ODTQA-FoRe: Open-Domain Tabular QA Dataset for Future Data Forecasting and Reasoning
The paper introduces ODTQA-FoRe, a new benchmark dataset for open-domain tabular question answering focused on time-series forecasting and forecast-based reasoning using real estate data. The authors also propose TimeFore, an LLM agent framework that decomposes the task into three roles: a SQL-generating Retriever, a Forecaster that calls external time-series models, and an Analyzer that synthesizes results. The work targets a gap in existing tabular QA systems, which typically cannot perform future-oriented numerical prediction. Experiments demonstrate TimeFore's effectiveness on the new benchmark.
Related guides (2)
Related events (8)
Docmatix: A Large-Scale Dataset for Document Visual Question Answering
Hugging Face released Docmatix, a large-scale dataset designed for Document Visual Question Answering (DocVQA) tasks. The dataset aims to address the scarcity of high-quality training data for document understanding in multimodal models. It is intended to improve fine-tuning of vision-language models on document comprehension tasks.
DocTrace: Structure-Aware On-Demand Hypergraph Memory for Long-Document QA
Researchers introduce DocTrace, a multi-agent RAG framework for long-document question answering that uses query-triggered knowledge organization rather than costly query-agnostic preprocessing. The system combines a lightweight document structural tree index, on-demand hypergraph working memory, and a graph-structured experience memory that stores successful reasoning plans for reuse. Evaluated on four long-document QA datasets, DocTrace outperforms the strongest baseline (ComoRAG) by up to 8.85% F1 and 4.40% EM while reducing computational cost by 53.32%.
Efficient Table Pre-training without Real Data: An Introduction to TAPEX
TAPEX is a table pre-training approach that avoids reliance on real tabular data by instead training a language model to simulate SQL query execution over synthetic tables. The method achieves strong performance on table-based question answering and fact verification benchmarks. This Hugging Face blog post introduces the technique and its integration into the Hugging Face ecosystem.
Back to The Future: Evaluating AI Agents on Predicting Future Events
This Hugging Face blog post introduces FutureBench, a benchmark designed to evaluate AI agents on their ability to predict future events, addressing the challenge of data contamination in standard benchmarks by using temporally forward-looking tasks. The approach tests whether agents can reason about and forecast outcomes beyond their training data cutoff. This framing positions future-event prediction as a rigorous, contamination-resistant evaluation methodology for frontier models and agents.
DABStep: Data Agent Benchmark for Multi-step Reasoning
Hugging Face introduces DABStep, a benchmark designed to evaluate data agents on multi-step reasoning tasks. The benchmark targets agentic systems that must perform complex, sequential data operations rather than single-step queries. It aims to fill a gap in evaluation tooling for realistic data analysis workflows involving tool use and chained reasoning.
LLM Agent Framework for Last-Mile Time Series Forecasting Revision
This paper introduces a 'last-mile forecasting' framework where an LLM agent sits atop a statistical forecasting backbone to incorporate weakly structured business context—holidays, campaigns, expert feedback, external events—into decision-ready forecasts. The system uses tool-invocation for contextual retrieval, converts reasoning into explicit revision actions under safety constraints, and supports long-horizon forecasting via map-reduce decomposition with a memory bank for post-hoc reflection. The authors validate the approach through real-world case studies, positioning it as a bridge between statistical prediction and operationally usable forecasts.
CADE framework proposes direct timestep embedding and contrastive alignment for time-series question answering
A new arXiv preprint introduces CADE (Contrastive Alignment with Direct Embedding), a framework for time-series question answering (TSQA) that bypasses the tokenization bottleneck of standard LLMs by mapping each timestep directly into the LLM embedding space via a point-wise linear encoder and MLP projector. The approach also introduces a one-directional supervised contrastive loss to align time-series embeddings with frozen class-name text anchors, bridging the semantic gap between numerical and language representations. Evaluated on the Time-MQA benchmark across six TSQA tasks, CADE outperforms both open-source and proprietary LLM baselines. The work addresses a concrete limitation of patch-based encoders — fixed granularity and poor cross-dataset transfer — with a cleaner architectural alternative.
Toto 2.0: Open-Weights Time Series Foundation Models Demonstrate Scaling Laws from 4M to 2.5B Parameters
Datadog releases Toto 2.0, a family of five open-weights time series forecasting models ranging from 4M to 2.5B parameters, demonstrating consistent forecast quality improvements with scale. The models achieve state-of-the-art results on three benchmarks: BOOM (observability), GIFT-Eval (general-purpose), and TIME (contamination-resistant). The release includes architectural details, a u-muP hyperparameter transfer pipeline, and all base checkpoints under Apache 2.0 license.