Reject the uncalibrated text-manifest tiling and verify Content Seal transforms

Tiled diffusion was never provider-oracle calibrated with verified text
restoration: the tiled VAE donor path ran anyway and produced results no
oracle had certified. The combination is now rejected at both the
pipeline and the engine seam (ValueError with the reason), and the CLI
help no longer implies support. The invisible help is generalized and
the metadata container list corrected (MKA/OGA/Opus/AAC).

scripts/contentseal_transforms.py reproduces the deterministic crop,
resize, and JPEG variants of the Content Seal corpus from manifest.csv,
hash-verifying every output; its README gains scripts/README.md context
and new data tests. The corpus README is honest about the one crop the
daily oracle limit left unchecked, and the eval CSVs carry the updated
verdicts. The byte-scan SynthID suppression hoists its soft-binding
lookup so the guard is computed once.

Staged on top of 0.33.1; no version bump in this commit.

src/remove_ai_watermarks/_internal/c2pa.py

@@ -624,16 +624,16 @@ def _populate_registry_fields(buffer: bytes, info: dict[str, Any]) -> bool:
 
     if b"c2pa.watermarked" in buffer:
         info["watermarked"] = True
+    soft_bindings = soft_binding_vendors_in(buffer)
     synthid = (
         []
-        if soft_binding_vendors_in(buffer)
+        if soft_bindings
         else synthid_evidence_vendors_in(buffer, has_watermark_action=info.get("watermarked", False))
     )
     if ai_source and synthid:
         info["synthid_vendors"] = synthid
         info["synthid_watermark"] = synthid_verdict(", ".join(synthid))
 
-    soft_bindings = soft_binding_vendors_in(buffer)
     if soft_bindings:
         info["soft_binding_vendors"] = soft_bindings
         info["soft_binding"] = ", ".join(soft_bindings)

src/remove_ai_watermarks/_internal/constants.py

@@ -70,7 +70,7 @@ C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
     _vendor(
         b"OpenAI",
         "OpenAI",
-        "OpenAI (ChatGPT / gpt-image / DALL-E / Sora)",
+        "OpenAI (ChatGPT / GPT Image / DALL·E / Sora)",
         "OpenAI",
         synthid=True,
         synthid_requires_watermark_action=True,
@@ -128,8 +128,8 @@ C2PA_AI_TOOLS = {
         ("GPT-4o", "GPT-4o"),
         ("ChatGPT", "ChatGPT"),
         ("Sora", "Sora"),
-        ("DALL-E", "DALL-E"),
-        ("DALL", "DALL-E"),
+        ("DALL-E", "DALL·E"),
+        ("DALL", "DALL·E"),
         ("Imagen", "Imagen"),
         ("Firefly", "Firefly"),
         ("Dreamina", "Dreamina"),
@@ -138,16 +138,18 @@ C2PA_AI_TOOLS = {
 
 C2PA_SOFT_BINDINGS = {
     b"com.adobe.trustmark": "Adobe TrustMark",
-    b"com.adobe.icn": "Adobe (content fingerprint)",
-    b"com.digimarc": "Digimarc",
+    b"com.adobe.icn": "Adobe Image Comparator Network",
+    b"com.digimarc": "Digimarc Validate",
     b"com.imatag.lamark": "Imatag (Lamark)",
     b"ai.steg": "Steg.AI",
     b"com.microsoft.invismark": "Microsoft InvisMark",
     b"com.microsoft.wavmark": "Microsoft WavMark",
     b"com.verimatrix": "Verimatrix",
     b"com.nagra.nexguard": "NAGRA NexGuard",
-    b"com.aiwatermark": "AIWatermark (Meta PixelSeal)",
-    b"ai.trufo": "Trufo",
+    b"com.aiwatermark.pixelseal": "AIWatermark PixelSeal",
+    b"com.aiwatermark.videoseal": "AIWatermark VideoSeal",
+    b"com.aiwatermark.audioseal": "AIWatermark AudioSeal",
+    b"ai.trufo": "Trufo PawPrint",
     b"app.overlai": "Overlai",
     b"com.markany": "MarkAny",
     b"com.mentaport": "Mentaport",

src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py

@@ -1074,29 +1074,14 @@ class QwenZImagePipeline:
         fidelity_anchor: bool = False,
     ) -> Image.Image:
         """Execute global regeneration and masked face repair."""
+        if text_manifest is not None and tile:
+            raise ValueError("Verified text restoration is not calibrated with tiled diffusion")
         self._require_cuda()
         seed = resolve_seed(seed)
         donor = None
         if text_manifest is not None:
             self._progress("Reconstructing the verified text donor with the Qwen VAE...")
-            if tile and max(image.size) > tile_size:
-                from remove_ai_watermarks._internal.tiling import run_tiled
-
-                donor = run_tiled(
-                    self._qwen_vae_roundtrip,
-                    image,
-                    tile_size,
-                    tile_overlap,
-                    lambda message: self._progress(
-                        message.replace(
-                            "Tiled diffusion",
-                            "Reconstructing the verified text donor",
-                            1,
-                        )
-                    ),
-                )
-            else:
-                donor = self._qwen_vae_roundtrip(image)
+            donor = self._qwen_vae_roundtrip(image)
         global_strength = (
             resolution_adaptive_denoise(image.width, image.height) if strength is None else float(strength)
         )

src/remove_ai_watermarks/_text_mark_engine.py

@@ -90,7 +90,7 @@ class TextMarkConfig:
     asset_name: str  # bundled alpha PNG under assets/ (e.g. "doubao_alpha.png")
     corner: Literal[
         "br", "bl", "tl", "bc"
-    ]  # bottom-right (Doubao/Jimeng), bottom-left (Samsung), top-left (RunningHub), bottom-center (LibLibAI)
+    ]  # bottom-right (Doubao/Jimeng), bottom-left (Samsung), top-left (RunningHub), bottom-center (LiblibAI)
     margin_floor: int  # min margin in px for locate (4 for br marks, 2 for Samsung)
     # locate geometry (fraction of scale_base -- see scale_base())
     width_frac: float
@@ -695,7 +695,7 @@ class TextMarkEngine:
 
         OVERRIDABLE, and the override contract is specifically the DETECTOR'S MATCH BOX:
         a mark whose removable footprint reaches beyond what the NCC localizes -- Baidu's
-        flat white tag right of the text run, LibLibAI's triangle logo left of the
+        flat white tag right of the text run, LiblibAI's triangle logo left of the
         wordmark -- supplies its own extension here and inherits the rest of the
         footprint path. The blob-bbox branch never routes through an override.
         """
@@ -771,7 +771,7 @@ class TextMarkEngine:
         """Footprint policy for a mark whose fill must be bounded by the DETECTOR's match
         box and never by the binary glyph blob.
 
-        Baidu's white tag has a flat interior a top-hat cannot answer, and LibLibAI's
+        Baidu's white tag has a flat interior a top-hat cannot answer, and LiblibAI's
         blob bleeds up into background structure; in both cases the blob bbox is
         measurably wrong and the NCC match box is right. ``force`` takes priority here,
         unlike the default policy: a ``--no-detect`` caller named the mark, so the whole

src/remove_ai_watermarks/api.py

@@ -157,7 +157,7 @@ def remove_visible(
     """Remove every detected known visible AI mark through localize then fill.
 
     The registry currently covers the Gemini sparkle; Doubao, Jimeng, Qwen, Kling,
-    Yuanbao, Samsung, RunningHub, Baidu, and LibLibAI text marks; and the Jimeng
+    Yuanbao, Samsung, RunningHub, Baidu, and LiblibAI text marks; and the Jimeng
     pill. Returns ``(result_bgr, [labels removed])``.
 
     ``source`` is a file path OR a BGR ndarray. For a PATH, metadata provenance is read

src/remove_ai_watermarks/cli.py

@@ -307,7 +307,7 @@ _seed_option = click.option(
     help="Random seed for reproducibility. Default 0: both profiles are certified "
     "at a fixed seed, because SynthID removal near the strength floor is seed-dependent.",
 )
-_hf_token_option = click.option("--hf-token", type=str, default=None, help="HuggingFace API token.")
+_hf_token_option = click.option("--hf-token", type=str, default=None, help="Hugging Face API token.")
 _humanize_option = click.option(
     "--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)."
 )
@@ -859,7 +859,7 @@ def cmd_invisible(
     text_manifest: Path | None,
     fidelity_anchor: bool,
 ) -> None:
-    """Remove invisible AI watermarks (SynthID, StableSignature, TreeRing).
+    """Attempt to disrupt invisible AI watermarks through pixel regeneration.
 
     Regenerates the pixels with the two-stage diffusion profile. CUDA-only:
     pip install 'remove-ai-watermarks[qwen-zimage]'
@@ -1001,8 +1001,8 @@ def cmd_metadata(
     Strips EXIF AI tags, PNG text chunks, C2PA provenance manifests, and the
     China TC260 AIGC label. Beyond images (PNG/JPEG/WebP/AVIF/HEIF/JXL) it also
     strips provenance metadata from MP4/MOV/M4V/M4A containers and, via ffmpeg,
-    from WebM/MKV/AVI/FLV/MP3/WAV/FLAC/OGG. The coded image, audio, and video
-    data are left untouched.
+    from WebM/MKV/MKA/AVI/FLV/MP3/WAV/FLAC/OGG/OGA/Opus/AAC. The coded image,
+    audio, and video data are left untouched.
     """
     from remove_ai_watermarks.metadata import get_ai_metadata, has_ai_metadata, strip_and_verify
 

src/remove_ai_watermarks/identify.py

@@ -145,7 +145,7 @@ _INVISIBLE_WM_CAVEAT = (
     "or resizing, so it confirms origin only on a pristine (un-re-encoded) file."
 )
 _HF_JOB_CAVEAT = (
-    "The hf-job-id tag marks a HuggingFace-hosted job (commonly diffusion "
+    "The hf-job-id tag marks a Hugging Face-hosted job (commonly diffusion "
     "generation) but names neither the model nor the content type, so it is a "
     "medium-confidence signal, not proof the pixels are AI-generated."
 )
@@ -445,7 +445,7 @@ def evidence_from_metadata_record(
     if iptc_system:
         ai_metadata.setdefault("ai_system", f"IPTC 2025.1 AI disclosure ({iptc_system})")
     if hf_job:
-        ai_metadata.setdefault("huggingface_job", f"HuggingFace-hosted job ({hf_job})")
+        ai_metadata.setdefault("huggingface_job", f"Hugging Face-hosted job ({hf_job})")
     if samsung is not None:
         ai_metadata.setdefault("samsung_genai", f"Samsung Galaxy AI editing marker (genAIType={samsung})")
 
@@ -953,7 +953,7 @@ def _visible_sparkle(image_path: Path, *, image: NDArray[Any] | None = None) ->
 # metadata label); the per-engine detection thresholds live in the registry.
 # Text mark -> the platform sentence this report prints when that mark is the strongest
 # evidence, DERIVED from the registry rows so registering a mark is one edit. It was a
-# hand-maintained copy, and that class of copy is how LibLibAI ended up registered but
+# hand-maintained copy, and that class of copy is how LiblibAI ended up registered but
 # missing from the pill veto. Insertion order is the registry's, which is what fixes the
 # scan order below. The Gemini sparkle and the capture-less pill carry no platform of
 # their own (`KnownMark.platform is None`) and are excluded here: the sparkle has its
@@ -1426,17 +1426,17 @@ def _identify_from_evidence(
             platform = "xAI (Grok / Aurora)"
         ai_vendor_claims["xai"] = "xAI"
 
-    # ── HuggingFace-hosted job marker (hf-job-id PNG text chunk) ─────
+    # ── Hugging Face-hosted job marker (hf-job-id PNG text chunk) ─────
     # Marks the hosting job, not a model -- medium confidence (commonly diffusion
     # output). Like the visible sparkle, it lifts an otherwise-Unknown verdict to
     # a tentative AI, but never overrides a high-confidence metadata signal.
     hf_job = evidence.huggingface_job
     if hf_job:
-        signals.append(Signal("hf_job", f"HuggingFace job {hf_job}", "medium"))
-        watermarks.append("HuggingFace-hosted job (hf-job-id)")
+        signals.append(Signal("hf_job", f"Hugging Face job {hf_job}", "medium"))
+        watermarks.append("Hugging Face-hosted job (hf-job-id)")
         caveats.append(_HF_JOB_CAVEAT)
         if platform is None:
-            platform = "HuggingFace-hosted job (model not identified)"
+            platform = "Hugging Face-hosted job (model not identified)"
 
     # ── Samsung Galaxy AI editing marker (genAIType) ─────────────────
     # Galaxy AI tools stamp a proprietary genAIType in PhotoEditor_Re_Edit_Data.

src/remove_ai_watermarks/invisible_engine.py

@@ -33,7 +33,7 @@ warnings.filterwarnings("ignore", category=UserWarning, module="huggingface_hub"
 warnings.filterwarnings("ignore", category=UserWarning, module="diffusers")
 warnings.filterwarnings("ignore", module="transformers")
 
-# Suppress HuggingFace internal logging
+# Suppress Hugging Face internal logging
 os.environ["TRANSFORMERS_VERBOSITY"] = "error"
 os.environ["DIFFUSERS_VERBOSITY"] = "error"
 
@@ -104,7 +104,7 @@ class InvisibleEngine:
                 global pass, vendor-adaptive strength because an SDXL global stage
                 needs more of it). BOTH ARE CUDA-ONLY -- there is no CPU or MPS path
                 for invisible-watermark removal.
-            hf_token: HuggingFace API token.
+            hf_token: Hugging Face API token.
             progress_callback: Optional callback for progress messages.
             controlnet_conditioning_scale: Canny ControlNet structure-preservation
                 strength on the global stage of both profiles.
@@ -187,9 +187,8 @@ class InvisibleEngine:
                 pixels. Enables the experimental Qwen-VAE ``vae-glyphs`` post-pass.
                 Requires the ``text-restoration`` extra and the ``qwen-zimage``
                 profile. Incompatible with downscaling, humanize, unsharp, and
-                adaptive polish. Tiling is supported: the VAE donor uses the same
-                overlapping tiles as the global pass, then glyph restore runs on
-                the blended full frame.
+                adaptive polish. Tiling is rejected because that combination has
+                no provider-oracle calibration.
             fidelity_anchor: Blend 15% of the Qwen-VAE donor across the whole frame
                 before glyph restoration. OFF by default since 0.27.1: that global
                 blend was measured to return detector-visible OpenAI SynthID on
@@ -211,6 +210,8 @@ class InvisibleEngine:
         if text_manifest is not None:
             if self._remover.model_profile != QWEN_ZIMAGE_PROFILE:
                 raise ValueError("--text-manifest is supported only by the qwen-zimage profile")
+            if tile:
+                raise ValueError("--text-manifest is not calibrated with --tile")
             if max_resolution != 0:
                 raise ValueError("--text-manifest requires --max-resolution 0")
             if humanize > 0.0 or unsharp > 0.0 or adaptive_polish:

src/remove_ai_watermarks/liblib_engine.py

@@ -1,13 +1,13 @@
-"""LibLibAI visible watermark detector/localizer.
+"""LiblibAI visible watermark detector/localizer.
 
-LibLibAI (哩布哩布AI, USCC 91110105MACJ6K1C8A) stamps its generations with a
-white triangle logo + "LibLibAI" latin wordmark at **bottom-center** (not a
+LiblibAI (哩布哩布AI, USCC 91110105MACJ6K1C8A) stamps its generations with a
+white triangle logo + "LiblibAI" Latin wordmark at **bottom-center** (not a
 corner -- the locate box is horizontally centered). Detection matches the
-bundled font-rendered "LibLibAI" silhouette (the triangle logo is NOT rendered
+bundled font-rendered "LiblibAI" silhouette (the triangle logo is NOT rendered
 -- logos vary, the wordmark discriminates); removal is the shared **localize ->
 fill** (the glyph blob covers logo + wordmark, both bright).
 
-This module supplies only LibLibAI's tuned :class:`TextMarkConfig`
+This module supplies only LiblibAI's tuned :class:`TextMarkConfig`
 (``assets/liblib_alpha.png`` from ``scripts/render_vendor_silhouettes.py``,
 never cut from an upload).
 
@@ -64,7 +64,7 @@ _ALPHA_HEIGHT_FRAC = 0.026
 _LADDER = (0.9, 1.0, 1.1)
 
 _CONFIG = TextMarkConfig(
-    name="LibLibAI",
+    name="LiblibAI",
     asset_name="liblib_alpha.png",
     corner="bc",
     margin_floor=4,
@@ -90,12 +90,12 @@ _CONFIG = TextMarkConfig(
 
 
 def _alpha_template() -> NDArray[Any] | None:
-    """The bundled LibLibAI alpha template (float [0,1]), or None."""
+    """The bundled LiblibAI alpha template (float [0,1]), or None."""
     return _text_mark_engine.load_alpha_template(_CONFIG.asset_name)
 
 
 class LibLibEngine(TextMarkEngine):
-    """Detect/localize the visible LibLibAI wordmark (bottom-center; localize -> fill)."""
+    """Detect/localize the visible LiblibAI wordmark (bottom-center; localize -> fill)."""
 
     # Per-mark size floor prevents small generic icons from matching the wordmark.
     _MIN_SHORT_SIDE = 480

src/remove_ai_watermarks/metadata.py

@@ -199,7 +199,7 @@ def parse_tc260_aigc_json(value: bytes) -> dict[str, str] | None:
     return fields if TC260_AIGC_FIELDS & fields.keys() else None
 
 
-# HuggingFace-hosted GPU jobs (Jobs / Spaces) stamp generated PNGs with this
+# Hugging Face-hosted GPU jobs (Jobs / Spaces) stamp generated PNGs with this
 # ``tEXt`` chunk key holding the job UUID. It marks the hosting job, not a
 # specific model -- a medium-confidence AI signal (commonly diffusion output).
 _HF_JOB_KEY: str = "hf-job-id"
@@ -510,7 +510,7 @@ def has_ai_metadata(image_path: Path) -> bool:
     # only the XMP form; the raw-JSON tEXt chunk needs the PIL-based parse).
     if aigc_label(image_path) is not None:
         return True
-    # HuggingFace-hosted job marker (hf-job-id PNG text chunk).
+    # Hugging Face-hosted job marker (hf-job-id PNG text chunk).
     if huggingface_job(image_path):
         return True
     # xAI / Grok: no C2PA/IPTC/XMP -- only the EXIF Signature + UUID-Artist pair.
@@ -682,10 +682,10 @@ def c2pa_cloud_manifest(image_path: Path) -> str | None:
 
 
 def _huggingface_job_impl(image_path: Path) -> str | None:
-    """Return the HuggingFace job id if the image carries an ``hf-job-id`` PNG
+    """Return the Hugging Face job id if the image carries an ``hf-job-id`` PNG
     text chunk, else None.
 
-    HuggingFace-hosted GPU jobs (Jobs / Spaces) stamp generated PNGs with an
+    Hugging Face-hosted GPU jobs (Jobs / Spaces) stamp generated PNGs with an
     ``hf-job-id`` ``tEXt`` chunk holding the job's UUID. It identifies the
     *hosting job*, not a specific model, and is most commonly seen on diffusion-
     generation output -- a medium-confidence AI signal, not proof of AI pixels
@@ -1216,9 +1216,9 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]:
     if system := iptc_ai_system(image_path):
         result.setdefault("ai_system", f"IPTC 2025.1 AI disclosure ({system})")
 
-    # HuggingFace-hosted job marker (hf-job-id PNG text chunk).
+    # Hugging Face-hosted job marker (hf-job-id PNG text chunk).
     if job := huggingface_job(image_path):
-        result.setdefault("huggingface_job", f"HuggingFace-hosted job ({job})")
+        result.setdefault("huggingface_job", f"Hugging Face-hosted job ({job})")
     # Samsung Galaxy AI editing marker (genAIType in PhotoEditor_Re_Edit_Data).
     if (genai := samsung_genai(image_path)) is not None:
         result.setdefault("samsung_genai", f"Samsung Galaxy AI editing marker (genAIType={genai})")

src/remove_ai_watermarks/video.py

@@ -179,8 +179,8 @@ _VISIBLE_PLATFORM = {
     "veo": "Google Veo",
     "seedance": "ByteDance Seedance",
     "dola": "ByteDance Dola",
-    "hailuo": "MiniMax Hailuo",
-    "kling": "Kuaishou Kling",
+    "hailuo": "MiniMax Hailuo AI",
+    "kling": "Kuaishou Kling AI",
 }
 
 

src/remove_ai_watermarks/watermark_registry.py

@@ -17,17 +17,17 @@ localizer stays cheap (cv2/numpy, CPU) so a memory-tight caller can run it on a
 small worker; the heavy fill (MI-GAN / LaMa) is opt-in and chosen by the caller.
 
 Entries:
-  - ``gemini`` -- Google Gemini / Nano Banana sparkle, bottom-right.
+  - ``gemini`` -- Google Gemini / Nano Banana visible watermark (sparkle), bottom-right.
   - ``doubao`` -- ByteDance Doubao "豆包AI生成" text strip, bottom-right.
   - ``jimeng`` -- ByteDance Jimeng / Dreamina "★ 即梦AI" wordmark, bottom-right.
   - ``qwen`` -- Alibaba Qwen "千问AI生成" text strip, bottom-right.
-  - ``kling`` -- Kuaishou Kling "可灵AI 3.0" text strip, bottom-right.
+  - ``kling`` -- Kuaishou Kling AI "可灵AI 3.0" text strip, bottom-right.
   - ``yuanbao`` -- Tencent Yuanbao "元宝 / AI生成" two-line mark, bottom-right.
   - ``samsung`` -- Samsung Galaxy AI "Contenuti generati dall'AI" strip, bottom-left.
   - ``jimeng_pill`` -- Jimeng-basic "AI生成" pill, top-left (capture-less).
   - ``runninghub`` -- RunningHub "RunningHub AI生成" text, top-left (gray front-end).
   - ``baidu`` -- Baidu "百度 AI生成" text + white tag, bottom-right.
-  - ``liblib`` -- LibLibAI "LibLibAI" wordmark, bottom-center.
+  - ``liblib`` -- LiblibAI "LiblibAI" wordmark, bottom-center.
 """
 
 from __future__ import annotations
@@ -620,7 +620,7 @@ _REGISTRY: tuple[KnownMark, ...] = (
     # is None so it can never act as a TC260 sibling in _keep_pill.
     KnownMark(
         "gemini",
-        "Google Gemini sparkle",
+        "Google Gemini visible watermark (sparkle)",
         "bottom-right",
         True,
         "gemini",
@@ -656,9 +656,9 @@ _REGISTRY: tuple[KnownMark, ...] = (
     ),
     _text_mark(
         "kling",
-        "Kling 可灵AI 3.0 text",
+        "Kling AI 可灵AI 3.0 text",
         "bottom-right",
-        platform="Kuaishou Kling (visible 可灵AI 3.0 mark detected)",
+        platform="Kuaishou Kling AI (visible 可灵AI 3.0 mark detected)",
         tc260_producer_codes=("91110108335469089C",),
     ),
     _text_mark(
@@ -693,9 +693,9 @@ _REGISTRY: tuple[KnownMark, ...] = (
     ),
     _text_mark(
         "liblib",
-        "LibLibAI wordmark",
+        "LiblibAI wordmark",
         "bottom-center",
-        platform="LibLibAI (visible LibLibAI mark detected)",
+        platform="LiblibAI (visible LiblibAI mark detected)",
         tc260_producer_codes=("91110105MACJ6K1C8A",),
     ),
     # Same product as the Jimeng wordmark -- the one pair that cross-relaxes.
@@ -794,7 +794,7 @@ def tc260_producer_vendors() -> dict[str, str]:
 def _pill_suppressors() -> set[str]:
     """Marks whose detection vetoes the capture-less pill: same label regime as the
     pill, different product. Derived so a newly registered TC260 mark cannot be
-    forgotten here -- which is exactly how LibLibAI ended up missing."""
+    forgotten here -- which is exactly how LiblibAI ended up missing."""
     pill = get_mark("jimeng_pill")
     return {
         m.key
@@ -822,9 +822,9 @@ def _keep_pill(keys: set[str], *, provenance: frozenset[str], footprint_flat: bo
     No confirmation at all -> never remove (blocks false fires on non-Jimeng content).
 
     The suppressor set is DERIVED from the registry (same label regime, different
-    product), not hand-listed. The hand-written list had drifted: LibLibAI was
+    product), not hand-listed. The hand-written list had drifted: LiblibAI was
     registered alongside RunningHub and Baidu but never added to it, so a confident
-    LibLibAI detection did not veto the pill the way its two siblings did. Marks
+    LiblibAI detection did not veto the pill the way its two siblings did. Marks
     outside the TC260 regime (Gemini, Samsung) are deliberately NOT suppressors --
     neither can put ``"jimeng"`` into ``provenance``, so neither can enable the arm
     they would be vetoing."""