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Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench

Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench

Posted by By MPRAUTO MPRAUTO July 8, 2026Posted inAI2 Comments
A deep dive into 2026 AI agent benchmarks: SWE-bench Verified, GAIA, and tau-bench — what they measure, how they leak, and how to read agent leaderboards honestly.
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Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

Posted by By MPRAUTO MPRAUTO July 2, 2026Posted inAINo Comments
Long-context LLM benchmarks in 2026: why 1M-token windows do not mean 1M-token reasoning, RULER, NIAH, effective context length, and how to test long-context models properly.
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Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

Posted by By MPRAUTO MPRAUTO July 2, 2026Posted inAINo Comments
Long-context LLM benchmarks in 2026: why 1M-token windows do not mean 1M-token reasoning, RULER, NIAH, effective context length, and how to test long-context models properly.
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AI Agent Trajectory Evaluation: 2026 Patterns

AI Agent Trajectory Evaluation: 2026 Patterns

Posted by By MPRAUTO MPRAUTO June 20, 2026Posted inTech1 Comment
How to evaluate AI agents in 2026: trajectory vs outcome metrics, step-level scoring, LLM-as-judge pitfalls, and a reusable agent eval harness pattern.
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inmation in 2026: Architecture, Pros, Cons, Alternatives

inmation in 2026: Architecture, Pros, Cons, Alternatives

Posted by By mprcba June 20, 2026Posted iniotNo Comments
inmation software in 2026: how its industrial DataOps architecture works, real pros and cons, where it fits vs PI System and UNS, and an evaluation checklist.
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Text-to-SQL LLM Benchmark: Accuracy and Latency (2026)

Text-to-SQL LLM Benchmark: Accuracy and Latency (2026)

Posted by By MPRAUTO MPRAUTO June 17, 2026Posted inAINo Comments
A 2026 text-to-SQL benchmark methodology: execution accuracy, schema linking, latency, and cost across model tiers - plus where generated SQL goes wrong.
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Small vs Large LLMs for Agentic Tasks: A 2026 Benchmark

Small vs Large LLMs for Agentic Tasks: A 2026 Benchmark

Posted by By MPRAUTO MPRAUTO June 9, 2026Posted inAI1 Comment
A reproducible 2026 benchmark methodology comparing small and large LLMs on agentic tasks: cost, latency, tool-call accuracy, and when small wins.
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