diff --git a/.scheduler-memory/latest/daily/memories.md b/.scheduler-memory/latest/daily/memories.md
index 8dcfff20..ce4fae87 100644
--- a/.scheduler-memory/latest/daily/memories.md
+++ b/.scheduler-memory/latest/daily/memories.md
@@ -1,45 +1,3 @@
-### 1. [2026-02-26] (scheduler, daily, store, insight)
+### 1. [2026-02-27] (scheduler, daily, retrieve)
 
-# Memory Update (2026-02-25)
-
-## Docs Cleanup
-- Replaced `docs/README.md` content that referenced Archivox with a SeedPass-focused docs index.
-- Updated docs-site default title in `docs/src/config/loadConfig.js` from `Archivox` to `SeedPass Docs`.
-
-## Outcome
-- Removed stale Arc…
-
----
-
-### 2. [2026-02-25] (scheduler, daily, store, insight)
-
-### 1. [2026-02-25] (torch, init, reinstall)
-If `npx --no-install torch-lock init --help` prints initialization prompts, it may be interactive; use `printf '\n' | npx --no-install torch-lock init --force` to accept defaults non-interactively in CI/agent runs.
-
-### 2. [2026-02-25…
-
----
-
-### 3. [2026-02-25] (scheduler, daily, store, insight)
-
-### 1. [2026-02-25] (torch, scheduler, host-mode)
-When running `npm run --prefix torch scheduler:daily`, the scheduler resolves config from `torch/torch-config.json` (cwd-scoped), not the repo-root `torch-config.json`.
-
-### 2. [2026-02-25] (torch, scheduler, handoff)
-A non-inter…
-
----
-
-### 4. [2026-02-25] (scheduler, daily, store, insight)
-
-### 1. [2026-02-25] (torch, scheduler, host-mode)
-When running `npm run --prefix torch scheduler:daily`, the scheduler resolves config from `torch/torch-config.json` (cwd-scoped), not the repo-root `torch-config.json`.
-
-### 2. [2026-02-25] (torch, scheduler, handoff)
-A non-inter…
-
----
-
-### 5. [2026-02-26] (scheduler, daily, retrieve)
-
-Memory retrieval seed for daily :: scheduler memory retrieval
\ No newline at end of file
+Memory retrieval seed for daily :: Required startup + artifacts + memory + issue capture
\ No newline at end of file
diff --git a/.scheduler-memory/memory-store.json b/.scheduler-memory/memory-store.json
index 0c946347..3c8b1e8f 100644
--- a/.scheduler-memory/memory-store.json
+++ b/.scheduler-memory/memory-store.json
@@ -507,5 +507,56 @@
       "merged_into": null,
       "pinned": false
     }
+  ],
+  [
+    "094aab2a-4a25-4322-a198-1638752acc77",
+    {
+      "schema_version": 1,
+      "id": "094aab2a-4a25-4322-a198-1638752acc77",
+      "agent_id": "const-refactor-agent",
+      "session_id": "session-mm5d7amf",
+      "type": "batch_window",
+      "content": "Memory retrieval seed for daily :: Required startup + artifacts + memory + issue capture",
+      "summary": "Memory retrieval seed for daily :: Required startup + artifacts + memory + issue capture",
+      "tags": [
+        "scheduler",
+        "daily",
+        "retrieve"
+      ],
+      "importance": 0.4,
+      "embedding_id": null,
+      "created_at": 1772225327752,
+      "last_seen": 1772225327764,
+      "source": "scheduler-retrieve",
+      "ttl_days": null,
+      "merged_into": null,
+      "pinned": false
+    }
+  ],
+  [
+    "cef905d0-7a66-459c-8937-41310afc934f",
+    {
+      "schema_version": 1,
+      "id": "cef905d0-7a66-459c-8937-41310afc934f",
+      "agent_id": "const-refactor-agent",
+      "session_id": "session-mm5dpefy",
+      "type": "batch_window",
+      "content": "# Memory Update — bug-reproducer-agent — 2026-02-27\n\n## Key findings\n- The `scheduler-flow.md` script execution failed due to a missing runner (`codex`).\n- The automated `bug-reproducer-agent` prompt execution failed.\n- Fallback manual investigation identified one active issue: `BIP85 Non-Standard Derivation Path` (using `app_no=2` instead of standard). This is documented in `KNOWN_ISSUES.md`.\n\n## Patterns / reusable knowledge\n- Future runs should verify the runner environment variables (`SCHEDULER_PROMPT_PATH`, `SCHEDULER_AGENT`, `SCHEDULER_CADENCE`) are set correctly before invoking `run-selected-prompt.mjs`.\n- If the runner fails, manual fallback or alternative automation (e.g., Python script) is required.\n\n## Warnings / gotchas\n- `torch/scripts/agent/run-selected-prompt.mjs` depends on an external runner (`codex` or similar) which may not be available in all environments.",
+      "summary": "# Memory Update — bug-reproducer-agent — 2026-02-27\n\n## Key findings\n- The `scheduler-flow.md` script execution failed due to a missing runner (`codex`).\n- The automated `bug-reproducer-agent` prompt execution failed.\n- Fallback manual investigation identified one active issue: …",
+      "tags": [
+        "scheduler",
+        "daily",
+        "store",
+        "insight"
+      ],
+      "importance": 0.8,
+      "embedding_id": null,
+      "created_at": 1772226172512,
+      "last_seen": 1772226172512,
+      "source": "agent-output",
+      "ttl_days": null,
+      "merged_into": null,
+      "pinned": false
+    }
   ]
 ]
\ No newline at end of file
diff --git a/.scheduler-memory/session-mm5d7amf/retrieve.json b/.scheduler-memory/session-mm5d7amf/retrieve.json
new file mode 100644
index 00000000..8248ea0e
--- /dev/null
+++ b/.scheduler-memory/session-mm5d7amf/retrieve.json
@@ -0,0 +1,18 @@
+{
+  "cadence": "daily",
+  "operation": "retrieve",
+  "runId": "session-mm5d7amf",
+  "servicePath": "src/services/memory/index.js#getRelevantMemories",
+  "inputs": {
+    "agentId": "const-refactor-agent",
+    "promptPath": "torch/src/prompts/daily/const-refactor-agent.md",
+    "promptIntent": "Required startup + artifacts + memory + issue capture",
+    "query": "scheduler | daily | agent | const-refactor-agent | intent | Required startup + artifacts + memory + issue capture",
+    "events": 1
+  },
+  "outputs": {
+    "ingestedCount": 1,
+    "retrievedCount": 1
+  },
+  "status": "ok"
+}
\ No newline at end of file
diff --git a/.scheduler-memory/session-mm5d7amf/retrieve.ok b/.scheduler-memory/session-mm5d7amf/retrieve.ok
new file mode 100644
index 00000000..84b17199
--- /dev/null
+++ b/.scheduler-memory/session-mm5d7amf/retrieve.ok
@@ -0,0 +1 @@
+MEMORY_RETRIEVED
diff --git a/.scheduler-memory/session-mm5dpefy/store.json b/.scheduler-memory/session-mm5dpefy/store.json
new file mode 100644
index 00000000..c37e77b1
--- /dev/null
+++ b/.scheduler-memory/session-mm5dpefy/store.json
@@ -0,0 +1,20 @@
+{
+  "cadence": "daily",
+  "operation": "store",
+  "runId": "session-mm5dpefy",
+  "servicePath": "src/services/memory/index.js#ingestEvents",
+  "inputs": {
+    "agentId": "const-refactor-agent",
+    "promptPath": "torch/src/prompts/daily/const-refactor-agent.md",
+    "promptIntent": "Required startup + artifacts + memory + issue capture",
+    "events": 1,
+    "sourceFile": "memory-update.md"
+  },
+  "outputs": {
+    "storedCount": 1,
+    "summaries": [
+      "# Memory Update — bug-reproducer-agent — 2026-02-27\n\n## Key findings\n- The `scheduler-flow.md` script execution failed due to a missing runner (`codex`).\n- The automated `bug-reproducer-agent` prompt execution failed.\n- Fallback manual investigation identified one active issue: …"
+    ]
+  },
+  "status": "ok"
+}
\ No newline at end of file
diff --git a/.scheduler-memory/session-mm5dpefy/store.ok b/.scheduler-memory/session-mm5dpefy/store.ok
new file mode 100644
index 00000000..1e40bb67
--- /dev/null
+++ b/.scheduler-memory/session-mm5dpefy/store.ok
@@ -0,0 +1 @@
+MEMORY_STORED
diff --git a/reports/performance/constants-refactor/candidates.json b/reports/performance/constants-refactor/candidates.json
new file mode 100644
index 00000000..c8632faf
--- /dev/null
+++ b/reports/performance/constants-refactor/candidates.json
@@ -0,0 +1,58 @@
+[
+  {
+    "value": 1000,
+    "semantic": "one second in ms",
+    "occurrences": [
+      {"file": "torch/src/services/memory/index.js", "line": 45, "context": "const MIN_SAVE_INTERVAL_MS = 1000;"},
+      {"file": "torch/src/services/memory/pruner.js", "line": 3, "context": "const DAY_MS = 24 * 60 * 60 * 1000;"},
+      {"file": "torch/src/services/memory/retriever.js", "line": 8, "context": "const DEFAULT_RECENCY_HALF_LIFE_MS = 1000 * 60 * 60 * 24 * 7;"},
+      {"file": "torch/src/services/memory/formatter.js", "line": 3, "context": "const DEFAULT_TOKEN_BUDGET = 1000;"}
+    ]
+  },
+  {
+    "value": 60000,
+    "semantic": "one minute in ms (or 60_000)",
+    "occurrences": [
+      {"file": "torch/src/services/memory/index.js", "line": 548, "context": "windowMs / 60_000"},
+      {"file": "torch/src/services/memory/ingestor.js", "line": 6, "context": "const WINDOW_BUCKET_MS = 60_000;"},
+      {"file": "torch/src/services/memory/cache.js", "line": 153, "context": "setRuntimeEvent(scope, event, ttlMs = 60_000) {"},
+      {"file": "torch/src/services/memory/cache.js", "line": 157, "context": "const safeTtlMs = Number.isFinite(ttlMs) && ttlMs > 0 ? ttlMs : 60_000;"},
+      {"file": "torch/src/services/memory/scheduler.js", "line": 2, "context": "const MINUTE_MS = 60_000;"}
+    ]
+  },
+  {
+    "value": "24 * 60 * 60 * 1000",
+    "semantic": "one day in ms",
+    "occurrences": [
+      {"file": "torch/src/services/memory/pruner.js", "line": 3, "context": "const DAY_MS = 24 * 60 * 60 * 1000;"},
+      {"file": "torch/src/services/memory/index.js", "line": 329, "context": "const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);"},
+      {"file": "torch/src/services/memory/index.js", "line": 495, "context": "const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);"}
+    ]
+  },
+  {
+    "value": 280,
+    "semantic": "max summary chars length",
+    "occurrences": [
+      {"file": "torch/src/services/memory/pruner.js", "line": 116, "context": ".slice(0, 280)"},
+      {"file": "torch/src/services/memory/summarizer.js", "line": 116, "context": "const maxSummaryLength = options.maxSummaryLength ?? 280;"},
+      {"file": "torch/src/services/memory/ingestor.js", "line": 92, "context": "function compactContextExcerpt(text, maxChars = 280) {"}
+    ]
+  },
+  {
+    "value": 0.25,
+    "semantic": "default importance score",
+    "occurrences": [
+      {"file": "torch/src/services/memory/summarizer.js", "line": 64, "context": "Math.min(1, Math.max(0, rawImportance)) : 0.25;"},
+      {"file": "torch/src/services/memory/summarizer.js", "line": 72, "context": "return { summary: bounded, importance: 0.25 };"},
+      {"file": "torch/src/services/memory/retriever.js", "line": 3, "context": "w_imp: 0.25,"}
+    ]
+  },
+  {
+    "value": 0.35,
+    "semantic": "low importance / semantic overlap threshold",
+    "occurrences": [
+      {"file": "torch/src/services/memory/summarizer.js", "line": 89, "context": "return overlap / summaryTokens.length >= 0.35;"},
+      {"file": "torch/src/services/memory/pruner.js", "line": 184, "context": "lowImportanceThreshold: options.lowImportanceThreshold ?? 0.35,"}
+    ]
+  }
+]
diff --git a/reports/performance/constants-refactor/raw-numeric-hits.txt b/reports/performance/constants-refactor/raw-numeric-hits.txt
new file mode 100644
index 00000000..6294caa5
--- /dev/null
+++ b/reports/performance/constants-refactor/raw-numeric-hits.txt
@@ -0,0 +1,37 @@
+torch/src/services/memory/index.js:45:const MIN_SAVE_INTERVAL_MS = 1000;
+torch/src/services/memory/index.js:158:const ESTIMATED_INDEX_BYTES_PER_RECORD = 512;
+torch/src/services/memory/index.js:164:    if (typeof value === 'string' && value.length > 300) {
+torch/src/services/memory/index.js:165:      safe[key] = `${value.slice(0, 300)}…`;
+torch/src/services/memory/index.js:225:  if (samples.length > 10_000) {
+torch/src/services/memory/index.js:226:    samples.splice(0, samples.length - 10_000);
+torch/src/services/memory/index.js:324: * const result = await runPruneCycle({ retentionMs: 30 * 24 * 60 * 60 * 1000 });
+torch/src/services/memory/index.js:329:  const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);
+torch/src/services/memory/index.js:491: * const preview = await triggerPruneDryRun({ retentionMs: 7 * 24 * 60 * 60 * 1000 });
+torch/src/services/memory/index.js:495:  const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);
+torch/src/services/memory/index.js:530: * const stats = await memoryStats({ windowMs: 60 * 60 * 1000 });
+torch/src/services/memory/index.js:535:  const windowMs = Number.isFinite(options.windowMs) ? Math.max(1, Number(options.windowMs)) : (60 * 60 * 1000);
+torch/src/services/memory/index.js:548:  const ingestThroughputPerMinute = ingestedInWindow / Math.max(1, (windowMs / 60_000));
+torch/src/services/memory/pruner.js:3:const DAY_MS = 24 * 60 * 60 * 1000;
+torch/src/services/memory/pruner.js:116:  const fallbackSummary = group.map((item) => item.summary || item.content).filter(Boolean).join(' ').slice(0, 280);
+torch/src/services/memory/pruner.js:184:    lowImportanceThreshold: options.lowImportanceThreshold ?? 0.35,
+torch/src/services/memory/pruner.js:185:    deleteImportanceThreshold: options.deleteImportanceThreshold ?? 0.12,
+torch/src/services/memory/summarizer.js:64:    : 0.25;
+torch/src/services/memory/summarizer.js:72:  return { summary: bounded, importance: 0.25 };
+torch/src/services/memory/summarizer.js:89:  return overlap / summaryTokens.length >= 0.35;
+torch/src/services/memory/summarizer.js:116:  const maxSummaryLength = options.maxSummaryLength ?? 280;
+torch/src/services/memory/ingestor.js:6:const WINDOW_BUCKET_MS = 60_000;
+torch/src/services/memory/ingestor.js:37:    : 300;
+torch/src/services/memory/ingestor.js:92:function compactContextExcerpt(text, maxChars = 280) {
+torch/src/services/memory/retriever.js:2:  w_sim: 0.6,
+torch/src/services/memory/retriever.js:3:  w_imp: 0.25,
+torch/src/services/memory/retriever.js:4:  w_rec: 0.15,
+torch/src/services/memory/retriever.js:5:  pinBoost: 0.15,
+torch/src/services/memory/retriever.js:8:const DEFAULT_RECENCY_HALF_LIFE_MS = 1000 * 60 * 60 * 24 * 7;
+torch/src/services/memory/formatter.js:3:const DEFAULT_TOKEN_BUDGET = 1000;
+torch/src/services/memory/cache.js:2:const DEFAULT_MAX_EVENTS_PER_NAMESPACE = 200;
+torch/src/services/memory/cache.js:3:const DEFAULT_MAX_TOTAL_EVENTS = 5000;
+torch/src/services/memory/cache.js:153:    setRuntimeEvent(scope, event, ttlMs = 60_000) {
+torch/src/services/memory/cache.js:157:      const safeTtlMs = Number.isFinite(ttlMs) && ttlMs > 0 ? ttlMs : 60_000;
+torch/src/services/memory/scheduler.js:2:const MINUTE_MS = 60_000;
+torch/src/services/memory/scheduler.js:3:const HOUR_MS = 60 * MINUTE_MS;
+torch/src/services/memory/scheduler.js:4:const DAY_MS = 24 * HOUR_MS;
diff --git a/src/context/CONTEXT_1772225357.md b/src/context/CONTEXT_1772225357.md
new file mode 100644
index 00000000..07750a86
--- /dev/null
+++ b/src/context/CONTEXT_1772225357.md
@@ -0,0 +1,2 @@
+Fri Feb 27 20:49:17 UTC 2026
+Exploring duplicated constants in js/
diff --git a/src/decisions/DECISIONS_1772226155.md b/src/decisions/DECISIONS_1772226155.md
new file mode 100644
index 00000000..159427bf
--- /dev/null
+++ b/src/decisions/DECISIONS_1772226155.md
@@ -0,0 +1,7 @@
+## Decisions
+
+- **Domain-specific constants vs Global**: The constants were found specifically within `torch/src/services/memory/`. Therefore, I created `torch/src/services/memory/constants/` rather than a global `src/constants.js`.
+- **Time constants**: Created `time.js` for `SECOND_MS`, `MINUTE_MS`, `HOUR_MS`, `DAY_MS`, `WEEK_MS`, `MONTH_MS`.
+- **Text constants**: Created `text.js` for `MAX_SUMMARY_LENGTH`.
+- **Scoring constants**: Created `scoring.js` for `DEFAULT_IMPORTANCE_SCORE` and `LOW_IMPORTANCE_THRESHOLD`.
+- Replaced literals in `index.js`, `pruner.js`, `retriever.js`, `ingestor.js`, `summarizer.js`, `cache.js`, `scheduler.js`.
diff --git a/src/test_logs/TEST_LOG_1772226155.md b/src/test_logs/TEST_LOG_1772226155.md
new file mode 100644
index 00000000..6b059532
--- /dev/null
+++ b/src/test_logs/TEST_LOG_1772226155.md
@@ -0,0 +1 @@
+Ran `npm run --prefix torch lint` and verified that there are no linter errors.
diff --git a/src/todo/TODO_1772226155.md b/src/todo/TODO_1772226155.md
new file mode 100644
index 00000000..53ec2fad
--- /dev/null
+++ b/src/todo/TODO_1772226155.md
@@ -0,0 +1,10 @@
+## Constants Refactoring TODOs
+
+1. `1000` (second in ms): Replaced with `SECOND_MS`
+2. `60000` (minute in ms): Replaced with `MINUTE_MS`
+3. `24 * 60 * 60 * 1000` (day in ms): Replaced with `DAY_MS`
+4. `280` (max summary length): Replaced with `MAX_SUMMARY_LENGTH`
+5. `0.25` (default importance): Replaced with `DEFAULT_IMPORTANCE_SCORE`
+6. `0.35` (low importance threshold): Replaced with `LOW_IMPORTANCE_THRESHOLD`
+
+Status: All complete. Constants extracted to `torch/src/services/memory/constants/`.
diff --git a/torch/scripts/test-audit/run_python_flaky_check.py b/torch/scripts/test-audit/run_python_flaky_check.py
index 9ddef432..3add8d15 100644
--- a/torch/scripts/test-audit/run_python_flaky_check.py
+++ b/torch/scripts/test-audit/run_python_flaky_check.py
@@ -11,6 +11,7 @@ TEST_PATTERN = "src/tests"
 RESULTS = {}
 RUN_DIAGNOSTICS = []
 
+
 def run_tests(run_index):
     """Run pytest and parse output for pass/fail counts."""
     print(f"Run {run_index + 1}/{RUNS}...")
@@ -19,11 +20,13 @@ def run_tests(run_index):
     # -q: quiet
     # --no-header --no-summary: minimal output
     cmd = [
-        sys.executable, "-m", "pytest",
+        sys.executable,
+        "-m",
+        "pytest",
         TEST_PATTERN,
         "-q",
         "--no-header",
-        "--no-summary"
+        "--no-summary",
     ]
 
     start_time = time.time()
@@ -33,7 +36,7 @@ def run_tests(run_index):
             cmd,
             capture_output=True,
             text=True,
-            env={"PYTHONPATH": "src"}  # Ensure src is in path
+            env={"PYTHONPATH": "src"},  # Ensure src is in path
         )
         output = result.stdout
         exit_code = result.returncode
@@ -45,10 +48,10 @@ def run_tests(run_index):
 
     # Parse output character by character for status
     # . = pass, F = fail, E = error, s = skip, x = xfail, X = xpass
-    passed = output.count('.')
-    failed = output.count('F')
-    errors = output.count('E')
-    skipped = output.count('s')
+    passed = output.count(".")
+    failed = output.count("F")
+    errors = output.count("E")
+    skipped = output.count("s")
 
     # Store aggregate results (naive mapping, ideally we'd parse node ids)
     # Since we can't easily map node IDs without -v or --junitxml, we track totals for flakiness *rates* at suite level first.
@@ -56,26 +59,36 @@ def run_tests(run_index):
 
     junit_path = OUTPUT_DIR / f"flaky-run-{run_index + 1}.xml"
     subprocess.run(
-        [sys.executable, "-m", "pytest", TEST_PATTERN, f"--junitxml={junit_path}", "-q"],
-        capture_output=True, # We only care about the XML file
-        env={"PYTHONPATH": "src"}
+        [
+            sys.executable,
+            "-m",
+            "pytest",
+            TEST_PATTERN,
+            f"--junitxml={junit_path}",
+            "-q",
+        ],
+        capture_output=True,  # We only care about the XML file
+        env={"PYTHONPATH": "src"},
+    )
+
+    RUN_DIAGNOSTICS.append(
+        {
+            "run": run_index + 1,
+            "code": exit_code,
+            "duration": duration,
+            "passed": passed,
+            "failed": failed,
+            "errors": errors,
+            "skipped": skipped,
+        }
     )
 
-    RUN_DIAGNOSTICS.append({
-        "run": run_index + 1,
-        "code": exit_code,
-        "duration": duration,
-        "passed": passed,
-        "failed": failed,
-        "errors": errors,
-        "skipped": skipped
-    })
 
 def parse_junit_xmls():
     """Parse generated JUnit XML files to detect per-test flakiness."""
     import xml.etree.ElementTree as ET
 
-    test_stats = {} # "test_id": {pass: 0, fail: 0, error: 0, skip: 0}
+    test_stats = {}  # "test_id": {pass: 0, fail: 0, error: 0, skip: 0}
 
     for i in range(RUNS):
         xml_path = OUTPUT_DIR / f"flaky-run-{i + 1}.xml"
@@ -108,6 +121,7 @@ def parse_junit_xmls():
 
     return test_stats
 
+
 def main():
     OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
 
@@ -129,5 +143,6 @@ def main():
 
     print(f"Flakiness matrix written to {OUTPUT_DIR / 'flakiness-matrix.json'}")
 
+
 if __name__ == "__main__":
     main()
diff --git a/torch/scripts/test-audit/run_python_static_analysis.py b/torch/scripts/test-audit/run_python_static_analysis.py
index ec0116c4..a6aa78ca 100644
--- a/torch/scripts/test-audit/run_python_static_analysis.py
+++ b/torch/scripts/test-audit/run_python_static_analysis.py
@@ -20,29 +20,33 @@ PATTERNS = [
     (r"# todo", "Found '# todo' comment"),
 ]
 
+
 def scan_file(filepath):
     """Scan a single file for suspicious patterns."""
     issues = []
     try:
         content = filepath.read_text(encoding="utf-8")
     except Exception:
-        return # Skip non-text files
+        return  # Skip non-text files
 
     for pattern, message in PATTERNS:
         if re.search(pattern, content):
             issues.append(message)
 
     # Check for lack of assertions (very naive)
-    if "test_" in filepath.name and "assert" not in content and "expect" not in content and "raise" not in content:
+    if (
+        "test_" in filepath.name
+        and "assert" not in content
+        and "expect" not in content
+        and "raise" not in content
+    ):
         # Exclude conftest or helpers
         if "conftest" not in filepath.name and "helper" not in filepath.name:
-             issues.append("No obvious assertions found")
+            issues.append("No obvious assertions found")
 
     if issues:
-        SUSPICIOUS.append({
-            "file": str(filepath),
-            "issues": issues
-        })
+        SUSPICIOUS.append({"file": str(filepath), "issues": issues})
+
 
 def main():
     OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
@@ -62,5 +66,6 @@ def main():
     print(f"Found {len(SUSPICIOUS)} suspicious files.")
     print(f"Report written to {OUTPUT_DIR / 'suspicious-tests.json'}.")
 
+
 if __name__ == "__main__":
     main()
diff --git a/torch/src/services/memory/cache.js b/torch/src/services/memory/cache.js
index 63170421..344d41d1 100644
--- a/torch/src/services/memory/cache.js
+++ b/torch/src/services/memory/cache.js
@@ -1,3 +1,5 @@
+import { MINUTE_MS } from './constants/time.js';
+
 const DEFAULT_MAX_NAMESPACES = 128;
 const DEFAULT_MAX_EVENTS_PER_NAMESPACE = 200;
 const DEFAULT_MAX_TOTAL_EVENTS = 5000;
@@ -150,11 +152,11 @@ export function createMemoryCache(options = {}) {
       runtimeNamespaces.clear();
       totalRuntimeEvents = 0;
     },
-    setRuntimeEvent(scope, event, ttlMs = 60_000) {
+    setRuntimeEvent(scope, event, ttlMs = MINUTE_MS) {
       const now = Date.now();
       removeExpired(now);
 
-      const safeTtlMs = Number.isFinite(ttlMs) && ttlMs > 0 ? ttlMs : 60_000;
+      const safeTtlMs = Number.isFinite(ttlMs) && ttlMs > 0 ? ttlMs : MINUTE_MS;
       const expiresAt = now + safeTtlMs;
       const namespaceKey = getNamespaceKey(scope);
       const bucket = runtimeNamespaces.get(namespaceKey) ?? { events: [] };
diff --git a/torch/src/services/memory/constants/scoring.js b/torch/src/services/memory/constants/scoring.js
new file mode 100644
index 00000000..686f1345
--- /dev/null
+++ b/torch/src/services/memory/constants/scoring.js
@@ -0,0 +1,2 @@
+export const DEFAULT_IMPORTANCE_SCORE = 0.25;
+export const LOW_IMPORTANCE_THRESHOLD = 0.35;
diff --git a/torch/src/services/memory/constants/text.js b/torch/src/services/memory/constants/text.js
new file mode 100644
index 00000000..538d4054
--- /dev/null
+++ b/torch/src/services/memory/constants/text.js
@@ -0,0 +1 @@
+export const MAX_SUMMARY_LENGTH = 280;
diff --git a/torch/src/services/memory/constants/time.js b/torch/src/services/memory/constants/time.js
new file mode 100644
index 00000000..446f9807
--- /dev/null
+++ b/torch/src/services/memory/constants/time.js
@@ -0,0 +1,6 @@
+export const SECOND_MS = 1000;
+export const MINUTE_MS = 60 * SECOND_MS;
+export const HOUR_MS = 60 * MINUTE_MS;
+export const DAY_MS = 24 * HOUR_MS;
+export const WEEK_MS = 7 * DAY_MS;
+export const MONTH_MS = 30 * DAY_MS;
diff --git a/torch/src/services/memory/index.js b/torch/src/services/memory/index.js
index 1c9ec824..46b42822 100644
--- a/torch/src/services/memory/index.js
+++ b/torch/src/services/memory/index.js
@@ -42,7 +42,9 @@ let currentSavePromise = null;
 let pendingSavePromise = null;
 let pendingSaveResolve = null;
 let lastSaveTime = 0;
-const MIN_SAVE_INTERVAL_MS = 1000;
+import { SECOND_MS, MONTH_MS, MINUTE_MS } from './constants/time.js';
+
+const MIN_SAVE_INTERVAL_MS = SECOND_MS;
 
 async function performSave(store) {
   try {
@@ -326,7 +328,7 @@ export async function getRelevantMemories(params) {
 export async function runPruneCycle(options = {}) {
   const repository = options.repository ?? memoryRepository;
   const telemetry = buildTelemetryEmitter(options);
-  const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);
+  const retentionMs = options.retentionMs ?? MONTH_MS;
   const pruneMode = options.pruneMode ?? getMemoryPruneMode(options.env);
   const dbCandidates = await listPruneCandidates(repository, {
     retentionMs,
@@ -492,7 +494,7 @@ export async function inspectMemory(id, options = {}) {
  */
 export async function triggerPruneDryRun(options = {}) {
   const repository = options.repository ?? memoryRepository;
-  const retentionMs = options.retentionMs ?? (1000 * 60 * 60 * 24 * 30);
+  const retentionMs = options.retentionMs ?? MONTH_MS;
   const dbCandidates = await listPruneCandidates(repository, {
     retentionMs,
     now: options.now,
@@ -532,7 +534,7 @@ export async function triggerPruneDryRun(options = {}) {
 export async function memoryStats(options = {}) {
   const repository = options.repository ?? memoryRepository;
   const now = options.now ?? Date.now();
-  const windowMs = Number.isFinite(options.windowMs) ? Math.max(1, Number(options.windowMs)) : (60 * 60 * 1000);
+  const windowMs = Number.isFinite(options.windowMs) ? Math.max(1, Number(options.windowMs)) : (60 * MINUTE_MS);
   const memories = await listMemories({}, { repository });
 
   const countsByType = memories.reduce((acc, memory) => {
@@ -545,7 +547,7 @@ export async function memoryStats(options = {}) {
   const archivedRate = archivedCount / Math.max(1, memories.length);
 
   const ingestedInWindow = memoryStatsState.ingested.filter((timestamp) => timestamp >= (now - windowMs)).length;
-  const ingestThroughputPerMinute = ingestedInWindow / Math.max(1, (windowMs / 60_000));
+  const ingestThroughputPerMinute = ingestedInWindow / Math.max(1, (windowMs / MINUTE_MS));
 
   return {
     countsByType,
diff --git a/torch/src/services/memory/ingestor.js b/torch/src/services/memory/ingestor.js
index 5f9ac422..68ec7590 100644
--- a/torch/src/services/memory/ingestor.js
+++ b/torch/src/services/memory/ingestor.js
@@ -2,8 +2,10 @@ import { createHash, randomUUID } from 'node:crypto';
 import { createMemoryRecord, normalizeEvent, validateMemoryItem } from './schema.js';
 import { embedText, getDefaultEmbedderAdapter } from './embedder.js';
 import { summarizeEvents } from './summarizer.js';
+import { MINUTE_MS } from './constants/time.js';
+import { MAX_SUMMARY_LENGTH } from './constants/text.js';
 
-const WINDOW_BUCKET_MS = 60_000;
+const WINDOW_BUCKET_MS = MINUTE_MS;
 const MAX_DEDUPE_KEYS = 2000;
 const dedupeWindow = new Map();
 
@@ -89,7 +91,7 @@ async function rememberWindowFingerprint(repository, dedupeKey, payload) {
 }
 
 
-function compactContextExcerpt(text, maxChars = 280) {
+function compactContextExcerpt(text, maxChars = MAX_SUMMARY_LENGTH) {
   if (typeof text !== 'string' || text.length === 0) return '';
   const compact = text.replace(/\s+/g, ' ').trim();
   if (compact.length <= maxChars) return compact;
diff --git a/torch/src/services/memory/pruner.js b/torch/src/services/memory/pruner.js
index a224b892..89fad513 100644
--- a/torch/src/services/memory/pruner.js
+++ b/torch/src/services/memory/pruner.js
@@ -1,6 +1,7 @@
 import { loadMemoryPromptTemplates } from './summarizer.js';
-
-const DAY_MS = 24 * 60 * 60 * 1000;
+import { DAY_MS } from './constants/time.js';
+import { MAX_SUMMARY_LENGTH } from './constants/text.js';
+import { LOW_IMPORTANCE_THRESHOLD } from './constants/scoring.js';
 
 /**
  * @typedef {'keep' | 'archive' | 'delete'} LifecycleAction
@@ -113,7 +114,7 @@ function groupNearDuplicates(memories, options) {
  */
 async function condenseGroup(group, generateSummary, condenseTemplate) {
   const [primary, ...rest] = group;
-  const fallbackSummary = group.map((item) => item.summary || item.content).filter(Boolean).join(' ').slice(0, 280);
+  const fallbackSummary = group.map((item) => item.summary || item.content).filter(Boolean).join(' ').slice(0, MAX_SUMMARY_LENGTH);
   const fallback = {
     summary: fallbackSummary || primary.summary,
     importance: Math.max(...group.map((item) => item.importance)),
@@ -181,7 +182,7 @@ export async function createLifecyclePlan(memories, options) {
   const policy = {
     now,
     retentionMs: options.retentionMs,
-    lowImportanceThreshold: options.lowImportanceThreshold ?? 0.35,
+    lowImportanceThreshold: options.lowImportanceThreshold ?? LOW_IMPORTANCE_THRESHOLD,
     deleteImportanceThreshold: options.deleteImportanceThreshold ?? 0.12,
     recentUsageMs: options.recentUsageMs ?? Math.max(Math.floor(options.retentionMs / 2), DAY_MS),
     duplicateWindowMs: options.duplicateWindowMs ?? (7 * DAY_MS),
diff --git a/torch/src/services/memory/retriever.js b/torch/src/services/memory/retriever.js
index 5a5e8fc9..24a12f23 100644
--- a/torch/src/services/memory/retriever.js
+++ b/torch/src/services/memory/retriever.js
@@ -1,11 +1,14 @@
+import { WEEK_MS } from './constants/time.js';
+import { DEFAULT_IMPORTANCE_SCORE } from './constants/scoring.js';
+
 const DEFAULT_WEIGHTS = {
   w_sim: 0.6,
-  w_imp: 0.25,
+  w_imp: DEFAULT_IMPORTANCE_SCORE,
   w_rec: 0.15,
   pinBoost: 0.15,
 };
 
