{"id":40320,"date":"2026-04-10T19:11:37","date_gmt":"2026-04-10T17:11:37","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/posting-a-small-artifact-from-the-control-plane-built-on-top-of-earlier-arxiv-patent-stage-1-dataset-work\/"},"modified":"2026-04-10T19:11:37","modified_gmt":"2026-04-10T17:11:37","slug":"posting-a-small-artifact-from-the-control-plane-built-on-top-of-earlier-arxiv-patent-stage-1-dataset-work","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/posting-a-small-artifact-from-the-control-plane-built-on-top-of-earlier-arxiv-patent-stage-1-dataset-work\/","title":{"rendered":"Posting A Small Artifact From The Control Plane Built On Top Of Earlier ArXiv\/patent Stage-1 Dataset Work."},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p><em>WIP<\/em> My attempt at pulling useful information out of a large dataset.<\/p>\n<p>High-level loop:<\/p>\n<p>artifact -&gt; frontier -&gt; bounded claim -&gt; probe family -&gt; evidence bundle -&gt; route -&gt; reasoner -&gt; confidence update -&gt; next-evidence request -&gt; validation -&gt; frontier advance<\/p>\n<p>What it is trying to do is take stage-1 corpus outputs and turn them into a controlled evidence loop: pick one bounded question, gather support or contradiction around it, decide what evidence to ask for next, and only move forward when the result holds up.<\/p>\n<p>This HF link is one visible slice of the larger system, not the whole thing. Here it shows repeated bridge and contrast structure plus one stable rare finding across follow-up validation. <\/p>\n<h1><\/h1>\n<p>partial artifact, more on the link below <\/p>\n<p>&#8220;cluster_ids&#8221;: [ 406 ], &#8220;member_count&#8221;: 1, &#8220;observation_count&#8221;: 3, &#8220;source_bundle_ids&#8221;: [ &#8220;tier1_bundle_mixed_region_0410c17c2515bf49&#8221; ], &#8220;source_result_ids&#8221;: [ &#8220;finding:406&#8221; ], &#8220;primary_terms&#8221;: [ &#8220;anisotropy&#8221;, &#8220;coherent&#8221;, &#8220;high-confidence&#8221;, &#8220;rare&#8221;, &#8220;centered&#8221;, &#8220;gmims-related&#8221;, &#8220;text&#8221;, &#8220;magnetic&#8221;, &#8220;medium&#8221;, &#8220;film&#8221; ], &#8220;supporting_results&#8221;: [ { &#8220;result_type&#8221;: &#8220;finding&#8221;, &#8220;cluster_ids&#8221;: [ 406 ], &#8220;bundle_id&#8221;: &#8220;tier1_bundle_mixed_region_0410c17c2515bf49&#8221;, &#8220;summary&#8221;: &#8220;Cluster 406 is a coherent, high-confidence rare cluster centered on GMIMS-related text about anisotropy, magnetic medium, and film\/samples, with 22 rows and a strong mean probability of 0.814.&#8221;, &#8220;confidence&#8221;: 0.76608, &#8220;support_cycles&#8221;: 3, &#8220;next_probe_name&#8221;: &#8220;neighbor_cluster_comparison&#8221;, &#8220;next_probe_cluster_ids&#8221;: [ 406 ], &#8220;next_probe_reason&#8221;: &#8220;The card lists centroid neighbors 285 and 282 with very high cosine similarity, making them the most direct follow-up for checking whether the GMIMS\/anisotropy pattern extends to adjacent clusters.&#8221; } ],<\/p>\n<p>link to hf with artifacts : <a href=\"https:\/\/huggingface.co\/datasets\/cjc0013\/reasoningovercorpusartifacts\/tree\/main\">https:\/\/huggingface.co\/datasets\/cjc0013\/reasoningovercorpusartifacts\/tree\/main<\/a><\/p>\n<p>link to previous dataset post : <a href=\"https:\/\/old.reddit.com\/r\/datasets\/comments\/1sej8ro\/fused_patent_arxiv_clustering_dataset_9m_raw_388m\/\">https:\/\/old.reddit.com\/r\/datasets\/comments\/1sej8ro\/fused_patent_arxiv_clustering_dataset_9m_raw_388m\/<\/a><\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Either_Pound1986\"> \/u\/Either_Pound1986 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1shro3t\/posting_a_small_artifact_from_the_control_plane\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1shro3t\/posting_a_small_artifact_from_the_control_plane\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-40320 jlk' href='javascript:void(0)' data-task='like' data-post_id='40320' data-nonce='65e0e39b87' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-40320 lc'>0<\/span><\/a><\/div><\/div> <div class='status-40320 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>WIP My attempt at pulling useful information out of a large dataset. High-level loop: artifact -&gt; frontier&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-40320","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=40320"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40320\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=40320"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=40320"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=40320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}