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🟡 Our principles — score 50
Sources: lab_blog/OpenAI
Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work.
📄 New Papers
| Title | Category | Score | Link |
|---|---|---|---|
| Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines | cs.AI | 0 | Open |
| K-SENSE: A Knowledge-Guided Self-Augmented Encoder for Neuro-Semantic Evaluation of Mental Health Conditions on Social Media | cs.AI | 0 | Open |
| Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph | cs.AI | 0 | Open |
| Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather | cs.AI | 0 | Open |
| Uncertainty Propagation in LLM-Based Systems | cs.AI | 0 | Open |
| Autocorrelation Reintroduces Spectral Bias in KANs for Time Series Forecasting | cs.AI | 0 | Open |
| Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong | cs.AI | 0 | Open |
| MTRouter: Cost-Aware Multi-Turn LLM Routing with History-Model Joint Embeddings | cs.AI | 0 | Open |
| Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model | cs.AI | 0 | Open |
| MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation | cs.AI | 0 | Open |
| Pref-CTRL: Preference Driven LLM Alignment using Representation Editing | cs.AI | 0 | Open |
| COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training | cs.AI | 0 | Open |
| Hardware-Efficient FPGA Implementation of Sigmoid Function Using Mixed-Radix Hyperbolic Rotation CORDIC | cs.AI | 0 | Open |
| On the Memorization of Consistency Distillation for Diffusion Models | cs.AI | 0 | Open |
| DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making | cs.AI | 0 | Open |
🏢 Lab Blog Posts
- OpenAI: Our principles