·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’1h◆Kimi: Threat or menace?3h◆The apps, gadgets, and tools every reader needs10h◆Neil Rimer thinks the AI money is coming back out17h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach18h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study18h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap18h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation18h◆The Steering Budget: Examples beat Knobs18h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs18h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination18h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization18h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models18h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation18h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System18h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets18h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility18h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation18h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks18h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios18h◆Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’1h◆Kimi: Threat or menace?3h◆The apps, gadgets, and tools every reader needs10h◆Neil Rimer thinks the AI money is coming back out17h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach18h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study18h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap18h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation18h◆The Steering Budget: Examples beat Knobs18h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs18h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination18h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization18h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models18h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation18h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System18h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets18h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility18h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation18h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks18h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios18h◆
News/EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
arxiv
PublishedJune 24, 2026 at 4:00 AM
—neutral

EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering

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

arXiv:2606.23724v1 Announce Type: cross Abstract: Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes financial workflows. A fluent answer can blend directly grounded state

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

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
arxivDemocratizing Agent Deployment Safety: A Structural Monitoring Approach18harxivHarnessing LLMs for Reliable Academic Supervision: A Comparative Study18harxivAccelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap18harxivTIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation18h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews