·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Figma announces new features to define how agents work on its platform4h◆Narrowing the Collaboration Gap, Probably5h◆Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs5h◆ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing5h◆MoLE: Mixture of Latent Experts for Complementary Visual Reasoning5h◆Distribution Matching Evolutionary Algorithms for Rare Event Sampling5h◆Why Software Engineering Is Indispensable in the Age of Coding Agents5h◆NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime5h◆Structure-Preserving Document-Level Data Augmentation via Discourse Parsing5h◆One Block Ahead: Promoting Sampler State in Diffusion Language Models5h◆Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding5h◆AFCS: Atomic-Fact-Conditioned Synthesis for Factual Knowledge Pretraining5h◆Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization5h◆Function-Space Temporal Self-Distillation for Policy Optimization5h◆Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction5h◆AVA-Encoder: Towards Agent-Native Video Representation Learning5h◆TRACER: Trace-Regularized Training Against Indirect Prompt Injection5h◆Towards Training and Inference of Graph Neural Networks in Graph Databases5h◆CAST: History-Adaptive Spectral Clipping for LLM Pretraining5h◆Beyond DFT electronic band structures via dual-space orbital learning5h◆Figma announces new features to define how agents work on its platform4h◆Narrowing the Collaboration Gap, Probably5h◆Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs5h◆ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing5h◆MoLE: Mixture of Latent Experts for Complementary Visual Reasoning5h◆Distribution Matching Evolutionary Algorithms for Rare Event Sampling5h◆Why Software Engineering Is Indispensable in the Age of Coding Agents5h◆NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime5h◆Structure-Preserving Document-Level Data Augmentation via Discourse Parsing5h◆One Block Ahead: Promoting Sampler State in Diffusion Language Models5h◆Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding5h◆AFCS: Atomic-Fact-Conditioned Synthesis for Factual Knowledge Pretraining5h◆Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization5h◆Function-Space Temporal Self-Distillation for Policy Optimization5h◆Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction5h◆AVA-Encoder: Towards Agent-Native Video Representation Learning5h◆TRACER: Trace-Regularized Training Against Indirect Prompt Injection5h◆Towards Training and Inference of Graph Neural Networks in Graph Databases5h◆CAST: History-Adaptive Spectral Clipping for LLM Pretraining5h◆Beyond DFT electronic band structures via dual-space orbital learning5h◆
News/How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study
arxiv
PublishedSeptember 7, 2026 at 4:00 AM
—neutral

How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2609.04797v1 Announce Type: new Abstract: Deep models for sales forecasting, such as WaveNet-style dilated convolutional networks, are accurate but opaque: when a single model predicts sales for one of many series, it offers no account of why. We add a post-hoc, architecture-agnostic counterfa

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivNarrowing the Collaboration Gap, Probably5harxivDivide, Consult, Conquer: Capability Laundering Through Aligned LLMs5harxivViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing5harxivMoLE: Mixture of Latent Experts for Complementary Visual Reasoning5h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews