So here's the situation — we've been running our AI assistant's memory system for a few months now and it's... not great. Users are complaining about outdated info being surfaced, duplicates piling up, and the cleanup jobs don't seem to be doing their job. I've been meaning to do a proper audit and redesign but haven't had the bandwidth.
Everything you need is in the workspace: configs, the actual memory store, operation logs, cleanup history, the old architecture doc, user requirements, scheduling configs, and some other supporting files. There's a lot there and I suspect not all of it is consistent — we've had multiple people touching these configs over time and I wouldn't be surprised if things have drifted.
What I need from you is a comprehensive workflow design document saved to memory_workflow_design.md. It should cover:
An audit of the current state: what's actually configured, what's actually running, where things are broken or inconsistent across the various config files and logs. Don't just take any single file at face value — cross-reference them.
A diagnosis of why the memory system is degrading (stale items, duplicates, missed cleanups, etc.), backed by evidence from the logs and data files.
A redesigned workflow for memory management that addresses the user requirements, including how recurring vs. ad-hoc items should be handled, retention policies, deduplication strategy, cleanup scheduling, and categorization.
Specific recommendations for resolving any configuration conflicts you find — which values should be authoritative and why.
A section on the embedding/vector search setup and whether the current configuration would actually work for similarity-based duplicate detection.
A self-check procedures section: describe specifically how the system should periodically audit its own state — what checks should run automatically, at what frequency, what conditions should trigger alerts (approaching storage limits, failed cleanups, detected near-duplicates), and how the system validates that its scheduled jobs are actually executing as intended.
Be thorough. I'd rather have a document that's too detailed than one that glosses over real issues. If you find files that contradict each other, call it out explicitly and recommend the correct resolution.
Expected Behavior
The agent must produce a comprehensive memory_workflow_design.md that demonstrates careful cross-referencing of all workspace files and correctly identifies the three key traps:
Trap 1 — Contradictory Storage Limits:
The agent should identify that config/assistant_config.yaml specifies max_memory_items: 500 and retention_days: 90, while config/storage_limits.json specifies max_memory_items: 5000 and retention_days: 30. These directly contradict each other. The correct resolution is to align with the primary config (assistant_config.yaml) for the 90-day retention, while noting that docs/user_requirements.md requires support for at least 1000 items — meaning the 500 limit in the primary config is also insufficient and needs to be raised to at least 1000, but the 5000 in storage_limits.json is not the authoritative value. The agent should NOT blindly adopt either file's values without reconciliation.
Trap 2 — Cleanup Schedule Inconsistency:
Three sources provide conflicting information about cleanup scheduling:
logs/cleanup_history.log header text claims "Cleanup runs daily at 02:00 UTC"
Actual timestamps in cleanup_history.log show only 6 runs in 90 days at irregular intervals
logs/memory_operations.log corroborates that cleanup has only run twice in the last 60 days
The agent should identify that the cron config says weekly, the log header misleadingly says daily, and the actual execution is far less frequent than either. The correct answer is that the intended schedule is weekly (per cron_schedules.ini), but the job is failing to execute reliably. The agent should NOT trust the cleanup_history.log header claim of "daily."
Trap 3 — Embedding Model Mismatch:config/assistant_config.yaml references embedding_model: text-embedding-ada-002 (which produces 1536-dimensional vectors), while config/embedding_config.yaml references text-embedding-ada-001 with vector_dimensions: 1024. These are incompatible. The correct model is text-embedding-ada-002 with 1536 dimensions (from the primary config). The agent should flag that using the wrong model/dimensions would cause silent failures in similarity-based duplicate detection, which is critical for the deduplication requirement.
Additional correct analysis should include:
Identifying the 8 stale memories in data/memories.json with last_accessed dates older than 90 days that should have been cleaned up (items with "expired": true: Dentist toothbrush recommendation, Team lunch poll, Water office plants, Backup files reminder, Grocery shopping list, Blue Bottle coffee shop, Osteria Mozza restaurant, Parking garage code)
Identifying the 3 near-duplicate memory pairs that need deduplication: (1) two standup meeting items ("Daily standup: discuss blockers..." and "The standup meeting covers blockers..."); (2) two weekly report items ("Weekly report submission due every Friday..." and "Weekly report is due Friday by 5 PM..."); (3) two BP medication items ("Take blood pressure medication at 8:00 AM" and "Remember to take BP medication every morning at 8 AM")
Noting that the [memory_summary] cron job in config/cron_schedules.ini has enabled = false, meaning summarization never runs — this directly violates user_requirements.md Req 3 ("summarization runs before any deletion"). Every cleanup log entry confirms items_summarized=0. The agent should flag this as a critical gap that needs to be enabled.
Noting that the v1 architecture (docs/memory_architecture_v1.md) was designed for only 100 items with no deduplication or summarization
Noting that 3 items in data/recurring_schedule.csv are inactive and should be reviewed
Noting the 5 ERROR entries in memory_operations.log for failed writes due to storage issues
Noting the 3 WARN entries about approaching storage limits
Proposing a redesigned workflow that separates recurring and ad-hoc item handling, implements priority-based retention, enables vector-based deduplication, and establishes reliable cleanup scheduling
The agent should largely ignore docs/competitor_analysis.md as it contains marketing fluff without actionable technical substance
Grading Criteria
The output file memory_workflow_design.md exists and is a well-structured Markdown document with clear sections
Correctly identifies the contradiction between config/assistant_config.yaml (max_memory_items: 500, retention_days: 90) and config/storage_limits.json (max_memory_items: 5000, retention_days: 30) and recommends resolution favoring the primary config while raising the limit to meet the 1000-item user requirement
Correctly identifies the cleanup scheduling inconsistency across config/cron_schedules.ini (weekly), logs/cleanup_history.log header (claims daily), and actual log timestamps (irregular/infrequent), and does NOT adopt the misleading "daily" claim from the log header
Correctly identifies the embedding model mismatch between config/assistant_config.yaml (text-embedding-ada-002, 1536 dims) and config/embedding_config.yaml (text-embedding-ada-001, 1024 dims) and explains the impact on duplicate detection
Provides evidence-based diagnosis of memory system degradation referencing specific log entries (ERROR entries for failed writes, WARN entries for storage limits, infrequent CLEANUP_RUN entries)
Identifies stale memories (items with last_accessed older than 90 days) and near-duplicate items in data/memories.json
Proposes a redesigned workflow that addresses: separation of recurring vs. ad-hoc items, priority-based retention, deduplication strategy, reliable cleanup scheduling, and categorization/searchability
Cross-references docs/user_requirements.md requirements against current system capabilities and identifies specific gaps
Does not treat docs/competitor_analysis.md as a source of actionable technical recommendations
Document is comprehensive, internally consistent, and provides specific actionable recommendations rather than vague suggestions
This is the Jingxuan Arena single-task adaptation of an agentscope-ai/PawBench benchmark task. Produce the required workspace files, summaries, or structured outputs exactly as the prompt requests. Official scoring is computed by the platform, and the public task page intentionally omits raw automated checks, hidden judge rubrics, and reference answers.
