Evolving Memory

Evolving Memory System

ScienceClaw learns from every research session. Each completed analysis enriches a persistent memory that makes future research faster, more accurate, and less error-prone.

Memory Record Types

All records are stored in ~/.scienceclaw/memory/findings.jsonl (append-only, one JSON object per line).

Type 1: Finding (existing)

A verified scientific discovery with evidence.

{"type":"finding","date":"2026-03-18","gene":"THBS2","disease":"pancreatic cancer","finding":"THBS2 overexpressed in 17/33 TCGA cancer types (Wilcoxon p<0.001)","significance":"high","sources":["PMID:32273438"],"tags":["expression","pan-cancer"],"project":"thbs2-tumor-2026-03-10"}

Type 2: Ideation

Records whether a research direction proved viable or not. Prevents revisiting dead ends and reinforces promising paths.

{"type":"ideation","date":"2026-03-18","direction":"THBS2+CA19-9 as pancreatic cancer diagnostic panel","feasibility":"low","reason":"Prospective AUC dropped from 0.96 to 0.69; biomarker validation failed in independent cohort","tags":["diagnostic","validation-failure","liquid-biopsy"],"project":"thbs2-tumor-2026-03-10"}

Fields:

• direction: The research direction or hypothesis explored
• feasibility: "high" | "medium" | "low" | "dead-end"
• reason: Why this feasibility assessment was reached (with data)

Type 3: Strategy

Records an analysis approach that worked well (or better than the default). Enables progressively better methodology.

{"type":"strategy","date":"2026-03-18","task":"survival_analysis","strategy":"Use surv_cutpoint() for optimal expression cutoff instead of median split","outcome":"Better separation: p=0.003 vs p=0.047 with median; HR 2.31 vs 1.68","tools":["R:survival","R:survminer"],"tags":["survival","cutoff-optimization"],"project":"thbs2-tumor-2026-03-10"}

Fields:

• task: The type of analysis (e.g., survival_analysis, enrichment, deseq2, literature_search)
• strategy: What was done differently from the default approach
• outcome: Quantitative evidence that this strategy was better
• tools: Software/packages used

Type 4: Pitfall

Records errors, API quirks, data issues, and their fixes. Prevents repeating the same mistakes.

{"type":"pitfall","date":"2026-03-18","context":"TCGA PAAD via cBioPortal","issue":"cBioPortal API returns duplicate samples when a study has multiple cohorts (e.g., TCGA-PAAD has both 'tcga_pan_can_atlas_2018' and 'paad_tcga')","fix":"Always deduplicate by sample_id before analysis; prefer the pan_can_atlas study for cross-cancer comparisons","tags":["cbioportal","tcga","deduplication"],"project":"thbs2-tumor-2026-03-10"}

Fields:

• context: Where the problem occurred (database, API, analysis step)
• issue: What went wrong (specific, reproducible)
• fix: How to avoid or resolve it

When to Write Memory

After every Recipe completion

Extract and append records using this checklist:

1. Findings: Any statistically significant result (p < 0.05 with meaningful effect size), contradiction between studies, or validated pipeline
2. Ideation: For each research direction explored — was it viable? Record feasibility with evidence
3. Strategy: Did any analysis step use a non-default approach that produced better results? Record the approach and quantitative comparison
4. Pitfalls: Did any tool call fail, return unexpected data, or require a workaround? Record the issue and fix

Implementation

After completing a deep task, append records:

cat >> ~/.scienceclaw/memory/findings.jsonl << 'JSONL'
{"type":"strategy","date":"2026-03-18","task":"immune_infiltration","strategy":"Use CIBERSORT absolute mode instead of relative for correlation with gene expression","outcome":"Absolute scores gave r=0.59 vs relative r=0.31 for THBS2-macrophage correlation","tools":["R:CIBERSORT"],"tags":["immune","cibersort"],"project":"thbs2-tumor-2026-03-18"}
{"type":"pitfall","date":"2026-03-18","context":"Enrichr API","issue":"Enrichr addList endpoint returns 500 if gene list exceeds 2000 genes","fix":"Limit gene list to top 500 by fold change before submitting to Enrichr","tags":["enrichr","api-limit"],"project":"thbs2-tumor-2026-03-18"}
JSONL

