3arXiv cs.CL (Computation and Language)·
JobHop v2: Large-scale career trajectory dataset extracted from 440K resumes via LLM pipeline
Researchers release JobHop v2, a dataset of 355,315 career trajectories extracted from ~440,000 pseudonymized multilingual resumes provided by VDAB, the Flemish Public Employment Service. The extraction pipeline uses reasoning-controlled LLM inference with a retry mechanism achieving 100% JSON parse rate, annotating trajectories with ESCO occupational codes, temporal information, and education attainment. The work addresses a gap in publicly available, authentic (non-synthesized) career trajectory data for workforce planning and labour market analysis. Dataset and code are publicly released.
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STEP: Career-path recommendation system using LLMs, temporal GRU, and contrastive occupation embeddings
Researchers introduce STEP (Sequential Trajectory of Employment Prediction), a career-path recommendation system that extracts structured trajectory data from unstructured resumes using LLMs and models it with a time-decay GRU, FiLM conditioning on educational attainment, and attention-based pooling. They also introduce ROUTE, a two-stage contrastive pretraining procedure for multilingual occupation embeddings. STEP is evaluated on four career-trajectory datasets including an improved version of the public JobHop dataset, outperforming prior baselines on next-job prediction. Code and data are publicly released.
SARLO-80: Large-scale VHR SAR-optical-text dataset for multimodal foundation model training
Researchers from ONERA release SARLO-80, a dataset of 119,566 triplets combining very-high-resolution complex SAR imagery, aligned optical patches, and natural-language captions covering 257 locations across 72 countries. The dataset is built from Umbra spotlight acquisitions standardized to an 80cm slant-range grid, with three caption variants per sample to support vision-language training and evaluation. It addresses a recognized gap in SAR-optical multimodal resources, which have historically been limited to low-resolution intensity-only products. The dataset and preprocessing code are publicly released on Hugging Face Hub.
TraceLab: Real-world coding agent workload traces reveal serving optimization opportunities
Researchers at UW release TraceLab, a dataset of ~4,300 coding-agent sessions comprising ~350,000 LLM steps and ~430,000 tool calls drawn from real day-to-day use of Claude Code and Codex. Analysis reveals characteristic patterns: long autonomous loops, long contexts with short outputs, heavily-tailed tool call distributions, and high but imperfect prefix cache hit rates. The findings motivate concrete serving-system improvements including lower-overhead tool calling, append-length-aware prefill, and improved KV-cache management. The dataset, pipeline, and analysis code are publicly released.
TalentCLEF 2026 overview: NLP benchmarks for job-person matching and skill classification
The second edition of TalentCLEF, a shared evaluation challenge at CLEF 2026, introduced two tasks: contextualized job-person matching (English and Spanish) and job-skill matching with skill type classification. The challenge attracted 113 registered teams and over 400 submissions, indicating significant community interest in NLP benchmarks for Human Capital Management. The paper describes datasets, evaluation settings, and results across participating teams.
OpenMedReason: Large-scale multimodal medical reasoning corpus with 450K instances for clinical VLM training
Researchers introduce OpenMedReason, a 450K-instance open multimodal medical reasoning corpus with reasoning traces derived from human-authored biomedical literature rather than synthetic chains of thought. The dataset covers diverse medical imaging modalities and is paired with OpenMedReason-Bench, a held-out benchmark evaluating LVLMs on perception, medical knowledge, and rationale axes. Training with OpenMedReason yields a 20% average VQA accuracy improvement over base models and achieves performance within 4.2% of leading comparable-scale medical VLMs. Both the dataset and code are publicly released.
WorkflowView: LLM-based framework abstracts user action logs into interpretable workflows
Researchers introduce WorkflowView, a framework using LLMs to convert low-level interaction logs into high-level activity descriptions across diverse domains. The system achieves strong results on three tasks: zero-shot browser log reconstruction (semantic similarity 0.91), few-shot MOOC dropout prediction (F1=0.90 with five examples), and privacy-preserving analysis of AI tool usage in Microsoft Word. The work addresses limitations of prior deep learning clustering approaches, which struggled with noise and cross-application generalization, and discusses deployment considerations including computational efficiency and privacy.
Stanford EDGAR Filings Dataset: 152B-token open corpus of SEC filings for LLM pretraining
Stanford researchers introduce the Stanford EDGAR Filings Dataset (SEFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown, releasing a 152B-token initial snapshot with a larger 550B-token archive described. The dataset targets the growing scarcity of high-quality long-context pretraining data, with less than 0.1% overlap with Common Crawl-derived corpora. Two derived benchmarks are also introduced: EDGAR-Forecast for filing-grounded numerical forecasting and EDGAR-OCR for complex financial table transcription. The work addresses a real gap in open long-context training data outside narrow domains like code.
Anthropic Launches Economic Index: First Large-Scale Empirical Study of AI's Labor Market Impact
Anthropic has released the Anthropic Economic Index, an initiative tracking AI's effects on labor markets using anonymized data from approximately one million Claude.ai conversations matched to U.S. Department of Labor O*NET occupational tasks. Key findings show AI use is concentrated in software development and technical writing, with 36% of occupations seeing AI use in at least 25% of their tasks, and usage skewing toward augmentation (57%) over automation (43%). The underlying dataset is being open-sourced to enable independent research, and Anthropic is inviting economists and policy experts to contribute to the ongoing initiative. The analysis was enabled by Clio, Anthropic's privacy-preserving internal conversation analysis tool.