Introduces the TVR active viewpoint-matching task and TVRBench indoor simulation benchmark, where foundation models start at low single-digit success rates but reach 51.4% after visual-action SFT and multi-turn GRPO post-training.
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GPT-4o System Card
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abstract
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
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- abstract GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while
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citing papers explorer
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From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
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POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference
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SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of LLMs
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MetaLint: Easy-to-Hard Generalization for Code Linting
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SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
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Probing Privacy Leaks in LLM-based Code Generation via Test Generation
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MEMCoder: Multi-dimensional Evolving Memory for Private-Library-Oriented Code Generation
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Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
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AnomalyGen: Enhancing Log-Based Anomaly Detection with Code-Guided Data Augmentation
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LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments
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Can Old Tests Do New Tricks for Resolving SWE Issues?
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Multi-LLM Orchestration for High-Quality Code Generation: Exploiting Complementary Model Strengths
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Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
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Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments
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Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
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Human-aligned AI Model Cards with Weighted Hierarchy Architecture
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