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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

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No description, website, or topics provided.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

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0 stars

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0 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

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0 stars

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0 watching

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Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

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0 stars

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0 watching

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Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Epoch AI Labs

Epoch AI Labs

Research for the next regime.

We study the transitions that reshape intelligent systems: how they learn, scale, reason, and alter the world around them.

Website [Under construction] · Research · Contact


What we study

Epoch AI Labs is an independent research lab working at the thresholds where scale changes behavior, existing evaluations break down, and new scientific language becomes necessary.

Our work currently spans:

  • Emergent capabilities - identifying when quantitative improvements produce qualitative shifts in model behavior.
  • Evaluation science - building measurements that remain meaningful as systems become more general and adaptive.
  • Machine reasoning - studying the structures, limits, and failure modes behind increasingly capable reasoning systems.
  • Model topology - locating and testing the internal structures that shape behavior, with an emphasis on precise and auditable intervention.

How we work

Build the instrument. Run the experiment. Publish what survives.

We replace broad claims with falsifiable hypotheses and inspectable evidence. Our research artifacts are designed to expose methods, controls, uncertainty, limitations, and failed approaches alongside results.

PrincipleIn practice
Measure before declaringClaims begin with experiments, baselines, and controlled comparisons.
Make uncertainty visibleConfidence is an output of the research, not a property of the presentation.
Work in the openReproducible methods and honest limitations belong in the scientific record.

Current direction

We are developing methods for understanding, modifying, combining, and compressing neural networks while preserving unrelated capabilities. Our near-term work centers on behavioral circuit audits: finding small, behavior-associated model regions, testing whether they are causal, and measuring the collateral effects of changing them.

We use open-weight models as experimental substrates. Released systems must show a measurable change, controlled comparisons with standard baselines, and reproducible evidence explaining how the result was produced.


Independent research for the next regime.

About

No description, website, or topics provided.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors