Clean up release highlights copy and removing emdash from the docs (#5984)

docs/platform/features/memory-decay.mdx

@@ -7,12 +7,12 @@ description: "Boost recently-used memories and gently dampen stale ones at searc
 
 Older memories drift in relevance at different speeds. A user's coffee order matters every morning; a one-off project name from last quarter rarely matters again. Memory Decay makes that intuition explicit at search time: every time a memory is returned in a search it gets a small reinforcement, and memories that haven't been touched in a while have their ranking score gently dampened.
 
-It is **a soft ranking bias, never a filter.** Decay never zeroes a candidate out — at worst it scales its score by `0.3×`. Anything that would have surfaced without decay can still surface with decay on, just with a different ranking among similarly-scored results.
+It is **a soft ranking bias, never a filter.** Decay never zeroes a candidate out: at worst it scales its score by `0.3×`. Anything that would have surfaced without decay can still surface with decay on, just with a different ranking among similarly-scored results.
 
 <Info>
   **Use Memory Decay when…**
   - Search results are crowded with old facts the user no longer cares about.
-  - You want recently-used memories to drift to the top automatically — without writing custom scoring logic.
+  - You want recently-used memories to drift to the top automatically: without writing custom scoring logic.
   - You want this preference applied per project so cohorts can be compared side-by-side.
 </Info>
 
@@ -30,7 +30,7 @@ Every memory carries a small piece of bookkeeping: when was it last retrieved, a
 | Touched today | 1.2 – 1.4× | Mild boost |
 | Idle for a few days | 0.6 – 1.0× | Mild dampening |
 | Idle for weeks | 0.4 – 0.6× | Stronger dampening |
-| Idle for many months / years | ≈ **0.3×** | Floor — never lower |
+| Idle for many months / years | ≈ **0.3×** | Floor: never lower |
 
 The bounds matter: `0.3` is the floor and `1.5` is the ceiling, so decay can meaningfully reorder candidates without ever dominating the underlying relevance score.
 
@@ -41,16 +41,16 @@ At search time the pipeline:
 3. Sorts on the unclamped product so the full `0.3×–1.5×` range can rearrange candidates.
 4. Returns the public `score` clamped to `[0, 1]` so the API contract is preserved.
 5. Truncates to the `top_k` you requested.
-6. Records a fire-and-forget reinforcement against each returned memory — its access history grows by one, capped at the most recent 20 touches.
+6. Records a fire-and-forget reinforcement against each returned memory: its access history grows by one, capped at the most recent 20 touches.
 
-Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
+Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
 
 ## Configure access
 
 - Set `MEM0_API_KEY` in your environment, or pass it to the SDK constructor.
 - Initialize the client with the organization and project you want to scope to.
 
-The toggle lives on the project. You enable decay by patching the project's `decay` field; everything else — your `add` calls, your `search` calls, your application code — stays exactly the same.
+The toggle lives on the project. You enable decay by patching the project's `decay` field; everything else: your `add` calls, your `search` calls, your application code: stays exactly the same.
 
 ## Enable decay for a project
 
@@ -137,23 +137,23 @@ curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PR
 
 ## What stays the same
 
-- **Public API shape** — every endpoint accepts the same parameters and returns the same fields. You don't touch your client code.
-- **Threshold semantics on the request side** — your `threshold` is still applied during candidate selection.
-- **Memory creation and storage** — every new memory still lands the same way. Decay is a search-time concern.
-- **Per-memory data** — categories, metadata, timestamps, embeddings: untouched.
+- **Public API shape**: every endpoint accepts the same parameters and returns the same fields. You don't touch your client code.
+- **Threshold semantics on the request side**: your `threshold` is still applied during candidate selection.
+- **Memory creation and storage**: every new memory still lands the same way. Decay is a search-time concern.
+- **Per-memory data**: categories, metadata, timestamps, embeddings: untouched.
 
