utk7rsh/Arbiter_Gen1
1
1<!DOCTYPE html>2<html lang="en">3<head>4<meta charset="UTF-8">5<title>graphify - graphify-out\graph.html</title>6<script src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>7<style>8 * { box-sizing: border-box; margin: 0; padding: 0; }9 body { background: #0f0f1a; color: #e0e0e0; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; display: flex; height: 100vh; overflow: hidden; }10 #graph { flex: 1; }11 #sidebar { width: 280px; background: #1a1a2e; border-left: 1px solid #2a2a4e; display: flex; flex-direction: column; overflow: hidden; }12 #search-wrap { padding: 12px; border-bottom: 1px solid #2a2a4e; }13 #search { width: 100%; background: #0f0f1a; border: 1px solid #3a3a5e; color: #e0e0e0; padding: 7px 10px; border-radius: 6px; font-size: 13px; outline: none; }14 #search:focus { border-color: #4E79A7; }15 #search-results { max-height: 140px; overflow-y: auto; padding: 4px 12px; border-bottom: 1px solid #2a2a4e; display: none; }16 .search-item { padding: 4px 6px; cursor: pointer; border-radius: 4px; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }17 .search-item:hover { background: #2a2a4e; }18 #info-panel { padding: 14px; border-bottom: 1px solid #2a2a4e; min-height: 140px; }19 #info-panel h3 { font-size: 13px; color: #aaa; margin-bottom: 8px; text-transform: uppercase; letter-spacing: 0.05em; }20 #info-content { font-size: 13px; color: #ccc; line-height: 1.6; }21 #info-content .field { margin-bottom: 5px; }22 #info-content .field b { color: #e0e0e0; }23 #info-content .empty { color: #555; font-style: italic; }24 .neighbor-link { display: block; padding: 2px 6px; margin: 2px 0; border-radius: 3px; cursor: pointer; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; border-left: 3px solid #333; }25 .neighbor-link:hover { background: #2a2a4e; }26 #neighbors-list { max-height: 160px; overflow-y: auto; margin-top: 4px; }27 #legend-wrap { flex: 1; overflow-y: auto; padding: 12px; }28 #legend-wrap h3 { font-size: 13px; color: #aaa; margin-bottom: 10px; text-transform: uppercase; letter-spacing: 0.05em; }29 .legend-item { display: flex; align-items: center; gap: 8px; padding: 4px 0; cursor: pointer; border-radius: 4px; font-size: 12px; }30 .legend-item:hover { background: #2a2a4e; padding-left: 4px; }31 .legend-item.dimmed { opacity: 0.35; }32 .legend-dot { width: 12px; height: 12px; border-radius: 50%; flex-shrink: 0; }33 .legend-label { flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }34 .legend-count { color: #666; font-size: 11px; }35 #stats { padding: 10px 14px; border-top: 1px solid #2a2a4e; font-size: 11px; color: #555; }36</style>37</head>38<body>39<div id="graph"></div>40<div id="sidebar">41 <div id="search-wrap">42 <input id="search" type="text" placeholder="Search nodes..." autocomplete="off">43 <div id="search-results"></div>44 </div>45 <div id="info-panel">46 <h3>Node Info</h3>47 <div id="info-content"><span class="empty">Click a node to inspect it</span></div>48 </div>49 <div id="legend-wrap">50 <h3>Communities</h3>51 <div id="legend"></div>52 </div>53 <div id="stats">189 nodes · 220 edges · 29 communities</div>54</div>55<script>56const RAW_NODES = [{"id": "config_py", "label": "config.py", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "config.py", "community": 20, "community_name": "Community 20", "source_file": "config.py", "file_type": "code", "degree": 0}, {"id": "integration_test_py", "label": "integration_test.py", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 24.2, "font": {"size": 12, "color": "#ffffff"}, "title": "integration_test.py", "community": 0, "community_name": "Community 0", "source_file": "integration_test.py", "file_type": "code", "degree": 9}, {"id": "integration_test_record", "label": "record()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "record()", "community": 0, "community_name": "Community 0", "source_file": "integration_test.py", "file_type": "code", "degree": 2}, {"id": "integration_test_section", "label": "section()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "section()", "community": 0, "community_name": "Community 0", "source_file": "integration_test.py", "file_type": "code", "degree": 1}, {"id": "integration_test_schemaerror", "label": "SchemaError", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "SchemaError", "community": 0, "community_name": "Community 0", "source_file": "integration_test.py", "file_type": "code", "degree": 2}, {"id": "exception", "label": "Exception", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Exception", "community": 0, "community_name": "Community 0", "source_file": "", "file_type": "code", "degree": 1}, {"id": "integration_test_validate_action_schema", "label": "validate_action_schema()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "validate_action_schema()", "community": 0, "community_name": "Community 0", "source_file": "integration_test.py", "file_type": "code", "degree": 2}, {"id": "integration_test_load_lora", "label": "load_lora()", "color": {"background": "#4E79A7", 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"border": "#E15759"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "renderClaim()", "community": 12, "community_name": "Community 12", "source_file": "arbiter\\demo\\frontend\\components\\ContrastPanel.jsx", "file_type": "code", "degree": 1}, {"id": "contrastpanel_renderside", "label": "renderSide()", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "renderSide()", "community": 12, "community_name": "Community 12", "source_file": "arbiter\\demo\\frontend\\components\\ContrastPanel.jsx", "file_type": "code", "degree": 1}, {"id": "arbiter_demo_frontend_components_episodecontrols_jsx", "label": "EpisodeControls.jsx", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "EpisodeControls.jsx", "community": 17, "community_name": "Community 17", "source_file": "arbiter\\demo\\frontend\\components\\EpisodeControls.jsx", "file_type": "code", "degree": 1}, {"id": "episodecontrols_getmodelactiveclass", "label": "getModelActiveClass()", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "getModelActiveClass()", "community": 17, "community_name": "Community 17", "source_file": "arbiter\\demo\\frontend\\components\\EpisodeControls.jsx", "file_type": "code", "degree": 1}, {"id": "arbiter_demo_frontend_components_hypothesistracker_jsx", "label": "HypothesisTracker.jsx", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "HypothesisTracker.jsx", "community": 18, "community_name": "Community 18", "source_file": "arbiter\\demo\\frontend\\components\\HypothesisTracker.jsx", "file_type": "code", "degree": 1}, {"id": "hypothesistracker_getcard", "label": "getCard()", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "getCard()", "community": 18, "community_name": "Community 18", "source_file": "arbiter\\demo\\frontend\\components\\HypothesisTracker.jsx", "file_type": "code", "degree": 1}, {"id": "arbiter_demo_frontend_components_rewardpanel_jsx", "label": "RewardPanel.jsx", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "RewardPanel.jsx", "community": 19, "community_name": "Community 19", "source_file": "arbiter\\demo\\frontend\\components\\RewardPanel.jsx", "file_type": "code", "degree": 1}, {"id": "rewardpanel_renderbar", "label": "renderBar()", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "renderBar()", "community": 19, "community_name": "Community 19", "source_file": "arbiter\\demo\\frontend\\components\\RewardPanel.jsx", "file_type": "code", "degree": 1}, {"id": "arbiter_demo_frontend_components_statspage_jsx", "label": "StatsPage.jsx", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "StatsPage.jsx", "community": 13, "community_name": "Community 13", "source_file": "arbiter\\demo\\frontend\\components\\StatsPage.jsx", "file_type": "code", "degree": 2}, {"id": "statspage_bestidx", "label": "bestIdx()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "bestIdx()", "community": 13, "community_name": "Community 13", "source_file": "arbiter\\demo\\frontend\\components\\StatsPage.jsx", "file_type": "code", "degree": 1}, {"id": "statspage_baranim", "label": "barAnim()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "barAnim()", "community": 13, "community_name": "Community 13", "source_file": "arbiter\\demo\\frontend\\components\\StatsPage.jsx", "file_type": "code", "degree": 1}, {"id": "arbiter_demo_frontend_hooks_usebackend_js", "label": "useBackend.js", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "useBackend.js", "community": 22, "community_name": "Community 22", "source_file": "arbiter\\demo\\frontend\\hooks\\useBackend.js", "file_type": "code", "degree": 0}, {"id": "arbiter_demo_frontend_hooks_useepisode_js", "label": "useEpisode.js", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "useEpisode.js", "community": 14, "community_name": "Community 14", "source_file": "arbiter\\demo\\frontend\\hooks\\useEpisode.js", "file_type": "code", "degree": 2}, {"id": "useepisode_autolayout", "label": "_autoLayout()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_autoLayout()", "community": 14, "community_name": "Community 14", "source_file": "arbiter\\demo\\frontend\\hooks\\useEpisode.js", "file_type": "code", "degree": 1}, {"id": "useepisode_getscriptedaction", "label": "_getScriptedAction()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_getScriptedAction()", "community": 14, "community_name": "Community 14", "source_file": "arbiter\\demo\\frontend\\hooks\\useEpisode.js", "file_type": "code", "degree": 1}, {"id": "arbiter_training_evaluate_py", "label": "evaluate.py", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "evaluate.py", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "code", "degree": 5}, {"id": "evaluate_load_model", "label": "load_model()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "load_model()", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "code", "degree": 2}, {"id": "evaluate_rule_based_action", "label": "rule_based_action()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "rule_based_action()", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "code", "degree": 3}, {"id": "evaluate_generate_llm_action", "label": "generate_llm_action()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "generate_llm_action()", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "code", "degree": 3}, {"id": "evaluate_run_episode_with_model", "label": "run_episode_with_model()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "run_episode_with_model()", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "code", "degree": 5}, {"id": "evaluate_rationale_1", "label": "Three-Condition Evaluation for ARBITER. Runs 10 held-out Level 3 episodes und", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Three-Condition Evaluation for ARBITER. Runs 10 held-out Level 3 episodes und", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "rationale", "degree": 1}, {"id": "evaluate_rationale_50", "label": "Load a model and tokenizer for inference.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Load a model and tokenizer for inference.", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "rationale", "degree": 1}, {"id": "evaluate_rationale_77", "label": "Simple rule-based agent for the untrained baseline (no GPU needed for demo).", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Simple rule-based agent for the untrained baseline (no GPU needed for demo).", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "rationale", "degree": 1}, {"id": "evaluate_rationale_105", "label": "Generate an action from an LLM.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate an action from an LLM.", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "rationale", "degree": 1}, {"id": "evaluate_rationale_149", "label": "Run one episode. Returns metrics dict.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Run one episode. Returns metrics dict.", "community": 5, "community_name": "Community 5", "source_file": "arbiter\\training\\evaluate.py", "file_type": "rationale", "degree": 1}, {"id": "arbiter_training_grpo_trainer_py", "label": "grpo_trainer.py", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "grpo_trainer.py", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "code", "degree": 4}, {"id": "grpo_trainer_generate_action", "label": "generate_action()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "generate_action()", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "code", "degree": 3}, {"id": "grpo_trainer_run_episode", "label": "run_episode()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "run_episode()", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "code", "degree": 4}, {"id": "grpo_trainer_grpo_update", "label": "grpo_update()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "grpo_update()", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "code", "degree": 3}, {"id": "grpo_trainer_rationale_1", "label": "GRPO Training Loop for ARBITER. Runs dense-reward GRPO reinforcement learning", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "GRPO Training Loop for ARBITER. Runs dense-reward GRPO reinforcement learning", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "rationale", "degree": 1}, {"id": "grpo_trainer_rationale_81", "label": "Query the LLM for the next action given observation.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Query the LLM for the next action given observation.", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "rationale", "degree": 1}, {"id": "grpo_trainer_rationale_127", "label": "Run one complete episode with the LLM. Returns (total_reward, step_rewards,", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Run one complete episode with the LLM. Returns (total_reward, step_rewards,", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "rationale", "degree": 1}, {"id": "grpo_trainer_rationale_169", "label": "Simplified GRPO update: - Compute advantage = reward - mean(batch_rewards)", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Simplified GRPO update: - Compute advantage = reward - mean(batch_rewards)", "community": 0, "community_name": "Community 0", "source_file": "arbiter\\training\\grpo_trainer.py", "file_type": "rationale", "degree": 1}, {"id": "arbiter_training_sft_generator_py", "label": "sft_generator.py", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 19.5, "font": {"size": 12, "color": "#ffffff"}, "title": "sft_generator.py", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 6}, {"id": "sft_generator_anthropicclient", "label": "_AnthropicClient", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "_AnthropicClient", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 4}, {"id": "sft_generator_anthropicclient_init", "label": ".