From aa71db203469d23cfd0144a8dada85ce5790c013 Mon Sep 17 00:00:00 2001
From: Luca Fiaschi <luca.fiaschi@gmail.com>
Date: Mon, 15 Jun 2026 17:55:10 -0400
Subject: [PATCH 1/2] add semantic similarity rating

---
 README.md               |  74 +++-
 audiencekit/__init__.py |  23 +-
 audiencekit/ssr.py      | 325 ++++++++++++++
 audiencekit/survey.py   | 137 ++++++
 pyproject.toml          |   3 +
 skills/survey/SKILL.md  |  17 +
 tests/test_ssr.py       |  87 ++++
 tests/test_survey.py    |  74 +++-
 uv.lock                 | 959 +++++++++++++++++++++++++++++++++++++++-
 9 files changed, 1691 insertions(+), 8 deletions(-)
 create mode 100644 audiencekit/ssr.py
 create mode 100644 tests/test_ssr.py

diff --git a/README.md b/README.md
index 0ec4835..caa5b5d 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,8 @@ rows.
   with an id and optional weight column can become an audience.
 - **Structured studies**: define stimuli, treatment arms, Likert items, choices,
   and open-ended questions as reusable specs.
+- **SSR scoring for Likert items**: optionally elicit text first, then map it to
+  Likert probability distributions with semantic-similarity rating.
 - **Provider flexibility**: Gemini is the default backend, with OpenAI,
   Anthropic, and custom backend objects supported.
 - **Agent-ready workflows**: included skills help an agent structure surveys and
@@ -61,6 +63,12 @@ uv venv
 uv pip install -e ".[dev]"
 ```
 
+For local SSR embeddings, install the optional SSR extra:
+
+```bash
+uv pip install -e ".[dev,ssr]"
+```
+
 Set a model API key. Gemini is the default backend:
 
 ```bash
@@ -114,6 +122,58 @@ By default, `ak.load_panel()` prepares the bundled public GSS 2024 Stata file.
 For trend work across years, download the full GSS 1972-2024 cumulative file
 from NORC and pass it to `ak.load_gss(...)`.
 
+## Semantic Similarity Rating
+
+AudienceKit supports semantic-similarity rating (SSR) as an opt-in scoring layer
+for Likert questions. Instead of asking the model for a number directly,
+`run_ssr_survey(...)` asks for a short natural-language answer, embeds that text,
+compares it with reference anchors, and returns a probability mass function over
+the rating scale.
+
+```python
+rater = ak.SemanticSimilarityRater.for_purchase_intent()
+
+ssr_results = ak.SyntheticPanel(respondents).run_ssr_survey(
+    study,
+    rater=rater,
+    reference_set_id="mean",
+)
+
+print(ssr_results[[
+    "respondent_id",
+    "consideration_text",
+    "consideration",
+    "consideration_pmf_1",
+    "consideration_pmf_5",
+]].head())
+```
+
+The expected score is stored in the original question column, such as
+`consideration`. The elicited text is stored in `consideration_text`, the most
+likely rating in `consideration_most_likely`, and the distribution in
+`consideration_pmf_1` through `consideration_pmf_5`.
+
+SSR is implemented as a generic rater, not as a GSS feature. You can provide
+custom anchors for any ordered scale:
+
+```python
+anchors = ak.SSRAnchorSet("message_clarity", {
+    1: "The message is very unclear to me.",
+    2: "The message is somewhat unclear to me.",
+    3: "The message is neither clear nor unclear.",
+    4: "The message is somewhat clear to me.",
+    5: "The message is very clear to me.",
+})
+
+rater = ak.SemanticSimilarityRater([anchors])
+```
+
+By default, `SemanticSimilarityRater` uses `sentence-transformers` through the
+`ssr` extra. For hosted embeddings, local models, or tests, pass any object with
+`encode(texts)` or any callable that returns a two-dimensional NumPy-compatible
+array. This keeps SSR independent from the model backend used to generate the
+synthetic responses.
+
 ## How It Works
 
 ```mermaid
@@ -365,11 +425,15 @@ Recommended reading:
   and ratings are mapped from embedding similarity to reference statements. The
   paper reports stronger purchase-intent replication than direct numeric
   Likert prompting.
-
-AudienceKit v0.1 keeps direct structured Likert questions because they are
-simple, inspectable, and useful for within-run pressure tests. Treat SSR-style
-text-first scoring as a stronger validation direction for future adapters or
-custom backends, not as a feature this release already implements.
+- [pymc-labs/semantic-similarity-rating](https://github.com/pymc-labs/semantic-similarity-rating/)
+  provides the standalone research implementation that informed AudienceKit's
+  SSR-compatible rating layer.
+
+AudienceKit supports both direct structured Likert prompting and SSR-style
+text-first scoring. Direct Likert runs are simple and cheap to inspect. SSR runs
+preserve the synthetic respondent's rationale and usually provide a better
+methodological default for purchase-intent style studies, provided your anchors
+match the question being scored.
 
 When reporting results, be precise:
 
diff --git a/audiencekit/__init__.py b/audiencekit/__init__.py
index 3df50da..3c0609d 100644
--- a/audiencekit/__init__.py
+++ b/audiencekit/__init__.py
@@ -14,7 +14,22 @@
 from .gss import load_gss, prepare_gss_persona_frame, write_gss_panel
 from .personas import build_persona, is_luxury_household, load_panel, sample_panel
 from .primitives import AudienceFrame, PersonaTemplate
-from .survey import Question, Study, SyntheticPanel, build_survey_prompt, parse_json_response, render_persona
+from .ssr import (
+    SSRAnchorSet,
+    SSRResult,
+    SemanticSimilarityRater,
+    SentenceTransformerEmbeddings,
+    purchase_intent_anchor_sets,
+)
+from .survey import (
+    Question,
+    Study,
+    SyntheticPanel,
+    build_ssr_survey_prompt,
+    build_survey_prompt,
+    parse_json_response,
+    render_persona,
+)
 
 __all__ = [
     "AudienceFrame",
@@ -24,9 +39,14 @@
     "OpenAIBackend",
     "PersonaTemplate",
     "Question",
+    "SSRAnchorSet",
+    "SSRResult",
+    "SemanticSimilarityRater",
+    "SentenceTransformerEmbeddings",
     "Study",
     "SyntheticPanel",
     "build_persona",
+    "build_ssr_survey_prompt",
     "build_survey_prompt",
     "is_luxury_household",
     "load_gss",
@@ -34,6 +54,7 @@
     "make_backend",
     "parse_json_response",
     "prepare_gss_persona_frame",
+    "purchase_intent_anchor_sets",
     "render_persona",
     "sample_panel",
     "write_gss_panel",
diff --git a/audiencekit/ssr.py b/audiencekit/ssr.py
new file mode 100644
index 0000000..cee28d9
--- /dev/null
+++ b/audiencekit/ssr.py
@@ -0,0 +1,325 @@
+"""Semantic-similarity rating for mapping text responses to Likert PMFs."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Protocol
+
+import numpy as np
+import pandas as pd
+
+
+class TextEmbeddings(Protocol):
+    """Minimal text embedding protocol used by SSR."""
