What are ‘Computer-Use Agents’? From Web to OS—A Technical ExplainerMarkTechPost TL;DR: Computer-use agents are VLM-driven UI agents that act like users on unmodified software. Baselines on OSWorld started at 12.24% (human 72.36%); Claude Sonnet 4.5 now reports 61.4%. Gemini 2.5 Computer Use leads several web benchmarks (Online-Mind2Web 69.0%, WebVoyager 88.9%) but is not yet OS-optimized. Next steps center on OS-level robustness, sub-second action loops, and
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TL;DR: Computer-use agents are VLM-driven UI agents that act like users on unmodified software. Baselines on OSWorld started at 12.24% (human 72.36%); Claude Sonnet 4.5 now reports 61.4%. Gemini 2.5 Computer Use leads several web benchmarks (Online-Mind2Web 69.0%, WebVoyager 88.9%) but is not yet OS-optimized. Next steps center on OS-level robustness, sub-second action loops, and
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Self-Improving LLM Agents at Test-Timecs.AI updates on arXiv.org arXiv:2510.07841v1 Announce Type: cross
Abstract: One paradigm of language model (LM) fine-tuning relies on creating large training datasets, under the assumption that high quantity and diversity will enable models to generalize to novel tasks after post-training. In practice, gathering large sets of data is inefficient, and training on them is prohibitively expensive; worse, there is no guarantee that the resulting model will handle complex scenarios or generalize better. Moreover, existing techniques rarely assess whether a training sample provides novel information or is redundant with the knowledge already acquired by the model, resulting in unnecessary costs. In this work, we explore a new test-time self-improvement method to create more effective and generalizable agentic LMs on-the-fly. The proposed algorithm can be summarized in three steps: (i) first it identifies the samples that model struggles with (self-awareness), (ii) then generates similar examples from detected uncertain samples (self-data augmentation), and (iii) uses these newly generated samples at test-time fine-tuning (self-improvement). We study two variants of this approach: Test-Time Self-Improvement (TT-SI), where the same model generates additional training examples from its own uncertain cases and then learns from them, and contrast this approach with Test-Time Distillation (TT-D), where a stronger model generates similar examples for uncertain cases, enabling student to adapt using distilled supervision. Empirical evaluations across different agent benchmarks demonstrate that TT-SI improves the performance with +5.48% absolute accuracy gain on average across all benchmarks and surpasses other standard learning methods, yet using 68x less training samples. Our findings highlight the promise of TT-SI, demonstrating the potential of self-improvement algorithms at test-time as a new paradigm for building more capable agents toward self-evolution.
arXiv:2510.07841v1 Announce Type: cross
Abstract: One paradigm of language model (LM) fine-tuning relies on creating large training datasets, under the assumption that high quantity and diversity will enable models to generalize to novel tasks after post-training. In practice, gathering large sets of data is inefficient, and training on them is prohibitively expensive; worse, there is no guarantee that the resulting model will handle complex scenarios or generalize better. Moreover, existing techniques rarely assess whether a training sample provides novel information or is redundant with the knowledge already acquired by the model, resulting in unnecessary costs. In this work, we explore a new test-time self-improvement method to create more effective and generalizable agentic LMs on-the-fly. The proposed algorithm can be summarized in three steps: (i) first it identifies the samples that model struggles with (self-awareness), (ii) then generates similar examples from detected uncertain samples (self-data augmentation), and (iii) uses these newly generated samples at test-time fine-tuning (self-improvement). We study two variants of this approach: Test-Time Self-Improvement (TT-SI), where the same model generates additional training examples from its own uncertain cases and then learns from them, and contrast this approach with Test-Time Distillation (TT-D), where a stronger model generates similar examples for uncertain cases, enabling student to adapt using distilled supervision. Empirical evaluations across different agent benchmarks demonstrate that TT-SI improves the performance with +5.48% absolute accuracy gain on average across all benchmarks and surpasses other standard learning methods, yet using 68x less training samples. Our findings highlight the promise of TT-SI, demonstrating the potential of self-improvement algorithms at test-time as a new paradigm for building more capable agents toward self-evolution. Read More
Google Open-Sources an MCP Server for the Google Ads API, Bringing LLM-Native Access to Ads DataMarkTechPost Google has open-sourced a Model Context Protocol (MCP) server that exposes read-only access to the Google Ads API for agentic and LLM applications. The repository googleads/google-ads-mcp implements an MCP server in Python that surfaces two tools today: search (GAQL queries over Ads accounts) and list_accessible_customers (enumeration of customer resources). It includes setup via pipx, Google
