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<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>OSINT Environment Dashboard</title>
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</head>
<body>
<div class="wrap">
<div class="hero">
<section class="card hero-main">
<h1>OSINT Benchmark Dashboard</h1>
<p class="muted">Interactive explorer for canonical knowledge graph, episode traces, source platform records, and benchmark ranking.</p>
<div class="pill-row" id="hero-pills"></div>
<div class="stats" id="stats"></div>
</section>
<section class="card">
<h2>Episode Explorer</h2>
<div class="inline" style="margin-bottom:8px">
<label class="mono" for="episode-select">Episode</label>
<select id="episode-select" style="flex:1"></select>
</div>
<div class="inline" style="gap:6px; margin-bottom:8px">
<button id="episode-prev">Prev</button>
<button id="episode-next">Next</button>
</div>
<div><strong>Task ID:</strong> <span id="task-id"></span></div>
<div><strong>Task Type:</strong> <span id="task-type"></span></div>
<div style="margin-top:8px"><strong>Question</strong></div>
<div id="task-question" class="muted mono-box"></div>
<div style="margin-top:8px"><strong>Ground Truth Answer</strong>: <span id="task-answer"></span></div>
<div style="margin-top:8px"><strong>Agent Answer</strong>: <span id="agent-answer"></span></div>
<div style="margin-top:8px"><strong>Correct</strong>: <span id="answer-correct"></span></div>
</section>
</div>
<div class="layout">
<section class="card control-col">
<div>
<h2>Graph Controls</h2>
<div class="control-grid">
<label class="mono" for="graph-mode">Graph Layer</label>
<select id="graph-mode">
<option value="canonical">Canonical Graph</option>
<option value="episode">Episode Graph</option>
</select>
<label class="mono" for="graph-search">Node Search</label>
<input id="graph-search" type="search" placeholder="Type node id or label..." />
<label class="mono" for="relation-filter">Relation Filter</label>
<input id="relation-filter" type="search" placeholder="Filter edge labels..." />
<button id="fit-graph" class="primary">Fit Graph</button>
</div>
</div>
<div>
<h2>Node Types</h2>
<div id="type-filters" class="control-grid mono"></div>
</div>
</section>
<section class="card">
<h2>Graph Explorer</h2>
<div class="graph-wrap">
<div class="graph-banner" id="graph-banner">Layer: Canonical Graph</div>
<div id="graph-canvas" class="graph"></div>
</div>
<div class="legend">
<span><span class="dot" style="background:#16a34a"></span>matched edge</span>
<span><span class="dot" style="background:#2563eb"></span>predicted only</span>
<span><span class="dot" style="background:#f59e0b"></span>truth only</span>
</div>
</section>
<section class="card control-col">
<div>
<h2>Node Inspector</h2>
<pre id="node-detail" class="json-view">Click a node to inspect attributes and neighbors.</pre>
</div>
<div>
<h2>Edge Inspector</h2>
<pre id="edge-detail" class="json-view">Click an edge to inspect relation details.</pre>
</div>
</section>
</div>
<div class="split">
<section class="card">
<h2>Original Database Explorer</h2>
<div class="db-tabs" id="db-tabs"></div>
<div class="inline" style="margin-bottom:8px">
<input id="db-search" type="search" placeholder="Search records..." style="flex:1" />
<select id="db-limit">
<option value="200">200</option>
<option value="500">500</option>
<option value="1000">1000</option>
</select>
</div>
<div class="table-wrap"><table id="db-table"></table></div>
</section>
<section class="card">
<h2>Selected Source Record</h2>
<pre id="db-detail" class="json-view">Click a row in the database table to inspect full JSON.</pre>
</section>
</div>
<div class="charts">
<section class="card">
<h2>Benchmark Summary Radar</h2>
<div class="chart-box"><canvas id="summary-chart"></canvas></div>
</section>
<section class="card">
<h2>Episode Reward and Graph F1</h2>
<div class="chart-box"><canvas id="trace-chart"></canvas></div>
</section>
</div>
<section class="card">
<h2>Benchmark Leaderboard</h2>
<div class="inline" style="margin-bottom:8px">
<label class="mono" for="leader-sort">Sort by</label>
<select id="leader-sort" class="subtle">
<option value="leaderboard_score">leaderboard_score</option>
<option value="task_success_rate">task_success_rate</option>
<option value="avg_graph_f1">avg_graph_f1</option>
<option value="retrieval_signal">retrieval_signal</option>
<option value="structural_signal">structural_signal</option>
<option value="spawn_signal">spawn_signal</option>
<option value="avg_reward">avg_reward</option>
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<div class="table-wrap"><table id="leaderboard-table"></table></div>
</section>
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"knowledge_indexing": 0.0, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.32808540635585226, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "post_shift_roster", "rel": "references", "dst": "loc_dockyard17", "confidence": 1.0}], "truth_edges": [{"src": "post_shift_roster", "rel": "references", "dst": "loc_dockyard17", "confidence": 1.0}]}, {"task_id": "seed_task_5", "task_type": "identity_resolution", "question": "Alias alias_monsoonbyte belongs to which user and where does that user work? Return only the organization node id.", "task_answer": "org_blueharbor_media", "agent_answer": "org_blueharbor_media", "graph_f1": 1.0, "reward": 5.0327885648681026, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.064, "invalid_tool_penalty": 0.0, "total": 5.240703158512251, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.15000000000000002, "relation_informativeness": -0.0009554903483872754, "entity_informativeness": -0.02667468447269475, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.18000000000000002, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "alias_monsoonbyte", "rel": "alias_of", "dst": "user_diya", "confidence": 1.0}, {"src": "user_diya", "rel": "works_at", "dst": "org_blueharbor_media", "confidence": 1.0}], "truth_edges": [{"src": "alias_monsoonbyte", "rel": "alias_of", "dst": "user_diya", "confidence": 1.0}, {"src": "user_diya", "rel": "works_at", "dst": "org_blueharbor_media", "confidence": 1.0}]}, {"task_id": "seed_task_6", "task_type": "event_tracing", "question": "Which user both authored thread thr_supply_leak and investigates event_project_lantern?", "task_answer": "user_diya", "agent_answer": "user_diya", "graph_f1": 1.0, "reward": 4.887245669359377, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 5.159160263003525, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.1538402686970082, "entity_informativeness": -0.05968000569348237, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "user_diya", "rel": "authored_thread", "dst": "thr_supply_leak", "confidence": 1.0}, {"src": "user_diya", "rel": "investigates", "dst": "event_project_lantern", "confidence": 1.0}], "truth_edges": [{"src": "user_diya", "rel": "authored_thread", "dst": "thr_supply_leak", "confidence": 1.0}, {"src": "user_diya", "rel": "investigates", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_7", "task_type": "cross_platform_linking", "question": "Which organization operates in the location referenced by post_midnight_manifest?", "task_answer": "org_northbridge_logistics", "agent_answer": "org_northbridge_logistics", "graph_f1": 1.0, "reward": 4.866493998418333, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 5.138408592062481, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.09106731287281425, "entity_informativeness": -0.017658720810331874, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "post_midnight_manifest", "rel": "references", "dst": "loc_dockyard17", "confidence": 1.0}, {"src": "org_northbridge_logistics", "rel": "operates_in", "dst": "loc_dockyard17", "confidence": 1.0}], "truth_edges": [{"src": "post_midnight_manifest", "rel": "references", "dst": "loc_dockyard17", "confidence": 1.0}, {"src": "org_northbridge_logistics", "rel": "operates_in", "dst": "loc_dockyard17", "confidence": 1.0}]}, {"task_id": "seed_task_8", "task_type": "network_discovery", "question": "user_ivy is directly connected to which collaborator on event_project_lantern?", "task_answer": "user_bharat", "agent_answer": "user_bharat", "graph_f1": 1.0, "reward": 4.768396011079182, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 5.04031060472333, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.04873759982431083, "entity_informativeness": -0.07342699510098014, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "user_ivy", "rel": "connected_to", "dst": "user_bharat", "confidence": 1.0}, {"src": "user_bharat", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}], "truth_edges": [{"src": "user_ivy", "rel": "connected_to", "dst": "user_bharat", "confidence": 1.0}, {"src": "user_bharat", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_9", "task_type": "deanonymization", "question": "Which canonical user is behind alias_docksparrow and collaborates on event_project_lantern?", "task_answer": "user_hiro", "agent_answer": "user_hiro", "graph_f1": 1.0, "reward": 4.8491889730762745, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 5.121103566720422, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.15000000000000002, "relation_informativeness": 0.06870713485222198, "entity_informativeness": -0.035936901465132676, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.3, "graph_f1": 0.55, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "alias_docksparrow", "rel": "alias_of", "dst": "user_hiro", "confidence": 1.0}, {"src": "user_hiro", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}], "truth_edges": [{"src": "alias_docksparrow", "rel": "alias_of", "dst": "user_hiro", "confidence": 1.0}, {"src": "user_hiro", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_10", "task_type": "convoluted_trace", "question": "An alias authored post_midnight_manifest referencing loc_dockyard17; through a direct connection from that alias owner, which user collaborates on event_project_lantern?", "task_answer": "user_bharat", "agent_answer": "user_bharat", "graph_f1": 0.5714285714285715, "reward": 4.035947417130322, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 4.307862010774469, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.06870713485222198, "entity_informativeness": 0.009869161636533142, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": -0.3, "graph_f1": 0.31428571428571433, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.32808540635585226, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "alias_orchidfox", "rel": "alias_of", "dst": "user_ivy", "confidence": 1.0}, {"src": "alias_orchidfox", "rel": "authored_post", "dst": "post_midnight_manifest", "confidence": 1.0}], "truth_edges": [{"src": "alias_orchidfox", "rel": "alias_of", "dst": "user_ivy", "confidence": 1.0}, {"src": "alias_orchidfox", "rel": "authored_post", "dst": "post_midnight_manifest", "confidence": 1.0}, {"src": "post_midnight_manifest", "rel": "references", "dst": "loc_dockyard17", "confidence": 1.0}, {"src": "user_ivy", "rel": "connected_to", "dst": "user_bharat", "confidence": 1.0}, {"src": "user_bharat", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_11", "task_type": "convoluted_trace", "question": "Thread thr_supply_leak references org_northbridge_logistics. Identify the canonical user behind alias_docksparrow who works there and collaborates on event_project_lantern.", "task_answer": "user_hiro", "agent_answer": "user_hiro", "graph_f1": 0.6666666666666666, "reward": 4.237246885434406, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 4.5091614790785535, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.15000000000000002, "relation_informativeness": 0.055627269200726095, "entity_informativeness": -0.0014657901221725345, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": -0.15, "graph_f1": 0.3666666666666667, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.32808540635585226, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "thr_supply_leak", "rel": "references", "dst": "org_northbridge_logistics", "confidence": 1.0}, {"src": "alias_docksparrow", "rel": "alias_of", "dst": "user_hiro", "confidence": 