Paper Citation Record · LEDGER

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

As of 24 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2505.01396.

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

pith.paper-citation-record.v1
2505.01396 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:24:21.474479Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T10:57:40.128651Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:58:02.473826Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy13
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7657da88-63ef-4663-bcdd-d47e2827ccb1 · outbound

This paper cites GPT-4 Technical Report.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.341416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9d0ae40-9b23-442e-b76f-e12e7e068893 · outbound

This paper cites From imitation to refinement–residual rl for precise visual assembly.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration From imitation to refinement–residual rl for precise visual assembly

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.839594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80f8ce61-729f-43f7-b43d-140ca8bd98cc · outbound

This paper cites RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.349525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c538e2bf-adc6-425b-ba2a-6683cd0cc79e · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.354753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5b5107e-f891-4705-a175-c1c3ff9d2ef9 · outbound

This paper cites Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.358697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57355d6e-1263-44dc-8bb1-1e0ee6d633a6 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learn- ing via Action Diffusion.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Diffusion Policy: Visuomotor Policy Learn- ing via Action Diffusion

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.362993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d79a4490-6383-4a5b-9c62-75141e65eba2 · outbound

This paper cites Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.366451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16b9573b-15e2-4438-a4f6-63f15535d697 · outbound

This paper cites Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.823569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e9d460da-c12c-4afb-a9ae-ee4feb673e38 · outbound

This paper cites AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.373632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d8815ec-9c11-4ca4-ab53-e48b2493ce0c · outbound

This paper cites Implicit behavioral cloning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Implicit behavioral cloning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.376637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e29f6717-cb5f-4ba5-9800-5cf9f7de8aa9 · outbound

This paper cites Off-policy deep reinforcement learning without exploration.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Off-policy deep reinforcement learning without exploration

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.807200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e52ffbf8-4e62-4db8-989e-1e9946ff9164 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.797656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ff6fc4d-68f2-4903-82fa-0b2964f678a9 · outbound

This paper cites Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.384976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 340a3d7e-a9d0-4411-9a8a-a92d0362ede5 · outbound

This paper cites Deep residual learning for image recog- nition.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Deep residual learning for image recog- nition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.787892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee8a2335-4dc3-429b-8cfe-dedc1ed1de81 · outbound

This paper cites TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.391501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c22eb3-1e93-43e5-a296-d1332fa71037 · outbound

This paper cites DROID: A Large-Scale In-The- Wild Robot Manipulation Dataset.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration DROID: A Large-Scale In-The- Wild Robot Manipulation Dataset

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.778001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a45945fc-e060-442f-9feb-e99f01c63889 · outbound

This paper cites Segment anything.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Segment anything

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.769160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5cfdbf5-3315-406c-8edb-2d77df49581e · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Offline Reinforcement Learning with Implicit Q-Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.402454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 732c6017-f3e6-4a10-9247-6cc86c744c69 · outbound

This paper cites Conservative q-learning for offline re- inforcement learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Conservative q-learning for offline re- inforcement learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.759778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 244773de-6409-4a48-89ba-5c170241b00e · outbound

This paper cites Continuous control with deep reinforcement learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Continuous control with deep reinforcement learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.411447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b56abd12-cba5-4069-bc5a-1b7f796f5424 · outbound

This paper cites Robot learning on the job: Human- in-the-loop autonomy and learning during deployment.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Robot learning on the job: Human- in-the-loop autonomy and learning during deployment

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.750808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7aad825-a44d-4d10-9945-ac9067e8aa22 · outbound

This paper cites Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.418807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3acae2ea-6e90-4ea9-9fa3-59f38f3693f0 · outbound

This paper cites Serl: A software suite for sample-efficient robotic reinforcement learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Serl: A software suite for sample-efficient robotic reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.741821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation abb5c72a-0cb4-4305-9e1f-fcb7c006bc8a · outbound

This paper cites Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.425185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 351fb2c5-4728-41b0-ace0-84f14ab44bd6 · outbound

This paper cites Sam-rl: Sensing-aware model-based reinforce- ment learning via differentiable physics-based simulation and rendering.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Sam-rl: Sensing-aware model-based reinforce- ment learning via differentiable physics-based simulation and rendering

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.732889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ff452d4-17e7-4557-b7b9-eaf3ad7fb776 · outbound

This paper cites What Matters in Learning from Offline Human Demonstrations for Robot Manipulation.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.432445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 679e4985-8878-426d-a0b6-6e2679eee82d · outbound

This paper cites So You Think You Can Scale Up Autonomous Robot Data Collection?.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration So You Think You Can Scale Up Autonomous Robot Data Collection?

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.723650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23a93c59-f635-418f-bdee-a9e29a9c0449 · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collabora- tion.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collabora- tion

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.438624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54dcfa2d-7be0-44a0-9ab0-5c55240067db · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.441776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec11a19d-8e68-4e59-8835-dc8d075ef115 · outbound

This paper cites CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.445267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ac55430-72c5-408a-9cc7-49b1d2662d8d · outbound

This paper cites Proximal Policy Optimization Algorithms.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Proximal Policy Optimization Algorithms

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.449716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 269c62b3-797f-4f35-bebd-af11667aa5de · outbound

This paper cites Behavior transformers: Cloning k modes with one stone.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Behavior transformers: Cloning k modes with one stone

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.453017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f9d3fe6-390c-4b9f-b48e-e46c00f774a8 · outbound

This paper cites Residual Policy Learning.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Residual Policy Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.456230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66da45e2-0cde-4c23-8277-c710f7af89a5 · outbound

This paper cites Denoising Diffusion Implicit Models.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Denoising Diffusion Implicit Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.459897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce636175-18d4-4c72-a790-63aa47e07bc1 · outbound

This paper cites RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.463783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c4417a8-f9df-48f6-94f2-5725723e94c1 · outbound

This paper cites Exponentially weighted imitation learning for batched historical data.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Exponentially weighted imitation learning for batched historical data

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:24:21.700924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a35557b9-47ce-4cc4-95b6-172f129c4be9 · outbound

This paper cites Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.470866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d1981e4-f377-425e-adda-d7325de55126 · outbound

This paper cites Learning Fine-Grained Bimanual Manip- ulation with Low-Cost Hardware.

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration Learning Fine-Grained Bimanual Manip- ulation with Low-Cost Hardware

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T04:24:21.474479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 065d804c-7470-4df8-8637-5acc0bb5e053 · inbound

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation cites this paper.

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:10:57.873959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7973617-bd6a-46ce-9e52-db008fe9748b · inbound

WorldSample: Closed-loop Real-robot RL with World Modelling cites this paper.

WorldSample: Closed-loop Real-robot RL with World Modelling SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:58:02.475614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.