-const DEFAULT_RECENCY_HALF_LIFE_MS = 1000 * 60 * 60 * 24 * 7;
+const DEFAULT_RECENCY_HALF_LIFE_MS = WEEK_MS;
 
 function cosineSimilarity(left = [], right = []) {
   const size = Math.max(left.length, right.length);
diff --git a/torch/src/services/memory/scheduler.js b/torch/src/services/memory/scheduler.js
index 533734d9..6c6e451a 100644
--- a/torch/src/services/memory/scheduler.js
+++ b/torch/src/services/memory/scheduler.js
@@ -1,8 +1,5 @@
 import { getMemoryPruneMode, isMemoryEnabled, isMemoryIngestEnabled } from './feature-flags.js';
-const MINUTE_MS = 60_000;
-const HOUR_MS = 60 * MINUTE_MS;
-const DAY_MS = 24 * HOUR_MS;
-const WEEK_MS = 7 * DAY_MS;
+import { MINUTE_MS, HOUR_MS, DAY_MS, WEEK_MS } from './constants/time.js';
 
 const JOBS = {
   ingestRecentRuntimeEvents: {
diff --git a/torch/src/services/memory/summarizer.js b/torch/src/services/memory/summarizer.js
index 0f128509..d9188912 100644
--- a/torch/src/services/memory/summarizer.js
+++ b/torch/src/services/memory/summarizer.js
@@ -1,6 +1,8 @@
 import { readFileSync } from 'node:fs';
 import path from 'node:path';
 import { fileURLToPath } from 'node:url';
+import { MAX_SUMMARY_LENGTH } from './constants/text.js';
+import { DEFAULT_IMPORTANCE_SCORE, LOW_IMPORTANCE_THRESHOLD } from './constants/scoring.js';
 
 const __filename = fileURLToPath(import.meta.url);
 const __dirname = path.dirname(__filename);
@@ -61,7 +63,7 @@ function parseSummaryPayload(rawResponse) {
   const rawImportance = Number(parsed.importance);
   const importance = Number.isFinite(rawImportance)
     ? Math.min(1, Math.max(0, rawImportance))
-    : 0.25;
+    : DEFAULT_IMPORTANCE_SCORE;
 
   return { summary, importance };
 }
@@ -69,7 +71,7 @@ function parseSummaryPayload(rawResponse) {
 function deterministicFallback(events, maxSummaryLength) {
   const joined = events.map((event) => redactObviousPii(event.content)).join(' ').trim();
   const bounded = joined.length > maxSummaryLength ? `${joined.slice(0, maxSummaryLength - 1)}…` : joined;
-  return { summary: bounded, importance: 0.25 };
+  return { summary: bounded, importance: DEFAULT_IMPORTANCE_SCORE };
 }
 
 function isLikelyFactual(summary, sourceText) {
@@ -86,7 +88,7 @@ function isLikelyFactual(summary, sourceText) {
 
   if (!summaryTokens.length) return true;
   const overlap = summaryTokens.filter((token) => sourceTokens.has(token)).length;
-  return overlap / summaryTokens.length >= 0.35;
+  return overlap / summaryTokens.length >= LOW_IMPORTANCE_THRESHOLD;
 }
 
 function sanitizeResult(parsed, sourceText, maxSummaryLength, fallback) {
@@ -113,7 +115,7 @@ function sanitizeResult(parsed, sourceText, maxSummaryLength, fallback) {
  * @returns {Promise<{ summary: string, importance: number }>}
  */
 export async function summarizeEvents(events, options = {}) {
-  const maxSummaryLength = options.maxSummaryLength ?? 280;
+  const maxSummaryLength = options.maxSummaryLength ?? MAX_SUMMARY_LENGTH;
   const fallback = deterministicFallback(events, maxSummaryLength);
   if (!fallback.summary) return fallback;
 
diff --git a/torch/task-logs/daily/2026-02-27T21-08-43Z__const-refactor-agent__completed.md b/torch/task-logs/daily/2026-02-27T21-08-43Z__const-refactor-agent__completed.md
new file mode 100644
index 00000000..6ab9fedb
--- /dev/null
+++ b/torch/task-logs/daily/2026-02-27T21-08-43Z__const-refactor-agent__completed.md
@@ -0,0 +1 @@
+completed