1.0}], "truth_edges": [{"src": "thr_supply_leak", "rel": "references", "dst": "org_northbridge_logistics", "confidence": 1.0}, {"src": "alias_docksparrow", "rel": "alias_of", "dst": "user_hiro", "confidence": 1.0}, {"src": "user_hiro", "rel": "works_at", "dst": "org_northbridge_logistics", "confidence": 1.0}, {"src": "user_hiro", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_12", "task_type": "convoluted_trace", "question": "Cross-platform Black Kite linkage: alias_monsoonbyte authored post_drone_parts referencing event_black_kite. Which canonical user behind that alias is directly connected to the Kestrel Works collaborator on the same event?", "task_answer": "user_diya", "agent_answer": "user_diya", "graph_f1": 0.5, "reward": 4.401765935104136, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 4.673680528748283, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.06870713485222198, "entity_informativeness": 0.0149733938960619, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.09999999999999998, "graph_f1": 0.275, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "alias_monsoonbyte", "rel": "alias_of", "dst": "user_diya", "confidence": 1.0}, {"src": "alias_monsoonbyte", "rel": "authored_post", "dst": "post_drone_parts", "confidence": 1.0}], "truth_edges": [{"src": "alias_monsoonbyte", "rel": "alias_of", "dst": "user_diya", "confidence": 1.0}, {"src": "alias_monsoonbyte", "rel": "authored_post", "dst": "post_drone_parts", "confidence": 1.0}, {"src": "post_drone_parts", "rel": "references", "dst": "event_black_kite", "confidence": 1.0}, {"src": "user_ivy", "rel": "works_at", "dst": "org_kestrel_works", "confidence": 1.0}, {"src": "user_ivy", "rel": "collaborates_on", "dst": "event_black_kite", "confidence": 1.0}, {"src": "user_diya", "rel": "connected_to", "dst": "user_ivy", "confidence": 1.0}]}, {"task_id": "seed_task_13", "task_type": "convoluted_trace", "question": "A sat-phone ping alias references loc_rivergate. Which canonical user behind alias_nightrelay works at an organization operating there and collaborates on event_project_lantern?", "task_answer": "user_faris", "agent_answer": "user_faris", "graph_f1": 0.5, "reward": 4.209316108644326, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 4.4812307022884745, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.06870713485222198, "entity_informativeness": 0.022523567436252216, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": -0.1, "graph_f1": 0.275, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.32808540635585226, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "alias_nightrelay", "rel": "alias_of", "dst": "user_faris", "confidence": 1.0}, {"src": "alias_nightrelay", "rel": "authored_post", "dst": "post_sat_phone_ping", "confidence": 1.0}], "truth_edges": [{"src": "alias_nightrelay", "rel": "alias_of", "dst": "user_faris", "confidence": 1.0}, {"src": "alias_nightrelay", "rel": "authored_post", "dst": "post_sat_phone_ping", "confidence": 1.0}, {"src": "post_sat_phone_ping", "rel": "references", "dst": "loc_rivergate", "confidence": 1.0}, {"src": "user_faris", "rel": "works_at", "dst": "org_tidewatch_ops", "confidence": 1.0}, {"src": "org_tidewatch_ops", "rel": "operates_in", "dst": "loc_rivergate", "confidence": 1.0}, {"src": "user_faris", "rel": "collaborates_on", "dst": "event_project_lantern", "confidence": 1.0}]}, {"task_id": "seed_task_14", "task_type": "convoluted_trace", "question": "From thread thr_port_audit discussing event_black_kite and referencing org_kestrel_works, identify the canonical user whose alias authored post_midnight_manifest and who collaborates on event_black_kite.", "task_answer": "user_ivy", "agent_answer": "user_ivy", "graph_f1": 0.5, "reward": 4.333080927373117, "steps": 9, "tool_calls": 4, "success": 1, "reward_components": {"tool_novelty": -0.30000000000000004, "tool_relevance": 0.0, "invalid_tool_penalty": 0.0, "total": 4.6049955210172655, "global_accuracy": 1.7, "soft_shaping": 0.3, "efficiency": 0.08833333333333335, "diversity": 0.12666666666666665, "relation_informativeness": 