<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ContentLora: Frontier AI</title><description>New and updated Frontier AI pages on ContentLora.</description><link>https://contentlora.com/</link><language>en</language><atom:link href="https://contentlora.com/topics/frontier-ai/feed.xml" rel="self" type="application/rss+xml"/><item><title>ARC-AGI</title><link>https://contentlora.com/wiki/arc-agi/</link><guid isPermaLink="true">https://contentlora.com/wiki/arc-agi/</guid><description>ARC-AGI is a benchmark series of tasks easy for people and hard for AI. ARC-AGI-3 went from 0.51% to 62.7% for AI within six months.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>science</category></item><item><title>Claude Mythos</title><link>https://contentlora.com/wiki/claude-mythos/</link><guid isPermaLink="true">https://contentlora.com/wiki/claude-mythos/</guid><description>Claude Mythos is Anthropic&apos;s most capable model class, first released as a gated preview for cyber defence and later as Claude Fable.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category></item><item><title>DeepSeek-R1</title><link>https://contentlora.com/wiki/deepseek-r1/</link><guid isPermaLink="true">https://contentlora.com/wiki/deepseek-r1/</guid><description>DeepSeek-R1 reported that reinforcement learning alone can teach a language model to reason. Its findings, publication and successors.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>science</category></item><item><title>METR</title><link>https://contentlora.com/wiki/metr/</link><guid isPermaLink="true">https://contentlora.com/wiki/metr/</guid><description>METR is an AI evaluation group known for measuring how long a task AI agents can complete, and for trials of AI&apos;s effect on developers.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>science</category></item><item><title>Model Context Protocol (MCP)</title><link>https://contentlora.com/wiki/model-context-protocol/</link><guid isPermaLink="true">https://contentlora.com/wiki/model-context-protocol/</guid><description>The Model Context Protocol is an open standard for connecting AI apps and agents to tools and data, now run by the Linux Foundation.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>OpenAI o1</title><link>https://contentlora.com/wiki/openai-o1/</link><guid isPermaLink="true">https://contentlora.com/wiki/openai-o1/</guid><description>OpenAI o1 is a reasoning model series trained with reinforcement learning to think in a chain of thought before answering.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category></item><item><title>SWE-bench</title><link>https://contentlora.com/wiki/swe-bench/</link><guid isPermaLink="true">https://contentlora.com/wiki/swe-bench/</guid><description>SWE-bench tests whether AI can fix real GitHub issues. How it works, its Verified subset, and how scores rose from 2% to near 100%.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>Test-time compute</title><link>https://contentlora.com/wiki/test-time-compute/</link><guid isPermaLink="true">https://contentlora.com/wiki/test-time-compute/</guid><description>Test-time compute is the computing power an AI model spends while answering. Spending more of it is how reasoning models improve.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Wiki</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>Frontier AI tracker: reasoning models and agents</title><link>https://contentlora.com/events/frontier-ai-tracker/</link><guid isPermaLink="true">https://contentlora.com/events/frontier-ai-tracker/</guid><description>A dated timeline of frontier AI milestones in reasoning models and AI agents, from o1 and DeepSeek-R1 to GPT-6, Claude Mythos and Gemini 4.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Developing</category><category>frontier-ai</category><category>ai</category></item><item><title>How fast are AI agents really improving?</title><link>https://contentlora.com/analysis/ai-agent-progress-debate/</link><guid isPermaLink="true">https://contentlora.com/analysis/ai-agent-progress-debate/</guid><description>AI agents&apos; task horizons are doubling every few months on benchmarks, but real-world gains are harder to measure. The evidence, weighed.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Analysis</category><category>frontier-ai</category><category>ai</category></item><item><title>Do reasoning models really reason? The debate over their limits</title><link>https://contentlora.com/analysis/reasoning-models-limits-debate/</link><guid isPermaLink="true">https://contentlora.com/analysis/reasoning-models-limits-debate/</guid><description>Reasoning models win maths olympiads yet fail some simple tasks, and their written reasoning is not always faithful. The evidence, weighed.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Analysis</category><category>frontier-ai</category><category>ai</category><category>science</category></item><item><title>Frontier AI in 2026: a crash course</title><link>https://contentlora.com/explain/frontier-ai/</link><guid isPermaLink="true">https://contentlora.com/explain/frontier-ai/</guid><description>A crash course on frontier AI in 2026: how reasoning models and AI agents work, who builds them, and where the frontier stands now.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Explainer</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>How AI agents work</title><link>https://contentlora.com/explain/how-ai-agents-work/</link><guid isPermaLink="true">https://contentlora.com/explain/how-ai-agents-work/</guid><description>What an AI agent is and how it works: language models using tools in a loop, computer use, MCP connectors and long tasks.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Explainer</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>How frontier AI capabilities are measured</title><link>https://contentlora.com/explain/how-ai-capabilities-are-measured/</link><guid isPermaLink="true">https://contentlora.com/explain/how-ai-capabilities-are-measured/</guid><description>How researchers measure what frontier AI can do: benchmarks like SWE-bench, Humanity&apos;s Last Exam and ARC-AGI, and METR time horizons.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Explainer</category><category>frontier-ai</category><category>ai</category><category>science</category></item><item><title>How large language models work</title><link>https://contentlora.com/explain/how-large-language-models-work/</link><guid isPermaLink="true">https://contentlora.com/explain/how-large-language-models-work/</guid><description>A plain guide to large language models: the Transformer, scaling laws and human-feedback training, at beginner and expert level.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Explainer</category><category>frontier-ai</category><category>ai</category><category>computing</category></item><item><title>How reasoning models work</title><link>https://contentlora.com/explain/how-reasoning-models-work/</link><guid isPermaLink="true">https://contentlora.com/explain/how-reasoning-models-work/</guid><description>How AI reasoning models think step by step: chain of thought, reinforcement learning on checkable tasks and test-time compute.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>Explainer</category><category>frontier-ai</category><category>ai</category></item></channel></rss>