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Paper Citation Record · LEDGER

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

As of 11 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2602.10635.

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

pith.paper-citation-record.v1
2602.10635 v3

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:06:00.983061Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

26 of 26 outbound references displayed

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Outbound references

Observation f12bc8a5-3925-45ee-99b7-148ab2f59cc9 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 1

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Observation 479cb759-e01d-4cdb-a087-77f07b5b330d · outbound

This paper cites Constrained dynamical neural ode for time se- ries modelling: A case study on continuous emotion prediction.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Constrained dynamical neural ode for time se- ries modelling: A case study on continuous emotion prediction

Reference 4

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Observation f912b035-a23f-4a17-a9c6-22bebdab6706 · outbound

This paper cites an unresolved cited work.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Unresolved cited work

Reference 5

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Observation f021cf14-1a22-43d5-a995-c7775c858822 · outbound

This paper cites an unresolved cited work.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Unresolved cited work

Reference 6

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Observation 379506dd-ee90-4580-b518-7e1dde57f2bd · outbound

This paper cites A Closer Look at Deep Policy Gradients.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization A Closer Look at Deep Policy Gradients

Reference 7

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Observation 2f0e2477-2c29-4e4e-a905-03477808b965 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Adam: A Method for Stochastic Optimization

Reference 8

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Observation 31738d9a-f240-4e64-a665-29932d082eca · outbound

This paper cites Avec 2016: Depression, mood, and emotion recognition workshop and challenge.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Avec 2016: Depression, mood, and emotion recognition workshop and challenge

Reference 13

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Observation 4ea2e88a-9568-4ae7-a1c9-e41749e97fdc · outbound

This paper cites A novel markovian framework for integrating absolute and rela- tive ordinal emotion information.IEEE Transactions on Affective Computing, 14(3):2089–2101,.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization A novel markovian framework for integrating absolute and rela- tive ordinal emotion information.IEEE Transactions on Affective Computing, 14(3):2089–2101,

Reference 14

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Observation 60f870d4-7118-468c-b7ab-d2e2b501c9e1 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 16

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Observation 0d70d34c-4bea-46ba-a8b1-1b22923ce84b · outbound

This paper cites and Zuo, C.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization and Zuo, C

Reference 18

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Observation 4a83e5e2-bfa4-4e72-87d8-42e75a91dc1e · outbound

This paper cites The weighted F1 is then computed as: Weighted-F1= X c∈C nc N ·F1 c, wheren c is the number of true instances in classc,Nis the total number of instances, andCis the set of classes.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization The weighted F1 is then computed as: Weighted-F1= X c∈C nc N ·F1 c, wheren c is the number of true instances in classc,Nis the total number of instances, andCis the set of classes

Reference 19

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Observation d736e1a9-c1f2-4504-b9da-c8ec1ec6e5de · outbound

This paper cites On the other hand, we implement the reinforcement learning training algorithms in Tab.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization On the other hand, we implement the reinforcement learning training algorithms in Tab

Reference 20

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Observation 26704a28-d3ce-4bd9-9ca5-40ad58f9da15 · outbound

This paper cites Finally, we provide a overlong length penalty, rlen which follows Zhang & Zuo (2025) to prevent excessive length and verbosity of responses.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Finally, we provide a overlong length penalty, rlen which follows Zhang & Zuo (2025) to prevent excessive length and verbosity of responses

Reference 22

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Observation 606ef4aa-77bc-4e0d-80cf-774c5a9c8095 · outbound

This paper cites Each value is the arithmetic mean over datasets associated with the task.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Each value is the arithmetic mean over datasets associated with the task

Reference 23

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Observation 24f9f5cc-8648-48ac-aadd-3662e953c943 · outbound

This paper cites Additional Training Plots We provide additional training plots to empirically illustrate the training dynamics in our experiments.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Additional Training Plots We provide additional training plots to empirically illustrate the training dynamics in our experiments

Reference 25

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Observation afc19102-6fec-470d-80dc-ffcaa2801f03 · outbound

This paper cites an unresolved cited work.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-03T01:06:00.983061Z digest=sha256:30b31522f80222ba9d81627f37272bc94d9d0f88d806c7920163954b00b8a65d

Observation b5260150-f11b-4100-836f-9de08d0bad6c · outbound

This paper cites Gemma 3 Technical Report.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Gemma 3 Technical Report

Reference 1999

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source=pdf_text observed=2026-08-03T01:06:00.540337Z digest=sha256:57e9ac7ed3952ca6a64da1178a1eac87737cd3e58adf78996e4aeb83d93fdd6b

Observation 7ec840dc-86cf-4f35-a63e-17064f35c7f0 · outbound

This paper cites R., Solar-Lezama, A., and Liang, P.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization R., Solar-Lezama, A., and Liang, P

Reference 2014

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Observation 2c0b38e1-2239-435a-9567-a92735cf59a5 · outbound

This paper cites Proximal Policy Optimization Algorithms.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Proximal Policy Optimization Algorithms

Reference 2015

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Observation 139aee29-5406-4717-aa0b-f2237a0ee3a3 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2017

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Observation a5403f7c-d68e-468e-b1f9-7e8b52a930e6 · outbound

This paper cites K., Rahman, W., Zadeh, A.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization K., Rahman, W., Zadeh, A

Reference 2018

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Observation da63f133-7b5e-4a82-9817-c685d5efc9f1 · outbound

This paper cites MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Reference 2020

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Observation 79b87b2c-e996-4802-98ee-8c56dd83ec4b · outbound

This paper cites Qwen2.5-Omni Technical Report.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Qwen2.5-Omni Technical Report

Reference 2022

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Observation e55070f8-c239-4661-bfa7-89f31f9a80a7 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 2023

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source=pdf_text observed=2026-08-03T01:06:00.346002Z digest=sha256:83df64df2235a2e2706297fc1f02723f0c4b32c24a3818673171472f10022560

Observation f402657a-3fd1-49cf-839a-7b9207d68587 · outbound

This paper cites Qwen2.5-VL Technical Report.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Qwen2.5-VL Technical Report

Reference 2024

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Observation 4a07d16f-2563-4631-852d-7fbaf2949c9f · outbound

This paper cites Gpg: A simple and strong reinforcement learning baseline for model reasoning.arXiv preprint arXiv:2504.02546,.

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization Gpg: A simple and strong reinforcement learning baseline for model reasoning.arXiv preprint arXiv:2504.02546,

Reference 2025

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Pith citing papers

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