With architecture and data held fixed, a checkpoint-only reconstruction attack recovers training images best from Muon-trained models and worst from Adam-trained models, making the optimizer choice a control axis for training-data privacy.
Astrophysics research must juggle fast-growing literature, multiwavelength observations and complex physical models, and AstroGenesis places these in one multi-agent environment where a Supervisor Agent and Planner/Replanner coordinate literature retrieval, observational data analysis, theoretical modeling and research ideation. The current implementation focuses on blazar research, and its Theoretical Modeling Agent uses pretrained neural-network surrogate models to support broadband and multimessenger modeling through natural language. On single-paper and multi-paper literature benchmarks, at least one relevant publication appeared in the top five results for 76.6% and 79.2% of questions.
The work introduces Deep Persona, a psychologically grounded three-layered architecture that organizes personas into observable expression, latent beliefs, and core motivational drives, governed by scripted determinism and bounded agency, together with a reference-free evaluation framework (ADOS-inspired metrics for pragmatic fluency, joint attention, affective congruence, and emotional expression diversity, plus a Mahalanobis-distance Dialogue Naturalness Score, DNS); on human-human and human-LLM dialogue datasets such as DailyDialog and CounselChat, LLMs show high pragmatic fluency (0.88-0.
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
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