__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": ".__init__()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 1}, {"id": "sft_generator_anthropicclient_chat", "label": ".chat()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": ".chat()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 1}, {"id": "sft_generator_geminiclient", "label": "_GeminiClient", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "_GeminiClient", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 4}, {"id": "sft_generator_geminiclient_init", "label": ".__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": ".__init__()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 1}, {"id": "sft_generator_geminiclient_chat", "label": ".chat()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": ".chat()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 1}, {"id": "sft_generator_groqclient", "label": "_GroqClient", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "_GroqClient", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 5}, {"id": "sft_generator_groqclient_init", "label": ".__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": ".__init__()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 1}, {"id": "sft_generator_groqclient_chat", "label": ".chat()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".chat()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 2}, {"id": "sft_generator_generate_trajectory", "label": "generate_trajectory()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "generate_trajectory()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 5}, {"id": "sft_generator_main", "label": "main()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "main()", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "code", "degree": 5}, {"id": "sft_generator_rationale_1", "label": "SFT Trajectory Generator for ARBITER. Generates 400 training trajectories using", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "SFT Trajectory Generator for ARBITER. Generates 400 training trajectories using", "community": 4, "community_name": "Community 4", "source_file": "D:\\vraj\\scalar\\Arbiter\\arbiter\\training\\sft_generator.py", "file_type": "rationale", "degree": 1}, {"id": "sft_generator_rationale_129", "label": "Groq inference \u2014 very fast, generous free tier.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Groq inference \u2014 very fast, generous free tier.", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "rationale", "degree": 1}, {"id": "sft_generator_rationale_165", "label": "Run one episode with the LLM as the Auditor. Returns list of (prompt, response)", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Run one episode with the LLM as the Auditor. Returns list of (prompt, response)", "community": 4, "community_name": "Community 4", "source_file": "arbiter\\training\\sft_generator.py", "file_type": "rationale", "degree": 1}, {"id": "arbiter_training_train_sft_py", "label": "train_sft.py", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "train_sft.py", "community": 11, "community_name": "Community 11", "source_file": "arbiter\\training\\train_sft.py", "file_type": "code", "degree": 2}, {"id": "train_sft_load_trajectories", "label": "load_trajectories()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "load_trajectories()", "community": 11, "community_name": "Community 11", "source_file": "arbiter\\training\\train_sft.py", "file_type": "code", "degree": 2}, {"id": "train_sft_rationale_1", "label": "SFT Training Script for ARBITER. Fine-tunes Qwen 2.5 1.5B on the generated tr", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "SFT Training Script for ARBITER. Fine-tunes Qwen 2.5 1.5B on the generated tr", "community": 11, "community_name": "Community 11", "source_file": "arbiter\\training\\train_sft.py", "file_type": "rationale", "degree": 1}, {"id": "train_sft_rationale_87", "label": "Load JSONL trajectories and format as chat turns.", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Load JSONL trajectories and format as chat turns.", "community": 11, "community_name": "Community 11", "source_file": "arbiter\\training\\train_sft.py", "file_type": "rationale", "degree": 1}, {"id": "arbiter_training_visualize_py", "label": "visualize.py", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "visualize.py", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "code", "degree": 4}, {"id": "visualize_smooth", "label": "smooth()", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "smooth()", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "code", "degree": 