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With_In_Memory_250k

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WithIn Us AI — MemoryGenesis (God Level) 250K

Developer: WithIn Us AI

MemoryGenesis is a 250,000-example dataset designed to train LLMs to behave like memory-first agents:

capture new information at runtime
store durable and ephemeral memory safely (TTL)
retrieve and cite relevant memories (RAG-style behavior)
update, correct, merge, deduplicate, and compact memories
resolve conflicts via provenance/recency/confidence
enforce privacy (no secrets stored), redaction, and safe policies
approximate "instant knowledge injection" without weight updates by using external memory tools

Important: This dataset trains runtime memory behaviors. It does not perform real-time weight updates or replace model fine-tuning compute; instead it teaches a model to use external memory so new facts can be injected immediately.

Files

train.jsonl — 245,000 examples
valid.jsonl — 5,000 examples
sample_200.jsonl — 200 examples for inspection

Record schema (JSONL)

Each line includes both instruct and chat formats:

prompt_instruct / response_instruct (instruction SFT)
messages (chat SFT: system/user/assistant)
task_type: memory_write, recall, update, merge/dedup, compaction, TTL, privacy, schema, indexing, eval, Q&A
metadata.runtime_memory_only = true and metadata.no_weight_updates = true

Tool protocol (in-text)

Responses may include structured tool calls inside code blocks:

memory.write (key/value/tags/confidence/ttl_days)
memory.search (query/k/tags)
memory.update / memory.delete
memory.compact

These are represented as JSON under TOOL_CALL or TOOL_CALLS.

Safety constraints

Never store secrets (API keys, passwords, private keys).
Store only safe derived information.
Ask user to confirm when evidence is missing or conflicting.

Suggested fine-tuning usage

Instruct SFT

Map:

input: prompt_instruct
target: response_instruct

Chat SFT

Map:

messages: messages

Evaluation suggestions

Measure:

precision@k for retrieval
staleness/conflict rate
user correction rate
privacy redaction compliance