AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Mathematical reasoning remains underexplored for low-resource languages, including Dialectal Arabic. To bridge this gap, we introduce the DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) Benchmark, the first manually curated benchmark for evaluating mathematical reasoning in Dialectal Arabic. DAR-MGSM consist...
EvoNote is introduced, an agentic framework that enables health Community Notes generation to self-evolve through an evolving experience memory of prior misinformation correction episodes, positioning self-evolving note generation as a promising paradigm for scalable health misinformation governance.
SQLStructEval is introduced, a framework that analyzes this behavior through canonical abstract syntax tree representations and adopts a pipeline that first generates structured intermediate representations and then deterministically compiles them into SQL, improving execution accuracy and structural agreement among co...
Claims paired with AI-generated images are a rapidly growing form of misinformation. Existing automated fact-checking (AFC) methods mainly treat this as a provenance problem, detecting low-level synthesis artifacts to decide whether an image is AI-generated. However, such methods do not verify what human fact-checkers...
Jev has the lowest cost and median response time among the evaluated configurations, while hosted language models achieve higher baseline accuracy and Rankings by baseline accuracy differ from rankings by correctness across every condition and repeat, although small differences in the latter do not establish a general...
Co-FactChecker is proposed, a framework for human-AI collaborative claim verification that translates expert feedback into trace-edits that introduce targeted modifications to the trace, sidestepping the shortcomings of dialogue-based interaction.
It is suggested that robust meme understanding requires multimodal training, varies substantially across semantic categories, and remains sensitive to over-specialization when models are fine-tuned on individual datasets rather than trained in a unified setting.
A novel two-step multi-class classification system to identify varying degrees of machine involvement in Bulgarian text improves the reliable identification of human authorship and enhances the overall accuracy of multi-class text detection compared to single-step methods.
ArabCulture-Dialogue is introduced, a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both MSA and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics, to address the performance gap between MSA and Arabic dialects.
Across MedSNIP-Bench, HealthFC, and MedHallu, snippet-level verification preserves or improves false-class F1, with gains concentrated where answers are long enough to fragment and where the verifier is strong enough to exploit the recovered structure.
Results on UrduMMLU show that current LLMs have uneven Urdu knowledge, particularly for content grounded in the regional context, and many models lose 25 to 40 points on Urdu-centered Humanities subjects compared with STEM.
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