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_dashboard_state.json

@@ -1,31 +1,31 @@
 {
-  "timestamp": 1772994420.314485,
-  "step": 20,
+  "timestamp": 1772999012.107634,
+  "step": 2,
   "episode_done": false,
-  "cumulative_reward": 1.8024931955050638,
-  "gen_time_s": 23.52,
-  "model_response_raw": "{\n\"action_type\": \"design_followup_experiment\",\n\"method\": \"\",\n\"parameters\": {\n\"disease_comparison_groups\": true,\n\"treatment_condition_focus\": false,\n\"criterion_for_target_genes\": \"Differentially Expressed Between Healthy vs AD Brain Cells\"\n},\n\"Justification\": \"The primary goal remains identifying DGEs across conditions rather than targeting specific known pathways first. While 'filter_data' was previously performed but failed due to lack of validated markers from previous attempts, re-running filtering based solely on technical/quality criteria would still leave ambiguous candidates if noise pe",
+  "cumulative_reward": 2.3019429157970084,
+  "gen_time_s": 12.96,
+  "model_response_raw": "{\n\"action_type\": \"select_cohort\",\n\"method\": None,\n\"parameters\": {\n\"categorical_variables\": [],\n\"factors_to_stratify_by\": []\n},\n\"Justification\": \"The recent historical sample was from 'blood', but the target tissue specified in the query ('synovial_fluid') differs significantly due to disease pathology affecting local fluid composition. Stratifying based on organ/tissue type ensures cohort homogeneity required for comparing RA vs non-RA controls within the specific joint space.\",\n\"Confidence\": 0.9\n}",
   "model_thinking": "",
   "thinking_enabled": false,
   "task": {
-    "problem_statement": "Identify differentially expressed genes between Alzheimer's disease and healthy brain tissue using single-cell RNA sequencing.",
+    "problem_statement": "Determine the effect of JAK inhibitor treatment on T-cell activation states in rheumatoid arthritis.",
     "organism": "human",
-    "tissue": "brain",
+    "tissue": "synovial_fluid",
     "modality": "scRNA-seq",
     "conditions": [
-      "healthy",
-      "Alzheimer"
+      "untreated_RA",
+      "JAK_inhibitor_treated"
     ],
-    "budget_limit": 84032.3891133686,
-    "time_limit_days": 116.86792941835967
+    "budget_limit": 124112.45173009757,
+    "time_limit_days": 174.85852269116046
   },
   "resources": {
-    "budget_used": 28900.0,
-    "budget_remaining": 55132.39,
-    "time_used_days": 18.0,
-    "time_remaining_days": 98.9,
+    "budget_used": 5500.0,
+    "budget_remaining": 118612.45,
+    "time_used_days": 8.0,
+    "time_remaining_days": 166.9,
     "samples_consumed": 0,
-    "compute_hours_used": 12.0
+    "compute_hours_used": 0.0
   },
   "pipeline_history": [
     {
@@ -34,7 +34,7 @@
       "method": null,
       "output_summary": "Collected 6 samples (quality=0.56)",
       "success": true,
-      "quality_score": 0.562,
+      "quality_score": 0.563,
       "resource_cost": 5000.0,
       "time_cost_days": 7.0
     },
@@ -47,175 +47,44 @@
       "quality_score": 1.0,
       "resource_cost": 500.0,
       "time_cost_days": 1.0
-    },
-    {
-      "step_index": 3,
-      "action_type": "sequence_cells",
-      "method": null,
-      "output_summary": "Action blocked: Cannot sequence without library preparation",
-      "success": false,
-      "quality_score": 1.0,
-      "resource_cost": 15000.0,
-      "time_cost_days": 5.0
-    },
-    {
-      "step_index": 4,
-      "action_type": "prepare_library",
-      "method": null,
-      "output_summary": "Library prepared (complexity=0.75)",
-      "success": true,
-      "quality_score": 0.751,
-      "resource_cost": 8000.0,
-      "time_cost_days": 3.0
-    },
-    {
-      "step_index": 5,
-      "action_type": "sequence_cells",
-      "method": null,
