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Google Research proposes the Agile Deliberation framework
Google Research proposes the Agile Deliberation framework, which transforms the construction of subjective image classifiers into an iterative dialogue between humans and LLMs: users first break down subcategories and exclusions, then the VLM proactively selects semantically boundary-case images, and the feedback enters an automatic prompt optimization loop. Google Research validated this process in 18 real-human experiments of 1.5 hours each, stating that it reduces user fatigue and anxiety and that the resulting classifiers outperform standard baselines; the code and algorithms have been open-sourced on GitHub.
iers into an iterative dialogue between humans and LLMs: users first break down subcategories and exclusions, then the VLM proactively selects semantically boundary-case images, and the feedback enters an automatic prompt optimization loop. Google Research validated this process in 18 real-human experiments of 1.5 hours each, stating that it reduces user fatigue and anxiety and that the resulting classifiers outperform standard baselines; the code and algorithms have been open-sourced on GitHub.
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Goldman Sachs Restricts Non-Engineering Employees' Use of AI Coding Tools
Since October, Goldman Sachs has tightened access to AI coding tools such as Anthropic's Claude Code and Microsoft's GitHub, mainly restricting them to developers. Previously, some traders had used these tools to monitor client trading flows and model market changes and positions. According to Bloomberg, citing anonymous sources, the adjustment has upset some traders; a Goldman Sachs spokesperson said the reason is more precise monitoring of productivity and risk management, and stressed that the tools should be given to those best suited to long-term use. The restriction does not apply to Strats employees in roles at the intersection of trading, quantitative research and software development, and Goldman Sachs' in-house assistant, which integrates models from OpenAI, Anthropic and Alphabet, remains open to all employees.
Since October, Goldman Sachs has tightened access to AI coding tools such as Anthropic's Claude Code and Microsoft's GitHub, mainly restricting them to developers. Previously, some traders had used these tools to monitor client trading flows and model market changes and positions. According to Bloomberg, citing anonymous sources, the adjustment has upset some traders; a Goldman Sachs spokesperson said the reason is more precise monitoring of productivity and risk management, and stressed that th
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Goldman Sachs restricts non-engineering employees from using AI coding tools
Since October, Goldman Sachs has tightened access to AI coding tools such as Anthropic's Claude Code and Microsoft's GitHub, mainly restricting them to developers. Previously, some traders had used these tools to monitor client trading flows and model market changes and positions. According to Bloomberg, citing anonymous sources, the adjustment has upset some traders; a Goldman Sachs spokesperson said the reason is more precise monitoring of productivity and risk management, and stressed that the tools should be given to those best suited to long-term use. The restriction does not apply to Strats employees in roles at the intersection of trading, quantitative research and software development, and Goldman Sachs' in-house assistant, which integrates models from OpenAI, Anthropic and Alphabet, remains open to all employees.
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View story:Goldman Sachs Restricts Non-Engineering Employees' Use of AI Coding Tools
inclusionAI open-sources the dInfer diffusion language model inference framework
On October 10, 2025, Ant inclusionAI open-sourced the first version of dInfer, an inference framework for diffusion language models (dLLM). According to the introduction of its new GitHub repository, the framework splits inference into four modules: the model, the diffusion iteration manager, the decoder, and the KV-cache manager, and it supports batched inference and is compatible with the LLaDA, LLaDA-MoE, and LLaDA2 series. In terms of performance, on a single 8×H800 node, LLaDA-MoE exceeds 1,100 TPS on HumanEval at batch size 1, averages 800+ TPS across six benchmarks, and is 10 times faster than Fast-dLLM while maintaining accuracy. There are currently no public facts regarding further development progress.
n October 10, 2025, Ant inclusionAI open-sourced the first version of dInfer, an inference framework for diffusion language models (dLLM). According to the introduction of its new GitHub repository, the framework splits inference into four modules: the model, the diffusion iteration manager, the decoder, and the KV-cache manager, and it supports batched inference and is compatible with the LLaDA, LLaDA-MoE, and LLaDA2 series. In terms of performance, on a single 8×H800 node, LLaDA-MoE exceeds 1,
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NVIDIA Expands Cosmos Platform Training Tools and Data Blueprints
On October 22, 2026, NVIDIA announced the expansion of the Cosmos platform, adding training tools, simulation checkpoints, and Cosmos Data Factory blueprints to help developers build world models. According to information released by the NVIDIA Newsroom, Cosmos Data Factory provides five cloud-native blueprints, now available through GitHub, Microsoft Azure, Nebius, and CoreWeave; Porsche is using it to build a shared autonomous driving data flywheel, which is expected to reduce repetitive real-vehicle testing by up to 90% and shorten development cycles by up to 85%. Cosmos Framework's training, optimization, and deployment tools are being extended in the form of agent skills to open-source models such as GR00T, Alpamayo, QWEN, and Pi0.5, and the Cosmos 3 NIM microservice delivers up to 2x faster inference under validated configurations. There are currently no further developments.
s to help developers build world models. According to information released by the NVIDIA Newsroom, Cosmos Data Factory provides five cloud-native blueprints, now available through GitHub, Microsoft Azure, Nebius, and CoreWeave; Porsche is using it to build a shared autonomous driving data flywheel, which is expected to reduce repetitive real-vehicle testing by up to 90% and shorten development cycles by up to 85%. Cosmos Framework's training, optimization, and deployment tools are being extended
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