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    <title>PRYSM — Research Notes</title>
    <link>https://prysm1.com/</link>
    <description>Research-grade notes on AI economics, intensity calibration, methodology and reliability — written by the PRYSM team. Open benchmarks, cryptographic receipts, fail-safe by design.</description>
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    <copyright>Copyright 2026 PRYSM</copyright>
    <managingEditor>research@prysm1.com (PRYSM Research)</managingEditor>
    <webMaster>research@prysm1.com (PRYSM Research)</webMaster>
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      <title>PRYSM — Research Notes</title>
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      <title>How PRYSM fails safe — four reliability safeguards, test-verified</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Wed, 04 Jun 2026 18:00:00 GMT</pubDate>
      <category>Reliability</category>
      <author>research@prysm1.com (PRYSM Engineering)</author>
      <description><![CDATA[A frontier model wins a benchmark once. A reliable model wins trust every day. This week we shipped four fail-safe safeguards — null-safety on reasoning models, Halo self-recovery, an engine-wide circuit breaker, and request-input hardening — each one a small, verifiable promise rather than a marketing claim.]]></description>
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      <title>PRYSM-1 on AIME 2025: methodology, results, and reproducibility</title>
      <link>https://prysm1.com/</link>
      <guid isPermaLink="false">prysm1.com/blog/prysm-1-on-aime-2025</guid>
      <pubDate>Wed, 04 Jun 2026 00:00:00 GMT</pubDate>
      <category>Research</category>
      <author>research@prysm1.com (PRYSM Research)</author>
      <description><![CDATA[We measured PRYSM-1's four intensity tiers against the leading AI models on the 30-question 2025 AIME. Single trial, live providers, full cost and latency — methodology and reproducibility scripts in the open.]]></description>
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      <title>Why frontier accuracy alone doesn't define the best AI</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Thu, 28 May 2026 00:00:00 GMT</pubDate>
      <category>Analysis</category>
      <author>research@prysm1.com (PRYSM Research)</author>
      <description><![CDATA[A single number on a single benchmark is a shorthand, not a product. The right AI depends on what you're optimizing for — accuracy, cost, latency, or the cost of being wrong.]]></description>
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      <title>PrysmProof — cryptographic verification of AI execution</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Thu, 21 May 2026 00:00:00 GMT</pubDate>
      <category>Product</category>
      <author>research@prysm1.com (PRYSM Engineering)</author>
      <description><![CDATA[Every PRYSM-1 response carries a SHA-256 cryptographic receipt: what intensity was used, what it cost, when it ran, what it answered. Tamper-evident, audit-friendly, defensible in front of a finance team.]]></description>
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      <title>Why we report uncertainty: single-trial vs N=k</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Thu, 14 May 2026 00:00:00 GMT</pubDate>
      <category>Methodology</category>
      <author>research@prysm1.com (PRYSM Research)</author>
      <description><![CDATA[Most published benchmark numbers come from a single trial. We do the same when we say so — and we say so. Here's why N=1 results deserve uncertainty bars before they deserve headlines.]]></description>
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      <title>Benchmark contamination, and how we handle it</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Thu, 07 May 2026 00:00:00 GMT</pubDate>
      <category>Methodology</category>
      <author>research@prysm1.com (PRYSM Research)</author>
      <description><![CDATA[If a model has seen the test set during training, the benchmark measures memorization, not capability. Contamination is the silent epidemic of LLM evaluation. We document our exposure honestly and prefer benchmarks that minimize it.]]></description>
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      <title>Intensity selection: PRYSM-1's tier framework</title>
      <link>https://prysm1.com/</link>
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      <pubDate>Thu, 30 Apr 2026 00:00:00 GMT</pubDate>
      <category>Product</category>
      <author>research@prysm1.com (PRYSM Team)</author>
      <description><![CDATA[Foton-1 for chat. Halo-1 for everyday work. Laser-1 for hard problems. Nova-1 (preview) for the answer that has to be right. Same API, four intensities, one framework for picking the right one.]]></description>
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