ui updates

app.py

@@ -124,10 +124,11 @@ button, a, label, input, select, textarea,
 .gradio-container * {
     -webkit-tap-highlight-color: transparent !important;
 }
-html, body, .gradio-container, .main {
-    background: var(--pruna-bg-primary) !important;
-    color: var(--pruna-text-primary) !important;
-    font-family: var(--pruna-font) !important;
+
+.gradio-container {
+    --input-radius: 10px;
+    --container-radius: 10px;
+    --block-radius: 10px;
 }
 
 /* Subtle depth — not a marketing-site hero glow */
@@ -160,10 +161,9 @@ body, .gradio-container {
 .main-tabs,
 .main-tabs .tab-wrapper,
 .main-tabs .tabitem,
-.benchmark-catalogue,
-.benchmark-detail,
-.benchmark-panel,
 .leaderboard-controls,
+.workspace-shell,
+.workspace-filters,
 .view-filters {
     max-width: 100% !important;
 }
@@ -189,17 +189,26 @@ body, .gradio-container {
     position: relative;
     display: flex;
     flex-direction: column;
-    align-items: center;
+    align-items: stretch;
     width: 100%;
-    margin: 8px 0 16px;
-    padding: 8px 48px 0;
+    margin: 8px 0 0;
+    padding: 4px 0 10px;
     text-align: center;
 }
+.app-header-bar {
+    display: grid;
+    grid-template-columns: minmax(36px, 1fr) auto minmax(36px, 1fr);
+    align-items: center;
+    width: 100%;
+    column-gap: 8px;
+}
 .app-header .theme-toggle,
 button.theme-toggle {
-    position: absolute;
-    top: 8px;
-    right: 0;
+    position: static !important;
+    top: auto;
+    right: auto;
+    grid-column: 3;
+    justify-self: end;
     display: inline-flex !important;
     align-items: center !important;
     justify-content: center !important;
@@ -262,9 +271,11 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     align-items: center;
     justify-content: center;
     gap: 12px;
+    grid-column: 2;
+    justify-self: center;
     width: auto !important;
     max-width: 100%;
-    margin: 0 auto;
+    margin: 0;
 }
 .app-header-logo {
     display: block !important;
@@ -275,16 +286,43 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     flex: 0 0 auto;
 }
 
-/* —— Page tabs: centered underline nav (Gradio 5 uses tab-wrapper/container) —— */
+/* —— Page tabs: centered underline nav —— */
+.workspace-shell,
+.workspace-shell.block,
+.workspace-shell.column,
+.workspace-shell.gap {
+    display: flex !important;
+    flex-direction: column !important;
+    gap: 0 !important;
+    padding: 0 !important;
+    margin: 0 !important;
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+}
+.workspace-shell > .tabs,
+.workspace-shell > .main-tabs,
+.workspace-shell > .block:not(.workspace-filters),
+.workspace-shell .tabs.main-tabs {
+    display: contents !important;
+}
+.workspace-filters {
+    order: 2 !important;
+}
+.main-tabs .tabitem {
+    order: 3 !important;
+}
 .tabs {
     gap: 0 !important;
 }
 .main-tabs > .tab-wrapper {
+    order: 1 !important;
     height: auto !important;
     min-height: 0 !important;
     padding: 0 !important;
-    margin: 0 0 22px !important;
+    margin: 0 0 16px !important;
     justify-content: center !important;
+    width: 100% !important;
 }
 .main-tabs .tab-container {
     height: auto !important;
@@ -329,6 +367,7 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
 .main-tabs .tabitem {
     padding: 8px 0 0 !important;
     border-radius: 0 !important;
+    overflow: visible !important;
 }
 
 /* —— About: two columns on desktop, stacked on phone —— */
@@ -384,17 +423,14 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     .gradio-container .row,
     .gradio-container .column,
     .gradio-container .wrap,
-    .benchmark-catalogue,
-    .benchmark-detail,
-    .benchmark-panel,
     .leaderboard-controls,
     .view-filters {
         min-width: 0 !important;
         max-width: 100% !important;
     }
     .app-header {
-        padding: 4px 40px 0 8px;
-        margin: 4px 0 12px;
+        padding: 4px 0 8px;
+        margin: 4px 0 0;
     }
     .app-header-logo {
         width: 36px !important;
@@ -409,7 +445,7 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
         padding: 0 8px !important;
     }
     .main-tabs > .tab-wrapper {
-        margin: 0 0 14px !important;
+        margin: 4px 0 14px !important;
     }
     .main-tabs .tab-container {
         flex-wrap: wrap !important;
@@ -445,30 +481,6 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     .community-footer-links {
         gap: 10px 14px;
     }
-    .home-snapshots-grid {
-        grid-template-columns: 1fr;
-        gap: 22px;
-    }
-    .home-model {
-        overflow-wrap: anywhere;
-    }
-    .benchmark-catalogue-row,
-    .benchmark-catalogue-row.row,
-    .benchmark-catalogue-row .form {
-        flex-direction: column !important;
-        flex-wrap: nowrap !important;
-        align-items: stretch !important;
-        height: auto !important;
-        flex-grow: 0 !important;
-    }
-    .benchmark-catalogue-row .benchmark-card-col {
-        width: 100% !important;
-        flex: 0 0 auto !important;
-        align-self: stretch !important;
-        min-width: 0 !important;
-        min-height: 0 !important;
-        height: auto !important;
-    }
     .leaderboard-controls,
     .leaderboard-controls.row,
     .leaderboard-controls .form,
@@ -573,15 +585,28 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     }
 }
 
-/* —— Benchmark view menu (Leaderboards / Pareto plots / Compare) —— */
+/* —— Workspace filters (dataset / metric / models) —— */
+.workspace-filters,
+.workspace-filters.block,
+.workspace-filters.column,
+.workspace-filters.gap {
+    margin: 0 0 12px;
+    padding: 0 !important;
+    gap: 12px !important;
+    overflow: visible !important;
+}
 .view-filters {
     display: flex !important;
     flex-wrap: wrap !important;
     align-items: end !important;
-    gap: 10px !important;
-    margin: 0 0 8px;
+    gap: 12px !important;
+    margin: 0;
+    overflow: visible !important;
 }
-.view-filters > div {
+.view-filters > div,
+.view-filters > .block,
+.view-filters > .form {
+    flex: 1 1 0 !important;
     min-width: 0 !important;
 }
 .view-filters > .block,
@@ -591,286 +616,156 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     background: transparent !important;
     box-shadow: none !important;
     padding: 0 !important;
+    overflow: visible !important;
     --block-border-width: 0 !important;
 }
 .view-filters label {
+    display: block !important;
+    margin: 0 !important;
+    padding: 0 0 8px !important;
     color: var(--pruna-text-muted) !important;
     font-size: 0.8rem !important;
     font-weight: 500 !important;
+    line-height: 1.2 !important;
 }
-.view-filters .wrap,
-.view-filters .wrap-inner,
-.view-filters .secondary-wrap {
-    min-height: 40px !important;
-    border: 1px solid var(--pruna-input-border) !important;
-    border-radius: 10px !important;
-    background: var(--pruna-input-bg) !important;
-    box-shadow: none !important;
-}
-.view-title,
-.view-title.block,
-.view-title .padded {
-    border: none !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    padding: 0 !important;
-    margin: 0 0 8px !important;
-}
-.view-title h1,
-.view-title .prose h1,
-.gradio-container .view-title h1 {
-    margin: 0.35rem 0 0.4rem !important;
-    font-size: 1.45rem !important;
-}
-.workspace-back-btn,
-.workspace-back-btn.block,
-.workspace-back-btn .padded,
-.benchmark-back-btn,
-.benchmark-back-btn.block,
-.benchmark-back-btn .padded {
-    width: fit-content !important;
+.view-filters .wrap {
     min-width: 0 !important;
-    margin: 0 0 4px !important;
-    padding: 0 !important;
     border: none !important;
     background: transparent !important;
     box-shadow: none !important;
+    overflow: visible !important;
 }
-.workspace-back-btn button,
-.workspace-back-btn button.secondary,
-.workspace-back-btn button.lg,
-.workspace-back-btn button.sm,
-.benchmark-back-btn button,
-.benchmark-back-btn button.secondary,
-.benchmark-back-btn button.lg,
-.benchmark-back-btn button.sm {
-    display: inline-flex !important;
+.view-filters .wrap-inner {
+    display: flex !important;
+    flex-wrap: nowrap !important;
     align-items: center !important;
-    width: auto !important;
-    min-width: 0 !important;
-    min-height: 0 !important;
-    height: auto !important;
-    margin: 0 !important;
-    padding: 0 !important;
-    border: none !important;
-    border-radius: 0 !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    color: var(--pruna-text-muted) !important;
-    font-family: var(--pruna-font) !important;
-    font-size: 0.9rem !important;
-    font-weight: 500 !important;
-    letter-spacing: -0.01em !important;
-    line-height: 1.4 !important;
-}
-.workspace-back-btn button:hover,
-.benchmark-back-btn button:hover {
-    color: var(--pruna-link) !important;
-    background: transparent !important;
-    border: none !important;
-    box-shadow: none !important;
-}
-.benchmark-view-menu,
-.benchmark-view-menu.block,
-.benchmark-view-menu .form {
-    border: none !important;
-    background: transparent !important;
+    gap: 6px !important;
+    min-height: 40px !important;
+    height: 40px !important;
+    padding: 0 10px !important;
+    border: 1px solid var(--pruna-input-border) !important;
+    border-radius: 10px !important;
+    background: var(--pruna-input-bg) !important;
     box-shadow: none !important;
-    padding: 0 !important;
-    margin: 4px 0 20px !important;
-    max-width: 100% !important;
-    min-width: 0 !important;
-}
-.benchmark-view-menu .wrap {
-    display: grid !important;
-    grid-template-columns: repeat(3, minmax(0, 1fr));
-    gap: 4px !important;
-    width: 100%;
-    max-width: 100%;
-    min-width: 0;
-    padding: 4px !important;
-    border: 1px solid var(--pruna-menu-border) !important;
-    border-radius: 12px !important;
-    background: var(--pruna-menu-bg) !important;
+    overflow: hidden !important;
 }
-.benchmark-view-menu label {
+.view-filters .secondary-wrap {
     display: flex !important;
+    flex-wrap: nowrap !important;
     align-items: center !important;
-    justify-content: center !important;
+    flex: 1 1 auto !important;
+    min-width: 0 !important;
+    height: 40px !important;
     margin: 0 !important;
-    padding: 10px 12px !important;
+    padding: 0 !important;
     border: none !important;
-    border-radius: 8px !important;
     background: transparent !important;
     box-shadow: none !important;
-    color: var(--pruna-text-muted) !important;
-    font-family: var(--pruna-font) !important;
-    font-size: 0.9rem !important;
-    font-weight: 600 !important;
-    letter-spacing: -0.015em !important;
-    cursor: pointer;
+    overflow: visible !important;
 }
-.benchmark-view-menu label:hover {
-    color: var(--pruna-menu-hover-color) !important;
-    background: var(--pruna-menu-hover-bg) !important;
+.view-filters .filter-chips .wrap-inner {
+    overflow-x: auto !important;
+    overflow-y: hidden !important;
+    overscroll-behavior-x: contain;
+    scrollbar-width: thin;
+    scrollbar-color: var(--pruna-border) transparent;
 }
-.benchmark-view-menu label.selected {
-    color: var(--pruna-menu-selected-color) !important;
-    background: var(--pruna-menu-selected-bg) !important;
+.view-filters .filter-chips .secondary-wrap {
+    min-width: 2.5rem !important;
 }
-.benchmark-view-menu input[type="radio"] {
-    position: absolute !important;
-    opacity: 0 !important;
-    width: 0 !important;
-    height: 0 !important;
-    margin: 0 !important;
-    pointer-events: none !important;
+.view-filters .filter-chips .wrap-inner::-webkit-scrollbar {
+    height: 6px;
 }
-.benchmark-view-menu label span {
-    margin: 0 !important;
-    color: inherit !important;
+.view-filters .filter-chips .wrap-inner::-webkit-scrollbar-thumb {
+    background: var(--pruna-border);
+    border-radius: 99px;
 }
-@media (max-width: 700px) {
-    .benchmark-view-menu .wrap {
-        grid-template-columns: repeat(3, minmax(0, 1fr));
-    }
-    .benchmark-view-menu label {
-        padding: 8px 4px !important;
-        font-size: 0.75rem !important;
-        text-align: center;
-    }
+.view-filters .filter-chips .token {
+    flex: 0 0 auto !important;
+    white-space: nowrap !important;
+    word-break: keep-all !important;
 }
-
-/* Surfaces */
-.benchmark-catalogue,
-.benchmark-catalogue .row,
-.benchmark-catalogue .column,
-.benchmark-catalogue .form,
-.benchmark-detail,
-.benchmark-detail .row,
-.benchmark-detail .column,
-.benchmark-detail .form,
-.benchmark-panel,
-.benchmark-panel .row,
-.benchmark-panel .column,
-.benchmark-panel .form {
-    min-width: 0 !important;
-    max-width: 100% !important;
+.view-filters .filter-chips .token span {
+    white-space: nowrap !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    max-width: 14rem;
 }
-.benchmark-catalogue.column,
-.benchmark-catalogue.block,
-.benchmark-catalogue .stretch,
-.benchmark-panel.column,
-.benchmark-panel.block,
-.benchmark-panel .stretch {
-    flex-grow: 0 !important;
-    height: auto !important;
-    min-height: 0 !important;
-    justify-content: flex-start !important;
+.view-filters .token:empty {
+    display: none !important;
 }
-.benchmark-catalogue-row,
-.benchmark-catalogue-row.row,
-.benchmark-catalogue-row .form {
-    display: flex !important;
-    flex-direction: row !important;
-    flex-wrap: wrap !important;
+.view-filters input {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    min-height: 28px !important;
+    height: 28px !important;
     width: 100% !important;
-    gap: 16px !important;
-    align-items: stretch !important;
-    align-content: flex-start !important;
-    flex-grow: 0 !important;
-    height: auto !important;
-}
-.benchmark-catalogue-row .benchmark-card-col {
-    flex: 1 1 280px !important;
-    align-self: stretch !important;
-    min-width: 280px !important;
-    max-width: 100% !important;
-    height: auto !important;
-}
-.benchmark-card-col,
-.benchmark-card-col.block,
-.benchmark-card-col.column,
-.benchmark-card-col.stretch {
-    display: flex !important;
-    flex-direction: column !important;
-    justify-content: flex-start !important;
-    align-items: stretch !important;
-    gap: 12px !important;
-    height: auto !important;
-    min-height: 0 !important;
-    padding: 16px !important;
-    border: 1px solid var(--pruna-border) !important;
-    border-radius: 16px !important;
-    background: var(--pruna-card-bg) !important;
-    box-shadow: var(--pruna-card-shadow) !important;
+    min-width: 0 !important;
+    padding: 0 4px !important;
+    line-height: 28px !important;
 }
-.benchmark-card-col .form {
-    display: flex !important;
-    flex-direction: column !important;
-    justify-content: flex-start !important;
-    align-items: stretch !important;
-    gap: 12px !important;
-    width: 100% !important;
-    height: auto !important;
-    min-height: 0 !important;
+.view-filters .dropdown-arrow,
+.view-filters .icon-wrap {
     flex: 0 0 auto !important;
+    align-self: center !important;
+    margin-left: auto !important;
 }
-.benchmark-card-col:hover {
-    border-color: color-mix(in srgb, var(--pruna-accent) 50%, transparent) !important;
-}
-.benchmark-card-body,
-.benchmark-card-col > .block,
-.benchmark-card-col .html-container,
-.benchmark-card-col .prose,
-.benchmark-card-col .padded,
-.benchmark-card-col .block {
-    flex: 0 0 auto !important;
-    height: auto !important;
-    min-height: 0 !important;
-    margin: 0 !important;
-    padding: 0 !important;
+.view-title,
+.view-title.block,
+.view-title .padded {
     border: none !important;
     background: transparent !important;
     box-shadow: none !important;
+    padding: 0 !important;
+    margin: 0 0 8px !important;
 }
-.benchmark-card-title {
-    display: flex;
-    align-items: center;
-    gap: 8px;
-    color: var(--pruna-text-primary);
-    font-size: 1.15rem;
-    font-weight: 700;
-    line-height: 1.3;
-    margin: 0 0 6px;
+.view-title h1,
+.view-title .prose h1,
+.gradio-container .view-title h1 {
+    margin: 0.35rem 0 0.4rem !important;
+    font-size: 1.45rem !important;
 }
-.benchmark-card-emoji {
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
-    flex-shrink: 0;
-    line-height: 1;
+.filter-help-host,
+.filter-help-host.block,
+.filter-help-host .padded,
+.filter-help-host .html-container,
+.filter-help-host .prose {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    padding: 0 !important;
+    margin: 0 !important;
 }
-.benchmark-card-blurb {
-    margin: 0;
-    color: var(--pruna-text-body);
-    font-size: 0.9rem;
-    line-height: 1.45;
+.view-help-host,
+.view-help-host.block,
+.view-help-host .padded,
+.view-help-host .html-container,
+.view-help-host .prose {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    padding: 0 !important;
+    margin: 0 0 10px !important;
 }
-.benchmark-card-col .benchmark-open-btn,
-.benchmark-card-col button,
-.benchmark-card-col button.sm,
-.benchmark-card-col button.primary {
-    display: flex !important;
-    justify-content: center !important;
-    align-items: center !important;
-    width: 100% !important;
-    min-width: 0 !important;
+.filter-help,
+.prose .filter-help,
+.view-help,
+.prose .view-help {
     margin: 0 !important;
-    align-self: stretch !important;
-    flex: 0 0 auto !important;
+    color: var(--pruna-text-muted) !important;
+    font-size: 0.95rem !important;
+    line-height: 1.45 !important;
+    font-weight: 400 !important;
+}
+.view-filters span[data-testid="block-info"],
+.view-filters .info,
+.view-filters .block-info {
+    color: var(--pruna-text-muted) !important;
+    font-size: 0.75rem !important;
+    line-height: 1.35 !important;
+    margin-top: 4px !important;
 }
-.home-callouts > div,
 .compare-prompt-block {
     border: 1px solid var(--pruna-border) !important;
     border-radius: 16px !important;
@@ -912,260 +807,89 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     gap: 8px 16px;
     align-items: center;
 }
-.community-footer-links a {
-    color: var(--pruna-text-muted) !important;
-    text-decoration: none !important;
-    font-size: 0.9rem !important;
-    font-weight: 500 !important;
-}
-.community-footer-links a:hover {
-    color: var(--pruna-link) !important;
-}
-
-/* Citation accordion — full-width control, not a section heading */
-.citation-accordion {
-    margin: 0 0 28px !important;
-}
-.citation-accordion,
-.citation-accordion.block {
-    border: 1px solid var(--pruna-accordion-border) !important;
-    border-radius: 10px !important;
-    background: var(--pruna-accordion-bg) !important;
-    box-shadow: none !important;
-    overflow: hidden !important;
-}
-.citation-accordion > .label-wrap,
-.citation-accordion .label-wrap {
-    border: none !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    padding: 11px 14px !important;
-    font-size: 0.92rem !important;
-    font-weight: 500 !important;
-    color: var(--pruna-text-primary) !important;
-    letter-spacing: -0.01em !important;
-}
-.citation-accordion > .label-wrap:hover,
-.citation-accordion .label-wrap:hover {
-    background: var(--pruna-menu-hover-bg) !important;
-}
-.citation-accordion > .label-wrap span,
-.citation-accordion .label-wrap span {
-    color: var(--pruna-text-primary) !important;
-    font-size: 0.92rem !important;
-    font-weight: 500 !important;
-}
-.citation-accordion .icon,
-.citation-accordion .label-wrap .icon {
-    color: var(--pruna-text-muted) !important;
-    opacity: 0.9;
-}
-.citation-accordion .prose,
-.citation-accordion .markdown,
-.citation-accordion pre,
-.citation-accordion code {
-    color: var(--pruna-text-muted) !important;
-    background: transparent !important;
-    border: none !important;
-    box-shadow: none !important;
-}
-.citation-accordion .wrap,
-.citation-accordion > .wrap {
-    border-top: 1px solid var(--pruna-hairline) !important;
-    padding: 4px 14px 12px !important;
-    background: transparent !important;
-}
-.citation-accordion pre {
-    margin: 0 !important;
-    padding: 0 !important;
-    font-size: 0.8rem !important;
-    line-height: 1.55 !important;
-    overflow-x: auto;
-}
-
-.home-callouts-host,
-.home-callouts-host .html-container,
-.home-callouts-host .prose,
-.home-callouts-host.block,
-.home-callouts-host .padded,
-.home-section-title,
-.home-section-title.block,
-.home-section-title .prose,
-.home-section-title.padded {
-    border: none !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    padding: 0 !important;
-    margin: 0 !important;
-    width: 100% !important;
-    max-width: none !important;
-    overflow: visible !important;
-}
-.home-section-title h2,
-.home-section-title .prose h2,
-.gradio-container .home-section-title h2 {
-    margin: 1.25rem 0 0.6rem !important;
-}
-.home-callouts {
-    display: grid;
-    grid-template-columns: repeat(3, minmax(0, 1fr));
-    gap: 12px;
-    margin: 8px 0 8px;
-    width: 100%;
-}
-.home-callouts > div {
-    padding: 14px 16px;
-    background: var(--pruna-callout-bg) !important;
-    box-shadow: none !important;
-}
-.home-callouts span {
-    display: block;
-}
-.home-callouts strong {
-    display: block;
-    margin-top: 6px;
-    word-break: break-word;
-}
-.home-callouts em {
-    display: block;
-    margin-top: 4px;
-    font-style: normal;
-}
-.home-snapshots-host,
-.home-snapshots-host .html-container,
-.home-snapshots-host .prose,
-.home-snapshots-host.block,
-.home-snapshots-host .padded {
-    border: none !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    padding: 0 !important;
-    margin: 0 !important;
-    width: 100% !important;
-    max-width: none !important;
-    overflow: visible !important;
-}
-.home-snapshots-grid {
-    display: grid;
-    grid-template-columns: repeat(2, minmax(0, 1fr));
-    gap: 20px 32px;
-    width: 100%;
-    margin: 4px 0 8px;
-    align-items: stretch;
-}
-.home-snap-card {
-    display: flex;
-    flex-direction: column;
-    min-width: 0;
-    height: 100%;
-}
-.home-snap-card .home-snap-blurb {
-    margin-top: 6px !important;
-}
-.home-snap-card .home-snap-top {
-    margin-top: auto;
-}
-.home-snap-title,
-.prose .home-snap-title {
-    display: flex !important;
-    align-items: center !important;
-    gap: 8px !important;
-    margin: 0 !important;
-    min-height: 1.5rem;
-    font-size: 0.95rem !important;
-    font-weight: 600 !important;
-    letter-spacing: -0.01em;
-    line-height: 1.3 !important;
-    color: var(--pruna-text-primary) !important;
-}
-.home-snap-emoji {
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
-    width: 1.25rem;
-    height: 1.25rem;
-    flex-shrink: 0;
-    font-size: 1rem;
-    line-height: 1;
-}
-.home-snap-blurb,
-.prose .home-snap-blurb,
-.prose p.home-snap-blurb {
-    margin: 0 !important;
+.community-footer-links a {
     color: var(--pruna-text-muted) !important;
-    font-size: 0.875rem !important;
-    font-weight: 400 !important;
-    line-height: 1.45 !important;
+    text-decoration: none !important;
+    font-size: 0.9rem !important;
+    font-weight: 500 !important;
 }
-.home-snap-top {
-    padding-top: 10px;
-    border-top: 1px solid var(--pruna-hairline);
-    width: 100%;
+.community-footer-links a:hover {
+    color: var(--pruna-link) !important;
 }
-.home-top-label {
-    margin: 0;
+
+/* Citation accordion — full-width control, not a section heading */
+.citation-accordion {
+    margin: 0 0 28px !important;
 }
-.home-top-list {
-    list-style: none;
-    margin: 8px 0 0;
-    padding: 0;
+.citation-accordion,
+.citation-accordion.block {
+    border: 1px solid var(--pruna-accordion-border) !important;
+    border-radius: 10px !important;
+    background: var(--pruna-accordion-bg) !important;
+    box-shadow: none !important;
+    overflow: hidden !important;
 }
-.home-top-list li,
-.prose .home-top-list li {
-    display: grid;
-    grid-template-columns: 28px minmax(0, 1fr) 4.75rem;
-    gap: 10px;
-    align-items: center;
-    padding: 8px 0;
-    border-bottom: 1px solid var(--pruna-hairline);
+.citation-accordion > .label-wrap,
+.citation-accordion .label-wrap {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    padding: 11px 14px !important;
+    font-size: 0.92rem !important;
+    font-weight: 500 !important;
     color: var(--pruna-text-primary) !important;
+    letter-spacing: -0.01em !important;
 }
-@media (max-width: 700px) {
-    .home-snapshots-grid {
-        grid-template-columns: 1fr;
-        gap: 22px;
-    }
-}
-.home-rank,
-.prose .home-rank {
-    width: 28px;
-    height: 28px;
-    border-radius: 999px;
-    background: var(--pruna-rank-bg) !important;
-    color: var(--pruna-lavender) !important;
-    font-weight: 700;
-    font-size: 13px;
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
-    box-shadow: var(--pruna-rank-ring);
+.citation-accordion > .label-wrap:hover,
+.citation-accordion .label-wrap:hover {
+    background: var(--pruna-menu-hover-bg) !important;
 }
-.home-model,
-.prose .home-model {
-    font-weight: 600;
-    word-break: break-word;
+.citation-accordion > .label-wrap span,
+.citation-accordion .label-wrap span {
     color: var(--pruna-text-primary) !important;
+    font-size: 0.92rem !important;
+    font-weight: 500 !important;
 }
-.home-score,
-.prose .home-score {
-    font-variant-numeric: tabular-nums;
-    color: var(--pruna-score) !important;
-    font-weight: 600;
-    text-align: right;
+.citation-accordion .icon,
+.citation-accordion .label-wrap .icon {
+    color: var(--pruna-text-muted) !important;
+    opacity: 0.9;
 }
-.home-empty,
-.prose .home-empty {
+.citation-accordion .prose,
+.citation-accordion .markdown,
+.citation-accordion pre,
+.citation-accordion code {
     color: var(--pruna-text-muted) !important;
+    background: transparent !important;
+    border: none !important;
+    box-shadow: none !important;
 }
-
-@media (max-width: 700px) {
-    .home-callouts { grid-template-columns: 1fr; }
+.citation-accordion .wrap,
+.citation-accordion > .wrap {
+    border-top: 1px solid var(--pruna-hairline) !important;
+    padding: 4px 14px 12px !important;
+    background: transparent !important;
+}
+.citation-accordion pre {
+    margin: 0 !important;
+    padding: 0 !important;
+    font-size: 0.8rem !important;
+    line-height: 1.55 !important;
+    overflow-x: auto;
 }
 
-.pareto-plot { width: 100%; }
+.pareto-plot,
+.pareto-plot.block,
 .pareto-plot .plotly,
 .pareto-plot .js-plotly-plot,
-.pareto-plot .plot-container { width: 100% !important; }
+.pareto-plot .plot-container,
+.pareto-plot .svg-container,
+.pareto-plot .main-svg {
+    width: 100% !important;
+    max-width: 100% !important;
+    background: transparent !important;
+    border-color: var(--pruna-border) !important;
+}
 .pareto-plot .modebar { display: none !important; }
 
 .leaderboard-controls {
@@ -1174,6 +898,7 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     align-items: end !important;
     gap: 10px !important;
     margin-bottom: 12px;
+    overflow: visible !important;
 }
 .leaderboard-controls > div {
     min-width: 0 !important;
@@ -1192,16 +917,6 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     font-size: 0.8rem !important;
     font-weight: 500 !important;
 }
-.leaderboard-search label,
-.leaderboard-search .block-label,
-.leaderboard-search .block-label span {
-    color: var(--pruna-text-primary) !important;
-    font-size: 0.85rem !important;
-    font-weight: 700 !important;
-    letter-spacing: -0.01em !important;
-}
-.leaderboard-search input,
-.leaderboard-controls input,
 .leaderboard-controls textarea {
     min-height: 40px !important;
     height: 40px !important;
@@ -1216,29 +931,61 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     line-height: 40px !important;
     resize: none !important;
 }
-.leaderboard-search input {
-    font-weight: 600 !important;
-}
-.leaderboard-search input::placeholder,
-.leaderboard-controls input::placeholder {
-    color: var(--pruna-text-muted) !important;
-    opacity: 0.85;
-}
-.leaderboard-search input:focus,
-.leaderboard-controls input:focus,
 .leaderboard-controls textarea:focus {
     border-color: var(--pruna-focus-border) !important;
     outline: none !important;
     box-shadow: var(--pruna-focus-ring) !important;
 }
-.leaderboard-controls .wrap,
-.leaderboard-controls .wrap-inner,
-.leaderboard-controls .secondary-wrap {
+.leaderboard-controls .wrap {
+    min-height: 0 !important;
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    overflow: visible !important;
+}
+.leaderboard-controls .wrap-inner {
+    display: flex !important;
+    flex-wrap: wrap !important;
+    align-items: center !important;
+    gap: 6px !important;
     min-height: 40px !important;
+    height: auto !important;
+    padding: 4px 10px !important;
     border: 1px solid var(--pruna-input-border) !important;
     border-radius: 10px !important;
     background: var(--pruna-input-bg) !important;
     box-shadow: none !important;
+    overflow: visible !important;
+}
+.leaderboard-controls .secondary-wrap {
+    display: flex !important;
+    flex-wrap: wrap !important;
+    align-items: center !important;
+    gap: 6px !important;
+    flex: 1 1 auto !important;
+    min-width: 0 !important;
+    min-height: 0 !important;
+    height: auto !important;
+    margin: 0 !important;
+    padding: 0 !important;
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+}
+.leaderboard-controls .token {
+    display: inline-flex !important;
+    align-items: center !important;
+    max-width: 100% !important;
+    margin: 0 !important;
+}
+.leaderboard-controls .token:empty {
+    display: none !important;
+}
+.leaderboard-controls .dropdown-arrow,
+.leaderboard-controls .icon-wrap {
+    flex: 0 0 auto !important;
+    align-self: center !important;
+    margin-left: auto !important;
 }
 .ranking-table-host,
 .ranking-table-host .html-container,
@@ -1739,25 +1486,15 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
 .pareto-layout .form {
     display: flex !important;
     flex-wrap: wrap !important;
-    align-items: flex-start !important;
+    align-items: stretch !important;
     gap: 16px !important;
     width: 100% !important;
     max-width: 100% !important;
 }
 .pareto-layout > .pareto-col,
 .pareto-layout .form > .pareto-col {
-    flex: 1 1 320px !important;
-    min-width: 280px !important;
-    max-width: 100% !important;
-}
-.pareto-plot,
-.pareto-plot.block,
-.pareto-plot .plotly,
-.pareto-plot .js-plotly-plot,
-.pareto-plot .plot-container,
-.pareto-plot .svg-container,
-.pareto-plot .main-svg {
-    width: 100% !important;
+    flex: 1 1 360px !important;
+    min-width: 0 !important;
     max-width: 100% !important;
 }
 .compare-cell { min-width: 0; }
@@ -1885,31 +1622,6 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     font-weight: 400 !important;
     line-height: 1.55 !important;
 }
-.home-intro,
-.home-intro.block,
-.home-intro .prose,
-.home-intro.prose,
-.home-intro.padded {
-    border: none !important;
-    background: transparent !important;
-    box-shadow: none !important;
-    padding: 0 !important;
-    margin: 0 auto 12px !important;
-    max-width: 42rem;
-    width: 100%;
-    text-align: center;
-    overflow: visible !important;
-}
-.home-intro p,
-.home-intro .prose p,
-.gradio-container .home-intro p {
-    color: var(--pruna-text-body) !important;
-    font-size: 1.08rem !important;
-    font-weight: 400 !important;
-    line-height: 1.65 !important;
-    margin: 0 auto 0.7rem !important;
-    text-align: center !important;
-}
 .markdown li, .md li, .prose li {
     color: var(--pruna-text-body) !important;
     font-size: 0.95rem;
@@ -1920,45 +1632,6 @@ button.theme-toggle[data-mode="light"] .theme-icon-moon { display: block !import
     font-weight: 600 !important;
 }
 
-/* Card / list titles sit under section headings */
-.home-snap-title,
-.benchmark-card-title {
-    display: flex !important;
-    align-items: center !important;
-    gap: 8px !important;
-    margin: 0 0 6px !important;
-    font-size: 0.95rem !important;
-    font-weight: 600 !important;
-    letter-spacing: -0.01em;
-    line-height: 1.3;
-    color: var(--pruna-text-primary) !important;
-}
-.home-snap-blurb,
-.benchmark-card-blurb {
-    color: var(--pruna-text-muted) !important;
-    font-size: 0.875rem !important;
-    font-weight: 400 !important;
-}
-
-/* Meta labels only — not used for real headings */
-.home-callouts span,
-.home-top-label {
-    color: var(--pruna-text-muted) !important;
-    font-size: 11px !important;
-    font-weight: 600 !important;
-    letter-spacing: 0.06em !important;
-    text-transform: uppercase !important;
-}
-.home-callouts strong {
-    color: var(--pruna-text-primary) !important;
-    font-size: 1.15rem !important;
-    font-weight: 600 !important;
-}
-.home-callouts em {
-    color: var(--pruna-text-muted) !important;
-    font-size: 0.85rem !important;
-}
-
 label, .block-label span {
     color: var(--pruna-text-muted) !important;
     font-size: 0.8rem !important;
@@ -1988,45 +1661,48 @@ button.secondary:active, button.secondary:focus,
     border-color: var(--pruna-border) !important;
     background: transparent !important;
 }
-input, textarea, select, .wrap .secondary-wrap {
+input, textarea, select {
     background: var(--pruna-bg-card) !important;
     border-color: var(--pruna-border) !important;
     color: var(--pruna-text-primary) !important;
-    border-radius: 8px !important;
-}
-footer, .footer { display: none !important; }
-
-/* Search label lives in a Gradio BlockTitle span with its own font-weight */
-.leaderboard-search span[data-testid="block-info"],
-.leaderboard-search label > span {
-    color: var(--pruna-text-primary) !important;
-    font-size: 0.95rem !important;
-    font-weight: 700 !important;
-    letter-spacing: -0.015em !important;
+    border-radius: 10px !important;
 }
-.leaderboard-search input {
-    font-size: 0.95rem !important;
-    font-weight: 600 !important;
+.view-filters input,
+.leaderboard-controls .wrap-inner input {
+    background: transparent !important;
+    border: none !important;
+    box-shadow: none !important;
+    min-height: 28px !important;
+    height: 28px !important;
+    padding: 0 4px !important;
+    line-height: 28px !important;
 }
-.leaderboard-search input::placeholder {
-    color: var(--pruna-text-muted) !important;
-    font-size: 0.95rem !important;
-    font-weight: 600 !important;
-    opacity: 1 !important;
+.view-filters .wrap-inner:focus-within,
+.leaderboard-controls .wrap-inner:focus-within {
+    border-color: var(--pruna-focus-border) !important;
+    box-shadow: var(--pruna-focus-ring) !important;
 }
+footer, .footer { display: none !important; }
 
-.pareto-help {
-    color: var(--pruna-text-muted) !important;
-    font-size: 0.95rem;
-}
-.pareto-help-emphasis {
-    color: var(--pruna-text-primary) !important;
-    font-size: 1rem;
+.pareto-note,
+.pareto-note.block,
+.pareto-note .html-container,
+.pareto-note .prose {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    padding: 0 !important;
+    margin: 0 0 12px !important;
 }
-.pareto-panel-host {
-    display: flex;
-    flex-direction: column;
-    gap: 1.5rem;
+.pareto-note-copy {
+    margin: 0;
+    padding: 14px 16px;
+    background: var(--pruna-callout-bg, var(--pruna-bg-card));
+    border: 1px dashed color-mix(in oklab, var(--pruna-border) 70%, transparent);
+    border-radius: 10px;
+    color: var(--pruna-text-muted);
+    font-size: 0.95rem;
+    line-height: 1.5;
 }
 .pareto-metric-block {
     padding-bottom: 0.5rem;
@@ -2041,14 +1717,51 @@ footer, .footer { display: none !important; }
     font-size: 1.05rem;
     font-weight: 600;
 }
-.pareto-subhead {
-    margin: 0 0 0.35rem;
+.pareto-subhead,
+.pareto-subhead.block,
+.pareto-subhead .prose,
+.pareto-subhead .html-container {
+    border: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+    padding: 0 !important;
+    margin: 0 0 0.4rem !important;
     color: var(--pruna-text-muted);
-    font-size: 0.9rem;
+}
+.pareto-subhead h4,
+.pareto-subhead .prose h4 {
+    margin: 0 !important;
+    color: var(--pruna-text-muted) !important;
+    font-size: 0.95rem !important;
+    font-weight: 600 !important;
 }
 
 .options,
-ul.options,
+ul.options {
+    position: absolute !important;
+    top: calc(100% + 4px) !important;
+    bottom: auto !important;
+    left: 0 !important;
+    right: auto !important;
+    width: 100% !important;
+    max-height: min(280px, 60vh) !important;
+    background: var(--pruna-bg-card) !important;
+    color: var(--pruna-text-primary) !important;
+    border: 1px solid var(--pruna-border) !important;
+    border-radius: 10px !important;
+    overflow: auto !important;
+    z-index: 50 !important;
+}
+.options .item:first-child,
+.options li:first-child {
+    border-top-left-radius: 10px !important;
+    border-top-right-radius: 10px !important;
+}
+.options .item:last-child,
+.options li:last-child {
+    border-bottom-left-radius: 10px !important;
+    border-bottom-right-radius: 10px !important;
+}
 .dropdown-arrow-inner {
     background: var(--pruna-bg-card) !important;
     color: var(--pruna-text-primary) !important;
@@ -2067,14 +1780,6 @@ ul.options,
 input[type="range"] {
     accent-color: var(--pruna-accent);
 }
-.pareto-plot,
-.pareto-plot.block,
-.pareto-plot .plotly,
-.pareto-plot .js-plotly-plot,
-.pareto-plot .plot-container {
-    background: transparent !important;
-    border-color: var(--pruna-border) !important;
-}
 """
 
 theme = gr.themes.Base(
@@ -2267,12 +1972,8 @@ def load_oneig_dataframe(path):
         "OneIG Anime Elo",
         "OneIG Human Elo",
         "OneIG Object Elo",
-        "Median Inference Time",
-        "Median Inference Time (s)",
         "Median Generation Time (s)",
         "Min Generation Time (s)",
-        "Median Total Duration (s)",
-        "Price per Image",
         "Price / Image (USD)",
         "Evaluation Date (UTC)",
         "URL",
@@ -2634,10 +2335,7 @@ datasets = [
         "data": qwen_df,
         "columns": qwen_display_columns,
         "metric_ids": qwen_metric_ids,
-        "note": (
-            "> Ranked by the selected metric on the Qwen Image Dataset. "
-            "Rapidata Elo is a metric on this dataset, not a dataset of its own."
-        ),
+        "note": "Rapidata Elo is a metric on this dataset, not a dataset of its own.",
         "samples": qwen_samples,
     },
     {
@@ -2647,7 +2345,7 @@ datasets = [
         "columns": oneig_display_columns,
         "metric_ids": oneig_metric_ids,
         "note": (
-            "> Alignment Overall is the mean of the available category scores. "
+            "Alignment Overall is the mean of the available category scores. "
             "Missing categories are skipped for that model."
         ),
         "samples": oneig_samples,
@@ -2892,6 +2590,19 @@ custom_head = """
   });
 })();
 </script>
+<script>
+(() => {
+  if (window.__inferbenchChipScrollBound) return;
+  window.__inferbenchChipScrollBound = true;
+  document.addEventListener("wheel", (event) => {
+    const row = event.target.closest?.(".view-filters .filter-chips .wrap-inner");
+    if (!row || row.scrollWidth <= row.clientWidth + 1) return;
+    if (Math.abs(event.deltaY) < Math.abs(event.deltaX)) return;
+    row.scrollLeft += event.deltaY;
+    event.preventDefault();
+  }, { capture: true, passive: false });
+})();
+</script>
 """
 
 with gr.Blocks(

data/text_to_image.jsonl

@@ -1,31 +0,0 @@
-{"Platform": "Replicate", "Owner": "Pruna AI", "Device": "1xH100", "Model": "FLUX Schnell", "Optimization": "speed_mode_juiced", "URL": "https://replicate.com/prunaai/flux-schnell", "PartiPromts (ARNIQA)": 0.5665, "PartiPromts (ClipScore)": 27.4594, "PartiPromts (ClipIQA)": 0.8594, "PartiPromts (Sharpness - Laplacian Variance)": 4385.7579, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "HPS (v2.1)": 0.2106, "DrawBench (ClipScore)": 27.9985, "DrawBench (Image Reward)": 1.0057, "GenAI-Bench (VQA)": 0.79, "OneIG (Anime and Stylization) (Alignment Score)": null, "Median Inference Time": 0.9082, "Price per Image": 0.055}
-{"Platform": "fal.ai", "Owner": "fal.ai", "Device": "Undisclosed", "Model": "FLUX 1.1 Pro", "Optimization": "Undisclosed", "URL": "https://fal.ai/models/fal-ai/flux-pro/v1.1", "GenAI-Bench (VQA)": 0.7745, "HPS (v2.1)": 0.2093, "PartiPromts (ARNIQA)": 0.5998, "PartiPromts (ClipScore)": 26.7942, "PartiPromts (ClipIQA)": 0.9282, "PartiPromts (Sharpness - Laplacian Variance)": 14290.615, "Long Text Bench (edit_distance)": 163.5862, "Long Text Bench (text_word_accuracy)": null, "OneIG (Anime and Stylization) (Alignment Score)": null, "DrawBench (ClipScore)": 27.2099, "DrawBench (Image Reward)": 0.8609, "Median Inference Time": 4.031, "Price per Image": 0.04}
-{"Platform": "fal.ai", "Owner": "fal.ai", "Device": "Undisclosed", "Model": "FLUX Dev", "Optimization": "dev", "URL": "https://fal.ai/models/fal-ai/flux/dev", "Long Text Bench (edit_distance)": 177.7438, "Long Text Bench (text_word_accuracy)": null, "OneIG (Anime and Stylization) (Alignment Score)": null, "GenAI-Bench (VQA)": 0.7443, "DrawBench (ClipScore)": 27.0164, "DrawBench (Image Reward)": 0.8933, "PartiPromts (ARNIQA)": 0.6305, "PartiPromts (ClipScore)": 27.2592, "PartiPromts (ClipIQA)": 0.8593, "PartiPromts (Sharpness - Laplacian Variance)": 5212.9337, "HPS (v2.1)": 0.1932, "Median Inference Time": 1.9838, "Price per Image": 0.025}
-{"Platform": "Prodia", "Owner": "Prodia", "Device": "Undisclosed", "Model": "FLUX Dev", "Optimization": "fast", "URL": "https://app.prodia.com/models", "PartiPromts (ARNIQA)": 0.6224, "PartiPromts (ClipScore)": 26.8405, "PartiPromts (ClipIQA)": 0.9159, "PartiPromts (Sharpness - Laplacian Variance)": 5774.8605, "DrawBench (ClipScore)": 26.6909, "DrawBench (Image Reward)": 0.9675, "GenAI-Bench (VQA)": 0.7363, "OneIG (Anime and Stylization) (Alignment Score)": null, "Long Text Bench (edit_distance)": 178.3375, "Long Text Bench (text_word_accuracy)": null, "HPS (v2.1)": 0.1995, "Median Inference Time": 1.9977, "Price per Image": 0.02}
-{"Platform": "Prodia", "Owner": "Prodia", "Device": "Undisclosed", "Model": "FLUX 1.1 Pro", "Optimization": "Undisclosed", "URL": "https://app.prodia.com/models", "HPS (v2.1)": 0.2075, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "DrawBench (ClipScore)": 27.6498, "DrawBench (Image Reward)": 0.9484, "GenAI-Bench (VQA)": 0.7739, "OneIG (Anime and Stylization) (Alignment Score)": null, "PartiPromts (ARNIQA)": 0.6181, "PartiPromts (ClipScore)": 27.3402, "PartiPromts (ClipIQA)": 0.895, "PartiPromts (Sharpness - Laplacian Variance)": 6932.3266, "Median Inference Time": 3.285, "Price per Image": 0.04}
-{"Platform": "fal.ai", "Owner": "fal.ai", "Device": "Undisclosed", "Model": "FLUX Schnell", "Optimization": "schnell", "URL": "https://fal.ai/models/fal-ai/flux/schnell", "HPS (v2.1)": 0.206, "Long Text Bench (edit_distance)": 178.3125, "Long Text Bench (text_word_accuracy)": 0.0003, "PartiPromts (ARNIQA)": 0.6637, "PartiPromts (ClipScore)": 27.755, "PartiPromts (ClipIQA)": 0.8991, "PartiPromts (Sharpness - Laplacian Variance)": 6420.4761, "OneIG (Anime and Stylization) (Alignment Score)": null, "GenAI-Bench (VQA)": 0.7919, "DrawBench (ClipScore)": 28.0585, "DrawBench (Image Reward)": 0.9376, "Median Inference Time": 0.7981, "Price per Image": 0.003}
-{"Platform": "Together AI", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX Dev", "Optimization": "dev", "URL": "https://www.together.ai/models/flux-1-dev", "Long Text Bench (edit_distance)": 178.0437, "Long Text Bench (text_word_accuracy)": null, "GenAI-Bench (VQA)": 0.7592, "OneIG (Anime and Stylization) (Alignment Score)": null, "PartiPromts (ARNIQA)": 0.5982, "PartiPromts (ClipScore)": 27.5003, "PartiPromts (ClipIQA)": 0.8799, "PartiPromts (Sharpness - Laplacian Variance)": 5101.113, "HPS (v2.1)": 0.1973, "DrawBench (ClipScore)": 27.3007, "DrawBench (Image Reward)": 0.9612, "Median Inference Time": 3.862, "Price per Image": 0.025}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX 1.1 Pro", "Optimization": "Undisclosed", "URL": "https://replicate.com/black-forest-labs/flux-1.1-pro", "PartiPromts (ARNIQA)": 0.5879, "PartiPromts (ClipScore)": 27.8015, "PartiPromts (ClipIQA)": 0.8273, "PartiPromts (Sharpness - Laplacian Variance)": 6773.8274, "HPS (v2.1)": 0.2048, "OneIG (Anime and Stylization) (Alignment Score)": 0.87, "OneIG (General Object) (Alignment Score)": 0.83, "OneIG (Portrait) (Alignment Score)": 0.78, "DrawBench (ClipScore)": 27.7958, "DrawBench (Image Reward)": 0.9258, "Long Text Bench (edit_distance)": 163.28, "Long Text Bench (text_word_accuracy)": null, "GenAI-Bench (VQA)": 0.7813, "Median Inference Time": 2.8571, "Price per Image": 0.04}
-{"Platform": "Black Forest Labs", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX 2 pro", "Optimization": "Undisclosed", "URL": "https://bfl.ai/models", "PartiPromts (ARNIQA)": 0.6488, "PartiPromts (ClipScore)": 28.0808, "PartiPromts (ClipIQA)": 0.9172, "PartiPromts (Sharpness - Laplacian Variance)": 9335.3041, "GenAI-Bench (VQA)": 0.9633, "OneIG (Anime and Stylization) (Alignment Score)": null, "DrawBench (ClipScore)": 28.22, "DrawBench (Image Reward)": 1.0339, "Long Text Bench (edit_distance)": 159.2424, "Long Text Bench (text_word_accuracy)": null, "HPS (v2.1)": 0.1894, "Median Inference Time": 9.1787, "Price per Image": 0.1}
-{"Platform": "fal.ai", "Owner": "fal.ai", "Device": "Undisclosed", "Model": "FLUX Krea", "Optimization": "Undisclosed", "URL": "https://fal.ai/models/fal-ai/flux/krea", "DrawBench (ClipScore)": 28.1482, "DrawBench (Image Reward)": 0.9853, "OneIG (Anime and Stylization) (Alignment Score)": null, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "GenAI-Bench (VQA)": 0.7949, "PartiPromts (ARNIQA)": 0.615, "PartiPromts (ClipScore)": 27.7512, "PartiPromts (ClipIQA)": 0.848, "PartiPromts (Sharpness - Laplacian Variance)": 4043.6115, "HPS (v2.1)": 0.1902, "Median Inference Time": 2.0193, "Price per Image": 0.025}
-{"Platform": "Runware", "Owner": "Runware", "Device": "Undisclosed", "Model": "FLUX Dev", "Optimization": "Undisclosed", "URL": "https://runware.ai/models#", "PartiPromts (ARNIQA)": 0.638, "PartiPromts (ClipScore)": 27.3993, "PartiPromts (ClipIQA)": 0.8887, "PartiPromts (Sharpness - Laplacian Variance)": 6238.0671, "DrawBench (ClipScore)": 27.2074, "DrawBench (Image Reward)": 0.9923, "GenAI-Bench (VQA)": 0.7761, "Long Text Bench (edit_distance)": 178.3625, "Long Text Bench (text_word_accuracy)": null, "HPS (v2.1)": 0.194, "OneIG (Anime and Stylization) (Alignment Score)": null, "Median Inference Time": 4.2133, "Price per Image": 0.0038}
-{"Platform": "Black Forest Labs", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX 2 beta", "Optimization": "Undisclosed", "URL": "https://bfl.ai/models", "DrawBench (ClipScore)": 28.2489, "DrawBench (Image Reward)": 1.0498, "PartiPromts (ARNIQA)": 0.5988, "PartiPromts (ClipScore)": 28.2471, "PartiPromts (ClipIQA)": 0.8992, "PartiPromts (Sharpness - Laplacian Variance)": 7910.49, "Long Text Bench (edit_distance)": 155.9429, "Long Text Bench (text_word_accuracy)": null, "OneIG (Anime and Stylization) (Alignment Score)": null, "HPS (v2.1)": 0.2121, "GenAI-Bench (VQA)": 0.9673, "Median Inference Time": 15.5713, "Price per Image": 0.025}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX Schnell", "Optimization": "go_fast", "URL": "https://replicate.com/black-forest-labs/flux-schnell", "DrawBench (ClipScore)": 28.0363, "DrawBench (Image Reward)": 0.9009, "GenAI-Bench (VQA)": 0.7765, "OneIG (Anime and Stylization) (Alignment Score)": 0.88, "OneIG (Portrait) (Alignment Score)": 0.79, "OneIG (General Object) (Alignment Score)": 0.83, "PartiPromts (ARNIQA)": 0.5714, "PartiPromts (ClipScore)": 27.9771, "PartiPromts (ClipIQA)": 0.7947, "PartiPromts (Sharpness - Laplacian Variance)": 5685.5457, "HPS (v2.1)": 0.2086, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "Median Inference Time": 0.979, "Price per Image": 0.003}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX Krea", "Optimization": "go_fast", "URL": "https://replicate.com/black-forest-labs/flux-krea-dev", "DrawBench (ClipScore)": 28.3217, "DrawBench (Image Reward)": 0.9533, "PartiPromts (ARNIQA)": 0.6336, "PartiPromts (ClipScore)": 27.88, "PartiPromts (ClipIQA)": 0.8167, "PartiPromts (Sharpness - Laplacian Variance)": 4121.4541, "HPS (v2.1)": 0.1967, "OneIG (Anime and Stylization) (Alignment Score)": 0.88, "OneIG (Portrait) (Alignment Score)": 0.81, "OneIG (General Object) (Alignment Score)": 0.84, "GenAI-Bench (VQA)": 0.8218, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "Median Inference Time": 1.8774, "Price per Image": 0.025}
-{"Platform": "Prodia", "Owner": "Prodia", "Device": "Undisclosed", "Model": "FLUX Schnell", "Optimization": "fast", "URL": "https://app.prodia.com/models", "PartiPromts (ARNIQA)": 0.5726, "PartiPromts (ClipScore)": 27.388, "PartiPromts (ClipIQA)": 0.8827, "PartiPromts (Sharpness - Laplacian Variance)": 5464.0814, "OneIG (Anime and Stylization) (Alignment Score)": null, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "DrawBench (ClipScore)": 27.7805, "DrawBench (Image Reward)": 0.9845, "GenAI-Bench (VQA)": 0.7878, "HPS (v2.1)": 0.2128, "Median Inference Time": 0.7472, "Price per Image": 0.0015}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "1xH100", "Model": "FLUX Dev", "Optimization": "go_fast", "URL": "https://replicate.com/black-forest-labs/flux-dev", "PartiPromts (ARNIQA)": 0.5764, "PartiPromts (ClipScore)": 27.8144, "PartiPromts (ClipIQA)": 0.7859, "PartiPromts (Sharpness - Laplacian Variance)": 5164.9466, "Long Text Bench (edit_distance)": 177.8931, "Long Text Bench (text_word_accuracy)": null, "DrawBench (ClipScore)": 27.7887, "DrawBench (Image Reward)": 1.0318, "OneIG (Anime and Stylization) (Alignment Score)": 0.85, "OneIG (Portrait) (Alignment Score)": 0.78, "OneIG (General Object) (Alignment Score)": 0.8, "HPS (v2.1)": 0.1982, "GenAI-Bench (VQA)": 0.7686, "Median Inference Time": 1.8018, "Price per Image": 0.025}
-{"Platform": "Together AI", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX Schnell", "Optimization": "schnell", "URL": "https://www.together.ai/models/flux-1-schnell", "PartiPromts (ARNIQA)": 0.6082, "PartiPromts (ClipScore)": 27.6475, "PartiPromts (ClipIQA)": 0.898, "PartiPromts (Sharpness - Laplacian Variance)": 6446.8138, "DrawBench (ClipScore)": 27.6897, "DrawBench (Image Reward)": 0.9301, "GenAI-Bench (VQA)": 0.7706, "OneIG (Anime and Stylization) (Alignment Score)": null, "HPS (v2.1)": 0.2058, "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "Median Inference Time": 1.3602, "Price per Image": 0.003}
-{"Platform": "Runware", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX 1.1 Pro", "Optimization": "Undisclosed", "URL": "https://runware.ai/models#", "PartiPromts (ARNIQA)": 0.659, "PartiPromts (ClipScore)": 27.4929, "PartiPromts (ClipIQA)": 0.9074, "PartiPromts (Sharpness - Laplacian Variance)": 6874.5826, "Long Text Bench (edit_distance)": 167.1325, "Long Text Bench (text_word_accuracy)": null, "DrawBench (ClipScore)": 27.7755, "DrawBench (Image Reward)": 0.9277, "HPS (v2.1)": 0.1971, "OneIG (Anime and Stylization) (Alignment Score)": null, "GenAI-Bench (VQA)": 0.7723, "Median Inference Time": 4.2135, "Price per Image": 0.04}
-{"Platform": "Runware", "Owner": "Runware", "Device": "Undisclosed", "Model": "FLUX Schnell", "Optimization": "Undisclosed", "URL": "https://runware.ai/models#", "HPS (v2.1)": 0.2026, "PartiPromts (ARNIQA)": 0.6344, "PartiPromts (ClipScore)": 27.7251, "PartiPromts (ClipIQA)": 0.8873, "PartiPromts (Sharpness - Laplacian Variance)": 7144.7132, "GenAI-Bench (VQA)": 0.769, "OneIG (Anime and Stylization) (Alignment Score)": null, "DrawBench (ClipScore)": 27.9708, "DrawBench (Image Reward)": 0.9571, "Long Text Bench (edit_distance)": 178.3812, "Long Text Bench (text_word_accuracy)": 0.0005, "Median Inference Time": 1.7584, "Price per Image": 0.0013}
-{"Platform": "Together AI", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX Krea", "Optimization": "dev", "URL": "https://www.together.ai/models/flux-1-krea-dev", "Long Text Bench (edit_distance)": 178.5125, "Long Text Bench (text_word_accuracy)": null, "DrawBench (ClipScore)": 28.2254, "DrawBench (Image Reward)": 1.101, "GenAI-Bench (VQA)": 0.797, "PartiPromts (ARNIQA)": 0.618, "PartiPromts (ClipScore)": 27.6353, "PartiPromts (ClipIQA)": 0.8776, "PartiPromts (Sharpness - Laplacian Variance)": 5297.8361, "OneIG (Anime and Stylization) (Alignment Score)": null, "HPS (v2.1)": 0.1981, "Median Inference Time": 4.3831, "Price per Image": 0.025}
-{"Platform": "Bria", "Owner": "Bria", "Device": "Undisclosed", "Model":"FIBO", Optimization: "Undisclosed", "URL": "https://bria.ai/models", "OneIG (Anime and Stylization) (Alignment Score)": 0.87, "Median Inference Time": 15.8328 , "Price per Image": 0.04}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX 1.1 Pro Ultra", "Optimization": "Undisclosed", "URL": "", "OneIG (Anime and Stylization) (Alignment Score)": 0.89, "OneIG (Portrait) (Alignment Score)": 0.8, "OneIG (General Object) (Alignment Score)": 0.85}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX 2 Pro", "Optimization": "", URL: "", "OneIG (Anime and Stylization) (Alignment Score)": 0.92, "OneIG (Portrait) (Alignment Score)": 0.84, "OneIG (General Object) (Alignment Score)": 0.88}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "FLUX 2 Flex", "Optimization": "", URL: "", "OneIG (Anime and Stylization) (Alignment Score)": 0.92, "OneIG (Portrait) (Alignment Score)": 0.84, "OneIG (General Object) (Alignment Score)": 0.90}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "Flux 2 Max", "Optimization": "", URL: "", "OneIG (Anime and Stylization) (Alignment Score)": 0.93, "OneIG (Portrait) (Alignment Score)": 0.85, "OneIG (General Object) (Alignment Score)": 0.89}
-{"Platform": "Replicate", "Owner": "Black Forest Labs", "Device": "Undisclosed", "Model": "Flux 2 Pro", "Optimization": "", URL: "", "OneIG (Anime and Stylization) (Alignment Score)": null, "OneIG (Portrait) (Alignment Score)": 0.84, "OneIG (General Object) (Alignment Score)": 0.90}
-{"Platform": "Replicate", "Owner": "Open AI", "Device": "Undisclosed", "Model": "GPT Image 1.5", "Optimization": "", URL: "", "OneIG (Anime and Stylization) (Alignment Score)": 0.92, "OneIG (Portrait) (Alignment Score)": 0.85, "OneIG (General Object) (Alignment Score)": null}
-{"Platform": "Replicate", "Owner": "Pruna AI", "Device": "Undisclosed", "Model": "Hidream I1 Dev", "Optimzation": "Extra Juiced", "URL":"", "OneIG (Anime and Stylization) (Alignment Score)": 0.87, "OneIG (Portrait) (Alignment Score)": 0.79, "OneIG (General Object) (Alignment Score)": 0.82}
-{"Platform": "Replicate", "Owner": "Pruna AI", "Device": "Undisclosed", "Model": "Hidream I1 Faast", "Optimzation": "Extra Juiced", "URL":"", "OneIG (Anime and Stylization) (Alignment Score)": 0.87, "OneIG (Portrait) (Alignment Score)": 0.79, "OneIG (General Object) (Alignment Score)": 0.82}
-
-

requirements.txt

@@ -1,2 +1 @@
-gradio-leaderboard
-plotly
+plotly

ui.py

@@ -22,6 +22,22 @@ MAX_COMPARE_MODELS = 4
 DEFAULT_COMPARE_PROMPTS = 3
 MAX_COMPARE_PROMPTS = 8
 MAX_PARETO_METRICS = 8
+_PARETO_SLOT_COUNT = 1 + MAX_PARETO_METRICS * 8
+_PARETO_PRICE_COLUMN = "Price / Image (USD)"
+_PARETO_TIME_COLUMN = "Min Generation Time (s)"
+
+TAB_LEADERBOARDS = "leaderboards"
+TAB_PARETO = "pareto"
+TAB_SAMPLES = "samples"
+TAB_ABOUT = "about"
+
+_MODEL_CHOICES_CACHE = {}
+_VIEW_EVENTS = {
+    "show_progress": "hidden",
+    "trigger_mode": "always_last",
+    "concurrency_id": "workspace-views",
+    "concurrency_limit": 1,
+}
 
 ABOUT_OVERVIEW_CONTENT = """
 # About P-Bench
@@ -33,7 +49,7 @@ across P-Bench.
 
 ## How to read it
 
-1. Pick a **dataset** and a **metric**. The title is always Dataset | Metric.
+1. Pick a **dataset** and a **metric**.
 2. **Leaderboards**: ranked by that metric. Price and generation time sit in
    the same table.
 3. **Pareto plots**: mark models that are not beaten on both higher score
@@ -154,18 +170,20 @@ def render_header():
     gr.HTML(
         f"""
         <header class="app-header">
-          <button type="button" class="theme-toggle" data-mode="dark" aria-label="Switch to light mode" title="Switch to light mode">
-            <svg class="theme-icon-sun" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">
-              <circle cx="12" cy="12" r="4"></circle>
-              <path d="M12 2v2M12 20v2M4.93 4.93l1.41 1.41M17.66 17.66l1.41 1.41M2 12h2M20 12h2M4.93 19.07l1.41-1.41M17.66 6.34l1.41-1.41"></path>
-            </svg>
-            <svg class="theme-icon-moon" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">
-              <path d="M21 14.5A8.5 8.5 0 1 1 9.5 3 7 7 0 0 0 21 14.5z"></path>
-            </svg>
-          </button>
-          <div class="app-header-brand">
-            <img class="app-header-logo" src="{_MASCOT_DATA_URI}" alt="" />
-            <h1>P-Bench</h1>
+          <div class="app-header-bar">
+            <div class="app-header-brand">
+              <img class="app-header-logo" src="{_MASCOT_DATA_URI}" alt="" />
+              <h1>P-Bench</h1>
+            </div>
+            <button type="button" class="theme-toggle" data-mode="dark" aria-label="Switch to light mode" title="Switch to light mode">
+              <svg class="theme-icon-sun" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">
+                <circle cx="12" cy="12" r="4"></circle>
+                <path d="M12 2v2M12 20v2M4.93 4.93l1.41 1.41M17.66 17.66l1.41 1.41M2 12h2M20 12h2M4.93 19.07l1.41-1.41M17.66 6.34l1.41-1.41"></path>
+              </svg>
+              <svg class="theme-icon-moon" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">
+                <path d="M21 14.5A8.5 8.5 0 1 1 9.5 3 7 7 0 0 0 21 14.5z"></path>
+              </svg>
+            </button>
           </div>
           <p class="app-header-tagline">Compare text-to-image models on quality, speed, and price</p>
         </header>
@@ -201,6 +219,9 @@ def _coerce_sample_dataset(datasets, dataset_id):
     return dataset_id
 
 
+ALL_METRICS_ID = "__all__"
+
+
 def _metric_choices(datasets, metrics, dataset_id):
     dataset = _item(datasets, dataset_id)
     if not dataset:
@@ -209,41 +230,62 @@ def _metric_choices(datasets, metrics, dataset_id):
     data = dataset.get("data")
     columns = getattr(data, "columns", [])
     return [
-        (metric["name"], metric["id"])
+        (_display_label(metric["column"]), metric["id"])
         for metric in metrics
         if metric["id"] in allowed and metric["column"] in columns
     ]
 
 
-def _coerce_metric(datasets, metrics, dataset_id, metric_id):
-    """Keep a selected metric only if it is valid for this dataset."""
-    if isinstance(metric_id, (list, tuple)):
-        metric_id = metric_id[0] if metric_id else None
-    if metric_id is None or metric_id == "":
-        return None
+def _metric_dropdown_choices(datasets, metrics, dataset_id):
     choices = _metric_choices(datasets, metrics, dataset_id)
-    ids = [choice[1] for choice in choices]
-    if metric_id in ids:
-        return metric_id
-    return None
+    if not choices:
+        return []
+    return [("Select all", ALL_METRICS_ID)] + choices
+
+
+def _normalize_metric_ids(metric_id):
+    if metric_id is None or metric_id == "":
+        return []
+    if isinstance(metric_id, (list, tuple)):
+        return [item for item in metric_id if item]
+    return [metric_id]
+
+
+def _coerce_metric(datasets, metrics, dataset_id, metric_id):
+    """Valid metric ids for this dataset. Empty means all metrics."""
+    wanted = _normalize_metric_ids(metric_id)
+    valid_ids = [choice[1] for choice in _metric_choices(datasets, metrics, dataset_id)]
+    valid = set(valid_ids)
+    if ALL_METRICS_ID in wanted:
+        return list(valid_ids)
+    return [item for item in wanted if item in valid]
 
 
 def _metric_dropdown_value(metric_id):
-    """Gradio multiselect value for an optional single metric."""
-    return [metric_id] if metric_id else []
+    return [
+        item
+        for item in _normalize_metric_ids(metric_id)
+        if item != ALL_METRICS_ID
+    ]
 
 
 def _model_choices(datasets, dataset_id):
+    cached = _MODEL_CHOICES_CACHE.get(dataset_id)
+    if cached is not None:
+        return cached
     dataset = _item(datasets, dataset_id)
     data = dataset.get("data") if dataset else None
     if data is None or "Model" not in getattr(data, "columns", []):
+        _MODEL_CHOICES_CACHE[dataset_id] = []
         return []
     models = data["Model"].dropna().astype(str).unique().tolist()
     # (label, value) so the UI shows the shared name but filters on the raw id.
-    return sorted(
+    choices = sorted(
         ((display_model_name(model), model) for model in models),
         key=lambda item: item[0].casefold(),
     )
+    _MODEL_CHOICES_CACHE[dataset_id] = choices
+    return choices
 
 
 def _model_choice_values(choices):
@@ -267,10 +309,24 @@ def _metric_columns(datasets, metrics, dataset_id):
 def _view_title(datasets, metrics, dataset_id, metric_id):
     dataset = _item(datasets, dataset_id)
     dataset_name = dataset["name"] if dataset else "Dataset"
-    metric = _item(metrics, metric_id) if metric_id else None
-    if not metric:
+    selected = [
+        item
+        for item in _normalize_metric_ids(metric_id)
+        if item != ALL_METRICS_ID
+    ]
+    all_ids = [choice[1] for choice in _metric_choices(datasets, metrics, dataset_id)]
+    if not selected or set(selected) == set(all_ids):
         return dataset_name
-    return f"{dataset_name} | {metric['name']}"
+    names = []
+    for metric_key in selected:
+        metric = _item(metrics, metric_key)
+        if metric:
+            names.append(_display_label(metric["column"]))
+    if not names:
+        return dataset_name
+    if len(names) == 1:
+        return f"{dataset_name} | {names[0]}"
+    return f"{dataset_name} | {', '.join(names)}"
 
 
 _LEADERBOARD_META_COLUMNS = [
@@ -283,7 +339,12 @@ _LEADERBOARD_META_COLUMNS = [
 
 
 def _columns_for_metric(dataset, metric_column):
-    """When a metric is selected, show identity + that score + time/price."""
+    """When metrics are selected, show identity + those scores + time/price."""
+    metric_columns = (
+        [metric_column]
+        if isinstance(metric_column, str)
+        else [column for column in (metric_column or []) if column]
+    )
     available = list(getattr(dataset.get("data"), "columns", [])) or list(
         dataset.get("columns") or []
     )
@@ -293,8 +354,9 @@ def _columns_for_metric(dataset, metric_column):
         if column in available
     ]
     meta = [column for column in _LEADERBOARD_META_COLUMNS if column in available]
-    if metric_column and metric_column in available:
-        return [*identity, metric_column, *meta]
+    scores = [column for column in metric_columns if column in available]
+    if scores:
+        return [*identity, *scores, *meta]
     return [column for column in (dataset.get("columns") or available) if column != "URL"]
 
 
@@ -302,12 +364,21 @@ def resolve_view(datasets, metrics, dataset_id, metric_id):
     dataset = _item(datasets, dataset_id)
     if not dataset:
         return None
-    metric_id = _coerce_metric(datasets, metrics, dataset_id, metric_id)
-    metric = _item(metrics, metric_id) if metric_id else None
-    score_column = metric["column"] if metric else None
-    if score_column:
-        columns = _columns_for_metric(dataset, score_column)
-        score_columns = [score_column]
+    metric_ids = _coerce_metric(datasets, metrics, dataset_id, metric_id)
+    selected_metrics = []
+    for metric_key in metric_ids:
+        metric = _item(metrics, metric_key)
+        if metric:
+            selected_metrics.append(metric)
+    score_columns = [
+        metric["column"]
+        for metric in selected_metrics
+        if metric["column"] in getattr(dataset.get("data"), "columns", [])
+    ]
+    if score_columns:
+        columns = _columns_for_metric(dataset, score_columns)
+        score_column = score_columns[0]
+        metric = selected_metrics[0]
     else:
         columns = [
             column
@@ -315,11 +386,13 @@ def resolve_view(datasets, metrics, dataset_id, metric_id):
             if column != "URL"
         ]
         score_columns = _metric_columns(datasets, metrics, dataset_id)
+        score_column = score_columns[0] if score_columns else None
+        metric = None
     return {
         "dataset": dataset,
         "metric": metric,
-        "metric_id": metric_id,
-        "title": _view_title(datasets, metrics, dataset["id"], metric_id),
+        "metric_id": metric_ids,
+        "title": _view_title(datasets, metrics, dataset["id"], metric_ids),
         "data": dataset["data"],
         "columns": columns,
         "score_column": score_column,
@@ -385,6 +458,22 @@ def _leaderboard_cell_class(column):
     return "metric-score"
 
 
+def _assign_leaderboard_ranks(data, overall_column):
+    """Rank the full table by the selected metric. Filters keep these numbers."""
+    if data is None:
+        return data
+    ranked = data.copy()
+    if "Rank" in ranked.columns:
+        ranked = ranked.drop(columns=["Rank"])
+    if overall_column and overall_column in ranked.columns:
+        ranked = ranked.sort_values(
+            overall_column, ascending=False, na_position="last"
+        )
+    ranked = ranked.reset_index(drop=True)
+    ranked.insert(0, "Rank", ranked.index + 1)
+    return ranked
+
+
 def _leaderboard_html(data, columns, score_columns, overall_column):
     leaderboard = _leaderboard_dataframe(
         data, columns, score_columns, overall_column
@@ -441,28 +530,11 @@ def _filter_choices(data, column):
     return sorted(data[column].dropna().astype(str).unique().tolist())
 
 
-def _filter_leaderboard(data, search_term, platform, owner, optimized, models=None):
+def _filter_leaderboard(data, platform, owner, optimized, models=None):
     filtered = data.copy()
     if models:
         if "Model" in filtered.columns:
             filtered = filtered[filtered["Model"].astype(str).isin(models)]
-    if search_term:
-        search_columns = [
-            column
-            for column in ["Model", "Platform", "Endpoint Owner"]
-            if column in filtered.columns
-        ]
-        matches = pd.Series(False, index=filtered.index)
-        for column in search_columns:
-            matches |= filtered[column].astype(str).str.contains(
-                search_term, case=False, na=False
-            )
-        if "Model" in filtered.columns:
-            display_names = filtered["Model"].map(display_model_name).astype(str)
-            matches |= display_names.str.contains(
-                search_term, case=False, na=False
-            )
-        filtered = filtered[matches]
 
     for column, values in [
         ("Platform", platform),
@@ -475,7 +547,7 @@ def _filter_leaderboard(data, search_term, platform, owner, optimized, models=No
 
 
 def _leaderboard_dataframe(data, columns, score_columns, overall_column):  # noqa: ARG001
-    skip_columns = {"URL", "Rank"}
+    skip_columns = {"URL"}
     preferred_prefix = [
         column
         for column in ["Model", "Platform", "Endpoint Owner", "Optimized"]
@@ -515,20 +587,18 @@ def _leaderboard_dataframe(data, columns, score_columns, overall_column):  # noq
         if column not in seen:
             seen.add(column)
             ordered_columns.append(column)
+    if "Rank" in data.columns:
+        ordered_columns = ["Rank", *[c for c in ordered_columns if c != "Rank"]]
 
     leaderboard = data[ordered_columns].copy()
 
-    if overall_column and overall_column in data.columns:
-        leaderboard = (
-            leaderboard.assign(_sort_key=data[overall_column])
-            .sort_values("_sort_key", ascending=False, na_position="last")
-            .drop(columns=["_sort_key"])
-            .reset_index(drop=True)
+    if overall_column and overall_column in leaderboard.columns:
+        leaderboard = leaderboard.sort_values(
+            overall_column, ascending=False, na_position="last"
         )
-    else:
-        leaderboard = leaderboard.reset_index(drop=True)
-
-    leaderboard.insert(0, "Rank", leaderboard.index + 1)
+    leaderboard = leaderboard.reset_index(drop=True)
+    if "Rank" not in leaderboard.columns:
+        leaderboard.insert(0, "Rank", leaderboard.index + 1)
     return leaderboard.rename(columns=_display_label)
 
 
@@ -545,6 +615,7 @@ def _display_label(column):
         "P-Judge Overall": "P-Judger (Pruna)",
         "Datapoint Elo": "Datapoint Elo",
         "Rapidata Elo": "Rapidata Elo",
+        "Artificial Analysis Elo": "Artificial Analysis Elo",
         "Arena Elo": "Overall Elo",
         "Arena Branding / Commercial Elo": "Branding / Commercial",
         "Arena 3D Imaging Elo": "3D Imaging",
@@ -583,29 +654,52 @@ def _pareto_frontier_mask(x_values, scores):
     return mask
 
 
-def _empty_figure(message):
-    fig = go.Figure()
-    fig.add_annotation(
-        text=message,
-        xref="paper",
-        yref="paper",
-        x=0.5,
-        y=0.5,
-        showarrow=False,
-        font={"color": "#a3a3a3", "size": 14},
+def _pareto_unavailable_html(message):
+    return (
+        "<p class='pareto-note-copy'>"
+        f"{escape(message)}"
+        "</p>"
     )
-    fig.update_layout(
-        title=None,
-        autosize=True,
-        height=420,
-        margin={"l": 56, "r": 28, "t": 28, "b": 80},
-        plot_bgcolor="#1d1429",
-        paper_bgcolor="#171021",
-        font={"color": "#d4d4d4", "size": 13},
-        xaxis={"visible": False},
-        yaxis={"visible": False},
+
+
+def _pareto_note_update(message):
+    if message:
+        return gr.update(
+            value=_pareto_unavailable_html(message),
+            visible=True,
+        )
+    return gr.update(value="", visible=False)
+
+
+def _pareto_plot_update(fig):
+    if fig is not None:
+        return gr.update(value=fig, visible=True)
+    return gr.update(value=None, visible=False)
+
+
+def _skip_all(count):
+    return tuple(gr.skip() for _ in range(count))
+
+
+def _pareto_skip_updates():
+    return _skip_all(_PARETO_SLOT_COUNT)
+
+
+def _selection_key(dataset_id, metric_id, models):
+    return (
+        dataset_id,
+        tuple(_normalize_metric_ids(metric_id)),
+        tuple(models or ()),
+    )
+
+
+def _applied_key(view_state):
+    view_state = view_state or {}
+    return _selection_key(
+        view_state.get("dataset_id"),
+        view_state.get("metric_id"),
+        view_state.get("models"),
     )
-    return fig
 
 
 def _build_pareto_figure(
@@ -719,62 +813,129 @@ def _build_pareto_figure(
     return fig
 
 
+def _pareto_axis(data, score_column, x_column, x_title, missing_message, empty_message, **hover):
+    if x_column not in data.columns:
+        return None, missing_message
+    fig = _build_pareto_figure(
+        data,
+        score_column,
+        x_column=x_column,
+        x_title=x_title,
+        **hover,
+    )
+    if fig is None:
+        return None, empty_message
+    return fig, None
+
+
 def _pareto_pair(data, score_column):
+    score_missing = "No score data is available for this metric."
     if data is None or not score_column or score_column not in data.columns:
-        empty = _empty_figure("No score data is available yet.")
-        return empty, empty
-
-    price_column = "Price / Image (USD)"
-    time_column = "Min Generation Time (s)"
-    price_fig = None
-    time_fig = None
-    if price_column in data.columns:
-        price_fig = _build_pareto_figure(
-            data,
-            score_column,
-            x_column=price_column,
-            x_title="Price per image (USD)",
-            x_hover_prefix="$",
+        return None, score_missing, None, score_missing
+
+    price_fig, price_message = _pareto_axis(
+        data,
+        score_column,
+        _PARETO_PRICE_COLUMN,
+        "Price per image (USD)",
+        "Price per image isn't available for this dataset.",
+        "No models have both a score and a price for this metric.",
+        x_hover_prefix="$",
+    )
+    time_fig, time_message = _pareto_axis(
+        data,
+        score_column,
+        _PARETO_TIME_COLUMN,
+        "Min generation time (s)",
+        "Min generation time isn't available for this dataset.",
+        "No models have both a score and a min generation time for this metric.",
+        x_hover_suffix="s",
+    )
+    return price_fig, price_message, time_fig, time_message
+
+
+def _pareto_dataset_message(data):
+    has_price = data is not None and _PARETO_PRICE_COLUMN in data.columns
+    has_time = data is not None and _PARETO_TIME_COLUMN in data.columns
+    if has_price and has_time:
+        return None
+    if not has_price and not has_time:
+        return (
+            "Price per image and min generation time aren't available for "
+            "this dataset, so these plots can't be drawn."
         )
-    if time_column in data.columns:
-        time_fig = _build_pareto_figure(
-            data,
-            score_column,
-            x_column=time_column,
-            x_title="Min generation time (s)",
-            x_hover_suffix="s",
+    if not has_price:
+        return (
+            "Price per image isn't available for this dataset, so only min "
+            "generation time vs score is shown."
         )
     return (
-        price_fig or _empty_figure("No price data available."),
-        time_fig or _empty_figure("No min generation time data available."),
+        "Min generation time isn't available for this dataset, so only "
+        "price vs score is shown."
     )
 
 
+def _pareto_slot_note(price_fig, price_message, time_fig, time_message, data):
+    has_price = data is not None and _PARETO_PRICE_COLUMN in data.columns
+    has_time = data is not None and _PARETO_TIME_COLUMN in data.columns
+    notes = []
+    if price_fig is None and has_price:
+        notes.append(price_message)
+    if time_fig is None and has_time:
+        notes.append(time_message)
+    if len(notes) == 2 and notes[0] == notes[1]:
+        notes = notes[:1]
+    return " ".join(notes)
+
+
 def _pareto_slot_updates(data, score_columns):
     """Updates for a fixed bank of Gradio Plot slots (visible/hidden)."""
     score_columns = [column for column in (score_columns or []) if column]
-    updates = []
+    has_price = data is not None and _PARETO_PRICE_COLUMN in data.columns
+    has_time = data is not None and _PARETO_TIME_COLUMN in data.columns
+    dataset_note = _pareto_dataset_message(data)
+    updates = [_pareto_note_update(dataset_note)]
+    hide_all_slots = not has_price and not has_time
+    hidden_slot = (
+        gr.update(visible=False),
+        "",
+        _pareto_note_update(""),
+        gr.update(visible=False),
+        gr.update(visible=False),
+        _pareto_plot_update(None),
+        gr.update(visible=False),
+        _pareto_plot_update(None),
+    )
     for index in range(MAX_PARETO_METRICS):
-        if index < len(score_columns):
-            score_column = score_columns[index]
-            price_fig, time_fig = _pareto_pair(data, score_column)
-            updates.extend(
-                [
-                    gr.update(visible=True),
-                    f"#### {_display_label(score_column)}",
-                    price_fig,
-                    time_fig,
-                ]
-            )
-        else:
-            updates.extend(
-                [
-                    gr.update(visible=False),
-                    "",
-                    None,
-                    None,
-                ]
-            )
+        if hide_all_slots or index >= len(score_columns):
+            updates.extend(hidden_slot)
+            continue
+        score_column = score_columns[index]
+        price_fig, price_message, time_fig, time_message = _pareto_pair(
+            data, score_column
+        )
+        show_price = price_fig is not None
+        show_time = time_fig is not None
+        updates.extend(
+            [
+                gr.update(visible=True),
+                f"#### {_display_label(score_column)}",
+                _pareto_note_update(
+                    _pareto_slot_note(
+                        price_fig,
+                        price_message,
+                        time_fig,
+                        time_message,
+                        data,
+                    )
+                ),
+                gr.update(visible=show_price or show_time),
+                gr.update(visible=show_price),
+                _pareto_plot_update(price_fig),
+                gr.update(visible=show_time),
+                _pareto_plot_update(time_fig),
+            ]
+        )
     return updates
 
 
@@ -860,12 +1021,22 @@ def _build_compare_samples_html(samples, selected_models, num_prompts, seed=0):
     return "\n".join(blocks)
 
 
-def _title_markdown(title):
-    return f"# {title}"
+def _plain_note(note):
+    text = (note or "").strip()
+    if text.startswith(">"):
+        text = text.lstrip(">").strip()
+    return text
 
 
-def _note_markdown(note):
-    return note or ""
+def _leaderboard_intro_markdown(note):
+    extra = _plain_note(note)
+    parts = [
+        "Models are ranked by the selected metric, with price and generation "
+        "time in the same table."
+    ]
+    if extra:
+        parts.append(extra)
+    return "<p class='view-help'>" + " ".join(parts) + "</p>"
 
 
 def _filter_row(
@@ -877,8 +1048,6 @@ def _filter_row(
     require_samples=False,
     include_metric=True,
 ):
-    metric_choices = _metric_choices(datasets, metrics, default_dataset_id)
-    model_choices = _model_choices(datasets, default_dataset_id)
     metric_id = _coerce_metric(
         datasets, metrics, default_dataset_id, default_metric_id
     )
@@ -888,39 +1057,39 @@ def _filter_row(
             value=default_dataset_id,
             label="Dataset",
             type="value",
+            filterable=False,
             scale=2,
             min_width=160,
         )
         metric_dd = None
         if include_metric:
             metric_dd = gr.Dropdown(
-                choices=metric_choices,
+                choices=_metric_dropdown_choices(
+                    datasets, metrics, default_dataset_id
+                ),
                 value=_metric_dropdown_value(metric_id),
                 label="Metric",
                 type="value",
                 multiselect=True,
-                max_choices=1,
-                allow_custom_value=True,
+                allow_custom_value=False,
+                filterable=True,
                 scale=2,
                 min_width=180,
+                elem_classes="filter-chips",
             )
         models_dd = gr.Dropdown(
-            choices=model_choices,
+            choices=_model_choices(datasets, default_dataset_id),
             value=[],
             multiselect=True,
             label="Models",
             type="value",
-            allow_custom_value=True,
-            scale=3,
-            min_width=200,
+            allow_custom_value=False,
+            filterable=True,
+            scale=2,
+            min_width=180,
+            elem_classes="filter-chips",
         )
-    title = gr.Markdown(
-        _title_markdown(
-            _view_title(datasets, metrics, default_dataset_id, metric_id)
-        ),
-        elem_classes="view-title",
-    )
-    return dataset_dd, metric_dd, models_dd, title
+    return dataset_dd, metric_dd, models_dd
 
 
 def render_image_workspace(datasets, metrics, default_dataset_id, default_metric_id):
@@ -931,216 +1100,219 @@ def render_image_workspace(datasets, metrics, default_dataset_id, default_metric
     initial_data = initial["data"]
     initial_columns = initial["columns"]
     initial_score_columns = initial["score_columns"]
-    initial_samples = initial.get("samples")
-    initial_pareto_updates = _pareto_slot_updates(
-        _filter_leaderboard(initial_data, "", [], [], [], models=[]),
-        initial_score_columns,
-    )
-
-    with gr.Tabs(elem_classes="main-tabs"):
-        with gr.TabItem("Leaderboards"):
-            lb_dataset, lb_metric, lb_models, lb_title = _filter_row(
-                datasets, metrics, default_dataset_id, None
-            )
-            lb_note = gr.Markdown(_note_markdown(initial.get("note")))
-            platform_choices = _filter_choices(initial_data, "Platform")
-            owner_choices = _filter_choices(initial_data, "Endpoint Owner")
-            optimized_choices = _filter_choices(initial_data, "Optimized")
-            with gr.Row(elem_classes="leaderboard-controls"):
-                search = gr.Textbox(
-                    label="Search models",
-                    placeholder="Type a model or provider name…",
-                    scale=3,
-                    max_lines=1,
-                    elem_classes="leaderboard-search",
-                )
-                platform = gr.Dropdown(
-                    choices=platform_choices,
-                    value=[],
-                    label="Providers",
-                    multiselect=True,
-                    scale=1,
-                    visible=bool(platform_choices),
-                )
-                owner = gr.Dropdown(
-                    choices=owner_choices,
-                    value=[],
-                    label="Endpoint owners",
-                    multiselect=True,
-                    scale=1,
-                    visible=bool(owner_choices),
-                )
-                optimized = gr.Dropdown(
-                    choices=optimized_choices,
-                    value=[],
-                    label="Optimized",
-                    multiselect=True,
-                    scale=1,
-                    visible=bool(optimized_choices),
-                )
-            ranking = gr.HTML(
-                _leaderboard_html(
-                    initial_data,
-                    initial_columns,
-                    initial_score_columns,
-                    initial_score_columns[0] if initial_score_columns else None,
-                ),
-                padding=False,
-                elem_classes="ranking-table-host",
+    sample_dataset_id = _coerce_sample_dataset(datasets, default_dataset_id)
+    if sample_dataset_id == default_dataset_id:
+        initial_samples = initial.get("samples")
+    else:
+        initial_samples = resolve_view(
+            datasets, metrics, sample_dataset_id, None
+        ).get("samples")
+    with gr.Column(elem_classes="workspace-shell"):
+        with gr.Column(elem_classes="workspace-filters") as filters_host:
+            gr.Markdown(
+                "<p class='filter-help'>"
+                "These filters apply to Leaderboards, Pareto plots, and Samples. "
+                "Search in Models, or leave it empty to include every model."
+                "</p>",
+                elem_classes="filter-help-host",
             )
-
-        with gr.TabItem("Pareto Plots"):
-            pp_dataset, pp_metric, pp_models, pp_title = _filter_row(
+            dataset_dd, metric_dd, models_dd = _filter_row(
                 datasets, metrics, default_dataset_id, None
             )
-            gr.Markdown(
-                "<span class='pareto-help'>"
-                "Green = on the frontier (lower cost or time at the same or better score). "
-                "Lavender = below the frontier. Pick a metric to focus on one pair of plots; "
-                "leave it empty to see every metric."
-                "</span><br/>"
-                "<strong class='pareto-help-emphasis'>Hover a point to see which model it is.</strong>"
-            )
-            pareto_slots = []
-            for slot_index in range(MAX_PARETO_METRICS):
-                offset = slot_index * 4
-                visible = slot_index < len(initial_score_columns)
-                with gr.Column(
-                    visible=visible,
-                    elem_classes="pareto-metric-block",
-                ) as slot_group:
-                    slot_title = gr.Markdown(
-                        (
-                            f"#### {_display_label(initial_score_columns[slot_index])}"
-                            if visible
-                            else ""
-                        ),
-                        elem_classes="pareto-metric-title",
+        with gr.Tabs(elem_classes="main-tabs"):
+            with gr.TabItem("Leaderboards", id=TAB_LEADERBOARDS) as lb_tab:
+                lb_note = gr.Markdown(
+                    _leaderboard_intro_markdown(initial.get("note")),
+                    elem_classes="view-help-host",
+                )
+                platform_choices = _filter_choices(initial_data, "Platform")
+                owner_choices = _filter_choices(initial_data, "Endpoint Owner")
+                optimized_choices = _filter_choices(initial_data, "Optimized")
+                with gr.Row(
+                    elem_classes="leaderboard-controls",
+                    visible=bool(
+                        platform_choices or owner_choices or optimized_choices
+                    ),
+                ) as lb_controls:
+                    platform = gr.Dropdown(
+                        choices=platform_choices,
+                        value=[],
+                        label="Providers",
+                        multiselect=True,
+                        allow_custom_value=False,
+                        filterable=False,
+                        scale=1,
+                        visible=bool(platform_choices),
                     )
-                    with gr.Row(equal_height=False, elem_classes="pareto-layout"):
-                        with gr.Column(scale=1, min_width=320, elem_classes="pareto-col"):
-                            gr.Markdown("#### Price vs score")
-                            slot_price = gr.Plot(
-                                value=(
-                                    initial_pareto_updates[offset + 2]
-                                    if visible
-                                    else None
-                                ),
-                                show_label=False,
-                                elem_classes="pareto-plot",
-                            )
-                        with gr.Column(scale=1, min_width=320, elem_classes="pareto-col"):
-                            gr.Markdown("#### Min generation time vs score")
-                            slot_time = gr.Plot(
-                                value=(
-                                    initial_pareto_updates[offset + 3]
-                                    if visible
-                                    else None
-                                ),
-                                show_label=False,
-                                elem_classes="pareto-plot",
-                            )
-                pareto_slots.append(
-                    (slot_group, slot_title, slot_price, slot_time)
+                    owner = gr.Dropdown(
+                        choices=owner_choices,
+                        value=[],
+                        label="Endpoint owners",
+                        multiselect=True,
+                        allow_custom_value=False,
+                        filterable=False,
+                        scale=1,
+                        visible=bool(owner_choices),
+                    )
+                    optimized = gr.Dropdown(
+                        choices=optimized_choices,
+                        value=[],
+                        label="Optimized",
+                        multiselect=True,
+                        allow_custom_value=False,
+                        filterable=False,
+                        scale=1,
+                        visible=bool(optimized_choices),
+                    )
+                ranking = gr.HTML(
+                    _leaderboard_html(
+                        _assign_leaderboard_ranks(
+                            initial_data,
+                            initial_score_columns[0] if initial_score_columns else None,
+                        ),
+                        initial_columns,
+                        initial_score_columns,
+                        initial_score_columns[0] if initial_score_columns else None,
+                    ),
+                    padding=False,
+                    elem_classes="ranking-table-host",
                 )
 
-        with gr.TabItem("Samples"):
-            sample_default_dataset_id = _coerce_sample_dataset(
-                datasets, default_dataset_id
-            )
-            sm_dataset, _, sm_models, sm_title = _filter_row(
-                datasets,
-                metrics,
-                sample_default_dataset_id,
-                None,
-                require_samples=True,
-                include_metric=False,
-            )
-            with gr.Column(visible=bool(initial_samples)) as samples_panel:
+            with gr.TabItem("Pareto Plots", id=TAB_PARETO) as pp_tab:
                 gr.Markdown(
-                    f"""
-                    <p class="compare-samples-help">
-                    Filter models above (up to <strong>{MAX_COMPARE_MODELS}</strong> are
-                    shown). If none are selected, two defaults appear. Images come from
-                    the public generation URLs for this dataset. Prompts to show chooses
-                    how many shared prompts appear (1–{MAX_COMPARE_PROMPTS}).
-                    </p>
-                    """
+                    "<p class='view-help'>"
+                    "Score against price and generation time. Green points are on the "
+                    "frontier; lavender points sit below it. Hover a point to see "
+                    "which model it is."
+                    "</p>",
+                    elem_classes="view-help-host",
                 )
-                with gr.Row(equal_height=False, elem_classes="compare-controls"):
-                    prompt_count = gr.Slider(
-                        minimum=1,
-                        maximum=MAX_COMPARE_PROMPTS,
-                        value=DEFAULT_COMPARE_PROMPTS,
-                        step=1,
-                        label="Prompts to show",
-                        container=False,
-                        show_reset_button=False,
-                        scale=1,
-                        min_width=180,
-                        elem_classes="compare-prompt-count",
+                pareto_dataset_note = gr.HTML(
+                    "",
+                    padding=False,
+                    visible=False,
+                    elem_classes="pareto-note",
+                )
+                pareto_slots = []
+                for slot_index in range(MAX_PARETO_METRICS):
+                    with gr.Column(
+                        visible=False,
+                        elem_classes="pareto-metric-block",
+                    ) as slot_group:
+                        slot_title = gr.Markdown(
+                            "",
+                            elem_classes="pareto-metric-title",
+                        )
+                        slot_note = gr.HTML(
+                            "",
+                            padding=False,
+                            visible=False,
+                            elem_classes="pareto-note",
+                        )
+                        with gr.Row(
+                            equal_height=True,
+                            elem_classes="pareto-layout",
+                        ) as slot_layout:
+                            with gr.Column(
+                                scale=1,
+                                min_width=320,
+                                elem_classes="pareto-col",
+                            ) as slot_price_col:
+                                gr.Markdown(
+                                    "#### Price vs score",
+                                    elem_classes="pareto-subhead",
+                                )
+                                slot_price = gr.Plot(
+                                    value=None,
+                                    show_label=False,
+                                    elem_classes="pareto-plot",
+                                )
+                            with gr.Column(
+                                scale=1,
+                                min_width=320,
+                                elem_classes="pareto-col",
+                            ) as slot_time_col:
+                                gr.Markdown(
+                                    "#### Min generation time vs score",
+                                    elem_classes="pareto-subhead",
+                                )
+                                slot_time = gr.Plot(
+                                    value=None,
+                                    show_label=False,
+                                    elem_classes="pareto-plot",
+                                )
+                    pareto_slots.append(
+                        (
+                            slot_group,
+                            slot_title,
+                            slot_note,
+                            slot_layout,
+                            slot_price_col,
+                            slot_price,
+                            slot_time_col,
+                            slot_time,
+                        )
                     )
-                    shuffle_button = gr.Button(
-                        "Shuffle prompts",
-                        variant="primary",
-                        scale=0,
-                        min_width=140,
-                        elem_classes="compare-shuffle",
+
+            with gr.TabItem("Samples", id=TAB_SAMPLES) as sm_tab:
+                with gr.Column(visible=bool(initial_samples)) as samples_panel:
+                    gr.Markdown(
+                        f"<p class='view-help'>"
+                        f"The same prompts, side by side. Select up to "
+                        f"<strong>{MAX_COMPARE_MODELS}</strong> models above, or leave "
+                        f"Models empty for two defaults."
+                        f"</p>",
+                        elem_classes="view-help-host",
                     )
-            gallery = gr.HTML(
-                value=_samples_html(
-                    initial_samples, [], DEFAULT_COMPARE_PROMPTS, seed=0
-                ),
-                elem_classes="compare-gallery",
-            )
-            seed_state = gr.State(0)
+                    with gr.Row(equal_height=False, elem_classes="compare-controls"):
+                        prompt_count = gr.Slider(
+                            minimum=1,
+                            maximum=MAX_COMPARE_PROMPTS,
+                            value=DEFAULT_COMPARE_PROMPTS,
+                            step=1,
+                            label="Prompts to show",
+                            container=False,
+                            show_reset_button=False,
+                            scale=1,
+                            min_width=180,
+                            elem_classes="compare-prompt-count",
+                        )
+                        shuffle_button = gr.Button(
+                            "Shuffle prompts",
+                            variant="primary",
+                            scale=0,
+                            min_width=140,
+                            elem_classes="compare-shuffle",
+                        )
+                gallery = gr.HTML(
+                    value=_samples_html(
+                        initial_samples, [], DEFAULT_COMPARE_PROMPTS, seed=0
+                    ),
+                    elem_classes="compare-gallery",
+                )
+                seed_state = gr.State(0)
 
-        with gr.TabItem("About"):
-            render_about()
+            with gr.TabItem("About", id=TAB_ABOUT) as about_tab:
+                render_about()
 
     def _synced_filters(dataset_id, metric_id, models, *, clear_metric=False):
         if clear_metric:
-            metric_id = None
+            metric_id = []
         else:
             metric_id = _coerce_metric(datasets, metrics, dataset_id, metric_id)
         model_choices = _model_choices(datasets, dataset_id)
         model_values = set(_model_choice_values(model_choices))
         models = [model for model in (models or []) if model in model_values]
-        metric_choices = _metric_choices(datasets, metrics, dataset_id)
-        title = _title_markdown(
-            _view_title(datasets, metrics, dataset_id, metric_id)
-        )
-        dataset_update = gr.update(value=dataset_id)
-        metric_update = gr.update(
-            choices=metric_choices, value=_metric_dropdown_value(metric_id)
-        )
-        models_update = gr.update(choices=model_choices, value=models)
-
-        sample_dataset_id = _coerce_sample_dataset(datasets, dataset_id)
-        sample_model_choices = _model_choices(datasets, sample_dataset_id)
-        sample_model_values = set(_model_choice_values(sample_model_choices))
-        sample_models = [
-            model for model in (models or []) if model in sample_model_values
-        ]
-        sample_title = _title_markdown(
-            _view_title(datasets, metrics, sample_dataset_id, None)
-        )
+        metric_choices = _metric_dropdown_choices(datasets, metrics, dataset_id)
         return (
             dataset_id,
             metric_id,
             models,
-            sample_dataset_id,
-            dataset_update,
-            dataset_update,
-            gr.update(value=sample_dataset_id),
-            metric_update,
-            metric_update,
-            models_update,
-            models_update,
-            gr.update(choices=sample_model_choices, value=sample_models),
-            title,
-            title,
-            sample_title,
+            gr.update(
+                choices=metric_choices,
+                value=_metric_dropdown_value(metric_id),
+            ),
+            gr.update(choices=model_choices, value=models),
         )
 
     def _leaderboard_extras(data, platform_value, owner_value, optimized_value):
@@ -1175,78 +1347,186 @@ def render_image_workspace(datasets, metrics, default_dataset_id, default_metric
             platform_value,
             owner_value,
             optimized_value,
+            gr.update(
+                visible=bool(
+                    platform_choices or owner_choices or optimized_choices
+                )
+            ),
+        )
+
+    def _content_flags(tab):
+        return {
+            "include_leaderboard": tab == TAB_LEADERBOARDS,
+            "include_pareto": tab == TAB_PARETO,
+            "include_samples": tab == TAB_SAMPLES,
+        }
+
+    def _commit_state(
+        view_state,
+        dataset_id,
+        metric_id,
+        models,
+        tab,
+        flags,
+        extras=None,
+    ):
+        prev = dict(view_state or {})
+        extras = extras or {}
+        return {
+            "dataset_id": dataset_id,
+            "metric_id": metric_id,
+            "models": list(models or []),
+            "current_tab": tab,
+            "platform": list(
+                extras.get("platform", prev.get("platform") or [])
+            ),
+            "owner": list(extras.get("owner", prev.get("owner") or [])),
+            "optimized": list(
+                extras.get("optimized", prev.get("optimized") or [])
+            ),
+            "stale": {
+                TAB_LEADERBOARDS: not flags["include_leaderboard"],
+                TAB_PARETO: not flags["include_pareto"],
+                TAB_SAMPLES: not flags["include_samples"],
+            },
+        }
+
+    def _save_leaderboard_filters(
+        view_state, platform_value, owner_value, optimized_value
+    ):
+        view_state["platform"] = list(platform_value or [])
+        view_state["owner"] = list(owner_value or [])
+        view_state["optimized"] = list(optimized_value or [])
+        return view_state
+
+    def _restore_leaderboard_filters(
+        view_state, platform_value, owner_value, optimized_value
+    ):
+        stored_platform = (view_state or {}).get("platform") or []
+        stored_owner = (view_state or {}).get("owner") or []
+        stored_optimized = (view_state or {}).get("optimized") or []
+        view = resolve_view(
+            datasets,
+            metrics,
+            (view_state or {}).get("dataset_id"),
+            (view_state or {}).get("metric_id"),
+        )
+        extras = _leaderboard_extras(
+            view["data"] if view else None,
+            stored_platform,
+            stored_owner,
+            stored_optimized,
+        )
+        platform_update = extras[0] if list(platform_value or []) != extras[3] else gr.skip()
+        owner_update = extras[1] if list(owner_value or []) != extras[4] else gr.skip()
+        optimized_update = extras[2] if list(optimized_value or []) != extras[5] else gr.skip()
+        return (
+            extras[6],
+            platform_update,
+            owner_update,
+            optimized_update,
+            extras[3],
+            extras[4],
+            extras[5],
         )
 
     def _views(
         dataset_id,
         metric_id,
         models,
-        search_term,
         platform_value,
         owner_value,
         optimized_value,
         num_prompts,
         seed,
+        *,
+        include_leaderboard=True,
+        include_pareto=False,
+        include_samples=False,
     ):
         view = resolve_view(datasets, metrics, dataset_id, metric_id)
         data = view["data"]
-        filtered = _filter_leaderboard(
-            data,
-            search_term,
-            platform_value or [],
-            owner_value or [],
-            optimized_value or [],
-            models=models,
-        )
-        sort_column = view["score_column"] or (
-            view["score_columns"][0] if view["score_columns"] else None
-        )
-        ranking_html = _leaderboard_html(
-            filtered,
-            view["columns"],
-            view["score_columns"],
-            sort_column,
-        )
-        pareto_data = _filter_leaderboard(data, "", [], [], [], models=models)
-        pareto_updates = _pareto_slot_updates(pareto_data, view["score_columns"])
-        sample_dataset_id = _coerce_sample_dataset(datasets, dataset_id)
-        sample_view = resolve_view(datasets, metrics, sample_dataset_id, None)
-        samples = sample_view.get("samples") if sample_view else None
-        sample_models = [
-            model
-            for model in (models or [])
-            if model
-            in _model_choice_values(_model_choices(datasets, sample_dataset_id))
-        ]
-        samples_html = _samples_html(
-            samples,
-            sample_models,
-            int(num_prompts or DEFAULT_COMPARE_PROMPTS),
-            int(seed or 0),
-        )
+        if include_leaderboard:
+            sort_column = view["score_column"] or (
+                view["score_columns"][0] if view["score_columns"] else None
+            )
+            note = _leaderboard_intro_markdown(view.get("note"))
+            ranking_html = _leaderboard_html(
+                _filter_leaderboard(
+                    _assign_leaderboard_ranks(data, sort_column),
+                    platform_value or [],
+                    owner_value or [],
+                    optimized_value or [],
+                    models=models,
+                ),
+                view["columns"],
+                view["score_columns"],
+                sort_column,
+            )
+        else:
+            note = gr.skip()
+            ranking_html = gr.skip()
+        if include_pareto:
+            pareto_data = _filter_leaderboard(data, [], [], [], models=models)
+            pareto_updates = _pareto_slot_updates(pareto_data, view["score_columns"])
+        else:
+            pareto_updates = _pareto_skip_updates()
+        if include_samples:
+            sample_dataset_id = _coerce_sample_dataset(datasets, dataset_id)
+            sample_view = resolve_view(datasets, metrics, sample_dataset_id, None)
+            samples = sample_view.get("samples") if sample_view else None
+            sample_models = [
+                model
+                for model in (models or [])
+                if model
+                in _model_choice_values(_model_choices(datasets, sample_dataset_id))
+            ]
+            samples_html = _samples_html(
+                samples,
+                sample_models,
+                int(num_prompts or DEFAULT_COMPARE_PROMPTS),
+                int(seed or 0),
+            )
+            samples_visible = gr.update(visible=bool(samples))
+        else:
+            samples_html = gr.skip()
+            samples_visible = gr.skip()
         return (
-            _note_markdown(view.get("note")),
+            note,
             ranking_html,
             *pareto_updates,
             samples_html,
-            gr.update(visible=bool(samples)),
+            samples_visible,
         )
 
     def on_dataset(
         dataset_id,
         metric_id,
         models,
-        search_term,
         platform_value,
         owner_value,
         optimized_value,
         num_prompts,
         seed,
+        view_state,
     ):
+        view_state = dict(view_state or {})
+        tab = view_state.get("current_tab") or TAB_LEADERBOARDS
+        dataset_changed = dataset_id != view_state.get("dataset_id")
         synced = _synced_filters(
-            dataset_id, metric_id, models, clear_metric=True
+            dataset_id,
+            metric_id,
+            models,
+            clear_metric=dataset_changed,
         )
         dataset_id, metric_id, models = synced[:3]
+        if (
+            not dataset_changed
+            and _applied_key(view_state)
+            == _selection_key(dataset_id, metric_id, models)
+        ):
+            return _skip_all(len(dataset_outputs))
+        flags = _content_flags(tab)
         view = resolve_view(datasets, metrics, dataset_id, metric_id)
         extras = _leaderboard_extras(
             view["data"], platform_value, owner_value, optimized_value
@@ -1255,134 +1535,280 @@ def render_image_workspace(datasets, metrics, default_dataset_id, default_metric
             dataset_id,
             metric_id,
             models,
-            search_term,
             extras[3],
             extras[4],
             extras[5],
             num_prompts,
             seed,
+            **flags,
+        )
+        extras_payload = (
+            {
+                "platform": extras[3],
+                "owner": extras[4],
+                "optimized": extras[5],
+            }
+            if tab == TAB_LEADERBOARDS
+            else {}
+        )
+        new_state = _commit_state(
+            view_state,
+            dataset_id,
+            metric_id,
+            models,
+            tab,
+            flags,
+            extras=extras_payload,
+        )
+        return (
+            synced[3],
+            synced[4],
+            extras[6],
+            extras[0],
+            extras[1],
+            extras[2],
+            *views,
+            new_state,
         )
-        return (*synced[4:], extras[0], extras[1], extras[2], *views)
 
     def on_metric(
         dataset_id,
         metric_id,
         models,
-        search_term,
         platform_value,
         owner_value,
         optimized_value,
         num_prompts,
         seed,
+        view_state,
     ):
+        view_state = dict(view_state or {})
+        tab = view_state.get("current_tab") or TAB_LEADERBOARDS
+        selected_raw = _normalize_metric_ids(metric_id)
         metric_id = _coerce_metric(datasets, metrics, dataset_id, metric_id)
-        metric_choices = _metric_choices(datasets, metrics, dataset_id)
-        title = _title_markdown(
-            _view_title(datasets, metrics, dataset_id, metric_id)
-        )
-        metric_update = gr.update(
-            choices=metric_choices, value=_metric_dropdown_value(metric_id)
-        )
-        sample_title = _title_markdown(
-            _view_title(
-                datasets,
-                metrics,
-                _coerce_sample_dataset(datasets, dataset_id),
-                None,
-            )
-        )
+        models = list(models or [])
+        if (
+            ALL_METRICS_ID not in selected_raw
+            and _applied_key(view_state)
+            == _selection_key(dataset_id, metric_id, models)
+        ):
+            return _skip_all(len(metric_outputs))
+        flags = _content_flags(tab)
         views = _views(
             dataset_id,
             metric_id,
             models,
-            search_term,
             platform_value,
             owner_value,
             optimized_value,
             num_prompts,
             seed,
+            **flags,
+        )
+        extras_payload = (
+            {
+                "platform": platform_value or [],
+                "owner": owner_value or [],
+                "optimized": optimized_value or [],
+            }
+            if tab == TAB_LEADERBOARDS
+            else {}
+        )
+        new_state = _commit_state(
+            view_state,
+            dataset_id,
+            metric_id,
+            models,
+            tab,
+            flags,
+            extras=extras_payload,
+        )
+        metric_update = (
+            gr.update(
+                choices=_metric_dropdown_choices(datasets, metrics, dataset_id),
+                value=_metric_dropdown_value(metric_id),
+            )
+            if ALL_METRICS_ID in selected_raw
+            else gr.skip()
         )
         return (
             metric_update,
-            metric_update,
-            title,
-            title,
-            sample_title,
             *views,
+            new_state,
         )
 
     def on_models(
         dataset_id,
         metric_id,
         models,
-        search_term,
         platform_value,
         owner_value,
         optimized_value,
         num_prompts,
         seed,
+        view_state,
     ):
-        model_choices = _model_choices(datasets, dataset_id)
-        model_values = set(_model_choice_values(model_choices))
-        models = [model for model in (models or []) if model in model_values]
-        models_update = gr.update(choices=model_choices, value=models)
-
-        sample_dataset_id = _coerce_sample_dataset(datasets, dataset_id)
-        sample_model_choices = _model_choices(datasets, sample_dataset_id)
-        sample_model_values = set(_model_choice_values(sample_model_choices))
-        sample_models = [
-            model for model in (models or []) if model in sample_model_values
-        ]
-        sample_models_update = gr.update(
-            choices=sample_model_choices, value=sample_models
+        view_state = dict(view_state or {})
+        tab = view_state.get("current_tab") or TAB_LEADERBOARDS
+        metric_id = _coerce_metric(datasets, metrics, dataset_id, metric_id)
+        incoming = list(models or [])
+        model_values = set(
+            _model_choice_values(_model_choices(datasets, dataset_id))
         )
+        models = [model for model in incoming if model in model_values]
+        if _applied_key(view_state) == _selection_key(dataset_id, metric_id, models):
+            return _skip_all(len(models_outputs))
+        flags = _content_flags(tab)
         views = _views(
             dataset_id,
             metric_id,
             models,
-            search_term,
             platform_value,
             owner_value,
             optimized_value,
             num_prompts,
             seed,
+            **flags,
         )
-        return (
-            models_update,
-            models_update,
-            sample_models_update,
-            *views,
+        extras_payload = (
+            {
+                "platform": platform_value or [],
+                "owner": owner_value or [],
+                "optimized": optimized_value or [],
+            }
+            if tab == TAB_LEADERBOARDS
+            else {}
+        )
+        new_state = _commit_state(
+            view_state,
+            dataset_id,
+            metric_id,
+            models,
+            tab,
+            flags,
+            extras=extras_payload,
+        )
+        models_update = (
+            gr.update(value=models) if models != incoming else gr.skip()
+        )
+        return (models_update, *views, new_state)
+
+    def on_tab_select(
+        tab,
+        dataset_id,
+        metric_id,
+        models,
+        platform_value,
+        owner_value,
+        optimized_value,
+        num_prompts,
+        seed,
+        view_state,
+    ):
+        view_state = dict(view_state or {})
+        prev_tab = view_state.get("current_tab") or TAB_LEADERBOARDS
+        if prev_tab == TAB_LEADERBOARDS:
+            _save_leaderboard_filters(
+                view_state,
+                platform_value,
+                owner_value,
+                optimized_value,
+            )
+        view_state["current_tab"] = tab
+        metric_id = _coerce_metric(datasets, metrics, dataset_id, metric_id)
+        models = list(models or [])
+        view_state["dataset_id"] = dataset_id
+        view_state["metric_id"] = metric_id
+        view_state["models"] = models
+        show_filters = tab != TAB_ABOUT
+        show_metric = tab in (TAB_LEADERBOARDS, TAB_PARETO)
+        was_filters = prev_tab != TAB_ABOUT
+        was_metric = prev_tab in (TAB_LEADERBOARDS, TAB_PARETO)
+        filters_vis = (
+            gr.update(visible=show_filters)
+            if show_filters != was_filters
+            else gr.skip()
+        )
+        metric_vis = (
+            gr.update(visible=show_metric)
+            if show_metric != was_metric
+            else gr.skip()
+        )
+        if tab == TAB_LEADERBOARDS:
+            restored = _restore_leaderboard_filters(
+                view_state,
+                platform_value,
+                owner_value,
+                optimized_value,
+            )
+            platform_value = restored[4]
+            owner_value = restored[5]
+            optimized_value = restored[6]
+            lb_filters = restored[:4]
+        else:
+            lb_filters = _skip_all(4)
+        stale = dict(view_state.get("stale") or {})
+        chrome = (filters_vis, metric_vis, *lb_filters)
+        if tab == TAB_ABOUT or not stale.get(tab, True):
+            return (
+                *chrome,
+                *_skip_all(len(view_outputs)),
+                view_state,
+            )
+        flags = _content_flags(tab)
+        views = _views(
+            dataset_id,
+            metric_id,
+            models,
+            platform_value,
+            owner_value,
+            optimized_value,
+            num_prompts,
+            seed,
+            **flags,
         )
+        stale[tab] = False
+        view_state["stale"] = stale
+        return (*chrome, *views, view_state)
 
     def on_leaderboard_filters(
         dataset_id,
         metric_id,
         models,
-        search_term,
         platform_value,
         owner_value,
         optimized_value,
+        view_state,
     ):
-        view = resolve_view(datasets, metrics, dataset_id, metric_id)
-        filtered = _filter_leaderboard(
-            view["data"],
-            search_term,
-            platform_value or [],
-            owner_value or [],
-            optimized_value or [],
-            models=models,
+        view_state = dict(view_state or {})
+        _save_leaderboard_filters(
+            view_state,
+            platform_value,
+            owner_value,
+            optimized_value,
         )
+        view = resolve_view(datasets, metrics, dataset_id, metric_id)
         sort_column = view["score_column"] or (
             view["score_columns"][0] if view["score_columns"] else None
         )
-        return _leaderboard_html(
-            filtered,
-            view["columns"],
-            view["score_columns"],
-            sort_column,
+        return (
+            _leaderboard_html(
+                _filter_leaderboard(
+                    _assign_leaderboard_ranks(view["data"], sort_column),
+                    platform_value or [],
+                    owner_value or [],
+                    optimized_value or [],
+                    models=models,
+                ),
+                view["columns"],
+                view["score_columns"],
+                sort_column,
+            ),
+            view_state,
         )
 
     def on_samples_controls(dataset_id, models, num_prompts, seed):
+        dataset_id = _coerce_sample_dataset(datasets, dataset_id)
         view = resolve_view(datasets, metrics, dataset_id, None)
         return _samples_html(
             view.get("samples") if view else None,
@@ -1392,6 +1818,7 @@ def render_image_workspace(datasets, metrics, default_dataset_id, default_metric
         )
 
     def on_shuffle(dataset_id, models, num_prompts, seed):
+        dataset_id = _coerce_sample_dataset(datasets, dataset_id)
         next_seed = int(seed or 0) + 1
         view = resolve_view(datasets, metrics, dataset_id, None)
         return next_seed, _samples_html(
@@ -1401,142 +1828,175 @@ def render_image_workspace(datasets, metrics, default_dataset_id, default_metric
             next_seed,
         )
 
-    no_metric = gr.State(None)
+    def _on_tab(tab):
+        def handler(
+            dataset_id,
+            metric_id,
+            models,
+            platform_value,
+            owner_value,
+            optimized_value,
+            num_prompts,
+            seed,
+            view_state,
+        ):
+            return on_tab_select(
+                tab,
+                dataset_id,
+                metric_id,
+                models,
+                platform_value,
+                owner_value,
+                optimized_value,
+                num_prompts,
+                seed,
+                view_state,
+            )
+
+        handler.__name__ = f"on_tab_{tab}"
+        return handler
+
+    view_state = gr.State(
+        {
+            "dataset_id": default_dataset_id,
+            "metric_id": None,
+            "models": [],
+            "current_tab": TAB_LEADERBOARDS,
+            "platform": [],
+            "owner": [],
+            "optimized": [],
+            "stale": {
+                TAB_LEADERBOARDS: False,
+                TAB_PARETO: True,
+                TAB_SAMPLES: False,
+            },
+        }
+    )
     pareto_outputs = [
-        component
-        for slot_group, slot_title, slot_price, slot_time in pareto_slots
-        for component in (slot_group, slot_title, slot_price, slot_time)
+        pareto_dataset_note,
+        *[
+            component
+            for slot_group, slot_title, slot_note, slot_layout, slot_price_col, slot_price, slot_time_col, slot_time in pareto_slots
+            for component in (
+                slot_group,
+                slot_title,
+                slot_note,
+                slot_layout,
+                slot_price_col,
+                slot_price,
+                slot_time_col,
+                slot_time,
+            )
+        ],
     ]
-
-    dataset_outputs = [
-        lb_dataset,
-        pp_dataset,
-        sm_dataset,
-        lb_metric,
-        pp_metric,
-        lb_models,
-        pp_models,
-        sm_models,
-        lb_title,
-        pp_title,
-        sm_title,
+    view_inputs = [
         platform,
         owner,
         optimized,
+        prompt_count,
+        seed_state,
+        view_state,
+    ]
+    view_outputs = [
         lb_note,
         ranking,
         *pareto_outputs,
         gallery,
         samples_panel,
     ]
-    for dataset_dd, metric_dd, models_dd in (
-        (lb_dataset, lb_metric, lb_models),
-        (pp_dataset, pp_metric, pp_models),
-        (sm_dataset, no_metric, sm_models),
-    ):
-        dataset_dd.change(
-            on_dataset,
-            inputs=[
-                dataset_dd,
-                metric_dd,
-                models_dd,
-                search,
-                platform,
-                owner,
-                optimized,
-                prompt_count,
-                seed_state,
-            ],
-            outputs=dataset_outputs,
-        )
+    filter_inputs = [dataset_dd, metric_dd, models_dd, *view_inputs]
+
+    dataset_outputs = [
+        metric_dd,
+        models_dd,
+        lb_controls,
+        platform,
+        owner,
+        optimized,
+        *view_outputs,
+        view_state,
+    ]
+    dataset_dd.change(
+        on_dataset,
+        inputs=filter_inputs,
+        outputs=dataset_outputs,
+        **_VIEW_EVENTS,
+    )
 
     metric_outputs = [
-        lb_metric,
-        pp_metric,
-        lb_title,
-        pp_title,
-        sm_title,
-        lb_note,
-        ranking,
-        *pareto_outputs,
-        gallery,
-        samples_panel,
+        metric_dd,
+        *view_outputs,
+        view_state,
     ]
-    for dataset_dd, metric_dd, models_dd in (
-        (lb_dataset, lb_metric, lb_models),
-        (pp_dataset, pp_metric, pp_models),
-    ):
-        metric_dd.change(
-            on_metric,
-            inputs=[
-                dataset_dd,
-                metric_dd,
-                models_dd,
-                search,
-                platform,
-                owner,
-                optimized,
-                prompt_count,
-                seed_state,
-            ],
-            outputs=metric_outputs,
-        )
+    metric_dd.change(
+        on_metric,
+        inputs=filter_inputs,
+        outputs=metric_outputs,
+        **_VIEW_EVENTS,
+    )
 
     models_outputs = [
-        lb_models,
-        pp_models,
-        sm_models,
-        lb_note,
-        ranking,
-        *pareto_outputs,
-        gallery,
-        samples_panel,
+        models_dd,
+        *view_outputs,
+        view_state,
+    ]
+    models_dd.change(
+        on_models,
+        inputs=filter_inputs,
+        outputs=models_outputs,
+        **_VIEW_EVENTS,
+    )
+
+    tab_outputs = [
+        filters_host,
+        metric_dd,
+        lb_controls,
+        platform,
+        owner,
+        optimized,
+        *view_outputs,
+        view_state,
     ]
-    for dataset_dd, metric_dd, models_dd in (
-        (lb_dataset, lb_metric, lb_models),
-        (pp_dataset, pp_metric, pp_models),
-        (sm_dataset, no_metric, sm_models),
+    for tab, tab_item in (
+        (TAB_LEADERBOARDS, lb_tab),
+        (TAB_PARETO, pp_tab),
+        (TAB_SAMPLES, sm_tab),
+        (TAB_ABOUT, about_tab),
     ):
-        models_dd.change(
-            on_models,
-            inputs=[
-                dataset_dd,
-                metric_dd,
-                models_dd,
-                search,
-                platform,
-                owner,
-                optimized,
-                prompt_count,
-                seed_state,
-            ],
-            outputs=models_outputs,
+        tab_item.select(
+            _on_tab(tab),
+            inputs=filter_inputs,
+            outputs=tab_outputs,
+            show_progress="hidden",
         )
 
-    for component in (search, platform, owner, optimized):
+    for component in (platform, owner, optimized):
         component.change(
             on_leaderboard_filters,
             inputs=[
-                lb_dataset,
-                lb_metric,
-                lb_models,
-                search,
+                dataset_dd,
+                metric_dd,
+                models_dd,
                 platform,
                 owner,
                 optimized,
+                view_state,
             ],
-            outputs=ranking,
+            outputs=[ranking, view_state],
+            show_progress="hidden",
         )
 
     prompt_count.change(
         on_samples_controls,
-        inputs=[sm_dataset, sm_models, prompt_count, seed_state],
+        inputs=[dataset_dd, models_dd, prompt_count, seed_state],
         outputs=gallery,
+        show_progress="hidden",
     )
     shuffle_button.click(
         on_shuffle,
-        inputs=[sm_dataset, sm_models, prompt_count, seed_state],
+        inputs=[dataset_dd, models_dd, prompt_count, seed_state],
         outputs=[seed_state, gallery],
+        show_progress="hidden",
     )
 
 def render_about():