Paper Citation Record · LEDGER

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2505.10522.

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

pith.paper-citation-record.v1
2505.10522 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:12:51.255232Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy22
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 094534ba-2ba0-4eb3-9943-b260e313936f · outbound

This paper cites of Aerospace & Mechanical Engineering University of Southern California Los Angeles, USA xinruiw@usc.edu Yan Jin* Dept.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation of Aerospace & Mechanical Engineering University of Southern California Los Angeles, USA xinruiw@usc.edu Yan Jin* Dept

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.656108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d7d6132-f442-443f-95fa-5770fcb3bc9c · outbound

This paper cites In the domain of robotic manipulation, machine learning has significantly advanced its capabilities in handling objects and executing complex tasks.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation In the domain of robotic manipulation, machine learning has significantly advanced its capabilities in handling objects and executing complex tasks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.643794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ed8b8e33-af71-4294-8536-44b1fa8786bb · outbound

This paper cites Reinforcement learning is one of the solutions, allowing agents to understand and optimize the process through continuous interaction with their environment [14-16].

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Reinforcement learning is one of the solutions, allowing agents to understand and optimize the process through continuous interaction with their environment [14-16]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.631314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0ce980a8-43fc-4db9-9c3a-ed3c98acf719 · outbound

This paper cites #$%=𝟏&!((.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation #$%=𝟏&!((

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.617590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e06f5607-d1e3-42f4-8770-368cf6e15da7 · outbound

This paper cites (𝑜/,𝑒)−250∆.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation (𝑜/,𝑒)−250∆

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.604739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d13aca93-5f51-432d-a75e-71487d9cdafe · outbound

This paper cites The default learning rate of 1e-4, as provided by the baseline, is used for all pre-training phases in the curriculum.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation The default learning rate of 1e-4, as provided by the baseline, is used for all pre-training phases in the curriculum

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.591622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a6568886-91ba-4975-9d29-213d09772f2b · outbound

This paper cites Our model significantly improves the top block's fractional success regardless of the conditions and causing learning sequence.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Our model significantly improves the top block's fractional success regardless of the conditions and causing learning sequence

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.577108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 330ae382-4758-40d5-a5f4-84c114d54daf · outbound

This paper cites an unresolved cited work.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:12:51.563780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6d66dcb8-2603-4a77-9f25-d819b42cdd1a · outbound

This paper cites This task requires the agent to differentiate between blocks and operate with higher precision to avoid collisions while successfully constructing the assembly.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation This task requires the agent to differentiate between blocks and operate with higher precision to avoid collisions while successfully constructing the assembly

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.550964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e338ef10-ae63-462a-b96f-0ab4c1e480a4 · outbound

This paper cites CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.173384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b75660b-bce3-4331-97c0-1d47a1117793 · outbound

This paper cites Robust Multi-Agent Reinforcement Learning with State Uncertainty.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Robust Multi-Agent Reinforcement Learning with State Uncertainty

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.177543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7cf3e55-3223-4b65-9b9f-44f7b84a56bf · outbound

This paper cites Enhancing Efficiency in Collision Avoidance: A Study on Transfer Reinforcement Learning in Autonomous Ships’ Navigation.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Enhancing Efficiency in Collision Avoidance: A Study on Transfer Reinforcement Learning in Autonomous Ships’ Navigation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.538397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1937d59d-7920-46e6-8fa2-72d348f323ab · outbound

This paper cites Simultaneous Thermal-Electrical Cloak and Camouflage via Level-Set-Based Topology Optimization.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Simultaneous Thermal-Electrical Cloak and Camouflage via Level-Set-Based Topology Optimization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.525243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 022e1b39-7d7d-4490-ac4b-1a6acac6ffde · outbound

This paper cites Topology optimization of multimaterial thermoelectric structures.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Topology optimization of multimaterial thermoelectric structures

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.512523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c049545-b5f0-48b4-b5a2-f9312a5c5641 · outbound

This paper cites Explainable reinforcement learning: A survey and comparative review.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Explainable reinforcement learning: A survey and comparative review

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.500165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c4fc7021-c907-4e88-bd2e-befd2b218ab8 · outbound

This paper cites Continuous control with deep reinforcement learning.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Continuous control with deep reinforcement learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.197950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0055748f-8fbc-4c64-a66d-83de6248a2b5 · outbound

This paper cites Human-level control through deep reinforcement learning.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Human-level control through deep reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.487507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 341c739f-c76d-4a1e-a255-0c33c7ecb8ae · outbound

This paper cites Data-driven distributionally robust optimization for vehicle balancing of mobility-on-demand systems.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Data-driven distributionally robust optimization for vehicle balancing of mobility-on-demand systems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.474832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c4bbfca-3279-446b-995d-69d0141eb296 · outbound

This paper cites Explicable reward design for reinforcement learning agents.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Explicable reward design for reinforcement learning agents

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.461966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b0fc9f39-c2a3-4440-adec-00f775f87bc4 · outbound

This paper cites Curriculum learning for reinforcement learning domains: A framework and survey.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Curriculum learning for reinforcement learning domains: A framework and survey

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.448542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f5378b93-24d1-4507-a476-c427814956a1 · outbound

This paper cites Source task creation for curriculum learning.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Source task creation for curriculum learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.435035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92ed0bea-4ae2-4f71-bb20-b76a74f9e243 · outbound

This paper cites CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.222480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dca62682-2a8b-4b20-9a25-3e8cb334d1de · outbound

This paper cites Proximal Curriculum for Reinforcement Learning Agents.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Proximal Curriculum for Reinforcement Learning Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.226856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3da57851-e655-400a-a054-2ed60e23416f · outbound

This paper cites Residual learning from demonstration: Adapting dmps for contact-rich manipulation.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Residual learning from demonstration: Adapting dmps for contact-rich manipulation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.420694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 570de16d-d99f-4e97-b08a-452764ee4085 · outbound

This paper cites Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:51.234894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d5ae36a-06b4-4659-8ec6-da68d75f6c7f · outbound

This paper cites $\mathrm {R}^{3} $: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation $\mathrm {R}^{3} $: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.407401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2fe001b1-00d4-4b4b-908c-010d9f8d0625 · outbound

This paper cites A critical review of Knowledge-Based Engineering: An identification of research challenges.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation A critical review of Knowledge-Based Engineering: An identification of research challenges

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.394737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bc9a14f5-9119-48c1-a727-126205750094 · outbound

This paper cites Machine learning model towards evaluating data gathering methods in manufacturing and mechanical engineering.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Machine learning model towards evaluating data gathering methods in manufacturing and mechanical engineering

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.381212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e1058d3-e557-499c-a344-2b7c29f9774b · outbound

This paper cites A recurrent neural network architecture for failure prediction in deep drawing sensory time series data.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation A recurrent neural network architecture for failure prediction in deep drawing sensory time series data

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:51.367627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.