When to Recall Memory

Before every Recipe execution

Before starting any Research Recipe, search memory for relevant prior experience:

python3 -c "
import json, sys

query_terms = sys.argv[1].lower().split()
records = {'strategy': [], 'pitfall': [], 'ideation': []}

with open('$HOME/.scienceclaw/memory/findings.jsonl') as f:
    for line in f:
        line = line.strip()
        if not line:
            continue
        try:
            r = json.loads(line)
        except json.JSONDecodeError:
            continue
        rtype = r.get('type', 'finding')
        if rtype not in records:
            continue
        searchable = json.dumps(r).lower()
        if any(t in searchable for t in query_terms):
            records[rtype].append(r)

for rtype in ['strategy', 'pitfall', 'ideation']:
    if records[rtype]:
        print(f'\n=== Relevant {rtype}s ({len(records[rtype])}) ===')
        for r in records[rtype][-5:]:
            if rtype == 'strategy':
                print(f'  [{r.get(\"task\",\"\")}] {r.get(\"strategy\",\"\")}')
                print(f'    Outcome: {r.get(\"outcome\",\"\")}')
            elif rtype == 'pitfall':
                print(f'  [{r.get(\"context\",\"\")}] {r.get(\"issue\",\"\")}')
                print(f'    Fix: {r.get(\"fix\",\"\")}')
            elif rtype == 'ideation':
                print(f'  [{r.get(\"feasibility\",\"\")}] {r.get(\"direction\",\"\")}')
                print(f'    Reason: {r.get(\"reason\",\"\")}')
" "GENE DISEASE KEYWORDS"

Replace GENE DISEASE KEYWORDS with terms relevant to the current task (e.g., "TP53 liver cancer survival").

How to use recalled memory

• Strategies: Apply proven approaches instead of defaults. Mention in progress: "基于之前的经验，使用 surv_cutpoint() 代替中位数分割..."
• Pitfalls: Proactively avoid known issues. Mention in progress: "注意 cBioPortal 去重（之前遇到过重复样本问题）..."
• Ideation: Skip dead-end directions. Mention: "之前已验证 THBS2+CA19-9 诊断组合前瞻性 AUC 不理想，跳过此方向..."

User Commands

/recall <topic>

Search memory for all records matching the topic. Group by type, sort by date (newest first). Show up to 10 per type.

/lessons

Show the most recent 20 strategy and pitfall records across all projects. Useful for reviewing accumulated knowledge.

/memory stats

Report memory statistics:

• Total records by type
• Projects with most entries
• Most common tags
• Memory file size

Memory Maintenance

Size management

When findings.jsonl exceeds 500 records, automatically compact on next session start:

1. Keep all records from the last 90 days
2. For older records: keep all findings and high-feasibility ideation; keep only the 100 most recent strategies and pitfalls
3. Write compacted file to findings.jsonl, backup original to findings.jsonl.bak

python3 -c "
import json, os
from datetime import datetime, timedelta

path = os.path.expanduser('~/.scienceclaw/memory/findings.jsonl')
if not os.path.exists(path):
    exit()

with open(path) as f:
    records = [json.loads(l) for l in f if l.strip()]

if len(records) <= 500:
    print(f'Memory OK: {len(records)} records (under 500 limit)')
    exit()

cutoff = (datetime.now() - timedelta(days=90)).strftime('%Y-%m-%d')
recent = [r for r in records if r.get('date', '9999') >= cutoff]
old = [r for r in records if r.get('date', '0000') < cutoff]

kept = list(recent)
for r in old:
    if r.get('type') == 'finding':
        kept.append(r)
    elif r.get('type') == 'ideation' and r.get('feasibility') in ('high', 'medium'):
        kept.append(r)

old_strat_pit = [r for r in old if r.get('type') in ('strategy', 'pitfall')]
old_strat_pit.sort(key=lambda x: x.get('date', ''), reverse=True)
kept.extend(old_strat_pit[:100])

import shutil
shutil.copy2(path, path + '.bak')
with open(path, 'w') as f:
    for r in kept:
        f.write(json.dumps(r, ensure_ascii=False) + '\n')

print(f'Compacted: {len(records)} → {len(kept)} records (backup saved)')
"

Integration with Session Greeting

At session start, after checking for watch alerts, also run memory maintenance (if needed) and briefly report:

📊 研究记忆: 127 条记录 (38 findings, 22 ideation, 45 strategy, 22 pitfalls)
   最近项目: thbs2-tumor-2026-03-10 (上次: THBS2 在 17/33 癌种显著上调)