FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance

Natural language interfaces for tabular data must address ambiguities in user queries. This paper reframes ambiguity as a feature of cooperative interaction, proposing a framework that shares responsibility for query specification between users and systems. It distinguishes between cooperative queries, which can be resolved through inference, and uncooperative queries that cannot. The study evaluates queries across 15 datasets, revealing a problematic mix of query types that complicates assessments of system accuracy and interpretation capabilities.

Large language models (LLMs) are increasingly utilized in finance and economics, where their ability to understand chronology is critical. A study tested this capability through various chronological ordering tasks, revealing that while models like GPT-4.1 and GPT-5 can maintain local order, they struggle with creating a consistent global timeline. The findings indicate a significant drop in exact match rates as task complexity increases, particularly in conditional sorting tasks, highlighting inherent limitations in LLMs' chronological reasoning.

Anomaly detection is essential in fields like cybersecurity and finance, particularly with large-scale tabular data. Traditional unsupervised methods struggle due to their reliance on a single global distribution, which does not account for the diverse contexts present in real-world data. This paper introduces a contextual learning framework that models normal behavior variations across different contexts, focusing on conditional data distributions instead of a global joint distribution, enhancing anomaly detection effectiveness.

The article presents a new framework called GMAT, which enhances Multiple Instance Learning (MIL) for whole slide image (WSI) classification. By integrating vision-language models (VLMs), GMAT aims to improve the generation of clinical descriptions that are more expressive and medically specific. This addresses limitations in existing methods that rely on large language models (LLMs) for generating descriptions, which often lack domain grounding and detailed medical specificity, thus improving alignment with visual features.

The research paper explores the challenge of false information and the effectiveness of large language models (LLMs) in verifying factual claims in English and Telugu. It presents a bilingual dataset and evaluates various approaches for classifying the veracity of claims. The study aims to enhance the efficiency of fact-checking processes, which are often labor-intensive and time-consuming.

Flaky tests, which unpredictably pass or fail, hinder developer productivity and delay software releases. FlakyGuard is introduced as a solution that leverages large language models (LLMs) to automatically repair these tests. Unlike previous methods like FlakyDoctor, FlakyGuard effectively addresses the context problem by structuring code as a graph and selectively exploring relevant contexts. Evaluation of FlakyGuard on real-world tests indicates a repair success rate of 47.6%, with 51.8% of fixes accepted by developers, marking a significant improvement over existing approaches.

DataSage is a novel multi-agent framework designed to enhance insight discovery in data analytics. It addresses limitations of existing data insight agents by incorporating external knowledge retrieval, a multi-role debating mechanism, and multi-path reasoning. These features aim to improve the depth of analysis and the accuracy of insights generated, thereby assisting organizations in making informed decisions in a data-driven environment.

The paper titled 'Beat the long tail: Distribution-Aware Speculative Decoding for RL Training' introduces a new framework called DAS, aimed at improving the efficiency of reinforcement learning (RL) rollouts for large language models (LLMs). The study identifies a bottleneck in the rollout phase, where long trajectories consume significant time. DAS employs an adaptive drafter and a length-aware speculation policy to optimize the rollout process without changing model outputs, enhancing the overall training efficiency.