+
+    def encode(self, texts: Sequence[str]) -> np.ndarray: ...
+
+
+EmbeddingProvider = TextEmbeddings | Callable[[Sequence[str]], np.ndarray]
+
+
+def _format_score(score: int | float) -> str:
+    if isinstance(score, float) and score.is_integer():
+        score = int(score)
+    return str(score).replace("-", "minus_").replace(".", "_")
+
+
+@dataclass(frozen=True)
+class SSRAnchorSet:
+    """Reference statements for one ordered rating scale."""
+
+    id: str
+    anchors: Mapping[int | float, str]
+
+    def __post_init__(self) -> None:
+        if not self.id:
+            raise ValueError("SSRAnchorSet needs a non-empty id")
+        if len(self.anchors) < 2:
+            raise ValueError("SSRAnchorSet needs at least two anchors")
+        normalized = {score: str(text).strip() for score, text in sorted(self.anchors.items())}
+        empty_scores = [score for score, text in normalized.items() if not text]
+        if empty_scores:
+            raise ValueError(f"SSRAnchorSet {self.id!r} has empty anchors for {empty_scores}")
+        object.__setattr__(self, "anchors", normalized)
+
+    @property
+    def scores(self) -> tuple[int | float, ...]:
+        return tuple(self.anchors.keys())
+
+    @property
+    def statements(self) -> tuple[str, ...]:
+        return tuple(self.anchors.values())
+
+
+@dataclass(frozen=True)
+class SSRResult:
+    """Per-response SSR probability mass functions."""
+
+    texts: tuple[str, ...]
+    scores: tuple[int | float, ...]
+    pmfs: np.ndarray
+    reference_set_id: str
+
+    @property
+    def expected_scores(self) -> np.ndarray:
+        return self.pmfs @ np.asarray(self.scores, dtype=float)
+
+    @property
+    def most_likely_scores(self) -> np.ndarray:
+        if len(self.pmfs) == 0:
+            return np.asarray([])
+        score_values = np.asarray(self.scores)
+        return score_values[np.argmax(self.pmfs, axis=1)]
+
+    def survey_pmf(self, weights: Sequence[float] | None = None) -> np.ndarray:
+        if len(self.pmfs) == 0:
+            return np.zeros(len(self.scores), dtype=float)
+        if weights is None:
+            return self.pmfs.mean(axis=0)
+        weight_array = np.asarray(weights, dtype=float)
+        if weight_array.shape != (len(self.pmfs),):
+            raise ValueError("weights must have one value per PMF row")
+        if weight_array.sum() <= 0:
+            raise ValueError("weights must sum to a positive value")
+        return np.average(self.pmfs, axis=0, weights=weight_array)
+
+    def to_frame(self, prefix: str = "ssr") -> pd.DataFrame:
+        data: dict[str, object] = {
+            f"{prefix}_text": list(self.texts),
+            prefix: self.expected_scores,
+            f"{prefix}_most_likely": self.most_likely_scores,
+        }
+        for idx, score in enumerate(self.scores):
+            data[f"{prefix}_pmf_{_format_score(score)}"] = self.pmfs[:, idx]
+        return pd.DataFrame(data)
+
+
+class SentenceTransformerEmbeddings:
+    """Lazy wrapper around sentence-transformers for local SSR embeddings."""
+
+    def __init__(self, model_name: str = "all-MiniLM-L6-v2", device: str | None = None):
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ImportError as exc:
+            raise ValueError(
+                "Install AudienceKit with the 'ssr' extra to use default SSR embeddings: "
+                "pip install 'audiencekit[ssr]'"
+            ) from exc
+
+        self.model = SentenceTransformer(model_name, device=device)
+
+    def encode(self, texts: Sequence[str]) -> np.ndarray:
+        return np.asarray(self.model.encode(list(texts)), dtype=float)
+
+
+class SemanticSimilarityRater:
+    """Map free-text answers to probability distributions over an ordered scale.
+
+    The rater is dataset-agnostic: callers provide anchor statements and an
+    embedding provider. It implements the SSR flow from the cited paper at the
+    abstraction level AudienceKit needs: embed response text, compare to anchor
+    text, shift similarities by the row minimum, normalize to a PMF, optionally
+    temperature-scale, and average across reference sets when requested.
