Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-13T04:55:54.572349Z
Paper Citation Record · LEDGER
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
As of 4 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 100 inbound Pith citation observations for arXiv:2508.19828.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T16:07:45.074949Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T12:15:01.137692Z
Reference resolution
26 of 26 outbound references displayed
- verified exact0
- verified fuzzy6
- unresolved15
- parse uncertain0
- malformed identifier1
- metadata mismatch4
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b8db0ad6-9b51-40f4-891a-bf0c0e6359bd · outbound
This paper cites On Memory Construction and Retrieval for Personalized Conversational Agents.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning On Memory Construction and Retrieval for Personalized Conversational Agents
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.593699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bdf96395-e160-4c6b-b9be-aaa744a738a6 · outbound
This paper cites MemInsight: Autonomous Memory Augmentation for LLM Agents.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning MemInsight: Autonomous Memory Augmentation for LLM Agents
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.599962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eec065dd-b0d4-4535-8f79-2884a9def5bf · outbound
This paper cites Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.604266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eb38a588-8758-402f-980a-04d6031fffcb · outbound
This paper cites How memory management impacts llm agents: An empirical study of experience-following behavior.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning How memory management impacts llm agents: An empirical study of experience-following behavior
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.608630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5ea8bde6-7a16-4ae7-a1da-96d1672a35a8 · outbound
This paper cites id" : "0.
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.659604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3f0bfc38-dbb1-404c-b71d-cbf9db41deb3 · outbound
This paper cites User likes to play cricket.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning User likes to play cricket
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.614722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eefd1641-455e-4c77-9b8c-e5d96a2b9314 · outbound
This paper cites id" : "1.
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.617392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b2dfa2bc-2c82-4e37-ad5e-d2c1155f2464 · outbound
This paper cites id" : "0.
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.619916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 651b99de-bf9e-4c39-a816-98c4559bcbd8 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.622609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1732c491-932f-4e34-ba63-cb7eb2e49612 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.625142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8f904a28-34ab-4b9a-b3c7-98ec99fe281d · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.627484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 38e04778-977b-4a90-8a40-e596881cea37 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.629680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4b9f0f99-e049-4fbb-9b14-f56691a3a1a0 · outbound
This paper cites last year.
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.632205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8a23e36b-eca7-4130-a421-c8036c418ab7 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.634393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 675c45d7-57a0-4b10-b824-f954da42f016 · outbound
This paper cites Do not confuse character names.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Do not confuse character names
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-13T04:55:54.636700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 65847871-0d8e-4e75-89ae-63b2a974a15c · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 16
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.639072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5dfb5d43-49e8-4ee2-80b3-c66814008cc0 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.641126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2b6b92b8-0663-472c-9d12-bf615fa23ee4 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.643371Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d05b612e-54dc-4088-a666-c7af1d3b7f09 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.645349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f6a14075-0201-4f39-bebc-a8c69829c6e7 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.647287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4928a6ef-5015-4c79-ba63-556dd0562911 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.649272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4032cf8c-bc9d-42fd-b6f3-c75b097ccbfa · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.651212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9e179b05-b165-402d-bd81-dc46cd4cbe83 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.653074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 444c364a-40b4-4752-850b-c9a31f9b06fc · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 24
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.655131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1f546e5f-9d52-4cc8-abb4-18bd1d1bf8a7 · outbound
This paper cites an unresolved cited work.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-05-13T04:55:54.657560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 55f274ea-2dbd-4c2b-a04d-bce6a681bd0b · outbound
This paper cites last Tuesday.
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning last Tuesday
Reference 26
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-13T04:55:54.612257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation 276140f0-9fa6-4b48-82e5-5182264eb8a6 · inbound
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems cites this paper.
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 109
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T23:21:42.217462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f7676747-a779-4ff8-8c62-f20b4b46d296 · inbound
Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.
Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 215
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T16:07:45.074949Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e56a12eb-2e15-4b60-880c-911aa9081fe7 · inbound
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle cites this paper.
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T06:20:58.406500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 74061e8e-8315-4d0c-a661-f995d44cf165 · inbound
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle cites this paper.
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T20:50:36.447479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6e149e2b-26ac-4e6c-b972-44b249d6765a · inbound
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 54
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-14T23:13:16.009641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 86cf777a-5b4e-4d26-bca9-71e330f35f0d · inbound
MemVerse: Multimodal Memory for Lifelong Learning Agents cites this paper.
MemVerse: Multimodal Memory for Lifelong Learning Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:49:02.781988Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac9f726e-2a40-4108-b002-f56f004108a8 · inbound
Agentic Reasoning for Large Language Models cites this paper.
Agentic Reasoning for Large Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-17T15:14:25.806772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1d3c4a7c-e76d-479f-a8f5-5dfe6dffb6ad · inbound
Toward Efficient Agents: Memory, Tool learning, and Planning cites this paper.
Toward Efficient Agents: Memory, Tool learning, and Planning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 162
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T09:21:47.532799Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5cab4dc2-71de-45ad-b4fd-c21a3270fe6e · inbound
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents cites this paper.
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T18:10:13.078382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bd4f494e-8b1a-4e31-82a2-632d4659fdde · inbound
Joint Optimization of Multi-agent Memory System cites this paper.
Joint Optimization of Multi-agent Memory System Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T12:15:34.586148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cdfba686-025c-4b9a-a94d-81ed0dd74e7b · inbound
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments cites this paper.
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 73
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-21T09:59:59.101743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 65ca5fe7-e6f9-465c-8677-eb0d04f7b938 · inbound
MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation cites this paper.
MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T19:45:45.674967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3d15e49-1ba0-4b20-8780-f0438f3c2564 · inbound
MemFactory: Unified Inference & Training Framework for Agent Memory cites this paper.
MemFactory: Unified Inference & Training Framework for Agent Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-14T00:03:28.640232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1e0bc2b8-68f1-4928-9b20-85257fc229cf · inbound
Decocted Experience Improves Test-Time Inference in LLM Agents cites this paper.
Decocted Experience Improves Test-Time Inference in LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3551058d-63e1-4d83-9207-cc8fa09de102 · inbound
Improving Sparse Memory Finetuning cites this paper.
Improving Sparse Memory Finetuning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 76b4225d-1646-491c-8204-b505178f267b · inbound
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation cites this paper.
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 76
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2a81a318-61b0-4e8e-babf-9e6a940c2534 · inbound
AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning cites this paper.
AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6b68786b-400d-4e09-bddd-3e4369aa5939 · inbound
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering cites this paper.
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 171
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2056625a-af3b-415c-a1f1-745940af6ab0 · inbound
Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards cites this paper.
Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b998d94a-12de-4aa9-871e-368b67c21fd1 · inbound
POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.
POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 52
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 32994def-542e-49dc-bec3-8d71bbbbfc9d · inbound
POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.
POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T16:18:25.319823Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1b40c59-520a-457d-a84e-472977ba8778 · inbound
SAGER: Self-Evolving User Policy Skills for Recommendation Agent cites this paper.
SAGER: Self-Evolving User Policy Skills for Recommendation Agent Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 22
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 95004cb7-fa47-4f4c-b1d5-29abc1001f46 · inbound
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents cites this paper.
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 27
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c3a7ee71-c9f4-4d3b-8edd-947a059edc49 · inbound
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search cites this paper.
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7e111cba-a842-4b56-acbf-fcd9cc5d841c · inbound
OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory cites this paper.
OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1d4bca65-141a-4f41-ba5f-aa17f932ba8f · inbound
Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction cites this paper.
Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 24
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 50d8f452-e968-4912-8d2b-6ea24e9dd90f · inbound
Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents cites this paper.
Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6f2d5c2d-107b-4865-980d-c6ee153add40 · inbound
Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.
Rethinking Agentic Reinforcement Learning In Large Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 110
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3c5c211a-17ae-4455-9a4a-a6d5d385b574 · inbound
Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.
Rethinking Agentic Reinforcement Learning In Large Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 110
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 84c62bfb-fd30-447e-afb0-6e92b3d894f5 · inbound
Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.
Rethinking Agentic Reinforcement Learning In Large Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 110
- Resolution
- verified exact
- local_arxiv, observed 2026-05-19T17:02:40.654235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation edb3a766-bccb-40cb-a386-e49c4da204f0 · inbound
MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents cites this paper.
MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 22
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c7eccb63-de60-419a-9824-b0b086528d33 · inbound
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory cites this paper.
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 168
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e5d74cf1-3223-4bc8-a9f7-7f96fac18c32 · inbound
What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis cites this paper.
What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 715b8b0d-820e-406a-be01-aed2ed4a091f · inbound
Tree-based Credit Assignment for Multi-Agent Memory System cites this paper.
Tree-based Credit Assignment for Multi-Agent Memory System Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cb693e4c-6dac-4261-9234-0e4fb31c4ab3 · inbound
Belief Memory: Agent Memory Under Partial Observability cites this paper.
Belief Memory: Agent Memory Under Partial Observability Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3bbe657c-2477-4261-9842-7e6c3889c5f2 · inbound
Belief Memory: Agent Memory Under Partial Observability cites this paper.
Belief Memory: Agent Memory Under Partial Observability Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b0174252-af52-4d25-af82-a07ebe8cf7cd · inbound
From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work cites this paper.
From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 41ae460f-7a01-42c3-9ef5-a605b7ec01cb · inbound
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents cites this paper.
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ab6757b4-eec9-4021-8b1b-2f3c2ef436a5 · inbound
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents cites this paper.
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T05:59:49.009335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 414b6233-aa59-4141-85dd-159a8ff8c713 · inbound
DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning cites this paper.
DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8ab730c6-a9ba-4818-a7d4-ff978f732035 · inbound
MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare cites this paper.
MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T06:42:26.184042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3a17be96-f78b-42f6-ba41-cca70f3e3fa6 · inbound
SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory cites this paper.
SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 256
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 183b0a1a-388b-4b01-b18c-077bdf8c3176 · inbound
Intermediate Artifacts as First-Class Citizens: A Data Model for Durable Intermediate Artifacts in Agentic Systems cites this paper.
Intermediate Artifacts as First-Class Citizens: A Data Model for Durable Intermediate Artifacts in Agentic Systems Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9d3b5a41-c2e5-470d-ac45-4429079ee858 · inbound
LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues cites this paper.
LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 102
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T04:55:54.660411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6f034bd7-6fe5-4ab5-a6ef-f22d934b0ece · inbound
Reinforced Collaboration in Multi-Agent Flow Networks cites this paper.
Reinforced Collaboration in Multi-Agent Flow Networks Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-05-14T20:42:57.190223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1ccd3b04-d9f1-45ab-82f5-bdeb4e44ad5b · inbound
R^2-Mem: Reflective Experience for Memory Search cites this paper.
R^2-Mem: Reflective Experience for Memory Search Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 4
- Resolution
- malformed identifier
- local_arxiv, observed 2026-05-14T19:12:50.920403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4a161864-bed5-4deb-a59e-4130fe18604c · inbound
EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents cites this paper.
EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 36
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-15T04:49:44.093656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 36743a27-5921-4e9d-87b4-5e41bb86d4f9 · inbound
Improving Multi-turn Dialogue Consistency with Self-Recall Thinking cites this paper.
Improving Multi-turn Dialogue Consistency with Self-Recall Thinking Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 35
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T20:25:02.581608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0178c60b-bbd9-4833-83db-33c0ffe43967 · inbound
DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory cites this paper.
DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T19:03:39.723273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 21185e0b-04cd-4665-831a-a6911702c8ae · inbound
DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory cites this paper.
DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-01T14:55:47.099446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eec704c2-f044-4fc3-bd50-bcfb8ac67fed · inbound
PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning cites this paper.
PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 46
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T15:13:24.847942Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3b9b0677-4996-42b9-bf73-b02745bd3da1 · inbound
Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery cites this paper.
Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T20:33:43.410682Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2f76fbd0-6bac-4b9c-9779-ddf7ebc76be6 · inbound
Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents cites this paper.
Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 36
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-21T05:39:40.794076Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 598af286-bd06-44e3-915c-3c8f2cb1931c · inbound
Mem-$\pi$: Adaptive Memory through Learning When and What to Generate cites this paper.
Mem-$\pi$: Adaptive Memory through Learning When and What to Generate Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T04:29:34.507653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 77a3ce97-ad3c-4617-9fdd-8b879aa94d90 · inbound
Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents cites this paper.
Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:21:21.766214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e6b463a1-625d-40a9-826f-54ae4030fc17 · inbound
Dynamic Mixture of Latent Memories for Self-Evolving Agents cites this paper.
Dynamic Mixture of Latent Memories for Self-Evolving Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-22T07:41:14.525338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 34689446-ff67-4006-816b-fac4817aa373 · inbound
DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Memory QA cites this paper.
DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Memory QA Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 51
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-22T07:14:42.576513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3d270236-e79e-4611-96cb-01b7753e9b84 · inbound
What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA cites this paper.
What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T05:30:23.048170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5a16a5bb-fdc6-4d97-bc4c-4472a85ca9f7 · inbound
MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems cites this paper.
MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T00:14:04.444762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c400382c-1e42-4fb4-bd9c-08ef26d7be38 · inbound
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents cites this paper.
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T22:04:00.067627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 15b06d91-ff82-47e2-8fe7-797abc166db2 · inbound
Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory cites this paper.
Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 47
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T21:23:58.153909Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3eb337f8-56d1-4551-a79f-8d380fd84fce · inbound
MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains cites this paper.
MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T07:33:13.834153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 67cf6c1c-fe00-4ba5-a92e-5b323501ecdc · inbound
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents cites this paper.
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T00:22:51.831125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d645301b-d0b3-45d0-84a0-a1c8f5ea2937 · inbound
MemPro: Agentic Memory Systems as Evolvable Programs cites this paper.
MemPro: Agentic Memory Systems as Evolvable Programs Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-28T19:22:34.742174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0383e28b-460f-49f7-84c7-3d3ce0bd1173 · inbound
Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue cites this paper.
Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 33
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-28T17:42:25.969045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a81cf93e-e8fb-4e7b-a3d2-8d91caf90892 · inbound
MemTrain: Self-Supervised Context Memory Training cites this paper.
MemTrain: Self-Supervised Context Memory Training Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 12
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T02:36:26.640139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a72396b0-3f93-4807-94df-1eec689dfbff · inbound
SaliMory: Orchestrating Cognitive Memory for Conversational Agents cites this paper.
SaliMory: Orchestrating Cognitive Memory for Conversational Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T03:16:35.069378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0784f7e0-b58e-4b6a-b250-e743d911897d · inbound
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline cites this paper.
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 45
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T07:36:45.450202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0b5cc28e-5c46-4709-8296-8fed98b6bf0e · inbound
Scaling Self-Evolving Agents via Parametric Memory cites this paper.
Scaling Self-Evolving Agents via Parametric Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T07:46:45.824770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 42cc7aec-7a92-41d5-9643-b14c42396758 · inbound
EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents cites this paper.
EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T13:26:58.761322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b8348652-4b3e-43c9-a9a2-5df00eac068d · inbound
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems cites this paper.
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 20
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T22:37:26.611871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 987f4751-8af1-4d9d-9111-2a728b5bbf26 · inbound
Co-Evolving Skill Generation and Policy Optimization cites this paper.
Co-Evolving Skill Generation and Policy Optimization Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T22:57:25.901885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation acac695c-772d-40dd-ac12-2a6f337c293f · inbound
ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning cites this paper.
ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-06-27T13:10:56.429154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f37c17e9-449a-466a-95d5-f44859a44380 · inbound
From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent cites this paper.
From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-06-27T07:20:42.053268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4f75f1c0-5a59-4ea6-8af8-ccea4a173556 · inbound
User as Engram: Internalizing Per-User Memory as Local Parametric Edits cites this paper.