 <Warning>
-  Because the scaling factor is applied *after* the threshold filter has already run, an item that passed the request `threshold` can come back with a public `score` slightly below it (a stale candidate dampened by `0.3×`). This is intentional — decay is a soft bias, not a filter. If you require a hard `score >= threshold` invariant on the response, filter client-side after the call.
+  Because the scaling factor is applied *after* the threshold filter has already run, an item that passed the request `threshold` can come back with a public `score` slightly below it (a stale candidate dampened by `0.3×`). This is intentional: decay is a soft bias, not a filter. If you require a hard `score >= threshold` invariant on the response, filter client-side after the call.
 </Warning>
 
 ## Lifecycle of a memory under decay
 
 | Stage | Scaling factor | Effect |
 |---|---|---|
-| Just added | ≈ 1.5× | Strong boost — fresh facts surface easily. |
+| Just added | ≈ 1.5× | Strong boost: fresh facts surface easily. |
 | Reinforced on a recent search | 1.2 – 1.5× | Sustains its boost for the next several searches. |
 | Idle for a few days | 0.6 – 1.0× | Falls back into the neutral band. |
-| Idle for weeks | 0.4 – 0.6× | Mild dampening — can still surface for strong matches. |
+| Idle for weeks | 0.4 – 0.6× | Mild dampening: can still surface for strong matches. |
 | Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `updated_at`: recently-updated entries land near 1.0×, long-stale entries approach the 0.3× floor. |
 
 The reinforcement is bounded: each memory tracks at most the last 20 access timestamps, so the boost stays well-behaved no matter how many times a memory is retrieved.
@@ -161,31 +161,31 @@ The reinforcement is bounded: each memory tracks at most the last 20 access time
 ## FAQ
 
 **Will decay ever drop a result that would otherwise surface?**
-No. The floor is `0.3×` — the scaling factor can dampen a score, never zero it. Threshold filtering happens *before* decay, so any candidate that cleared the threshold is in the pool decay reorders.
+No. The floor is `0.3×`: the scaling factor can dampen a score, never zero it. Threshold filtering happens *before* decay, so any candidate that cleared the threshold is in the pool decay reorders.
 
 **Why is the public score sometimes below my requested threshold?**
-The threshold is applied to the candidate pool pre-decay; the scaling factor then reshapes scores in the `0.3×–1.5×` band. A stale-but-relevant candidate can come back with a final score slightly under your threshold by design — the candidate stays visible but visibly dampened. Filter client-side if you need a hard floor on the response.
+The threshold is applied to the candidate pool pre-decay; the scaling factor then reshapes scores in the `0.3×–1.5×` band. A stale-but-relevant candidate can come back with a final score slightly under your threshold by design: the candidate stays visible but visibly dampened. Filter client-side if you need a hard floor on the response.
 
 **Does decay change how I add memories?**
 No. The `client.add(...)` path is unchanged. Decay is a search-time ranking adjustment.
 
 **What if I had memories before turning decay on?**
-They use a fallback: the memory's `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.
+They use a fallback: the memory's `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.
 
 **Can I tune how aggressively decay scales scores?**
-Not in this version. The current scaling is calibrated to be conservative — wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.
+Not in this version. The current scaling is calibrated to be conservative: wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.
 
 **Can I see the scaling factor per result?**
-Internal scoring details are persisted on the search Event for support and debugging. They aren't exposed in the public response by design — the response surface stays a single `score` field.
+Internal scoring details are persisted on the search Event for support and debugging. They aren't exposed in the public response by design: the response surface stays a single `score` field.
 
 **Does decay interact with reranking?**
-Yes — they layer cleanly. The reranker produces a richer relevance score; decay then biases that score by reinforcement history before final truncation to `top_k`.
+Yes: they layer cleanly. The reranker produces a richer relevance score; decay then biases that score by reinforcement history before final truncation to `top_k`.
 
 ## What's next
 
-This release is deliberately the simplest version of decay we could ship — every memory contributes to ranking through its access history alone, so the signal can be evaluated in isolation. On the roadmap:
+This release is deliberately the simplest version of decay we could ship: every memory contributes to ranking through its access history alone, so the signal can be evaluated in isolation. On the roadmap:
 
 - **Category-aware weighting.** A fact tagged `health` will be able to carry more weight than a passing observation tagged `misc`, so important categories don't get dampened the same way as noise.
-- **Auto-tuning per project.** Project-scoped automatic adjustment of how aggressively decay scales scores, based on observed access patterns — replacing the fixed scaling band with one that fits your workload.
+- **Auto-tuning per project.** Project-scoped automatic adjustment of how aggressively decay scales scores, based on observed access patterns: replacing the fixed scaling band with one that fits your workload.
 
-Both extensions are forward-compatible — no migration on your side will be needed when they ship.
+Both extensions are forward-compatible: no migration on your side will be needed when they ship.