TWIST: A Benchmark for What Memory Benchmarks Can't See

TWIST — a benchmark for what memory benchmarks can't see

Every AI memory vendor quotes a LoCoMo score. LoCoMo measures whether a system can recall what was said. Nothing measures whether it notices when what was said stops being true. We built that benchmark — and our own product fails it. Here's the spec, the data, and the honest first results.

The failures that actually cost money

Deployed memory systems don't lose money on forgotten facts. They lose it at change points — the moments a stored belief and reality diverge:

Several vendors ship features aimed at these behaviors — conflict detectors, temporal fact invalidation, draft vetting. None of these capabilities has a public benchmark. Every claim in this space is currently unfalsifiable — precisely the condition LoCoMo fixed for recall. TWIST (Tracking Within-speaker Inconsistencies, Stance-reversals, and Tensions) proposes to fix it for coherence, alignment, and governance.

Four tracks, one rule: every metric ships with its pair

TrackCapabilityPaired failure it scores
A — Tension detectionFlag a stance reversal unprompted, months apartFalse-flagging nostalgia, hypotheticals, pressure signals
B — Draft alignmentVet an outgoing message against the record, with citationsFlagging drafts that merely acknowledge a change
C — Belief supersessionAnswer with the current fact; retire the stale oneDeleting history, or retiring facts that still coexist
D — Safe recallPII and crisis content governed at write timeOver-blocking emotional-but-legitimate content

The pairing is the point. A detection system that flags everything aces recall and is unusable; a safety gate that suppresses everything leaks nothing and remembers nothing. TWIST's usage terms make citing one number without its pair invalid.

TWIST extends LoCoMo rather than replacing it: items are injected into LoCoMo's own conversations, so one corpus yields both a recall score and a TWIST profile. And it is designed against LoCoMo's documented weaknesses — the 6.4% answer-key error rate and the 62.8% deliberately-wrong-answer acceptance found by an independent audit of the LLM-judge evaluation convention ("LoCoMo-J") that memory-system papers popularized on top of the original F1-scored benchmark — with double annotation, structural evidence-ID scoring, judge decoy calibration reported alongside every run, and versioned errata.

What a scenario looks like

Contradicting draft (gold: flag it)
"Hey Caroline, hope you're enjoying your break from adoption plans for a while. Sometimes it's good to step back and reassess." — the record has Caroline, days earlier: "I passed the adoption agency interviews last Friday!" The draft asserts a pause the record refutes.
Why not a question? (a v0 → v1 lesson)
Our original v0 example here was question-form ("… Is that true?"). Human annotation taught us that was a generator error: asking whether something changed is a hard negative by TWIST's own definition, never a contradiction. Contradicting drafts must assert the wrong thing. We keep this correction visible on purpose — it is exactly the kind of item defect the validation protocol exists to catch.
Hard negative (gold: leave it alone)
"Caroline, it's awesome you're still committed to volunteering, despite how challenging things have been!" — contrastive framing, zero contradiction. Systems that flag this fail.

The full catalog — 26 scenario families across the four tracks, each detection family paired with a surface-matched hard negative — ships with the benchmark.

First results: neither system passes, in opposite directions

We built Track B first: 153 draft-vetting items over LoCoMo's ten conversations, every item gated by an independent verification pass. Two systems ran it — a flat-RAG baseline (embed, retrieve, ask an LLM) and MindTwin's own vetting path:

MetricMindTwinflat-RAG
Caught true contradictions (n=93)0.141.00
Didn't flag aligned drafts (n=41)0.980.78
Didn't flag hard negatives (n=19)1.000.37

Flat-RAG cries wolf: it catches everything and objects to two out of three perfectly safe messages. Deployed, users would mute it in a week. MindTwin stays silent too often: it never over-flags, but at ten-conversation scale it missed most true contradictions — half on retrieval coverage (a draft saying "you're not doing pottery anymore" embeds far from the positive statements it contradicts), and much of the rest, it turned out, on our own items: gold evidence whose standalone text didn't carry the contradiction. Both findings are in the public results, including the mechanism we shipped that didn't work.

That's the standard we're committing to. A benchmark authored by a vendor is only credible if it reports the vendor's failures with the same precision as its wins — and if competitors can run it themselves and dispute every item.

TWIST v1.0 update · September 2026
The regenerated 200-candidate Track B set completed independent double human annotation and adjudication: 39 items were removed (raw inter-annotator κ = 0.565; both annotators independently localized most defects to the same two generation batches), yielding a frozen 161-item v1.0 key with post-adjudication κ = 0.851 — above the pre-registered 0.8 bar. The full annotation trail (both annotators' raw answers, the adjudication queue and decisions, the disclosed 3-model council pre-screen) and per-item outputs for four system configurations are public in the GitHub repository. The full paper is on arXiv (2609.28575) and SSRN. On v1.0, no tested configuration simultaneously achieves high contradiction recall, high hard-negative specificity, and high evidence attribution — flat RAG detects well but falsely flags 13–53% of surface-matched safe drafts depending on backend; our own system almost never over-flags but catches only a third of true contradictions. The v0 results below are retained for provenance and should not be cited as current benchmark scores.

Run it, break it, dispute it

Any memory system that can wrap five calls — ingest, flag_tensions, recall, check_alignment, resolve — can run TWIST, and may decline tracks it doesn't support. The v0.1 spec, the scenario catalog, the Track B item set, and the raw per-item results are downloadable below. If you build memory systems: run it, tell us where the items are wrong, and we'll version the errata publicly. If it doesn't separate systems, it doesn't ship as v1 — that's pre-registered.