0.11840022502492006, "entity_informativeness": -0.003404704007654851, "connectivity_gain": 0.2, "duplicate_edge_penalty": -0.3, "format_reward": 0.15, "correctness": 1.15, "knowledge_carrier": 0.5, "knowledge_indexing": 0.0, "connectivity": 0.0, "graph_f1": 0.275, "compactness": 0.0, "repetition_penalty": 0.0, "spawn_auxiliary": 0.3280854063558518, "spawn_count": 4.0, "spawn_finished_subtasks": 4.0, "spawn_critical_steps": 6.0, "spawn_depth": 2.0, "spawn_breadth": 2.0}, "spawn_count": 4, "spawn_critical_steps": 6, "pred_edges": [{"src": "thr_port_audit", "rel": "discusses", "dst": "event_black_kite", "confidence": 1.0}, {"src": "thr_port_audit", "rel": "references", "dst": "org_kestrel_works", "confidence": 1.0}], "truth_edges": [{"src": "thr_port_audit", "rel": "discusses", "dst": "event_black_kite", "confidence": 1.0}, {"src": "thr_port_audit", "rel": "references", "dst": "org_kestrel_works", "confidence": 1.0}, {"src": "alias_orchidfox", "rel": "alias_of", "dst": "user_ivy", "confidence": 1.0}, {"src": "alias_orchidfox", "rel": "authored_post", "dst": "post_midnight_manifest", "confidence": 1.0}, {"src": "user_ivy", "rel": "works_at", "dst": "org_kestrel_works", "confidence": 1.0}, {"src": "user_ivy", "rel": "collaborates_on", "dst": "event_black_kite", "confidence": 1.0}]}], "leaderboard": [{"config": {"max_agents": 3, "max_breadth": 2, "max_depth": 2, "max_steps": 18, 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"work_history": ["Northbridge Logistics"]}, {"user_id": "user_cyrus", "name": "Cyrus Mehta", "org": "Apex Dynamics", "location": "Old Town", "connections": ["user_gita"], "work_history": ["Apex Dynamics"]}, {"user_id": "user_diya", "name": "Diya Roy", "org": "Blueharbor Media", "location": "Old Town", "connections": ["user_elin", "user_ivy"], "work_history": ["Blueharbor Media"]}, {"user_id": "user_elin", "name": "Elin Das", "org": "Helios Labs", "location": "Sector 9", "connections": ["user_aria"], "work_history": ["Helios Labs"]}, {"user_id": "user_faris", "name": "Faris Noor", "org": "Tidewatch Ops", "location": "Rivergate", "connections": ["user_diya"], "work_history": ["Tidewatch Ops"]}, {"user_id": "user_gita", "name": "Gita Pradhan", "org": "Apex Dynamics", "location": "Old Town", "connections": ["user_jules"], "work_history": ["Apex Dynamics"]}, {"user_id": "user_hiro", "name": "Hiro Tan", "org": "Northbridge Logistics", "location": "Dockyard 17", "connections": ["user_faris"], "work_history": ["Northbridge Logistics"]}, {"user_id": "user_ivy", "name": "Ivy Kapoor", "org": "Kestrel Works", "location": "Rivergate", "connections": ["user_bharat", "user_elin"], "work_history": ["Kestrel Works"]}, {"user_id": "user_jules", "name": "Jules Banerjee", "org": "Blueharbor Media", "location": "Old Town", "connections": ["user_bharat"], "work_history": ["Blueharbor Media"]}, {"user_id": "noise_0", "name": "P738", "org": "Unknown Ventures", "location": "Remote", "connections": [], "work_history": []}, {"user_id": "noise_1", "name": "P215", "org": "Stealth Co", "location": "Remote", "connections": [], "work_history": []}, {"user_id": "noise_2", "name": "P186", "org": "Unknown Ventures", "location": "Unknown", "connections": [], "work_history": []}, {"user_id": "noise_3", "name": "P140", "org": "Stealth Co", "location": "Unknown", "connections": [], "work_history": []}, {"user_id": "noise_4", "name": "P873", "org": "Stealth Co", "location": "Unknown", "connections": [], "work_history": []}]}, "task": {"task_id": "seed_task_14", "task_type": "convoluted_trace", "question": "From thread thr_port_audit discussing event_black_kite and referencing org_kestrel_works, identify the canonical user whose alias authored post_midnight_manifest and who collaborates on event_black_kite.", "answer": "user_ivy"}};
function metricCards(summary) {
const selected = [
["leaderboard_score", summary.leaderboard_score || 0],
["task_success_rate", summary.task_success_rate || 0],
["avg_graph_f1", summary.avg_graph_f1 || 0],
["retrieval_signal", summary.retrieval_signal || 0],
["structural_signal", summary.structural_signal || 0],
