Multi-hash ID embeddings can reduce embedding table size by more than 98 percent while preserving ranking quality in production GNN systems
The new sepsis index has potential as a decision support tool to complement clinical judgement based on its hourly prognostic information and consistency with clinical expectations
No learned alternative sepsis index derived directly from patient trajectories is currently in routine clinical use
Moral knowledge geometry in LLMs is consistent across architectures and scale, and emerges early in pre-training before probe accuracy saturates
Large language models organize moral knowledge geometrically, with moral foundation representations spanning near-maximal independent dimensions while sharing a positive common component
A scalable GNN ranking system increased friend additions from recommendations by 16 percent and unique friend adders by 11.5 percent in production
Message-passing GNNs can be successfully deployed on production-scale social graphs with hundreds of millions of users and tens of billions of edges
The new sepsis index separates non-survivors from survivors by 1.19-1.64 points on a 0-10 scale across all baseline SOFA-2 strata
LLMs represent moral tension itself in dilemmas rather than pre-resolved judgments, with dilemma directions partially composing from component foundations but majority variance encoding conflict-specific structure
The shared component in moral representation is moral-specific with much higher integration compared to matched non-moral concept batteries
Current sepsis severity indices use outdated fixed variables and weights from decades ago that no longer reflect modern critical care practices
Cross-institutional agreement for the sepsis index models trained on different sites achieved 70-77% of same-site correlation, demonstrating reasonable generalization
LLM moral representations reflect corpus statistics rather than the individualizing/binding distinction predicted by Moral Foundations Theory
Timestamp-sorted CSR storage with binary search reduces per-node temporal sampling cost from O(deg(v) + k) to O(log(deg(v)) + k)
A new sepsis index using mortality as a treatment-level ranking signal rather than per-state target allows credit redistribution non-uniformly across timesteps, improving on previous approaches
There is a crucial gap in the benchmarking ecosystem for corporate communication reasoning that needs to be filled
Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communities
The Latent Advertiser Mixture Auction (LAMA) mechanism satisfies Markov DSIC and IR properties while achieving near-optimal KL-regularized welfare
LLMs show increasingly poor performance as input size approaches realistic corporate communication scales of 230,000+ documents
Reproducing SmolLM3-3B requires over $700K in training costs
About $4.4K is sufficient to reach the performance of Qwen2-1.5B based on the Puro Cost Scaling Law