External benchmarks

An external benchmark is the only level that compares aizk with another system. AIZK has no published external benchmark score. This page assumes you have read how we evaluate and know the isolation rules on the eval CLI.

The GroupMemBench adapter

src/eval/groupmem.py reads the released conversation and question schemas directly. It does not copy the upstream implementation, whose repository declares no license. Messages come from data/final/<Domain>/synthetic_domain_channels_rolevariants_<Domain>.json and questions come from questions/<Domain>/<kind>.jsonl, both validated into frozen models before anything is stored.

Structure is preserved rather than flattened into text. Every message keeps its author, role, channel, reply target, phase, topic, decision marker and source time, and those become a CaptureContext on the ingested source. Every question is asked as the user who asked it, so a first-person question resolves against the right speaker.

  released corpus ──▶ adapter: fingerprint the selected messages
                        │
                        ├─▶ write path: ingest_texts into one deterministic scope
                        │        └─▶ build_graph over that scope
                        │
                        ├─▶ find: as the named asking user, k=10 ──▶ packed context
                        │
                        ├─▶ adapter: answer from that context only
                        │
                        └─▶ LLM judge: candidate against the gold answer ──▶ correct or not

The scope is derived from benchmark:GroupMemBench:<domain>:<fingerprint>, where the fingerprint is a SHA-256 over the adapter version and the exact selected messages, so two revisions of a corpus cannot share prepared state. Preparation is idempotent and verified, because corpus_state counts the imported documents and any chunk that has not finished graph extraction, and the runner refuses to score a corpus that is short a document or still has pending chunks. The scope is purged after the run unless you pass --keep.

The six question kinds

They stay separate in the report, so an overall score cannot hide a failure in one family.

Kind	What it tests
`multi_hop`	combining facts that no single message holds
`knowledge_update`	preferring the current value over a superseded one
`temporal`	when something was true, not only that it was
`user_implicit`	resolving first-person language from the asking speaker
`term_ambiguity`	telling apart terms a team uses in two senses
`abstention`	refusing when the memory genuinely does not hold an answer

An abstention case is only correct when the system abstains and invents nothing, which is why the answer model returns a typed BenchmarkAnswer with an explicit abstained flag rather than a sentence a judge has to interpret.

Publishable is a computed flag

BenchmarkReport.publishable is not a decision somebody makes when writing up a result. It is the conjunction of five conditions, and the rendered scorecard says publishable or diagnostic accordingly.

  complete_corpus        no --message-limit was used
  not sampled_questions  no --question-limit was used
  solvability_filtered   the domain is Finance or Technology
  reference_protocol     k = 10 and both agent and judge are gpt-5
  failed == 0            no network, generation, database or evaluator failure

Healthcare and Manufacturing stay useful as diagnostic domains, but their released questions are unfiltered, so a score there is not comparable to the published protocol. Reusing the local LLM also drops a run to diagnostic, and an operational failure is never turned into an ordinary wrong answer.

The honest status

No aizk GroupMemBench score exists. The adapter is implemented and unit-tested and the runner executes the complete path, but a full run needs the deployed embedding and extraction lanes against the whole 30,000 message domain, and that has not been done. A synthetic estimate is not an acceptable substitute, so there is no head-to-head claim here and there will not be one until a run comes back publishable.

For difficulty context only, the GroupMemBench paper reports its best evaluated system at 46 percent overall. That is a reference for how hard the task is, not an aizk result and not a target anybody here has hit.

Running one

A small diagnostic run is the normal way to check that a corpus directory parses and that the whole path still works end to end.

uv run --no-sync aizk-eval groupmem /path/to/GroupMemBench \
  --domain Finance --question-limit 2 --out /tmp/groupmem-smoke.json

That reroutes to the isolated evaluation database, resets it, imports the messages, builds the graph, answers and judges, then purges the scope. Because --question-limit is set it comes back marked diagnostic, which is the correct label and not a warning to work around. Drop both limits, point the answering and judging models at the reference protocol, and the same command produces a publishable report or tells you which of the five conditions failed.

Forgetting-aware scoring

FAMAScore in src/eval/metrics.py implements the Memora paper’s per-question equation.

score = max(0, MPA - weight * (1 - FAA))

  MPA     memory presence accuracy, the current memories the answer got right
  FAA     forgetting absence accuracy, the invalidated memories it correctly left out
  weight  forgetting criteria divided by all criteria

It penalizes exactly the failure a retrieval score misses. An answer that finds every relevant current memory and also repeats an invalidated one scores a perfect MPA and still loses credit, in proportion to how much of the question was about forgetting. When a question declares no forgetting criteria the absence term defaults to 1.0 and the score reduces to plain presence accuracy.

It is available and tested rather than in use. The GroupMemBench runner scores correctness with an LLM judge, so no report here carries a FAMA number.

The adapters that do not exist

src/eval/ contains one external adapter. LongMemEval-V2, Memora and Mem2ActBench are read, cited and used to shape the design, and briefs for each live in the corpus, but no code imports their corpora and no score for them exists. If a page anywhere claims otherwise it is wrong.

The action-memory boundary

Everything measured across these pages is retrieval and extraction. A retrieval score says the right evidence reached the top of a packed context. It says nothing about whether an agent then chose the right action, because aizk generates no actions and no benchmark here scores any.

That boundary matters because action-memory benchmarks are the ones people most want a memory system to win. Reporting a retrieval number against them would be a category error, so when a comparison needs an action-selection claim, the honest answer is that aizk has not measured it.

How we evaluate explains why the levels never borrow authority.
The eval CLI has the groupmem flags and the isolation rules.
Comparison places aizk beside other memory systems.
References and lineage has the papers these adapters read.