+    """
+
+    def __init__(
+        self,
+        reference_sets: Sequence[SSRAnchorSet],
+        *,
+        embeddings: EmbeddingProvider | None = None,
+        epsilon: float = 0.0,
+        temperature: float = 1.0,
+    ):
+        if not reference_sets:
+            raise ValueError("SemanticSimilarityRater needs at least one SSRAnchorSet")
+        self.reference_sets = {anchor_set.id: anchor_set for anchor_set in reference_sets}
+        if len(self.reference_sets) != len(reference_sets):
+            raise ValueError("SSR anchor set ids must be unique")
+
+        first_scale = reference_sets[0].scores
+        if any(anchor_set.scores != first_scale for anchor_set in reference_sets):
+            raise ValueError("All SSR anchor sets must use the same score scale")
+        self.scores = first_scale
+
+        if epsilon < 0:
+            raise ValueError("epsilon must be non-negative")
+        if temperature < 0:
+            raise ValueError("temperature must be non-negative")
+        self.epsilon = epsilon
+        self.temperature = temperature
+
+        self.embeddings = embeddings or SentenceTransformerEmbeddings()
+        self.reference_embeddings = {
+            anchor_set.id: self._encode(anchor_set.statements) for anchor_set in reference_sets
+        }
+
+    @classmethod
+    def for_purchase_intent(
+        cls,
+        *,
+        embeddings: EmbeddingProvider | None = None,
+        epsilon: float = 0.0,
+        temperature: float = 1.0,
+    ) -> "SemanticSimilarityRater":
+        return cls(
+            purchase_intent_anchor_sets(),
+            embeddings=embeddings,
+            epsilon=epsilon,
+            temperature=temperature,
+        )
+
+    @property
+    def available_reference_sets(self) -> list[str]:
+        return list(self.reference_sets)
+
+    def get_reference_sentences(self, reference_set_id: str) -> tuple[str, ...]:
+        return self.reference_sets[reference_set_id].statements
+
+    def score_texts(
+        self,
+        texts: Sequence[str],
+        *,
+        reference_set_id: str = "mean",
+        epsilon: float | None = None,
+        temperature: float | None = None,
+    ) -> SSRResult:
+        text_tuple = tuple(str(text) for text in texts)
+        if not text_tuple:
+            return SSRResult(text_tuple, self.scores, np.empty((0, len(self.scores))), reference_set_id)
+        response_embeddings = self._encode(text_tuple)
+        pmfs = self.score_embeddings(
+            response_embeddings,
+            reference_set_id=reference_set_id,
+            epsilon=epsilon,
+            temperature=temperature,
+        )
+        return SSRResult(text_tuple, self.scores, pmfs, reference_set_id)
+
+    def score_embeddings(
+        self,
+        response_embeddings: np.ndarray,
+        *,
+        reference_set_id: str = "mean",
+        epsilon: float | None = None,
+        temperature: float | None = None,
+    ) -> np.ndarray:
+        responses = np.asarray(response_embeddings, dtype=float)
+        if responses.ndim == 1:
+            responses = responses.reshape(1, -1)
+        if responses.ndim != 2:
+            raise ValueError("response_embeddings must be a 2D array")
+        if len(responses) == 0:
+            return np.empty((0, len(self.scores)))
+
+        if reference_set_id.lower() == "mean":
+            pmfs = np.asarray(
+                [
+                    self._pmfs_from_embeddings(responses, reference_embeddings, epsilon=epsilon)
+                    for reference_embeddings in self.reference_embeddings.values()
+                ]
+            ).mean(axis=0)
+        else:
+            if reference_set_id not in self.reference_embeddings:
+                raise KeyError(reference_set_id)
+            pmfs = self._pmfs_from_embeddings(
+                responses,
+                self.reference_embeddings[reference_set_id],
+                epsilon=epsilon,
+            )
+
+        return self._temperature_scale(pmfs, self.temperature if temperature is None else temperature)
+
+    def _encode(self, texts: Sequence[str]) -> np.ndarray:
+        if hasattr(self.embeddings, "encode"):
+            raw = self.embeddings.encode(list(texts))
+        elif callable(self.embeddings):
+            raw = self.embeddings(list(texts))
+        else:
+            raise TypeError("embeddings must provide encode(texts) or be callable")
+
+        array = np.asarray(raw, dtype=float)
+        if array.ndim == 1 and len(texts) == 1:
+            array = array.reshape(1, -1)
+        if array.ndim != 2 or array.shape[0] != len(texts):
+            raise ValueError("embedding provider must return a 2D array with one row per text")
+        if not np.all(np.isfinite(array)):
+            raise ValueError("embeddings must be finite")
+        if np.any(np.linalg.norm(array, axis=1) == 0):
+            raise ValueError("embeddings must not contain zero vectors")
+        return array
+
+    def _pmfs_from_embeddings(
+        self,
+        response_embeddings: np.ndarray,
+        reference_embeddings: np.ndarray,
+        *,
+        epsilon: float | None,
+    ) -> np.ndarray:
+        if response_embeddings.shape[1] != reference_embeddings.shape[1]:
+            raise ValueError("response and reference embeddings must have the same dimension")
+        eps = self.epsilon if epsilon is None else epsilon
+        if eps < 0:
+            raise ValueError("epsilon must be non-negative")
+
+        response_norms = np.linalg.norm(response_embeddings, axis=1, keepdims=True)
+        reference_norms = np.linalg.norm(reference_embeddings, axis=1, keepdims=True)
+        similarities = (response_embeddings / response_norms) @ (reference_embeddings / reference_norms).T
+        similarities = (1.0 + similarities) / 2.0
+
+        shifted = similarities - similarities.min(axis=1, keepdims=True)
+        numerator = shifted + eps
+        denominator = numerator.sum(axis=1, keepdims=True)
+        uniform = np.full_like(numerator, 1.0 / numerator.shape[1])
+        return np.divide(numerator, denominator, out=uniform, where=denominator > 0)
+
+    def _temperature_scale(self, pmfs: np.ndarray, temperature: float) -> np.ndarray:
+        if temperature < 0:
+            raise ValueError("temperature must be non-negative")
+        if temperature == 1.0 or len(pmfs) == 0:
+            return pmfs
+        if temperature == 0.0:
+            scaled = np.zeros_like(pmfs)
+            scaled[np.arange(pmfs.shape[0]), np.argmax(pmfs, axis=1)] = 1.0
+            uniform_rows = np.all(np.isclose(pmfs, pmfs[:, :1]), axis=1)
+            scaled[uniform_rows] = pmfs[uniform_rows]
+            return scaled
+        scaled = pmfs ** (1.0 / temperature)
+        return scaled / scaled.sum(axis=1, keepdims=True)
+
+
+def purchase_intent_anchor_sets() -> tuple[SSRAnchorSet, ...]:
+    """Default domain-neutral purchase-intent anchors for 1-5 SSR scoring."""
+
+    return (
+        SSRAnchorSet(
+            "purchase_intent_plain",
+            {
+                1: "I definitely would not buy it.",
+                2: "I probably would not buy it.",
+                3: "I am unsure whether I would buy it.",
+                4: "I probably would buy it.",
+                5: "I definitely would buy it.",
+            },
+        ),
+        SSRAnchorSet(
+            "purchase_intent_likelihood",
+            {
+                1: "There is almost no chance I would purchase this.",
+                2: "I would be unlikely to purchase this.",
+                3: "I might or might not purchase this.",
+                4: "I would be likely to purchase this.",
+                5: "There is a very strong chance I would purchase this.",
+            },
+        ),
+        SSRAnchorSet(
+            "purchase_intent_consideration",
+            {
+                1: "This is not something I would consider buying.",
+                2: "I would lean against buying this.",
+                3: "I would need more information before deciding.",
+                4: "I would lean toward buying this.",
+                5: "This is something I would strongly consider buying.",
+            },
+        ),
+    )
diff --git a/audiencekit/survey.py b/audiencekit/survey.py
index e758f33..3cf9aa6 100644
--- a/audiencekit/survey.py
+++ b/audiencekit/survey.py
@@ -25,6 +25,7 @@
 import concurrent.futures
 import json
 import re
+from collections.abc import Mapping
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any, Optional, Union
@@ -34,6 +35,7 @@
 from .backends import make_backend
 from .personas import build_persona
 from .primitives import PersonaTemplate
+from .ssr import SemanticSimilarityRater
 
 LIKERT_ANCHORS = (
     "1 = very low / strongly disagree\n"
@@ -188,6 +190,57 @@ def build_survey_prompt(
 No markdown, no commentary."""