-      "output_summary": "Sequenced 4574 cells, 9256 genes detected, median UMI=3083",
-      "success": true,
-      "quality_score": 0.47,
-      "resource_cost": 15000.0,
-      "time_cost_days": 5.0
-    },
-    {
-      "step_index": 6,
-      "action_type": "filter_data",
-      "method": null,
-      "output_summary": "Action blocked: Cannot filter data before QC",
-      "success": false,
-      "quality_score": 1.0,
-      "resource_cost": 50.0,
-      "time_cost_days": 0.2
-    },
-    {
-      "step_index": 7,
-      "action_type": "run_qc",
-      "method": null,
-      "output_summary": "QC metrics computed",
-      "success": true,
-      "quality_score": 0.829,
-      "resource_cost": 100.0,
-      "time_cost_days": 0.5
-    },
-    {
-      "step_index": 8,
-      "action_type": "filter_data",
-      "method": null,
-      "output_summary": "Filtered 4574 \u2192 3996 cells (87% retained)",
-      "success": true,
-      "quality_score": 0.874,
-      "resource_cost": 50.0,
-      "time_cost_days": 0.2
-    },
-    {
-      "step_index": 9,
-      "action_type": "normalize_data",
-      "method": null,
-      "output_summary": "Normalized with log_normalize",
-      "success": true,
-      "quality_score": 1.0,
-      "resource_cost": 50.0,
-      "time_cost_days": 0.2
-    },
-    {
-      "step_index": 10,
-      "action_type": "cluster_cells",
-      "method": null,
-      "output_summary": "Found 7 clusters",
-      "success": true,
-      "quality_score": 0.79,
-      "resource_cost": 100.0,
-      "time_cost_days": 0.5
-    },
-    {
-      "step_index": 11,
-      "action_type": "design_followup_experiment",
-      "method": null,
-      "output_summary": "Follow-up experiment design proposed (evidence_signals=1)",
-      "success": true,
-      "quality_score": 0.28,
-      "resource_cost": 100.0,
-      "time_cost_days": 0.5
-    },
-    {
-      "step_index": 12,
-      "action_type": "synthesize_conclusion",
-      "method": null,
-      "output_summary": "Action blocked: Cannot synthesise conclusion before discovering or validating markers; Cannot synthesise conclusion befo",
-      "success": false,
-      "quality_score": 1.0,
-      "resource_cost": 0.0,
-      "time_cost_days": 0.5
-    },
-    {
-      "step_index": 13,
-      "action_type": "marker_selection",
-      "method": null,
-      "output_summary": "Action blocked: Cannot select markers without DE results",
-      "success": false,
-      "quality_score": 1.0,
-      "resource_cost": 100.0,
-      "time_cost_days": 0.5
-    },
-    {
-      "step_index": 14,
-      "action_type": "synthesize_conclusion",
-      "method": null,
-      "output_summary": "Action blocked: Cannot synthesise conclusion before discovering or validating markers; Cannot synthesise conclusion befo",
-      "success": false,
-      "quality_score": 1.0,
-      "resource_cost": 0.0,
-      "time_cost_days": 0.5
     }
   ],
   "current_action": {
-    "action_type": "synthesize_conclusion",
+    "action_type": "select_cohort",
     "method": null,
     "parameters": {
-      "claims": [
-        {
-          "top_markers": [],
-          "causal_mechanisms": [],
-          "predicted_pathways": {},
-          "confidence": 0.5,
-          "claim_type": "correlational",
-          "claim": "forced terminal conclusion"
-        }
-      ]
+      "categorical_variables": [],
+      "factors_to_stratify_by": []
     },
-    "justification": "forced terminal conclusion",
-    "confidence": 0.5
+    "justification": "The recent historical sample was from 'blood', but the target tissue specified in the query ('synovial_fluid') differs significantly due to disease pathology affecting local fluid composition. Stratifying based on organ/tissue type ensures cohort homogeneity required for comparing RA vs non-RA controls within the specific joint space.",