2}, {"id": "visualize_load_log", "label": "load_log()", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "load_log()", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "code", "degree": 2}, {"id": "visualize_make_demo_data", "label": "make_demo_data()", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "make_demo_data()", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "code", "degree": 2}, {"id": "visualize_rationale_1", "label": "Arms Race Visualization for ARBITER. Generates 4 publication-quality plots:", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Arms Race Visualization for ARBITER. Generates 4 publication-quality plots:", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "rationale", "degree": 1}, {"id": "visualize_rationale_68", "label": "Exponential moving average smoothing.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Exponential moving average smoothing.", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "rationale", "degree": 1}, {"id": "visualize_rationale_78", "label": "Load JSONL reward log.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Load JSONL reward log.", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "rationale", "degree": 1}, {"id": "visualize_rationale_87", "label": "Generate plausible synthetic training curves for demo/presentation.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate plausible synthetic training curves for demo/presentation.", "community": 8, "community_name": "Community 8", "source_file": "arbiter\\training\\visualize.py", "file_type": "rationale", "degree": 1}, {"id": "arbiter_training_init_py", "label": "__init__.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "__init__.py", "community": 23, "community_name": "Community 23", "source_file": "arbiter\\training\\__init__.py", "file_type": "code", "degree": 0}, {"id": "app_rationale_45", "label": "Render the observable causal graph with color-coded nodes.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Render the observable causal graph with color-coded nodes.", "community": 24, "community_name": "Community 24", "source_file": "D:\\vraj\\scalar\\Arbiter\\arbiter\\demo\\app.py", "file_type": "rationale", "degree": 0}, {"id": "app_rationale_109", "label": "Format claims as colored HTML.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Format claims as colored HTML.", "community": 25, "community_name": "Community 25", "source_file": "D:\\vraj\\scalar\\Arbiter\\arbiter\\demo\\app.py", "file_type": "rationale", "degree": 0}, {"id": "sft_generator_rationale_62", "label": "Run one episode with Claude as the Auditor. Returns list of (prompt, response) p", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Run one episode with Claude as the Auditor. Returns list of (prompt, response) p", "community": 26, "community_name": "Community 26", "source_file": "D:\\vraj\\scalar\\Arbiter\\arbiter\\training\\sft_generator.py", "file_type": "rationale", "degree": 0}, {"id": "arbiter_idea_ARBITER", "label": "ARBITER System", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "ARBITER System", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 5}, {"id": "arbiter_idea_Auditor", "label": "Auditor Agent", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "Auditor Agent", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 5}, {"id": "arbiter_idea_Defender", "label": "Defender Agent", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "Defender Agent", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 3}, {"id": "arbiter_idea_MetaOverseer", "label": "Meta-Overseer Agent", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Meta-Overseer Agent", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_CausalGraph", "label": "Causal Decision Graph", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "Causal Decision Graph", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 5}, {"id": "arbiter_idea_ProxyDiscrimination", "label": "Proxy Discrimination (Type 1)", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Proxy Discrimination (Type 1)", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_AdversarialInjection", "label": "Adversarial Injection (Type 2)", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Adversarial Injection (Type 2)", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 1}, {"id": "arbiter_idea_ModelDrift", "label": "Model Drift (Type 3)", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Model Drift (Type 3)", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 1}, {"id": "arbiter_idea_CounterfactualClaim", "label": "Counterfactual Claim", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Counterfactual Claim", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_TheoryOfMind", "label": "Theory-of-Mind