51b1eed1bc · outbound
This paper cites an unresolved cited work.
Training Language Agents to Learn from Experience Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T07:14:46.698343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e5f32a59-ed69-4c71-a145-49271e60f199 · outbound
This paper cites an unresolved cited work.
Training Language Agents to Learn from Experience Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T07:14:46.691346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e97f10c0-e480-44c2-8264-38e5828d5b3a · outbound
This paper cites <" symbol. I will move east to investigate further. Action 2: step e Observation 2.
Training Language Agents to Learn from Experience <" symbol. I will move east to investigate further. Action 2: step e Observation 2
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:14:46.693384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
No inbound Pith citation observations are available.