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Google has open-sourced a Model Context Protocol (MCP) server that exposes read-only access to the Google Ads API for agentic and LLM applications. The repository googleads/google-ads-mcp implements an MCP server in Python that surfaces two tools today: search (GAQL queries over Ads accounts) and list_accessible_customers (enumeration of customer resources). It includes setup via pipx, Google
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Agentic Context Engineering (ACE): Self-Improving LLMs via Evolving Contexts, Not Fine-TuningMarkTechPost TL;DR: A team of researchers from Stanford University, SambaNova Systems and UC Berkeley introduce ACE framework that improves LLM performance by editing and growing the input context instead of updating model weights. Context is treated as a living “playbook” maintained by three roles—Generator, Reflector, Curator—with small delta items merged incrementally to avoid brevity bias and
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TL;DR: A team of researchers from Stanford University, SambaNova Systems and UC Berkeley introduce ACE framework that improves LLM performance by editing and growing the input context instead of updating model weights. Context is treated as a living “playbook” maintained by three roles—Generator, Reflector, Curator—with small delta items merged incrementally to avoid brevity bias and
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Gemini Enterprise: Google aims to put an AI agent on every deskAI News Google Cloud has launched Gemini Enterprise, a new platform it calls “the new front door for AI in the workplace”. Announced during a virtual press conference, the platform brings together Google’s Gemini models, first and third-party agents, and the core technology of what was formerly known as Google Agentspace to create a singular agentic platform.
The post Gemini Enterprise: Google aims to put an AI agent on every desk appeared first on AI News.
Google Cloud has launched Gemini Enterprise, a new platform it calls “the new front door for AI in the workplace”. Announced during a virtual press conference, the platform brings together Google’s Gemini models, first and third-party agents, and the core technology of what was formerly known as Google Agentspace to create a singular agentic platform.
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AI value remains elusive despite soaring investmentAI News A new report from Red Hat finds that 89 percent of businesses are yet to see any customer value from their AI endeavours. However, organisations anticipate a 32 percent increase in AI investment by 2026. The survey finds that AI and security are the joint top IT priorities for UK organisations over the next 18
The post AI value remains elusive despite soaring investment appeared first on AI News.
A new report from Red Hat finds that 89 percent of businesses are yet to see any customer value from their AI endeavours. However, organisations anticipate a 32 percent increase in AI investment by 2026. The survey finds that AI and security are the joint top IT priorities for UK organisations over the next 18
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10 Command-Line Tools Every Data Scientist Should KnowKDnuggets Get control of your data workflows with these essential CLI tools.
Get control of your data workflows with these essential CLI tools. Read More
Model Context Protocol (MCP) vs Function Calling vs OpenAPI Tools — When to Use Each?MarkTechPost Comparison Table Concern MCP Function Calling OpenAPI Tools Interface contract Protocol data model (tools/resources/prompts) Per-function JSON Schema OAS 3.1 document Discovery Dynamic via tools/list Static list provided to the model From OAS; catalogable Invocation tools/call over JSON-RPC session Model selects function; app executes HTTP request per OAS op Orchestration Host routes across many servers/tools App-local
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Comparison Table Concern MCP Function Calling OpenAPI Tools Interface contract Protocol data model (tools/resources/prompts) Per-function JSON Schema OAS 3.1 document Discovery Dynamic via tools/list Static list provided to the model From OAS; catalogable Invocation tools/call over JSON-RPC session Model selects function; app executes HTTP request per OAS op Orchestration Host routes across many servers/tools App-local
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Data Visualization Explained (Part 3): The Role of ColorTowards Data Science A simple and powerful guide to using color for more impactful data stories.
The post Data Visualization Explained (Part 3): The Role of Color appeared first on Towards Data Science.
A simple and powerful guide to using color for more impactful data stories.
The post Data Visualization Explained (Part 3): The Role of Color appeared first on Towards Data Science. Read More
Introducing the Gemini 2.5 Computer Use modelGoogle DeepMind Blog Available in preview via the API, our Computer Use model is a specialized model built on Gemini 2.5 Pro’s capabilities to power agents that can interact with user interfaces.
Available in preview via the API, our Computer Use model is a specialized model built on Gemini 2.5 Pro’s capabilities to power agents that can interact with user interfaces. Read More