+    "confidence": 0.9
   },
   "latest_output": {
-    "summary": "Action blocked: Cannot synthesise conclusion before discovering or validating markers; Cannot synthesise conclusion before inferring pathways or mechanisms",
-    "success": false,
+    "summary": "Selected cohort of 4 samples with criteria {}",
+    "success": true,
     "quality_score": 1.0,
     "uncertainty": 0.0,
     "warnings": [],
-    "data_preview": null
+    "data_preview": "{\"criteria\": {}, \"n_selected\": 4}"
   },
   "discovered_markers": [],
   "candidate_mechanisms": [],
-  "rule_violations": [
-    "Cannot synthesise conclusion before discovering or validating markers",
-    "Cannot synthesise conclusion before inferring pathways or mechanisms"
-  ],
+  "rule_violations": [],
   "uncertainty_summary": {
-    "avg_uncertainty": 0.224,
-    "avg_quality": 0.814
+    "avg_uncertainty": 0.0,
+    "avg_quality": 0.781
   },
   "reward_breakdown": {
-    "validity": -1.0,
-    "ordering": 0.0,
-    "info_gain": 0.0,
-    "efficiency": 0.0,
-    "novelty": 0.0,
-    "penalty": -1.0,
+    "validity": 0.3,
+    "ordering": 0.06,
+    "info_gain": 0.4,
+    "efficiency": 0.294,
+    "novelty": 0.1,
+    "penalty": -0.0,
     "shaping": 0.0,
     "terminal": 0.0,
-    "total": -2.0,
-    "hard_violations": 2.0,
+    "total": 1.154,
+    "tool_fit": 0.0,
     "term_validity": 0.0,
     "term_ordering": 0.0,
     "term_info_gain": 0.0,
@@ -229,83 +98,101 @@
   "latent": {
     "cell_populations": [
       {
-        "name": "excitatory_neuron",
-        "proportion": 0.349,
+        "name": "CD4_Th1",
+        "proportion": 0.239,
         "marker_genes": [
-          "SLC17A7",
-          "CAMK2A",
-          "NRGN"
+          "IFNG",
+          "TBX21",
+          "IL2"
         ],
-        "state": "stressed"
+        "state": "activated"
       },
       {
-        "name": "inhibitory_neuron",
-        "proportion": 0.209,
+        "name": "CD4_Th17",
+        "proportion": 0.137,
         "marker_genes": [
-          "GAD1",
-          "GAD2",
-          "SLC32A1"
+          "IL17A",
+          "RORC",
+          "CCR6"
         ],
-        "state": "normal"
+        "state": "activated"
       },
       {
-        "name": "astrocyte",
-        "proportion": 0.211,
+        "name": "CD4_Treg",
+        "proportion": 0.071,
         "marker_genes": [
-          "GFAP",
-          "AQP4",
-          "SLC1A3"
+          "FOXP3",
+          "IL2RA",
+          "CTLA4"
         ],
-        "state": "quiescent"
+        "state": "regulatory"
       },
       {
-        "name": "oligodendrocyte",
-        "proportion": 0.153,
+        "name": "CD8_cytotoxic",
+        "proportion": 0.161,
         "marker_genes": [
-          "MBP",
-          "PLP1",
-          "MOG"
+          "GZMB",
+          "PRF1",
+          "CD8A"
         ],
-        "state": "myelinating"
+        "state": "activated"
       },
       {
-        "name": "OPC",
-        "proportion": 0.078,
+        "name": "macrophage",
+        "proportion": 0.145,
         "marker_genes": [
-          "PDGFRA",
-          "CSPG4",
-          "OLIG2"
+          "CD68",
+          "CD163",
+          "MARCO"
         ],
-        "state": "progenitor"
+        "state": "inflammatory"
+      },
+      {
+        "name": "fibroblast",
+        "proportion": 0.14,
+        "marker_genes": [
+          "COL1A1",
+          "FAP",
+          "THY1"
+        ],
+        "state": "activated"
+      },
+      {
+        "name": "B_cell",
+        "proportion": 0.109,
+        "marker_genes": [
+          "CD19",
+          "MS4A1",
+          "CD79A"
+        ],
+        "state": "quiescent"
       }