 
 
+def build_ssr_survey_prompt(
+    attributes: dict,
+    survey: Study | dict[str, Any],
+    *,
+    persona_template: PersonaTemplate | str | Any | None = None,
+) -> str:
+    """Build a prompt that elicits text for Likert items before SSR scoring."""
+    survey_dict = _as_study_dict(survey)
+    persona = render_persona(attributes, persona_template)
+    stimulus = survey_dict.get("stimulus") or {}
+    stimulus_block = ""
+    if stimulus.get("description"):
+        stimulus_block = f"\n# What you are shown\n{stimulus['description']}\n"
+        if stimulus.get("image"):
+            stimulus_block += "You are also looking at an image of it.\n"
+
+    lines = []
+    has_likert = False
+    for q in survey_dict["questions"]:
+        if q["type"] == "likert":
+            lines.append(f'- "{q["id"]}" (short natural-language answer, not a number): {q["text"]}')
+            has_likert = True
+        elif q["type"] == "choice":
+            lines.append(f'- "{q["id"]}" (one of {q["options"]}): {q["text"]}')
+        else:
+            lines.append(f'- "{q["id"]}" (short natural sentence or two): {q["text"]}')
+    question_block = "\n".join(lines)
+
+    ssr_instruction = (
+        "\nDo not answer Likert questions with numbers or scale labels. "
+        "Write the thought you would naturally express before choosing a rating.\n"
+        if has_likert
+        else ""
+    )
+
+    return f"""# Role
+You are a consumer taking part in a market research interview.
+Answer naturally from your own point of view and stay consistent with this profile.
+There are no right answers. Be direct and honest — you can like some aspects and dislike others.
+
+# Who you are
+{persona}
+{stimulus_block}
+# Questionnaire
+Answer ALL of the following fields:
+{question_block}
+{ssr_instruction}
+Return a single JSON object with exactly those field names and nothing else.
+No markdown, no commentary."""
+
+
 def parse_json_response(raw: str) -> Optional[dict]:
     """Parse a model response into a dict, tolerating code fences and prose."""
     if not raw:
@@ -288,3 +341,87 @@ def _one(idx: int) -> dict:
             if q["type"] == "likert" and q["id"] in df.columns:
                 df[q["id"]] = pd.to_numeric(df[q["id"]], errors="coerce")
         return df
+
+    def run_ssr_survey(
+        self,
+        survey: Study | dict[str, Any],
+        *,
+        rater: SemanticSimilarityRater | Mapping[str, SemanticSimilarityRater],
+        reference_set_id: str | Mapping[str, str] = "mean",
+        image: Optional[Union[str, Path]] = None,
+        verbose: bool = True,
+    ) -> pd.DataFrame:
+        """Run a survey with textual Likert elicitation and SSR scoring.
+
+        Likert question values are expected scores. Raw elicited text is stored
+        in ``{question_id}_text`` and per-score PMFs in
+        ``{question_id}_pmf_{score}`` columns.
+        """
+        survey_dict = _as_study_dict(survey)
+        stimulus = survey_dict.get("stimulus") or {}
+        image_path = image or stimulus.get("image")
+
+        def _one(idx: int) -> dict:
+            row = self.respondents.iloc[idx].to_dict()
+            if self.prompt_builder:
+                prompt = self.prompt_builder(row, survey_dict)
+            else:
+                prompt = build_ssr_survey_prompt(row, survey_dict, persona_template=self.persona_template)
+            try:
+                raw = self.backend.get_completion(
+                    prompt, image=image_path, temperature=self.temperature
+                )
+                parsed = parse_json_response(raw)
+            except RuntimeError:
+                parsed = None
+            record = {
+                "respondent_id": row.get("id"),
+                "age": row.get("age"),
+                "sex": row.get("sex"),
+                "region": row.get("region"),
+                "income16": row.get("income16"),
+                "segment": row.get("segment", "broad"),
+                "valid": parsed is not None,
+            }
+            if parsed:
+                for q in survey_dict["questions"]:
+                    value = parsed.get(q["id"])
+                    if q["type"] == "likert":
+                        record[f"{q['id']}_text"] = None if value is None else str(value)
+                    else:
+                        record[q["id"]] = value
+            if verbose:
+                print(".", end="", flush=True)
+            return record
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as pool:
+            records = list(pool.map(_one, range(len(self.respondents))))
+        if verbose:
+            valid = sum(r["valid"] for r in records)
+            print(f" done ({valid}/{len(records)} valid)")
+
+        df = pd.DataFrame(records)
+        if "valid" in df.columns:
+            df["valid"] = df["valid"].astype(object)
+
+        for q in survey_dict["questions"]:
+            if q["type"] != "likert":
+                continue
+            qid = q["id"]
+            text_col = f"{qid}_text"
+            if text_col not in df.columns:
+                continue
+            scorer = rater[qid] if isinstance(rater, Mapping) else rater
+            ref_id = reference_set_id[qid] if isinstance(reference_set_id, Mapping) else reference_set_id
+            mask = df["valid"].astype(bool) & df[text_col].notna()
+            if not mask.any():
+                continue
+
+            scored = scorer.score_texts(df.loc[mask, text_col].astype(str).tolist(), reference_set_id=ref_id)
+            scored_frame = scored.to_frame(prefix=qid)
+            for column in scored_frame.columns:
+                if column not in df.columns:
+                    df[column] = pd.NA
+                df.loc[mask, column] = scored_frame[column].to_numpy()
+
+        return df
diff --git a/pyproject.toml b/pyproject.toml
index dfee850..e6c5603 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,9 @@ dependencies = [
 dev = [
     "pytest>=8.0",
 ]
+ssr = [
+    "sentence-transformers>=2.2",
+]
 
 [tool.setuptools]
 packages = ["audiencekit", "audiencekit.data"]
diff --git a/skills/survey/SKILL.md b/skills/survey/SKILL.md
index a28a510..6a63185 100644
--- a/skills/survey/SKILL.md
+++ b/skills/survey/SKILL.md
@@ -46,6 +46,23 @@ study = ak.Study.from_json("examples/my_study/study.json")
 results = ak.SyntheticPanel(respondents).run_survey(study)
 ```
 
+For purchase intent or other distribution-sensitive Likert items, prefer
+semantic-similarity rating (SSR) when embeddings are available:
+
+```python
+rater = ak.SemanticSimilarityRater.for_purchase_intent()
+results = ak.SyntheticPanel(respondents).run_ssr_survey(
+    study,
+    rater=rater,
+    reference_set_id="mean",
+)
+```
+
+Use custom `ak.SSRAnchorSet` anchors when the question is not purchase intent.