User as Engram: Internalizing Per-User Memory as Local Parametric Edits Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 67
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T01:09:19.509880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 897a3952-960b-4389-aed7-1f9ece3f075a · inbound
AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents cites this paper.
AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T17:03:41.244185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2cd48c8b-741c-4b93-8553-d9057d8d45a9 · inbound
Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines cites this paper.
Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T17:48:46.430095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2cfe6cc2-9b6f-42ab-aee3-faa6dee96450 · inbound
TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory cites this paper.
TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T18:30:01.337547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e9d8ba98-e457-44fa-9874-fd1f850dbfe1 · inbound
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning cites this paper.
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 94
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T11:34:37.976611Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 01de5598-3f94-49eb-8098-6695a032cc6d · inbound
When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents cites this paper.
When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 14
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T11:54:38.769948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7e021890-e3f1-46cf-b750-835bb07e327e · inbound
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory cites this paper.
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 31
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-01T09:55:41.324594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 29bae9e4-2df2-4cc6-aa4b-6bd3279ece7f · inbound
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL cites this paper.
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T09:45:40.238323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d455b28a-4434-474f-9241-ea5e80604346 · inbound
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL cites this paper.
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T07:12:34.663753Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43655a17-be80-446a-a3c1-c4f2fcddb350 · inbound
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL cites this paper.
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:17:13.036650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0484a20d-6ff2-4d9e-869b-0b85b883ac67 · inbound
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL cites this paper.
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:05:47.553673Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34eaea53-3d8e-4d1e-8c14-7dd0194ffb31 · inbound
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL cites this paper.
ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T04:36:26.206389Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d13b0fbb-d7b7-487c-bd68-5bc127b15170 · inbound
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory cites this paper.
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T23:07:26.364459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d7ca33a9-cf48-4547-9e85-6e8695ada625 · inbound
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory cites this paper.
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T00:39:16.343041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1d5edfd5-ca8b-42ec-8e34-ae0d5bb97fca · inbound
AutoMem: Automated Learning of Memory as a Cognitive Skill cites this paper.
AutoMem: Automated Learning of Memory as a Cognitive Skill Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T12:16:56.506955Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c1825435-f391-414d-8378-e3eb128426be · inbound
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models cites this paper.
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 42
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T20:18:56.110948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 08f98928-a6ec-4830-b2a9-8e47c8786b88 · inbound
HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control cites this paper.
HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T02:24:21.020383Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4f7e5b9-8a8e-483f-a28f-bb2190d9cf72 · inbound
From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space cites this paper.
From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-09T00:05:48.074070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 22ef9fbf-39c8-4e94-adb1-9f852b129d22 · inbound
MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning cites this paper.
MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T02:42:49.041257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17f0f6bf-6682-412c-95b0-5ce63d8a30f4 · inbound
Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents cites this paper.
Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:44:01.188413Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92664144-8754-4184-bfd8-dc1b35a4dc8f · inbound
Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration cites this paper.
Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T19:49:17.238060Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c3a1c51-1f6a-4009-a740-674313c20ff4 · inbound
Mechanistic Attention Guidance for Agent Memory Refinement cites this paper.
Mechanistic Attention Guidance for Agent Memory Refinement Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T17:30:41.878456Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 076370e5-aef2-4b1e-9fdd-bef4969fa61b · inbound
Mi-Memory: A Lifecycle Memory Framework for Personal AI cites this paper.
Mi-Memory: A Lifecycle Memory Framework for Personal AI Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:52:28.696381Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1edb7e3d-fe21-43ae-9484-9bdf21604ddd · inbound
CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents cites this paper.
CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T14:01:04.059259Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0c80203-ef61-48e4-92ad-e1b312b5e384 · inbound
NVIDIA-labs OO Agents: Native Python Object-Oriented Agents cites this paper.
NVIDIA-labs OO Agents: Native Python Object-Oriented Agents Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T09:39:47.056929Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32657a1c-15d5-4896-8079-4f2a9360029c · inbound
AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems cites this paper.
AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T13:56:33.736414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.