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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representative citing papers
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LiveBench: A Challenging, Contamination-Limited LLM Benchmark
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PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects
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Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
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Fine-grained Claim-level RAG Benchmark for Law
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Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding
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How Should LLMs Listen While Speaking? A Study of User-Stream Routing in Full-Duplex Spoken Dialogue
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TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents
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ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
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Evaluating Temporal Consistency in Multi-Turn Language Models
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ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
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MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation
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Culture-Aware Humorous Captioning: Multimodal Humor Generation across Cultural Contexts
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From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning
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ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents
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FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
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Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
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PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models
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CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
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Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models
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M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation
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FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
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Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
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LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
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Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs
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Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective
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FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
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MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
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Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?
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Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods
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Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
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EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation
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From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
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