+SSR results keep the elicited text in `{question_id}_text`, expected score in
+`{question_id}`, and probability columns in `{question_id}_pmf_1` through
+`{question_id}_pmf_5`.
+
 For a user-prepared panel:
 
 ```python
diff --git a/tests/test_ssr.py b/tests/test_ssr.py
new file mode 100644
index 0000000..467c08f
--- /dev/null
+++ b/tests/test_ssr.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from audiencekit import SSRAnchorSet, SemanticSimilarityRater
+
+
+class TinyEmbeddings:
+    def __init__(self, vectors: dict[str, list[float]]):
+        self.vectors = vectors
+
+    def encode(self, texts):
+        return np.array([self.vectors[text] for text in texts], dtype=float)
+
+
+def assert_valid_pmfs(pmfs: np.ndarray) -> None:
+    assert np.allclose(pmfs.sum(axis=1), 1.0)
+    assert np.all(pmfs >= 0)
+
+
+def test_ssr_scores_text_against_custom_anchor_set() -> None:
+    anchors = SSRAnchorSet(
+        "purchase",
+        {
+            1: "I definitely would not buy it.",
+            2: "I probably would not buy it.",
+            3: "I am unsure whether I would buy it.",
+            4: "I probably would buy it.",
+            5: "I definitely would buy it.",
+        },
+    )
+    embeddings = TinyEmbeddings(
+        {
+            "I definitely would not buy it.": [1, 0, 0, 0, 0],
+            "I probably would not buy it.": [0, 1, 0, 0, 0],
+            "I am unsure whether I would buy it.": [0, 0, 1, 0, 0],
+            "I probably would buy it.": [0, 0, 0, 1, 0],
+            "I definitely would buy it.": [0, 0, 0, 0, 1],
+            "I would absolutely buy this.": [0, 0, 0, 0, 1],
+        }
+    )
+
+    rater = SemanticSimilarityRater([anchors], embeddings=embeddings)
+    result = rater.score_texts(["I would absolutely buy this."], reference_set_id="purchase")
+
+    assert_valid_pmfs(result.pmfs)
+    assert result.scores == (1, 2, 3, 4, 5)
+    assert result.expected_scores.tolist() == [5.0]
+    assert result.most_likely_scores.tolist() == [5]
+    assert result.to_frame(prefix="fit").loc[0, "fit_pmf_5"] == pytest.approx(1.0)
+
+
+def test_ssr_mean_reference_set_averages_across_anchor_sets() -> None:
+    set_a = SSRAnchorSet("a", {1: "a1", 2: "a2", 3: "a3", 4: "a4", 5: "a5"})
+    set_b = SSRAnchorSet("b", {1: "b1", 2: "b2", 3: "b3", 4: "b4", 5: "b5"})
+    vectors = {
+        "a1": [1, 0, 0, 0, 0],
+        "a2": [0, 1, 0, 0, 0],
+        "a3": [0, 0, 1, 0, 0],
+        "a4": [0, 0, 0, 1, 0],
+        "a5": [0, 0, 0, 0, 1],
+        "b1": [1, 0, 0, 0, 0],
+        "b2": [0, 1, 0, 0, 0],
+        "b3": [0, 0, 1, 0, 0],
+        "b4": [0, 0, 0, 0, 1],
+        "b5": [0, 0, 0, 1, 0],
+        "clear yes": [0, 0, 0, 0, 1],
+    }
+
+    rater = SemanticSimilarityRater([set_a, set_b], embeddings=TinyEmbeddings(vectors))
+    result = rater.score_texts(["clear yes"], reference_set_id="mean")
+
+    assert_valid_pmfs(result.pmfs)
+    assert result.expected_scores.tolist() == [4.5]
+    assert result.pmfs.tolist() == [[0.0, 0.0, 0.0, 0.5, 0.5]]
+
+
+def test_ssr_rejects_anchor_sets_with_inconsistent_scales() -> None:
+    with pytest.raises(ValueError, match="same score scale"):
+        SemanticSimilarityRater(
+            [
+                SSRAnchorSet("five", {1: "no", 2: "maybe no", 3: "maybe", 4: "maybe yes", 5: "yes"}),
+                SSRAnchorSet("three", {1: "no", 2: "maybe", 3: "yes"}),
+            ],
+            embeddings=TinyEmbeddings({}),
+        )
diff --git a/tests/test_survey.py b/tests/test_survey.py
index d1567c2..adae4e9 100644
--- a/tests/test_survey.py
+++ b/tests/test_survey.py
@@ -2,7 +2,15 @@
 
 import pandas as pd
 
-from audiencekit import PersonaTemplate, Study, SyntheticPanel, build_survey_prompt, parse_json_response
+from audiencekit import (
+    PersonaTemplate,
+    SSRAnchorSet,
+    SemanticSimilarityRater,
+    Study,
+    SyntheticPanel,
+    build_survey_prompt,
+    parse_json_response,
+)
 
 
 class FakeBackend:
@@ -21,6 +29,28 @@ def get_completion(self, prompt, image=None, **kwargs):
         return '{"fit": 5}'
 
 
+class SSRBackend:
+    def __init__(self):
+        self.prompts = []
+
+    def get_completion(self, prompt, image=None, **kwargs):
+        self.prompts.append(prompt)
+        return '{"fit": "I would absolutely buy this.", "verbatim": "The compact size sounds useful."}'
+
+
+class TinyEmbeddings:
+    def encode(self, texts):
+        vectors = {
+            "I definitely would not buy it.": [1, 0, 0, 0, 0],
+            "I probably would not buy it.": [0, 1, 0, 0, 0],
+            "I am unsure whether I would buy it.": [0, 0, 1, 0, 0],
+            "I probably would buy it.": [0, 0, 0, 1, 0],
+            "I definitely would buy it.": [0, 0, 0, 0, 1],
+            "I would absolutely buy this.": [0, 0, 0, 0, 1],
+        }
+        return pd.DataFrame([vectors[text] for text in texts]).to_numpy(dtype=float)
+
+
 def test_parse_json_response_tolerates_fenced_json() -> None:
     assert parse_json_response('```json\n{"score": 5}\n```') == {"score": 5}
 
@@ -111,3 +141,45 @@ def test_synthetic_panel_accepts_custom_persona_template_for_non_gss_data() -> N
 
     assert results.loc[0, "fit"] == 5
     assert "You are 41, live in Midwest, and buy running shoes." in backend.prompts[0]
+
+
+def test_synthetic_panel_runs_ssr_survey_for_likert_questions() -> None:
+    respondents = pd.DataFrame(
+        [{"id": "r1", "age": "35", "sex": "Male", "region": "Pacific", "income16": "$50,000 to $59,999"}]
+    )
+    study = Study.from_dict(
+        {
+            "title": "Concept test",
+            "questions": [
+                {"id": "fit", "type": "likert", "text": "How likely would you be to buy it?"},
+                {"id": "verbatim", "type": "text", "text": "Reaction?"},
+            ],
+        }
+    )
+    rater = SemanticSimilarityRater(
+        [
+            SSRAnchorSet(
+                "purchase",
+                {
+                    1: "I definitely would not buy it.",
+                    2: "I probably would not buy it.",
+                    3: "I am unsure whether I would buy it.",
+                    4: "I probably would buy it.",
+                    5: "I definitely would buy it.",
+                },
+            )
+        ],
+        embeddings=TinyEmbeddings(),
+    )
+    backend = SSRBackend()
+
+    panel = SyntheticPanel(respondents, backend=backend)
+    results = panel.run_ssr_survey(study, rater=rater, reference_set_id="purchase", verbose=False)
+
+    assert "Do not answer Likert questions with numbers" in backend.prompts[0]
+    assert results.loc[0, "valid"] is True
+    assert results.loc[0, "fit_text"] == "I would absolutely buy this."
+    assert results.loc[0, "fit"] == 5.0
+    assert results.loc[0, "fit_most_likely"] == 5
+    assert results.loc[0, "fit_pmf_5"] == 1.0
+    assert results.loc[0, "verbatim"] == "The compact size sounds useful."
diff --git a/uv.lock b/uv.lock
index 27a25c1..334daae 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,6 +10,15 @@ resolution-markers = [
     "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -68,6 +77,9 @@ dependencies = [
 dev = [
     { name = "pytest" },
 ]
+ssr = [
+    { name = "sentence-transformers" },
+]
 
 [package.metadata]
 requires-dist = [
@@ -78,8 +90,9 @@ requires-dist = [
     { name = "openai", specifier = ">=1.40" },
     { name = "pandas", specifier = ">=2.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0" },
+    { name = "sentence-transformers", marker = "extra == 'ssr'", specifier = ">=2.2" },
 ]
-provides-extras = ["dev"]
+provides-extras = ["dev", "ssr"]
 
 [[package]]
 name = "certifi"
@@ -249,6 +262,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" },
 ]
 
+[[package]]
+name = "click"
+version = "8.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -396,6 +421,74 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/aa/50/a9caea39ad19c431c1a3f8a31114df65b260cdfe67786b6c7e7c040c4c44/cryptography-49.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:be9fcb48a55f023493482827d4f459bd263cc20efde64f204b97c123201850c6", size = 3783731, upload-time = "2026-06-12T20:02:43.319Z" },
 ]
 
+[[package]]
+name = "cuda-bindings"
+version = "13.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/51/6b/457ca12dad3ee9bfcc9a545cfd6b64b359ba49de40f776f6e028e678f262/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c5879712accf6e14bb01aa5e67440eb84998b8d104b509cc7a6dc0b8f656a474", size = 6053539, upload-time = "2026-05-29T23:11:43.19Z" },
+    { url = "https://files.pythonhosted.org/packages/95/7a/c5e3c34a409b148f5c0f5a4ea374158f95d488862c1dffedf9aa5c639df9/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:04436a9364059c84b8f9636f359eccda1cf814341f5b670c71d80d2f79dbc708", size = 6674166, upload-time = "2026-05-29T23:11:45.478Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/67/5e7dba1ba576dd73da5dee894ca076ca5e959450dfff66d6d510a255d1f7/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7855c4868aabc0cfae28abbe83d56734bdfbd08f08fc234ac1912a12858bf49", size = 6025351, upload-time = "2026-05-29T23:11:49.685Z" },
+    { url = "https://files.pythonhosted.org/packages/39/2a/6d2e9047d1fb243dbaa364b01e0297534b9ed7fd27dba1c9f361519cf69b/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e32d08f71ebcdf00f0f41eab2eb37e8da94c8ed411cc9f7f7a019ce6b34abe3a", size = 6657965, upload-time = "2026-05-29T23:11:52.227Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/6e/2394f8163360f8391f8f1b7e72d300a82724edb81a7b7084c799fbd4c91f/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9efb21c1ee64981e184b9e0ba5eb3179e5ba3d4b51665a6cb52b8ef3d01a7cbf", size = 5920504, upload-time = "2026-05-29T23:11:56.883Z" },
+    { url = "https://files.pythonhosted.org/packages/34/c2/ef9b6a63f7dc432712a462c816662e662e00d38caa9b861c8c2588195d03/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2732904099e0a4d4db774a5fc6d91ee95fae065b4d2ecabb4968c5fe2406c9d7", size = 6476660, upload-time = "2026-05-29T23:11:59.188Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/81/bff68ce829999c1e4209c761bbf903b1c06ec570416ddb25020864ad5907/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ab2f74ed65bfef4163ba07a8db16f1085e0729291db12a2423aff84ee8278b8", size = 6013639, upload-time = "2026-05-29T23:12:03.509Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/e0/c8a1f0c8f9ffdea4f5fe6dbab89b326cef4d85caf489dad39e209da89416/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd4c814d311ec08c981f6dded1dbe7d4b371067ee4f6c14cccec4bde9590f80", size = 6534419, upload-time = "2026-05-29T23:12:05.633Z" },
+    { url = "https://files.pythonhosted.org/packages/52/b8/83b1f563925b290f2d11a01a77a84013ba56052fe3653a5bef3ccfbb43d6/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c3c772dfff49681541d59630c90f858e173ac926b9c593a2b7123f2a1043cc76", size = 5809771, upload-time = "2026-05-29T23:12:10.422Z" },
+    { url = "https://files.pythonhosted.org/packages/12/20/e79b4bfe98f075195afb6343d41c498f9dbd2d161d7021d4d28bceb83581/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:36febb7c1079d68a981dbbd8d5a67235b399802b82075c9388624719607e52b9", size = 6358584, upload-time = "2026-05-29T23:12:12.767Z" },
+]
+
+[[package]]
+name = "cuda-pathfinder"
+version = "1.5.5"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/c8/26f2e4aae92f11522a96043892ba39a90eac610d5242523aa863212bc1c7/cuda_pathfinder-1.5.5-py3-none-any.whl", hash = "sha256:0228c023f95d1480f143ef5c8922d27a2ab052087a942e81dc289c9eb8f91689", size = 51671, upload-time = "2026-05-27T01:21:25.413Z" },
+]
+
+[[package]]
+name = "cuda-toolkit"
+version = "13.0.2"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/57/b2/453099f5f3b698d7d0eab38916aac44c7f76229f451709e2eb9db6615dcd/cuda_toolkit-13.0.2-py2.py3-none-any.whl", hash = "sha256:b198824cf2f54003f50d64ada3a0f184b42ca0846c1c94192fa269ecd97a66eb", size = 2364, upload-time = "2025-12-19T23:24:07.328Z" },
+]
+
+[package.optional-dependencies]
+cudart = [
+    { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" },
+]
+cufft = [
+    { name = "nvidia-cufft", marker = "sys_platform == 'linux'" },
+]
+cufile = [
+    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
+]
+cupti = [
+    { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" },
+]
+curand = [
+    { name = "nvidia-curand", marker = "sys_platform == 'linux'" },
+]
+cusolver = [
+    { name = "nvidia-cusolver", marker = "sys_platform == 'linux'" },
+]
+cusparse = [
+    { name = "nvidia-cusparse", marker = "sys_platform == 'linux'" },
+]
+nvjitlink = [
+    { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux'" },
+]
+nvrtc = [
+    { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux'" },
+]
+nvtx = [
+    { name = "nvidia-nvtx", marker = "sys_platform == 'linux'" },
+]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -423,6 +516,15 @@ wheels = [
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+[[package]]
+name = "filelock"
+version = "3.29.4"
+source = { registry = "https://pypi.org/simple" }
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From 9948a61bd819330083b7a527dd05886fb35a2141 Mon Sep 17 00:00:00 2001
From: Luca Fiaschi <luca.fiaschi@gmail.com>
Date: Mon, 15 Jun 2026 18:00:29 -0400
Subject: [PATCH 2/2] harden ssr validation and metadata

---
 README.md             | 26 ++++++++++++++++++++---
 audiencekit/ssr.py    | 14 ++++++++++---
 audiencekit/survey.py | 40 +++++++++++++++++++-----------------
 tests/test_ssr.py     | 47 ++++++++++++++++++++++++++++++++++++++++++
 tests/test_survey.py  | 48 +++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 150 insertions(+), 25 deletions(-)

diff --git a/README.md b/README.md
index caa5b5d..3e414cd 100644
--- a/README.md
+++ b/README.md
@@ -101,7 +101,7 @@ study = ak.Study.from_dict({
         {
             "id": "consideration",
             "type": "likert",
-            "text": "How likely would you be to consider it?"
+            "text": "How likely would you be to consider buying it?"
         },
         {
             "id": "first_reaction",
@@ -134,7 +134,22 @@ the rating scale.
 rater = ak.SemanticSimilarityRater.for_purchase_intent()
 
 ssr_results = ak.SyntheticPanel(respondents).run_ssr_survey(
-    study,
+    ak.Study.from_dict({
+        "title": "Compact EV purchase-intent test",
+        "stimulus": study.stimulus,
+        "questions": [
+            {
+                "id": "consideration",
+                "type": "likert",
+                "text": "How likely would you be to consider buying it?"
+            },
+            {
+                "id": "why",
+                "type": "text",
+                "text": "What is the main reason for your reaction?"
+            },
+        ],
+    }),
     rater=rater,
     reference_set_id="mean",
 )
@@ -316,7 +331,12 @@ template = ak.PersonaTemplate(
     "and currently buy {category} tools. Your budget authority is {budget_owner}."
 )
 
-panel = ak.SyntheticPanel(respondents, persona_template=template)
+panel = ak.SyntheticPanel(
+    respondents,
+    id_column="customer_id",
+    weight_column="survey_weight",
+    persona_template=template,
+)
 results = panel.run_survey(study)
 ```
 
diff --git a/audiencekit/ssr.py b/audiencekit/ssr.py
index cee28d9..bd8fcb5 100644
--- a/audiencekit/ssr.py
+++ b/audiencekit/ssr.py
@@ -80,6 +80,10 @@ def survey_pmf(self, weights: Sequence[float] | None = None) -> np.ndarray:
         weight_array = np.asarray(weights, dtype=float)
         if weight_array.shape != (len(self.pmfs),):
             raise ValueError("weights must have one value per PMF row")
+        if not np.all(np.isfinite(weight_array)):
+            raise ValueError("weights must be finite")
+        if np.any(weight_array < 0):
+            raise ValueError("weights must be non-negative")
         if weight_array.sum() <= 0:
             raise ValueError("weights must sum to a positive value")
         return np.average(self.pmfs, axis=0, weights=weight_array)
@@ -209,6 +213,7 @@ def score_embeddings(
             responses = responses.reshape(1, -1)
         if responses.ndim != 2:
             raise ValueError("response_embeddings must be a 2D array")
+        self._validate_embeddings(responses)
         if len(responses) == 0:
             return np.empty((0, len(self.scores)))
 
@@ -243,11 +248,14 @@ def _encode(self, texts: Sequence[str]) -> np.ndarray:
             array = array.reshape(1, -1)
         if array.ndim != 2 or array.shape[0] != len(texts):
             raise ValueError("embedding provider must return a 2D array with one row per text")
-        if not np.all(np.isfinite(array)):
+        self._validate_embeddings(array)
+        return array
+
+    def _validate_embeddings(self, embeddings: np.ndarray) -> None:
+        if not np.all(np.isfinite(embeddings)):
             raise ValueError("embeddings must be finite")
-        if np.any(np.linalg.norm(array, axis=1) == 0):
+        if len(embeddings) and np.any(np.linalg.norm(embeddings, axis=1) == 0):
             raise ValueError("embeddings must not contain zero vectors")
-        return array
 
     def _pmfs_from_embeddings(
         self,
diff --git a/audiencekit/survey.py b/audiencekit/survey.py
index 3cf9aa6..9ce4aa7 100644
--- a/audiencekit/survey.py
+++ b/audiencekit/survey.py
@@ -25,7 +25,7 @@
 import concurrent.futures
 import json
 import re
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any, Optional, Union
@@ -270,6 +270,9 @@ def __init__(
         backend: Any | None = None,
         persona_template: PersonaTemplate | str | Any | None = None,
         prompt_builder: Any | None = None,
+        id_column: str = "id",
+        weight_column: str | None = "weight",
+        metadata_columns: Sequence[str] | None = None,
         temperature: float = 0.7,
         max_workers: int = 8,
     ):
@@ -277,12 +280,25 @@ def __init__(
         self.backend = backend or make_backend(backend_type, model)
         self.persona_template = persona_template
         self.prompt_builder = prompt_builder
+        self.id_column = id_column
+        self.weight_column = weight_column
+        self.metadata_columns = tuple(metadata_columns or ("age", "sex", "region", "income16", "segment"))
         self.temperature = temperature
         self.max_workers = max_workers
 
     def __len__(self) -> int:
         return len(self.respondents)
 
+    def _base_record(self, row: dict[str, Any]) -> dict[str, Any]:
+        record: dict[str, Any] = {"respondent_id": row.get(self.id_column, row.get("id"))}
