搜索“RAG”

to introduce RoboBrain-X0, a groundbreaking cross-ontology foundation model designed to overcome the limitations of single-robot systems in heterogeneous ontology transfer. By leveraging End-Effector pose representation in SE(3) task space, coupled with a Unified Action Vocabulary (UAV) and action tokenizer, RoboBrain-X0 achieves efficient zero-shot generalization and complex task decomposition. Its Grouped Residual Quantizer (GRVQ) maps continuous control sequences from diverse degrees of freed

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al examples of the three inference modes (Incremental-Mode, Forward-Mode, and Backward-Mode). In practice, to predict the task state reward more accurately, we highly recommend averaging the inference reward results from all three modes to use as the final reward in RL. ✨ More Cases for Testing Many thanks to Robometer for providing more interesting test examples 🤗. To better demonstrate the usage of 'single-view without goal image' with our latest Robo-Dopamine-GRM-2.0-8B-Preview model, we also

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age generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary models such as pose detection and face detection; 9/10/2024: Introducing MemoRAG, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1); 9/2/2024: Start to maintain the tutorials. The contents within will be actively updated and eariched, stay tuned! 📚; 7/26/2024: Release a new embeddin

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first-party and third-party use (API and Enterprise) unless customers meet strict criteria for removal. To prevent CSAM generation, we have built a dedicated safety stack that leverages system mitigations used across our other products as well as additional safeguards developed specifically for Sora. For full details on our specific CSAM safety stack can be found on our Sora System Card1. OpenAI Sora System Card: https://openai.com/index/sora-system-card/↩︎; Teen Safety: Sora 2 has a number of a

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eroffensive challenges: Evasion: Covers the skills needed to avoid detection by security controls and monitoring systems; Network Attack Simulation: Assesses understanding and leveraging of commonly used attack flows, reconnaissance methods, and network protocols and components (such as firewalls and file servers); Vulnerability Discovery and Exploitation: Tests capabilities in reverse engineering, code analysis, cryptography, and exploitation Compared to o3-mini, o3 showed improved cyberoffensi

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mentally trained from GPT-5.5 with training data related to beneficial life sciences capability. Unlike GPT-5.5, it is trained not to refuse sophisticated biology queries, and leverages a trusted access and responsible deployment structure as the primary safeguard. 3. Beneficial Capability Evaluations The Beneficial Capability Evaluations below measure how well GPT-Rosalind-5.5 performs on the intended research-support tasks it was designed to improve. Preparedness evaluations, in the following

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fine-tuned the gpt-oss-120b model for these two categories. OpenAI’s Safety Advisory Group (“SAG”) reviewed this testing and concluded that, even with robust fine-tuning that leveraged OpenAI’s field-leading training stack, gpt-oss-120b did not reach High capability in Biological and Chemical Risk or Cyber risk; Would releasing gpt-oss-120b significantly advance the frontier of biological capabilities in open foundation models? We found that the answer is no: For most of the evaluations, the de

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is safe completion rate, checking that the model’s response and actions are not disallowed according to our safety policies. Our evaluations are run on the model without system-level safeguards to ensure the model’s underlying behavior meets our safety bar. We continue monito

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is safe completion rate, checking that the model’s response and actions are not disallowed according to our safety policies. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monit

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context, including differences in epidemiology, standard care practices, or access to care. These examples are each validated by 2+ physicians Each of these evaluations is the average of the relevant evaluations presented in Table 8 of the HealthBench work [8], for HealthBench Consensus and HealthBench Hard examples. Figure 7 We see large reductions in all three failure modes. For example, hallucinations on challenging conversations are reduced by 8x between OpenAI o3 and gpt-5-thinking. Errors

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rovements to prompt injection and instruction hierarchy robustness compared to their predecessors. GPT-6.1 Sol is also highly robust. The plot below shows defender success rate averaged per defender query on indirect prompt injection attacks (higher is better). Figure 3 Table 6: Defender success rate averaged per defender query on instruction hierarchy attacks (higher is better). 5. Health For a full description of these evaluations, please see the Health section in the GPT-6 Astra card. 5.1 Hea

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re built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the initial scores below. Error rates are not representative of average production traffic. Note these are new evaluations and may evolve over time. Table 1: Disallowed Content Evaluations - Production Benchmarks (higher is better) These are new evaluations that were not available when the August 15 model launched. We have run them retrospectively for these launches. 2.2 Jailbreaks We

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monitoring thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monitoring thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monitoring thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously launched models are from the latest versions of those models, and are subject to some variation. Values may vary slightly from values published at

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) We are continually improving our disallowed content evaluations and categories, and compare performance on thes

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