---
language:
- en
pretty_name: synthpercept-v2
size_categories:
- 10K<n<100K
annotations_creators:
- machine-generated
source_datasets:
- original
tags:
- synthetic
- interpretability
- activation-probes
- pragmatics
---

# synthpercept-v2

**57,638 single sentences, each labeled with the *induced percept* — the impression the sentence plants in a careful reader without ever stating it — plus an *abridged* twin that states exactly the same facts in plain wording, so the percept comes through much weaker.**

Built as training/eval data for natural-language activation probes ("activation oracles"): the sentence is the subject-model input, the percept is the gold verbalization target, and the fact-matched abridged twin is the control that separates *reading the percept from activations* from *guessing it from content*. Sentences are single-sentence and end in exactly one punctuation mark (the final token **is** that mark in 99.99% of rows), supporting clean final-token activation reads.

New over [synthpercept-v1](https://huggingface.co/datasets/cds-jb/synthpercept-v1):

- **Graded obviousness (`grade` 1–5)** — from `1` (faint tilt a reader could easily read past) to `5` (unmistakable, loaded to the edge of stating it, yet never named). Sampled at weights 10/15/25/30/20% (faint → blatant) for stratified evaluation.
- **Ambiguity tier (`ambiguous`, 19.8%)** — the sentence supports **two** substantively different plausible percepts (`percept` and `percept_b`); which one a reader lands on depends on the reader's priors, not on any missing fact. Instrument for verbalization-flip analysis during RL.
- **Much higher diversity** — structured scenario seeds with in-cell disjointness constraints plus a content-word dedup at filter time (the in-cell near-duplicate filter fired only 2× in 68k raw rows).

## Examples

**`veiled_menace` × `customer_service` · grade 5 · clear**

> **sentence:** `Marisol told Diego she'd wait a couple more days for her package, but her thumb was already so, so ready to type ninety-two reviews.`
> **percept:** Marisol is threatening to flood the seller with fake negative reviews if the delay continues.
> **abridged:** `Marisol told Diego she would wait a couple more days for her package before writing ninety-two reviews.`

**`nostalgia` × `military_field` · grade 3 · clear**

> **sentence:** `Do you still remember Sven's call sign on this frequency?`
> **percept:** The speaker wistfully recalls a former operator whose nightly voice is gone.
> **abridged:** `Can you recall what Sven's call sign was on this frequency?`

**`emotion_covert` × `fiction_fantasy` · grade 1 · clear**

> **sentence:** `You measured the crushed moth wings in half the time it takes me, so maybe teach a class sometime.`
> **percept:** The speaker resents the addressee's effortless talent.
> **abridged:** `You measured the crushed moth wings in half my usual time, so perhaps you could teach a class sometime.`

**`sarcasm_irony` × `fiction_fantasy` · grade 4 · ambiguous**

> **sentence:** `Logged: replaced the sparking coil in Zola's automaton at 10, noting it chose the exhibition hour to fail again.`
> **percept (A):** The diarist is quietly mocking the automaton's habit of failing at the worst possible moments.
> **percept_b (B):** The diarist is simply documenting a coincidental pattern in the automaton's malfunction timing for repair records.
> **abridged:** `I recorded that at 10 I replaced the sparking coil in Zola's automaton, since it failed again during the exhibition hour.`

## How the categories were generated

The corpus is generated down a fixed hierarchy. The two top levels are **hand-written category banks**; everything below them is produced by `claude-sonnet-5` via the Anthropic Message Batches API (pipeline code in [`pipeline/`](./pipeline)):

```
16 percept TYPES  ×  30 DOMAINS            hand-written banks  →  480 cells
  └─ 24 structured seeds per cell          Stage 1 (taxonomy2.py): one batch request per cell;
     {setting, actors, activity,           hard in-cell disjointness: no two seeds share their
      percept_instance}                    setting head-noun, activity verb, or percept shade
       └─ 6 rows per seed                  Stage 2 (generate2.py): per-row surface knobs sampled
          {sentence, percept[, percept_b],   deterministically: format(12) × register(6) × person(3)
           abridged, carriers}               × length bin × final punct × grade(1–5) × ambiguous(20%),
                                             plus per-seed entity pools (names, places, objects, …)
```

Stage 3 (`filter2.py`) validates each row (single sentence, exact final punctuation, the percept must never be named in the sentence — a banned-word/stem leak check, fact-parity checks on the abridged twin, `percept_b` distinctness for ambiguous rows), applies exact + in-cell content-word Jaccard dedup (threshold 0.6), tokenizes with the Qwen3-8B tokenizer, and holds out a **seed-level test split** (400 whole seeds → 2,073 rows; no seed crosses splits). 69,120 requested → 68,777 raw → **57,638 kept** (dominant drop: abridged-twin final-punctuation mismatch, 7,739).