+        if self.weight_column and self.weight_column in row:
+            record["weight"] = row.get(self.weight_column)
+        for column in self.metadata_columns:
+            if column in row:
+                record[column] = row.get(column)
+        record.setdefault("segment", row.get("segment", "broad"))
+        return record
+
     def run_survey(
         self,
         survey: Study | dict[str, Any],
@@ -312,15 +328,8 @@ def _one(idx: int) -> dict:
                 parsed = parse_json_response(raw)
             except RuntimeError:
                 parsed = None
-            record = {
-                "respondent_id": row.get("id"),
-                "age": row.get("age"),
-                "sex": row.get("sex"),
-                "region": row.get("region"),
-                "income16": row.get("income16"),
-                "segment": row.get("segment", "broad"),
-                "valid": parsed is not None,
-            }
+            record = self._base_record(row)
+            record["valid"] = parsed is not None
             if parsed:
                 for q in survey_dict["questions"]:
                     record[q["id"]] = parsed.get(q["id"])
@@ -374,15 +383,8 @@ def _one(idx: int) -> dict:
                 parsed = parse_json_response(raw)
             except RuntimeError:
                 parsed = None
-            record = {
-                "respondent_id": row.get("id"),
-                "age": row.get("age"),
-                "sex": row.get("sex"),
-                "region": row.get("region"),
-                "income16": row.get("income16"),
-                "segment": row.get("segment", "broad"),
-                "valid": parsed is not None,
-            }
+            record = self._base_record(row)
+            record["valid"] = parsed is not None
             if parsed:
                 for q in survey_dict["questions"]:
                     value = parsed.get(q["id"])
diff --git a/tests/test_ssr.py b/tests/test_ssr.py
index 467c08f..cfc1576 100644
--- a/tests/test_ssr.py
+++ b/tests/test_ssr.py
@@ -85,3 +85,50 @@ def test_ssr_rejects_anchor_sets_with_inconsistent_scales() -> None:
             ],
             embeddings=TinyEmbeddings({}),
         )
+
+
+def test_ssr_rejects_invalid_precomputed_response_embeddings() -> None:
+    anchors = SSRAnchorSet("purchase", {1: "no", 2: "maybe no", 3: "maybe", 4: "maybe yes", 5: "yes"})
+    rater = SemanticSimilarityRater(
+        [anchors],
+        embeddings=TinyEmbeddings(
+            {
+                "no": [1, 0, 0, 0, 0],
+                "maybe no": [0, 1, 0, 0, 0],
+                "maybe": [0, 0, 1, 0, 0],
+                "maybe yes": [0, 0, 0, 1, 0],
+                "yes": [0, 0, 0, 0, 1],
+            }
+        ),
+    )
+
+    with pytest.raises(ValueError, match="finite"):
+        rater.score_embeddings(np.array([[np.nan, 0, 0, 0, 1]]), reference_set_id="purchase")
+
+    with pytest.raises(ValueError, match="zero vectors"):
+        rater.score_embeddings(np.zeros((1, 5)), reference_set_id="purchase")
+
+
+def test_survey_pmf_rejects_negative_and_nonfinite_weights() -> None:
+    anchors = SSRAnchorSet("purchase", {1: "no", 2: "maybe no", 3: "maybe", 4: "maybe yes", 5: "yes"})
+    rater = SemanticSimilarityRater(
+        [anchors],
+        embeddings=TinyEmbeddings(
+            {
+                "no": [1, 0, 0, 0, 0],
+                "maybe no": [0, 1, 0, 0, 0],
+                "maybe": [0, 0, 1, 0, 0],
+                "maybe yes": [0, 0, 0, 1, 0],
+                "yes": [0, 0, 0, 0, 1],
+                "clear yes": [0, 0, 0, 0, 1],
+                "clear no": [1, 0, 0, 0, 0],
+            }
+        ),
+    )
+    result = rater.score_texts(["clear yes", "clear no"], reference_set_id="purchase")
+
+    with pytest.raises(ValueError, match="non-negative"):
+        result.survey_pmf(weights=[1.0, -0.5])
+
+    with pytest.raises(ValueError, match="finite"):
+        result.survey_pmf(weights=[1.0, np.nan])
diff --git a/tests/test_survey.py b/tests/test_survey.py
index adae4e9..cc8fee8 100644
--- a/tests/test_survey.py
+++ b/tests/test_survey.py
@@ -183,3 +183,51 @@ def test_synthetic_panel_runs_ssr_survey_for_likert_questions() -> None:
     assert results.loc[0, "fit_most_likely"] == 5
     assert results.loc[0, "fit_pmf_5"] == 1.0
     assert results.loc[0, "verbatim"] == "The compact size sounds useful."
+
+
+def test_synthetic_panel_ssr_preserves_custom_dataset_id_and_weight() -> None:
+    respondents = pd.DataFrame(
+        [
+            {
+                "person_id": "u1",
+                "survey_weight": 2.5,
+                "age": 41,
+                "region": "Midwest",
+                "category": "running shoes",
+            }
+        ]
+    )
+    study = Study.from_dict(
+        {
+            "title": "Concept test",
+            "questions": [{"id": "fit", "type": "likert", "text": "Fit?"}],
+        }
+    )
+    rater = SemanticSimilarityRater(
+        [
+            SSRAnchorSet(
+                "purchase",
+                {
+                    1: "I definitely would not buy it.",
+                    2: "I probably would not buy it.",
+                    3: "I am unsure whether I would buy it.",
+                    4: "I probably would buy it.",
+                    5: "I definitely would buy it.",
+                },
+            )
+        ],
+        embeddings=TinyEmbeddings(),
+    )
+
+    panel = SyntheticPanel(
+        respondents,
+        backend=SSRBackend(),
+        id_column="person_id",
+        weight_column="survey_weight",
+        persona_template=PersonaTemplate("You are {age}, live in {region}, and buy {category}."),
+    )
+    results = panel.run_ssr_survey(study, rater=rater, reference_set_id="purchase", verbose=False)
+
+    assert results.loc[0, "respondent_id"] == "u1"
+    assert results.loc[0, "weight"] == 2.5
+    assert results.loc[0, "fit"] == 5.0