Paper Citation Record · LEDGER

Efficiently Enhancing General Agents With Hierarchical-categorical Memory

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2505.22006.

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

pith.paper-citation-record.v1
2505.22006 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:29.291038Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:26.852386Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:21:29.650749Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy14
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 970d17da-e689-419f-a5c0-94b407a582f0 · outbound

This paper cites an unresolved cited work.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:21:31.992344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d858042e-41ee-46fc-8931-a9ad14065465 · outbound

This paper cites 1, com- prises two core components: the Hierarchical Memory Re- trieval (HMR) module and the Task-Type Oriented Experi- ence Learning (TOEL) module.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory 1, com- prises two core components: the Hierarchical Memory Re- trieval (HMR) module and the Task-Type Oriented Experi- ence Learning (TOEL) module

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.813274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 439ab456-88d1-4e4f-83a7-97dc74f4c657 · outbound

This paper cites Experimental Setup Datasets and Evaluation Protocol.To evaluate EHC, we conducted experiments using standard benchmark datasets and widely adopted evaluation metrics.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Experimental Setup Datasets and Evaluation Protocol.To evaluate EHC, we conducted experiments using standard benchmark datasets and widely adopted evaluation metrics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.638358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc1a6f62-effd-4a50-9e54-a1907fffc6c5 · outbound

This paper cites EHC consists of two core modules: Hier- archical Memory Retrieval (HMR) and Task-Oriented Expe- riential Learning (TOEL).

Efficiently Enhancing General Agents With Hierarchical-categorical Memory EHC consists of two core modules: Hier- archical Memory Retrieval (HMR) and Task-Oriented Expe- riential Learning (TOEL)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.506695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f38c5d1c-6702-4427-9f81-29b5cae4be69 · outbound

This paper cites Qwen-vl: A versatile vision-language model for understanding, localization,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Qwen-vl: A versatile vision-language model for understanding, localization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.110672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50186d48-8b58-4afb-b8e0-084e8a8032ca · outbound

This paper cites Multi- modal foundation models: From specialists to general- purpose assistants,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Multi- modal foundation models: From specialists to general- purpose assistants,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.357115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 980f9d5a-59ed-459f-943b-52df29e9c27d · outbound

This paper cites MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:27.449934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92e1b94e-fc35-4741-839a-2fa9babd519a · outbound

This paper cites Otter: A Multi-Modal Model with In-Context Instruction Tuning.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Otter: A Multi-Modal Model with In-Context Instruction Tuning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:27.568924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028041e2-6d97-4350-8117-5ed7c98e0ff9 · outbound

This paper cites Coarse-to-fine rea- soning for visual question answering,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Coarse-to-fine rea- soning for visual question answering,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:31.227421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec2b0b3c-4115-4b5d-85a3-c2ea12e07eed · outbound

This paper cites Clova: A closed-loop visual assistant with tool usage and update,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Clova: A closed-loop visual assistant with tool usage and update,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.648032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c4b53b8-cb69-4dee-adeb-38f426aa959b · outbound

This paper cites Visual programming: Compositional visual reasoning without training,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Visual programming: Compositional visual reasoning without training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.974128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c591ea08-b97e-4d78-a4bb-7a129cf57340 · outbound

This paper cites Vipergpt: Visual inference via python execution for reasoning,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Vipergpt: Visual inference via python execution for reasoning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.834494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b43fd50a-8b23-4dec-b77b-ace2e559025a · outbound

This paper cites Efficiently Enhancing General Agents With Hierarchical-categorical Memory.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Efficiently Enhancing General Agents With Hierarchical-categorical Memory

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:21:29.731844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e5e5ce8-5ef6-4e50-8c24-1091084ef81f · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.110695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2cf7928-cc31-438f-aaec-99f8b02fdae1 · outbound

This paper cites AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.195332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f605b9ec-d9c4-4626-be84-983b816a3dd5 · outbound

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

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Reasoning with Language Model is Planning with World Model

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.390418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4039664b-4bf5-4f48-9ef8-ef56e19f4cbd · outbound

This paper cites MemGPT: Towards LLMs as Operating Systems.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory MemGPT: Towards LLMs as Operating Systems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.487005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08deaa83-5241-4141-9330-baeaefea5063 · outbound

This paper cites Expel: Llm agents are experiential learners,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Expel: Llm agents are experiential learners,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.510522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 793a3b49-af7a-4909-9805-e00c4d566571 · outbound

This paper cites HAMMR: HierArchical MultiModal React agents for generic VQA.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory HAMMR: HierArchical MultiModal React agents for generic VQA

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.689451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae5a7fb6-97f4-468f-9562-bd01c226903d · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional ques- tion answering,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Gqa: A new dataset for real-world visual reasoning and compositional ques- tion answering,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.330710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e07ebcf-9613-4e70-8db6-7a20ae6a955c · outbound

This paper cites A Corpus for Reasoning About Natural Language Grounded in Photographs.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory A Corpus for Reasoning About Natural Language Grounded in Photographs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:28.923614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42cc94d6-1cb7-44e3-8d61-ed3d6e0e261c · outbound

This paper cites Modeling context in re- ferring expressions,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Modeling context in re- ferring expressions,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.238524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c98733e-5a03-4442-ba48-9decf778ad07 · outbound

This paper cites Referitgame: Referring to objects in photographs of natural scenes,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Referitgame: Referring to objects in photographs of natural scenes,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:30.104721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0a07778-8888-492c-9a13-7a6ff7d656b1 · outbound

This paper cites Magicbrush: A manually annotated dataset for instruction-guided image editing,.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory Magicbrush: A manually annotated dataset for instruction-guided image editing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:29.887962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38f17825-5e8f-42b1-8ccc-2750121edd80 · outbound

This paper cites ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning.

Efficiently Enhancing General Agents With Hierarchical-categorical Memory ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:21:29.497738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation b43fd50a-8b23-4dec-b77b-ace2e559025a · inbound