Claim", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Theory-of-Mind Claim", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_RewardFunction", "label": "Reward Function", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Reward Function", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_Curriculum", "label": "7-Level Curriculum", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "7-Level Curriculum", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 1}, {"id": "arbiter_idea_GRPO", "label": "GRPO RL Training", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 14.7, "font": {"size": 12, "color": "#ffffff"}, "title": "GRPO RL Training", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 3}, {"id": "arbiter_idea_SFT", "label": "SFT Behavioral Cloning", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "SFT Behavioral Cloning", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 1}, {"id": "arbiter_idea_ArmsRace", "label": "Arms Race Dynamic", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 16.3, "font": {"size": 12, "color": "#ffffff"}, "title": "Arms Race Dynamic", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 4}, {"id": "arbiter_idea_ScalableOversight", "label": "Scalable Oversight", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Scalable Oversight", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "arbiter_idea_OpenEnv", "label": "OpenEnv Wrapper", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "OpenEnv Wrapper", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 1}, {"id": "arbiter_idea_DenseReward", "label": "Dense Intermediate Reward", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Dense Intermediate Reward", "community": 3, "community_name": "Community 3", "source_file": "Arbiter-IDEA.txt", "file_type": "document", "degree": 2}, {"id": "tasklist_ProjectScaffolding", "label": "Project Scaffolding", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Project Scaffolding", "community": 27, "community_name": "Community 27", "source_file": "TASK_LIST.md", "file_type": "document", "degree": 0}, {"id": "tasklist_Validation", "label": "10-Episode Validation (70/70)", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "10-Episode Validation (70/70)", "community": 3, "community_name": "Community 3", "source_file": "TASK_LIST.md", "file_type": "document", "degree": 1}, {"id": "tasklist_GradioDemo", "label": "Gradio Demo Interface", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Gradio Demo Interface", "community": 3, "community_name": "Community 3", "source_file": "TASK_LIST.md", "file_type": "document", "degree": 2}, {"id": "tasklist_PitchDeck", "label": "Pitch Deck (11 Slides)", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Pitch Deck (11 Slides)", "community": 28, "community_name": "Community 28", "source_file": "TASK_LIST.md", "file_type": "document", "degree": 0}, {"id": "tasklist_HuggingFace", "label": "HuggingFace Space Deployment", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "HuggingFace Space Deployment", "community": 3, "community_name": "Community 3", "source_file": "TASK_LIST.md", "file_type": "document", "degree": 1}];57const RAW_EDGES = [{"from": "integration_test_py", "to": "integration_test_record", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_section", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_schemaerror", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_validate_action_schema", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_load_lora", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_run_model_episode", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_baseline_episode_reward", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_port_open", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_py", "to": "integration_test_rationale_1", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_record", "to": "validate_run_validation", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "integration_test_schemaerror", "to": "exception", "label": "inherits", "title": "inherits [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_validate_action_schema", "to": "integration_test_rationale_143", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_load_lora", "to": "integration_test_rationale_327", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_run_model_episode", "to": "integration_test_rationale_366", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_run_model_episode", "to": "test_steps_step", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "integration_test_baseline_episode_reward", "to": "integration_test_rationale_424", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "integration_test_baseline_episode_reward", "to": "test_steps_step", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "test_steps_py", "to": "test_steps_step", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "test_steps_step", "to": "validate_run_validation", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "test_steps_step", "to": "server_step_endpoint", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "test_steps_step", "to": "app_run_query", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "test_steps_step", "to": "app_run_agent_step", "label": "calls", "title": "calls [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "test_steps_step", 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{"cid": 3, "color": "#76B7B2", "label": "Community 3", "count": 21}, {"cid": 4, "color": "#59A14F", "label": "Community 4", "count": 15}, {"cid": 5, "color": "#EDC948", "label": "Community 5", "count": 10}, {"cid": 6, "color": "#B07AA1", "label": "Community 6", "count": 8}, {"cid": 7, "color": "#FF9DA7", "label": "Community 7", "count": 8}, {"cid": 8, "color": "#9C755F", "label": "Community 8", "count": 8}, {"cid": 9, "color": "#BAB0AC", "label": "Community 9", "count": 5}, {"cid": 10, "color": "#4E79A7", "label": "Community 10", "count": 4}, {"cid": 11, "color": "#F28E2B", "label": "Community 11", "count": 4}, {"cid": 12, "color": "#E15759", "label": "Community 12", "count": 3}, {"cid": 13, "color": "#76B7B2", "label": "Community 13", "count": 3}, {"cid": 14, "color": "#59A14F", "label": "Community 14", "count": 3}, {"cid": 15, "color": "#EDC948", "label": "Community 15", "count": 2}, {"cid": 16, "color": "#B07AA1", "label": "Community 16", "count": 2}, {"cid": 17, "color": "#FF9DA7", "label": "Community 17", "count": 2}, {"cid": 18, "color": "#9C755F", "label": "Community 18", "count": 2}, {"cid": 19, "color": "#BAB0AC", "label": "Community 19", "count": 2}, {"cid": 20, "color": "#4E79A7", "label": "Community 20", "count": 1}, {"cid": 21, "color": "#F28E2B", "label": "Community 21", "count": 1}, {"cid": 22, "color": "#E15759", "label": "Community 22", "count": 1}, {"cid": 23, "color": "#76B7B2", "label": "Community 23", "count": 1}, {"cid": 24, "color": "#59A14F", "label": "Community 24", "count": 1}, {"cid": 25, "color": "#EDC948", "label": "Community 25", "count": 1}, {"cid": 26, "color": "#B07AA1", "label": "Community 26", "count": 1}, {"cid": 27, "color": "#FF9DA7", "label": "Community 27", "count": 1}, {"cid": 28, "color": "#9C755F", "label": "Community 28", "count": 1}];59 60// HTML-escape helper — prevents XSS when injecting graph data into innerHTML61function esc(s) {62 return String(s).replace(/&/g,'&').replace(/</g,'<').replace(/>/g,'>').replace(/"/g,'"').replace(/'/g,''');63}64 65// Build vis datasets66const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({67 id: n.id, label: n.label, color: n.color, size: n.size,68 font: n.font, title: n.title,69 _community: n.community, _community_name: n.community_name,70 _source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,71})));72 73const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({74 id: i, from: e.from, to: e.to,75 label: '',76 title: e.title,77 dashes: e.dashes,78 width: e.width,79 color: e.color,80 arrows: { to: { enabled: true, scaleFactor: 0.5 } },81})));82 83const container = document.getElementById('graph');84const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {85 physics: {86 enabled: true,87 solver: 'forceAtlas2Based',88 forceAtlas2Based: {89 gravitationalConstant: -60,90 centralGravity: 0.005,91 springLength: 120,92 springConstant: 0.08,93 damping: 0.4,94 avoidOverlap: 0.8,95 },96 stabilization: { iterations: 200, fit: true },97 },98 interaction: {99 hover: true,100 tooltipDelay: 100,101 hideEdgesOnDrag: true,102 navigationButtons: false,103 keyboard: false,104 },105 nodes: { shape: 'dot', borderWidth: 1.5 },106 edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 },107});108 109network.once('stabilizationIterationsDone', () => {110 network.setOptions({ physics: { enabled: false } });111});112 113function showInfo(nodeId) {114 const n = nodesDS.get(nodeId);115 if (!n) return;116 const neighborIds = network.getConnectedNodes(nodeId);117 const neighborItems = neighborIds.map(nid => {118 const nb = nodesDS.get(nid);119 const color = nb ? nb.color.background : '#555';120 return `<span class="neighbor-link" style="border-left-color:${esc(color)}" onclick="focusNode(${JSON.stringify(nid)})">${esc(nb ? nb.label : nid)}</span>`;121 }).join('');122 document.getElementById('info-content').innerHTML = `123 <div class="field"><b>${esc(n.label)}</b></div>124 <div class="field">Type: ${esc(n._file_type || 'unknown')}</div>125 <div class="field">Community: ${esc(n._community_name)}</div>126 <div class="field">Source: ${esc(n._source_file || '-')}</div>127 <div class="field">Degree: ${n._degree}</div>128 ${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''}129 `;130}131 132function focusNode(nodeId) {133 network.focus(nodeId, { scale: 1.4, animation: true });134 network.selectNodes([nodeId]);135 showInfo(nodeId);136}137 138// Track hovered node — hover detection is more reliable than click params139let hoveredNodeId = null;140network.on('hoverNode', params => {141 hoveredNodeId = params.node;142 container.style.cursor = 'pointer';143});144network.on('blurNode', () => {145 hoveredNodeId = null;146 container.style.cursor = 'default';147});148container.addEventListener('click', () => {149 if (hoveredNodeId !== null) {150 showInfo(hoveredNodeId);151 network.selectNodes([hoveredNodeId]);152 }153});154network.on('click', params => {155 if (params.nodes.length > 0) {156 showInfo(params.nodes[0]);157 } else if (hoveredNodeId === null) {158 document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';159 }160});161 162const searchInput = document.getElementById('search');163const searchResults = document.getElementById('search-results');164searchInput.addEventListener('input', () => {165 const q = searchInput.value.toLowerCase().trim();166 searchResults.innerHTML = '';167 if (!q) { searchResults.style.display = 'none'; return; }168 const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);169 if (!matches.length) { searchResults.style.display = 'none'; return; }170 searchResults.style.display = 'block';171 matches.forEach(n => {172 const el = document.createElement('div');173 el.className = 'search-item';174 el.textContent = n.label;175 el.style.borderLeft = `3px solid ${n.color.background}`;176 el.style.paddingLeft = '8px';177 el.onclick = () => {178 network.focus(n.id, { scale: 1.5, animation: true });179 network.selectNodes([n.id]);180 showInfo(n.id);181 searchResults.style.display = 'none';182 searchInput.value = '';183 };184 searchResults.appendChild(el);185 });186});187document.addEventListener('click', e => {188 if (!searchResults.contains(e.target) && e.target !== searchInput)189 searchResults.style.display = 'none';190});191 192const hiddenCommunities = new Set();193const legendEl = document.getElementById('legend');194LEGEND.forEach(c => {195 const item = document.createElement('div');196 item.className = 'legend-item';197 item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div>198 <span class="legend-label">${c.label}</span>199 <span class="legend-count">${c.count}</span>`;200 item.onclick = () => {201 if (hiddenCommunities.has(c.cid)) {202 hiddenCommunities.delete(c.cid);203 item.classList.remove('dimmed');204 } else {205 hiddenCommunities.add(c.cid);206 item.classList.add('dimmed');207 }208 const updates = RAW_NODES209 .filter(n => n.community === c.cid)210 .map(n => ({ id: n.id, hidden: hiddenCommunities.has(c.cid) }));211 nodesDS.update(updates);212 };213 legendEl.appendChild(item);214});215</script>216<script>217// Render hyperedges as shaded regions218const hyperedges = [{"id": "three_agent_system", "label": "Three-Agent ARBITER System", "nodes": ["arbiter_idea_Auditor", "arbiter_idea_Defender", "arbiter_idea_MetaOverseer"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "Arbiter-IDEA.txt"}, {"id": "training_pipeline", "label": "Training Pipeline (SFT\u2192GRPO\u2192Arms Race)", "nodes": ["arbiter_idea_SFT", "arbiter_idea_GRPO", "arbiter_idea_ArmsRace"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "Arbiter-IDEA.txt"}, {"id": "anomaly_types", "label": "Three Anomaly Types", "nodes": ["arbiter_idea_ProxyDiscrimination", "arbiter_idea_AdversarialInjection", "arbiter_idea_ModelDrift"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "Arbiter-IDEA.txt"}];219// afterDrawing passes ctx already transformed to network coordinate space.220// Draw node positions raw — no manual pan/zoom/DPR math needed.221network.on('afterDrawing', function(ctx) {222 hyperedges.forEach(h => {223 const positions = h.nodes224 .map(nid => network.getPositions([nid])[nid])225 .filter(p => p !== undefined);226 if (positions.length < 2) return;227 ctx.save();228 ctx.globalAlpha = 0.12;229 ctx.fillStyle = '#6366f1';230 ctx.strokeStyle = '#6366f1';231 ctx.lineWidth = 2;232 ctx.beginPath();233 // Centroid and expanded hull in network coordinates234 const cx = positions.reduce((s, p) => s + p.x, 0) / positions.length;235 const cy = positions.reduce((s, p) => s + p.y, 0) / positions.length;236 const expanded = positions.map(p => ({237 x: cx + (p.x - cx) * 1.15,238 y: cy + (p.y - cy) * 1.15239 }));240 ctx.moveTo(expanded[0].x, expanded[0].y);241 expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y));242 ctx.closePath();243 ctx.fill();244 ctx.globalAlpha = 0.4;245 ctx.stroke();246 // Label247 ctx.globalAlpha = 0.8;248 ctx.fillStyle = '#4f46e5';249 ctx.font = 'bold 11px sans-serif';250 ctx.textAlign = 'center';251 ctx.fillText(h.label, cx, cy - 5);252 ctx.restore();253 });254});255</script>256</body>257</html>