The design lesson (after v1 came out too uniformly hard and diversity-poor) follows Anthropic's emotion-concepts pipeline: breadth via explicit orthogonal banks crossed at scale, validated on samples.

**The 16 percept types:** `emotion_covert` (unnamed emotional state), `sarcasm_irony` (real stance opposite the literal surface), `social_relation` (unstated relationship between two people), `power_dynamic` (who holds authority), `speaker_generation` (speaker's age cohort), `intimacy_distance` (emotional closeness of speaker and addressee), `physical_state` (unnamed bodily condition), `imminent_event` (something is about to happen), `attitude_valence` (admiration or contempt for a mentioned thing), `urgency_pressure` (acute time pressure), `confidence_doubt` (how sure the speaker really is), `evasion_deception` (dodging or hiding something), `veiled_menace` (threat under polite wording), `flirtation`, `nostalgia`, `setting_atmosphere` (unstated place or hour).

**The 30 domains:** v1's 20 (`everyday_home`, `workplace_office`, `science_lab`, `medicine_health`, `law_courtroom`, `sports`, `cooking_food`, `travel_transit`, `software_tech`, `finance_money`, `history`, `fiction_fantasy`, `casual_chat`, `news_current`, `education_school`, `games_puzzles`, `nature_outdoors`, `relationships_family`, `customer_service`, `engineering_construction`) + 10 new (`military_field`, `arts_theatre`, `religion_ritual`, `farming_rural`, `maritime_fishing`, `music_band`, `real_estate_moving`, `volunteering_community`, `parenting_childhood`, `aviation_space`).

## Fields

| column | description |
|---|---|
| `sentence` | the single carrier sentence (6–22 word target; one final punctuation mark, no quotes) |
| `percept` | gold induced percept: one declarative sentence going beyond the stated facts |
| `percept_b` | second plausible percept — non-empty only when `ambiguous` |
| `abridged` | fact-identical plain rewrite (same perspective, mood, and final punctuation) |
| `carriers` | ≤10 words naming the surface features carrying the percept |
| `type`, `domain` | the two hand-written category banks (16 × 30) |
| `seed` | JSON string: the structured scenario seed `{setting, actors, activity, percept_instance}` |
| `seed_id`, `row_idx` | seed identifier (`t<type>d<domain>s<seed>`) and row index within the seed |
| `grade` | obviousness 1 (faint) … 5 (unmistakable) |
| `ambiguous` | whether the row carries two prior-dependent percepts |
| `fmt`, `register`, `person`, `punct` | surface knobs assigned to the row |
| `n_tokens`, `final_token_is_punct` | Qwen3-8B token count; whether the last token is exactly the final mark |
| `split` | `train` (55,565) / `test` (2,073) — **split by seed**, use this column (the parquet is a single file) |

## Stats

- 57,638 rows · 11,509 seeds · 480 (type × domain) cells · mean sentence length 18.0 Qwen3-8B tokens (5–41)
- grade mix 1→5: 5,689 / 8,773 / 14,520 / 17,340 / 11,316 · ambiguous 11,428 (19.8%) · final-token-is-punct rate 0.9999

![stats](synthpercept_v2_stats.png)

## Provenance

Generated 2026-07-24/25 with `claude-sonnet-5` (Anthropic Message Batches, temperature 1.0, thinking disabled). Categories, prompts, filters, and the full pipeline are in [`pipeline/`](./pipeline) (`taxonomy2.py` → `generate2.py` → `filter2.py`; shared banks imported from the v1 pipeline). All content is synthetic; entity names are drawn from fixed international name/place banks.