     ],
     "true_markers": [
-      "TREM2",
-      "APOE",
-      "GFAP",
-      "C1QA"
+      "STAT1",
+      "SOCS1",
+      "IFNG"
     ],
     "causal_mechanisms": [
-      "TREM2-mediated microglial activation in amyloid clearance",
-      "complement-driven synaptic pruning",
-      "reactive astrogliosis amplifying neuroinflammation"
+      "JAK-STAT pathway inhibition reduces Th1/Th17 activation",
+      "Compensatory Treg expansion under JAK inhibition"
     ],
     "true_pathways": {
-      "complement_cascade": 0.839,
-      "neuroinflammation": 0.805,
-      "amyloid_processing": 0.666,
-      "synaptic_signalling": 0.394,
-      "lipid_metabolism": 0.674
+      "JAK_STAT_signalling": 0.3,
+      "Th1_differentiation": 0.35,
+      "Th17_differentiation": 0.4,
+      "cytokine_signalling": 0.45,
+      "regulatory_T_cell_function": 0.7
     },
-    "true_de_genes_count": 10,
+    "true_de_genes_count": 11,
     "true_regulatory_network_size": 0,
     "confounders": {},
-    "n_true_cells": 7619,
+    "n_true_cells": 13025,
     "technical": {
-      "ambient_rna_fraction": 0.04108598341080635,
-      "doublet_rate": 0.045763110874719674,
-      "dropout_rate": 0.07138299827651534,
-      "sample_quality": 0.9242864922806572,
+      "ambient_rna_fraction": 0.05940406458962544,
+      "doublet_rate": 0.02562860771136133,
+      "dropout_rate": 0.09574882286483327,
+      "sample_quality": 0.9514110325345917,
       "library_complexity": 0.8,
       "capture_efficiency": 0.6
     },
@@ -313,29 +200,31 @@
       "samples_collected": true,
       "cohort_selected": true,
       "cells_cultured": false,
-      "library_prepared": true,
+      "library_prepared": false,
       "perturbation_applied": false,
-      "cells_sequenced": true,
-      "qc_performed": true,
-      "data_filtered": true,
-      "data_normalized": true,
+      "cells_sequenced": false,
+      "qc_performed": false,
+      "data_filtered": false,
+      "data_normalized": false,
       "batches_integrated": false,
-      "cells_clustered": true,
+      "cells_clustered": false,
       "de_performed": false,
       "trajectories_inferred": false,
       "pathways_analyzed": false,
       "networks_inferred": false,
       "markers_discovered": false,
       "markers_validated": false,
-      "followup_designed": true,
+      "followup_designed": false,
       "subagent_review_requested": false,
       "conclusion_reached": false,
-      "n_cells_sequenced": 4574,
-      "n_cells_after_filter": 3996,
-      "n_clusters_found": "7",
+      "n_cells_sequenced": null,
+      "n_cells_after_filter": null,
+      "n_clusters_found": null,
       "n_de_genes_found": null,
       "n_markers_found": null
     },
-    "hidden_failure_conditions": []
+    "hidden_failure_conditions": [
+      "High ambient RNA may confound DE in low-abundance transcripts"
+    ]
   }
 }

run_agent.py

@@ -823,7 +823,11 @@ def main():
         """Read and consume a command file written by the dashboard."""
         try:
             raw = DASHBOARD_CMD_PATH.read_text(encoding="utf-8")
-            DASHBOARD_CMD_PATH.unlink(missing_ok=True)
+            try:
+                DASHBOARD_CMD_PATH.unlink(missing_ok=True)
+            except OSError:
+                # Windows: file may be locked by dashboard; still consumed
+                pass
             return json.loads(raw)
         except (FileNotFoundError, json.JSONDecodeError):
             return None
@@ -1064,7 +1068,10 @@ def main():
                     log(f"      Pathways: {c.predicted_pathways}")
         log("=" * 70)
 
-    DASHBOARD_CMD_PATH.unlink(missing_ok=True)
+    try:
+        DASHBOARD_CMD_PATH.unlink(missing_ok=True)
+    except OSError:
+        pass
     run_episode()
 
     while True: