Existing long-document QA benchmarks rarely combine large-scale human-authored questions with detailed visual evidence annotations, limiting diagnosis of evidence localization and interpretation failures. We present VELD-QA, a large-scale benchmark for Visual Evidence grounding and question answering in Long Documents....
Zhivar Sourati, Marianne Menglin Liu, Rong Wang et al.· 0 citations
Answering questions over heterogeneous data means combining evidence from files, databases, documents and the web. Data agents automate this, but their success depends on context. Three context-engineering techniques (CETs) help by shaping it: semantic layers describe the data, orchestration splits work among agents, a...
Zhivar Sourati, Marianne Menglin Liu, Zheng Wang et al.· 0 citations
Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, however, LLM-brain alignment is measured using one model for different regions of the brain, and then model performance is summarized across regions. This one-model-fits-all...
Zhivar Sourati, Meng-Hong Wu, Nona Ghazizadeh et al.· 0 citations
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