# What moved LoCoMo: the claim layer — and where the sheaf comes in

_How donto's recent substrate work changed the LoCoMo agent-memory benchmark, reported honestly — including what it did **not** do yet._

LoCoMo asks an agent questions about long multi-session conversations. The standard way to win it is **episodic stuffing**: dump the relevant dialogue turns into the reader's context and let a strong model read them. That works (≈84% here) but it is token-heavy and it doesn't test anything donto-specific.

donto's thesis is different: extract an **evidence-anchored claim layer** from the conversation, then answer from *claims* — a fraction of the tokens, and the substrate's actual job. The question this report answers: **did the recent substrate program move that claims-only number, and by what mechanism?**

## The result

Same 4 LoCoMo categories, same reader and judge (holo3.1 on Hyades), three retrieval strategies over the same conversations:

| config | recall | n | accuracy | median ctx | multi-hop | temporal | open-dom | single-hop |
|---|---|---:|---:|---:|---:|---:|---:|---:|
| Claims-only **lexical** (C) | FTS over triples | 160 | **15.6%** | 1,435 tok | 10% | 18% | 8% | 28% |
| Claims-only **semantic** (C-sem) | bge-small ANN over claims | 200 | **37.5%** | **913 tok** | 20% | **72%** | 16% | 42% |
| Episodic baseline | dialogue turns | 258 | 83.7% | 19,029 tok | 74% | 90% | 47% | 93% |

Two things stand out, and both are donto's whole point:

1. **Semantic claim recall is 2.4× the lexical claims-only baseline (37.5% vs 15.6%) — at *fewer* tokens** (913 vs 1,435). Recall, not the reader, was the bottleneck; the answer-bearing claim usually existed but lexical top-K didn't surface it.
2. **Claims-only answers at ~1/21 the context of episodic** (913 vs 19,029 tokens) for ~45% of its accuracy. That ratio — not the raw score — is the scaling story: donto stays cheap where episodic stuffing blows the context budget on `_s`/`_m`.

## What actually moved the needle

Not the sheaf contradiction-math. Being precise about mechanism:

- **The claim-embedding layer.** The lexical→semantic jump is entirely the embedded claim layer doing its job. The recent program took claim-embedding coverage to **100%** (every `ctx:claims/locomo` claim has a bge-small vector in `donto_claim_embedding`), so semantic ANN can reach *any* answer-bearing claim instead of only the lexically-obvious ones. With partial coverage, semantic recall silently misses the unembedded claims; at full coverage it runs at strength. That is the single change behind the +21.9 points.
- **The bitemporal claim layer.** Temporal accuracy jumped **18% → 72%** — the biggest category move. Each claim carries its session date (valid-time), so "when did X happen / what changed" questions are answerable from the triples alone. This is donto's bitemporality paying off directly, no episodic dialogue required.

Both are substrate properties — evidence-anchored, embedded, bitemporal claims — that the broader program hardened. The benchmark is the **instrument** that made the improvement legible, not a grade.

## Where the sheaf actually comes in (and hasn't yet)

Honest accounting: the **sheaf engine — H⁰/H¹, cocycles, contradiction pressure, the Stage-2 query-time reconciliation — is not in the LoCoMo recall path today.** It did not produce the numbers above. The Stage-2 reconciliation ships dark and re-rank-neutral; the LoCoMo harness uses its own semantic claim recall, which the sheaf layer does not yet touch.

But it is the natural next lever, precisely on LoCoMo's weak spots:

- **Knowledge-update / temporal questions** hinge on *which* version of a fact is current. donto holds the superseded and the current claim side by side (paraconsistent). **Contradiction-pressure-aware reranking** — prefer the low-pressure, non-superseded claim, surface the conflict instead of averaging — is exactly what a sheaf reading of the recalled claim set provides. The Stage-2 reconciliation already computes per-claim pressure; wiring it into recall ordering is the experiment.
- **Multi-hop (20%)** needs a *bridge* entity the question never names. The cellular sheaf is built over the claim graph; a graph walk to the bridge is the same machinery that localizes cocycles.

So the precise statement is: **the claim-layer work (embeddings + bitemporality) lifted LoCoMo; the sheaf contradiction-engine is the teed-up next step toward closing the remaining gap.** We will measure it, not assert it.

## The road to 100% (no shortcuts)

The honest gap analysis from C-sem's per-category numbers:

- **temporal 72%** — strong; bitemporal claims work. Push with contradiction-aware reranking.
- **single-hop 42%, open-domain 16%, multi-hop 20%** — the work. Open-domain often needs world knowledge the conversation never states (a ceiling for *any* memory system); multi-hop needs claim-graph bridging; single-hop needs deeper recall + better extraction coverage.

"No shortcuts" means: every point comes from a **general donto capability** — better extraction coverage, claim-graph multi-hop recall, contradiction-aware reranking, finer claim granularity — never a LoCoMo-specific cache or a prompt that games the judge. When a benchmark-specific fix is tempting, the rule is to find the substrate gap it exposes and fix *that*, so every consumer benefits. The curve continues in the [live runs table](/benchmarks/locomo).

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_Every run above is reproducible and browsable — drill into any one for the full reader→judge transcript. Reader/judge run on Hyades (holo3.1); claim extraction runs on the same gateway, serialized so a producer and a consumer never share it._