Paper Citation Record · LEDGER

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback

As of 23 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 3 inbound Pith citation observations for arXiv:2505.15572.

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

pith.paper-citation-record.v1
2505.15572 v1

Coverage vector

measured 63 of 63 reference resolution

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-06T21:29:10.676654Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation 6d540d20-0708-4e13-8ad5-a0b8610dec64 · outbound

This paper cites Artificial intelligence in physical sci- ences: Symbolic regression trends and perspectives.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Artificial intelligence in physical sci- ences: Symbolic regression trends and perspectives

Reference 1

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Observation 19a99033-cbf2-4f1f-af07-a94b8ebd05aa · outbound

This paper cites Multiple regression genetic programming.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Multiple regression genetic programming

Reference 2

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Observation f68fd1d8-ad59-489b-88ad-1882c7305709 · outbound

This paper cites Gorec: a generative cold-start recommendation framework.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Gorec: a generative cold-start recommendation framework

Reference 3

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Observation b9b87f5c-ef33-45a0-a458-3aa7448c04df · outbound

This paper cites Multimodality invariant learning for multimedia-based new item recommendation.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Multimodality invariant learning for multimedia-based new item recommendation

Reference 4

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This paper cites Neural symbolic regression that scales.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Neural symbolic regression that scales

Reference 5

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This paper cites Operon c++: an efficient genetic pro- gramming framework for symbolic regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Operon c++: an efficient genetic pro- gramming framework for symbolic regression

Reference 6

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This paper cites Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems

Reference 7

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Observation a94de04e-2f10-49c1-a792-4af181a83b0f · outbound

This paper cites Contemporary Symbolic Regression Methods and their Relative Performance.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Contemporary Symbolic Regression Methods and their Relative Performance

Reference 8

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This paper cites Multi-model approach for stock price prediction and trading recommendations.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Multi-model approach for stock price prediction and trading recommendations

Reference 9

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This paper cites Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression

Reference 10

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Observation ea0c6d7e-3f90-45d9-85d6-5da0f1b75b60 · outbound

This paper cites Discovering symbolic models from deep learning with inductive biases.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Discovering symbolic models from deep learning with inductive biases

Reference 11

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Observation ddf83fdf-d003-4fa0-aae4-a933e5400b26 · outbound

This paper cites Interaction–transformation evolutionary algorithm for symbolic regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Interaction–transformation evolutionary algorithm for symbolic regression

Reference 12

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This paper cites Deep symbolic regression for recurrence prediction.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Deep symbolic regression for recurrence prediction

Reference 13

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This paper cites Evolutionary large language model for automated feature transformation.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Evolutionary large language model for automated feature transformation

Reference 14

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Observation 6f45f2b5-2c32-4251-a34f-387db9e7481c · outbound

This paper cites Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming

Reference 15

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This paper cites Neuro-symbolic embedding for short and effective feature selection via autoregressive generation.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Neuro-symbolic embedding for short and effective feature selection via autoregressive generation

Reference 16

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Observation 32c5bf9a-1586-4af1-bc14-def11d384beb · outbound

This paper cites Gustafson, E.K.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Gustafson, E.K

Reference 17

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Observation 28c95cf9-df58-4340-900c-7c3784f72d5e · outbound

This paper cites Shape- constrained multi-objective genetic programming for symbolic regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Shape- constrained multi-objective genetic programming for symbolic regression

Reference 18

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This paper cites Double correction framework for denoising recommendation.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Double correction framework for denoising recommendation

Reference 19

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This paper cites Deep Generative Symbolic Regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Deep Generative Symbolic Regression

Reference 20

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This paper cites Reinforcement feature transformation for polymer property performance prediction.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Reinforcement feature transformation for polymer property performance prediction

Reference 21

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This paper cites Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting

Reference 22

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This paper cites Enhancing customer contact efficiency with graph neural networks in credit card fraud detection workflow.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Enhancing customer contact efficiency with graph neural networks in credit card fraud detection workflow

Reference 23

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This paper cites Bayesian Symbolic Regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Bayesian Symbolic Regression

Reference 24

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback End-to- end symbolic regression with transformers

Reference 25

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This paper cites Integration of neural network-based symbolic regression in deep learning for scientific discovery.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Integration of neural network-based symbolic regression in deep learning for scientific discovery

Reference 26

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This paper cites Inference of compact nonlinear dynamic models by epigenetic local search.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Inference of compact nonlinear dynamic models by epigenetic local search

Reference 27

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Epsilon-lexicase selection for regression

Reference 28

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This paper cites Learning concise representations for regression by evolving networks of trees.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Learning concise representations for regression by evolving networks of trees

Reference 29

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This paper cites A flexible symbolic regression method for constructing interpretable clinical prediction models.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback A flexible symbolic regression method for constructing interpretable clinical prediction models

Reference 30

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Sehf: A summary-enhanced hierarchical framework for financial report sentiment analysis

Reference 31

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Sade: A speaker-aware dual encoding model based on diagbert for medical triage and pre-diagnosis

Reference 32

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This paper cites Pth and the regulation of mesenchymal cells within the bone marrow niche.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Pth and the regulation of mesenchymal cells within the bone marrow niche

Reference 33

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression

Reference 34

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling

Reference 35

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Ffx: Fast, scalable, deterministic symbolic regression technology

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Symbolic Regression via Neural-Guided Genetic Programming Population Seeding

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Symbolic regression via deep reinforcement learning enhanced genetic programming seeding

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

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This paper cites Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Reference 40

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Age-fitness pareto optimization

Reference 41

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Transformer-based planning for symbolic regression

Reference 42

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This paper cites Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search

Reference 43

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Ai feynman: A physics-inspired method for symbolic regression

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

Reference 45

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Semantically-based crossover in genetic programming: application to real-valued symbolic regression

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:44.777505Z

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Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback SymbolicGPT: A Generative Transformer Model for Symbolic Regression

Reference 47

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This paper cites Scalable genetic pro- gramming by gene-pool optimal mixing and input-space entropy-based building-block learning.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Scalable genetic pro- gramming by gene-pool optimal mixing and input-space entropy-based building-block learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:44.571900Z

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Observation 340b7b71-be49-4dbe-b350-3f05e3aa44b1 · outbound

This paper cites Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:44.422431Z

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Observation 6b186ec6-dba3-4ed6-94a6-891f00154eb2 · outbound

This paper cites Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Reference 50

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Observation 6fd9a633-1f0f-41d5-8cae-7d6944274224 · outbound

This paper cites Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models

Reference 51

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This paper cites Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent

Reference 52

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Observation 25899c0c-c65a-45d0-a120-4842bf8ba3ab · outbound

This paper cites LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

Reference 53

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Observation 9ee983f7-21d8-4817-9a54-74dd08dd6b6c · outbound

This paper cites A successful hybrid deep learning model aiming at promoter identification.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback A successful hybrid deep learning model aiming at promoter identification

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:41.694888Z

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Observation 7a6d3922-2356-4339-ad17-952a3104e3fe · outbound

This paper cites Symbolic regression in materials science.MRS Communications, 9(3):793–805, 2019.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Symbolic regression in materials science.MRS Communications, 9(3):793–805, 2019

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:44.226917Z

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Observation 5aa6c0e1-5e8d-4293-b9f8-c176af2663cb · outbound

This paper cites Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:44.056245Z

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Observation 443bca91-424d-423d-a1d7-e21dc2b009e7 · outbound

This paper cites Topology-aware Reinforcement Feature Space Reconstruction for Graph Data.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Topology-aware Reinforcement Feature Space Reconstruction for Graph Data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:41.800742Z

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Observation a2f7bd7d-ad70-4cf2-91a9-3c9b5be86581 · outbound

This paper cites Feature selection as deep sequential generative learning.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Feature selection as deep sequential generative learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:43.852225Z

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Observation ae9de55b-8dc9-406e-b0ee-858c620c8a30 · outbound

This paper cites Revolutionizing biomarker discovery: Leveraging generative ai for bio-knowledge-embedded continuous space exploration.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Revolutionizing biomarker discovery: Leveraging generative ai for bio-knowledge-embedded continuous space exploration

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:43.675849Z

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Observation c2661aed-b642-48d3-9a7e-f9ab6fc370bd · outbound

This paper cites Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine-tuning.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine-tuning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:41.922971Z

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Observation 721bf089-eab2-4bcc-b062-a8f256d138bc · outbound

This paper cites A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:41.963199Z

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Observation 6b58a863-5e61-4e4a-8fdc-3368d0006cd4 · outbound

This paper cites Deep learning and symbolic regression for discovering parametric equations.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Deep learning and symbolic regression for discovering parametric equations

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:43.515932Z

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Observation edbb3fd1-d590-4632-86e3-38ee2398fe01 · outbound

This paper cites To simplify the notations, we replace the constant with ’C’ in the equations.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback To simplify the notations, we replace the constant with ’C’ in the equations

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:43.318152Z

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Pith citing papers

Observation 29e7564c-bfe2-4701-a592-88d4d32c04dc · inbound

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation cites this paper.

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:57.367920Z

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Observation 5ef5456a-03e5-46f3-9537-d6d86acd025b · inbound

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives cites this paper.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback

Reference 124

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:29:10.777100Z

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Observation 6f60a6fa-b119-48c1-8e63-d3b73d754a2b · inbound

Data-Efficient Symbolic Regression via Foundation Model Distillation cites this paper.

Data-Efficient Symbolic Regression via Foundation Model Distillation Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T16:58:57.979800Z

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