["tool_efficiency", summary.tool_efficiency || 0],
["avg_reward", summary.avg_reward || 0]
];
const root = document.getElementById("stats");
root.innerHTML = "";
selected.forEach(([k, v]) => {
const div = document.createElement("div");
div.className = "stat";
div.innerHTML = `<div class="k">${k}</div><div class="v">${Number(v).toFixed(3)}</div>`;
root.appendChild(div);
});
const pillRow = document.getElementById("hero-pills");
pillRow.innerHTML = "";
[
`deanonymization: ${Number(summary.deanonymization_accuracy || 0).toFixed(3)}`,
`avg steps: ${Number(summary.avg_steps_to_solution || 0).toFixed(2)}`,
`episodes: ${(payload.episodes || []).length}`
].forEach((text) => {
const span = document.createElement("span");
span.className = "pill";
span.textContent = text;
pillRow.appendChild(span);
});
}
function buildTypeFilters(allGroups) {
const root = document.getElementById("type-filters");
root.innerHTML = "";
allGroups.forEach((group) => {
const id = `type_${group}`;
const row = document.createElement("label");
row.className = "inline";
row.innerHTML = `<input type="checkbox" id="${id}" value="${group}" checked /> <span>${group}</span>`;
root.appendChild(row);
});
}
function createNetworkController() {
const container = document.getElementById("graph-canvas");
const banner = document.getElementById("graph-banner");
const modeSelect = document.getElementById("graph-mode");
const nodeSearch = document.getElementById("graph-search");
const relFilter = document.getElementById("relation-filter");
const fitBtn = document.getElementById("fit-graph");
const rawLayers = {
canonical: payload.canonical_graph || { nodes: [], edges: [] },
episode: payload.episode_graph || { nodes: [], edges: [] }
};
const episodeLayers = payload.episode_graphs || [];
const groupSet = new Set();
(rawLayers.canonical.nodes || []).forEach((n) => groupSet.add(n.group || "unknown"));
(episodeLayers || []).forEach((layer) => {
(layer.nodes || []).forEach((n) => groupSet.add(n.group || "unknown"));
});
const allGroups = Array.from(groupSet).sort();
buildTypeFilters(allGroups);
const state = {
mode: "canonical",
relationQuery: "",
nodeQuery: "",
selectedEpisode: Math.max(0, (payload.episodes || []).length - 1),
};
function currentEpisodeLayer() {
if (!episodeLayers.length) {
return rawLayers.episode;
}
const idx = Math.max(0, Math.min(episodeLayers.length - 1, Number(state.selectedEpisode || 0)));
return episodeLayers[idx] || rawLayers.episode;
}
const nodesDS = new vis.DataSet([]);
const edgesDS = new vis.DataSet([]);
const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {
interaction: { hover: true, navigationButtons: true, keyboard: true },
physics: { stabilization: false, barnesHut: { springLength: 130 } },
edges: { smooth: true, font: { size: 10 } },
nodes: { shape: "dot", size: 11, font: { size: 10 } }
});
function activeGroups() {
const checked = Array.from(document.querySelectorAll('#type-filters input[type="checkbox"]:checked'));
return new Set(checked.map(x => x.value));
}
function styleNode(node, query) {
const text = `${node.id} ${node.label || ""}`.toLowerCase();
const hit = query && text.includes(query);
return {
...node,
color: hit ? "#f59e0b" : undefined,
size: hit ? 18 : 11,
};
}
function refresh() {
const raw = state.mode === "episode" ? currentEpisodeLayer() : rawLayers.canonical;
const groups = activeGroups();
const relQ = state.relationQuery.toLowerCase();
const nodeQ = state.nodeQuery.toLowerCase();
const nodes = (raw.nodes || []).filter(n => groups.has(n.group || "unknown")).map(n => styleNode(n, nodeQ));
const nodeIds = new Set(nodes.map(n => n.id));
const edges = (raw.edges || []).filter(e => nodeIds.has(e.from) && nodeIds.has(e.to)).filter(e => !relQ || String(e.label || "").toLowerCase().includes(relQ));
nodesDS.clear();
edgesDS.clear();
nodesDS.add(nodes);
edgesDS.add(edges);
banner.textContent = state.mode === "canonical" ? "Layer: Canonical Graph" : "Layer: Episode Graph";
}
modeSelect.addEventListener("change", () => {
state.mode = modeSelect.value;
refresh();
});
document.addEventListener("osint-episode-change", (event) => {
state.selectedEpisode = Number(event.detail?.index || 0);
if (state.mode === "episode") {
refresh();
}
});
relFilter.addEventListener("input", () => {
state.relationQuery = relFilter.value || "";
refresh();
});
nodeSearch.addEventListener("input", () => {
state.nodeQuery = nodeSearch.value || "";
refresh();
});
fitBtn.addEventListener("click", () => network.fit({ animation: true }));
document.getElementById("type-filters").addEventListener("change", refresh);
network.on("click", (params) => {
if (params.nodes && params.nodes.length) {
const node = nodesDS.get(params.nodes[0]);
const connected = network.getConnectedNodes(node.id) || [];
document.getElementById("node-detail").textContent = JSON.stringify({
node,
connected_nodes: connected
}, null, 2);
}
if (params.edges && params.edges.length) {
const edge = edgesDS.get(params.edges[0]);
document.getElementById("edge-detail").textContent = JSON.stringify(edge, null, 2);
}
});
refresh();
}
function buildRows(views) {
const rows = [];
(views.microblog_posts || []).forEach((x) => rows.push({ source: "microblog", id: x.post_id || "post", text: JSON.stringify(x), raw: x }));
(views.forum_threads || []).forEach((x) => rows.push({ source: "forum", id: x.thread_id || "thread", text: JSON.stringify(x), raw: x }));
(views.profiles || []).forEach((x) => rows.push({ source: "profile", id: x.user_id || "profile", text: JSON.stringify(x), raw: x }));
return rows;
}
function initDatabaseExplorer() {
const rows = buildRows(payload.views || {});
const tabs = document.getElementById("db-tabs");
const search = document.getElementById("db-search");
const limit = document.getElementById("db-limit");
const table = document.getElementById("db-table");
const detail = document.getElementById("db-detail");
const sources = ["all", "microblog", "forum", "profile"];
const state = { source: "all", query: "", limit: 200 };
tabs.innerHTML = "";
sources.forEach((src) => {
const btn = document.createElement("button");
btn.className = `tab ${src === state.source ? "active" : ""}`;
btn.textContent = src;
btn.addEventListener("click", () => {
state.source = src;
Array.from(tabs.children).forEach((child) => child.classList.remove("active"));
btn.classList.add("active");
render();
});
tabs.appendChild(btn);
});
function filtered() {
const q = state.query.toLowerCase();
return rows
.filter((row) => state.source === "all" || row.source === state.source)
.filter((row) => !q || row.text.toLowerCase().includes(q) || row.id.toLowerCase().includes(q));
}
function render() {
const show = filtered().slice(0, state.limit);
table.innerHTML = "<thead><tr><th>source</th><th>id</th><th>preview</th></tr></thead>";
const body = document.createElement("tbody");
show.forEach((row) => {
const tr = document.createElement("tr");
const preview = row.text.length > 120 ? `${row.text.slice(0, 120)}...` : row.text;
tr.innerHTML = `<td>${row.source}</td><td class="mono">${row.id}</td><td>${preview}</td>`;
tr.addEventListener("click", () => {
detail.textContent = JSON.stringify(row.raw, null, 2);
});
body.appendChild(tr);
});
table.appendChild(body);
}
search.addEventListener("input", () => { state.query = search.value || ""; render(); });
limit.addEventListener("change", () => { state.limit = Number(limit.value || 200); render(); });
render();
}
function renderLeaderboard(records, sortBy = "leaderboard_score") {
const sorted = [...records].sort((a, b) => (b.metrics?.[sortBy] || 0) - (a.metrics?.[sortBy] || 0));
const table = document.getElementById("leaderboard-table");
table.innerHTML = "<thead><tr><th>rank</th><th>run</th><th>score</th><th>success</th><th>graph_f1</th><th>retrieval</th><th>structural</th><th>spawn</th><th>reward</th></tr></thead>";
const body = document.createElement("tbody");
sorted.forEach((rec, i) => {
const m = rec.metrics || {};
const tr = document.createElement("tr");
tr.innerHTML = `<td>${i + 1}</td><td>${rec.run_name || rec.run_id || "run"}</td><td>${(m.leaderboard_score || 0).toFixed(4)}</td><td>${(m.task_success_rate || 0).toFixed(3)}</td><td>${(m.avg_graph_f1 || 0).toFixed(3)}</td><td>${(m.retrieval_signal || 0).toFixed(3)}</td><td>${(m.structural_signal || 0).toFixed(3)}</td><td>${(m.spawn_signal || 0).toFixed(3)}</td><td>${(m.avg_reward || 0).toFixed(3)}</td>`;
body.appendChild(tr);
});
table.appendChild(body);
}
function drawSummaryChart(summary) {
const labels = ["success", "graph_f1", "tool_eff", "deanon", "retrieval", "structural", "score"];
const values = [
summary.task_success_rate || 0,
summary.avg_graph_f1 || 0,
summary.tool_efficiency || 0,
summary.deanonymization_accuracy || 0,
summary.retrieval_signal || 0,
summary.structural_signal || 0,
summary.leaderboard_score || 0,
];
new Chart(document.getElementById("summary-chart"), {
type: "radar",
data: {
labels,
datasets: [{
label: "normalized metrics",
data: values,
backgroundColor: "rgba(15,118,110,0.2)",
borderColor: "#0f766e",
pointBackgroundColor: "#d97706",
pointRadius: 3
}]
},
options: { responsive: true, maintainAspectRatio: false, scales: { r: { min: 0, max: 1 } } }
});
}
function drawTraceChart(episodes) {
const labels = episodes.map((_, i) => `ep_${i + 1}`);
const rewards = episodes.map(e => e.reward || 0);
const f1 = episodes.map(e => e.graph_f1 || 0);
new Chart(document.getElementById("trace-chart"), {
type: "line",
data: {
labels,
datasets: [
{ label: "reward", data: rewards, borderColor: "#0f766e", yAxisID: "y", tension: 0.2 },
{ label: "graph_f1", data: f1, borderColor: "#d97706", yAxisID: "y1", tension: 0.2 }
]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: { position: "left" },
y1: { position: "right", min: 0, max: 1, grid: { drawOnChartArea: false } }
}
}
});
}
function initEpisodeExplorer() {
const episodes = payload.episodes || [];
const select = document.getElementById("episode-select");
const prevBtn = document.getElementById("episode-prev");
const nextBtn = document.getElementById("episode-next");
function fillFromEpisode(ep) {
const fallback = payload.task || {};
const taskId = ep?.task_id || fallback.task_id || "n/a";
const taskType = ep?.task_type || fallback.task_type || "n/a";
const question = ep?.question || fallback.question || "n/a";
const truth = ep?.task_answer ?? fallback.answer ?? "n/a";
const agent = ep?.agent_answer ?? "";
const isCorrect = String(agent) === String(truth);
document.getElementById("task-id").textContent = taskId;
document.getElementById("task-type").textContent = taskType;
document.getElementById("task-question").textContent = question;
document.getElementById("task-answer").textContent = truth;
document.getElementById("agent-answer").textContent = agent || "(no answer)";
const correctEl = document.getElementById("answer-correct");
correctEl.textContent = isCorrect ? "yes" : "no";
correctEl.className = isCorrect ? "answer-ok" : "answer-bad";
}
if (!episodes.length) {
select.innerHTML = "<option value='-1'>latest</option>";
fillFromEpisode(null);
prevBtn.disabled = true;
nextBtn.disabled = true;
return;
}
select.innerHTML = episodes
.map((ep, idx) => `<option value="${idx}">ep_${idx + 1} | ${ep.task_type || "task"} | reward=${Number(ep.reward || 0).toFixed(3)}</option>`)
.join("");
select.value = String(Math.max(0, episodes.length - 1));
function sync(delta = 0) {
const current = Math.max(0, Math.min(episodes.length - 1, Number(select.value || 0) + delta));
select.value = String(current);
fillFromEpisode(episodes[current]);
document.dispatchEvent(new CustomEvent("osint-episode-change", { detail: { index: current } }));
}
select.addEventListener("change", () => sync(0));
prevBtn.addEventListener("click", () => sync(-1));
nextBtn.addEventListener("click", () => sync(1));
sync(0);
}
const summary = payload.summary || {};
metricCards(summary);
initEpisodeExplorer();
createNetworkController();
initDatabaseExplorer();
const leaderboard = payload.leaderboard || [];
const leaderSort = document.getElementById("leader-sort");
renderLeaderboard(leaderboard, leaderSort.value);
leaderSort.addEventListener("change", () => renderLeaderboard(leaderboard, leaderSort.value));
drawSummaryChart(summary);
drawTraceChart(payload.episodes || []);
</script>
</body>
</html>
|