Depression affects more than 280 million people worldwide, and social media text provides a large-scale resource for automated mental health assessment. Large language model (LLM)-based approaches have shown promising performance for social media-based depression detection; however, their cross-lingual robustness for...
Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive methods encode temporal dependencies implicitly in encoder weights, without an explicit model of how node representations evolve, making them brittle under distribution shif...
This study re-run two recent methods, DEMO and NSReg, together with OUTPOST, a small first-order detector built for this study, and finds that a 0.002 tie band for hyperparameter selection lies below the paired standard error on all six graphs tested, even at ten seeds.
Fine-tuning an aligned language model on narrow, flawed data can induce harmful behavior far outside the training domain, known as emergent misalignment (EM). Prior work has localized EM in model weights, activations, and training documents, but it remains unclear which training tokens carry the relevant fine-tuning si...
This work proposes an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, supported by structured or unstructured test cases, and introduces QUBOBench, a benchmark containing 100 combinatorial optimization problems across 12 application domains.
Computer-use agents ground natural-language instructions in screenshots to locate interface elements, yet existing benchmarks do not isolate whether models bind relational language to the correct element. We introduce GUI-Primitives, a 994-item benchmark of contrastive instruction pairs over seven spatial relations in...
Md Abrar Jahin, Md. Rizwan Parvez· 0 citations
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