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	---
	license: other
	library_name: pytorch
	pipeline_tag: image-text-to-text
	tags:
	- world-model
	- multimodal
	- robotics
	- computer-vision
	- sovereign
	- mother-exo
	---

	# MOTHER EXO — Model Card

	Model: MOTHER EXO — one sovereign world model (`world_model_exo_final_vN.pt`)
	Architecture: frozen sovereign backbone ⊕ trainable head-only weights
	- CORE-7B (frozen) — sovereign text/reasoning backbone (48 layers, 3072 d)
	- MOTHER DeepVision (frozen) — sovereign SigLIP-SO400M ViT (27 layers, 1152 d, 896 px)
	- cross-attn adapters + per-head projections + task heads — the trained weights
	Posture: non-weapon · observe-and-advise · meaningful human control (HITL) · consent/GDPR/EU-AI-Act · sovereign (only MOTHER's own weights run in the stack; external models are data-only teachers; codecs/vocoders are renderers, not models).
	Build: `training/build_final_model.py` blends the frozen base + every head into one atomic checkpoint (base fingerprint preserved). Load/serve: `training/load_exo.py` (`MotherExo`) + `mother_exo_sim/models/exo_node_server.py` (`:8000`).

	---

	## Modality cheat-sheet — what each weight consumes

	\| Input modality \| Weights that consume it \| How it arrives \|
	\|---\|---\|---\|
	\| Video / image frames \| vision, detect, track, worldmodel, face, relations \| a JPEG/PNG frame (data-URL/base64 or fetchable URL) \|
	\| Audio waveform \| audio (ASR), speech (as TTS output) \| a WAV (base64) \|
	\| Text + world context (data points) \| reasoning, memory \| a query string + `buildContext()` (detections, relations, analytics, data_points) \|

	So: HLS video → decode to frames → the six vision weights. Audio feeds → decode to WAV → the audio weight. All other data points → JSON summaries → the reasoning/memory context.

	---

	## The weights

	### 1. reasoning (adapters) · kind: language
	- Does: vision-grounded language reasoning, threat/HITL advice, tool-call planning, orchestration. CORE-7B ⊕ cross-attn adapters + projection.
	- Train: `training/adapters/train_integrated.py` (streams the `reasoning` manifest: mother_core_v31 + golden + `mother_reason.jsonl` + `mother_cot.jsonl` …). Format: `Question:\n\n…\n\nAnswer:`.
	- Checkpoint: `~/.mother-exo/heads/integrated_adapters.pt`
	- Inference: `exo.reason(prompt, image=None, use_memory=False)` · node `POST /reason {query,image?}` · SIM `/api/v1/exo/reason`
	- Wire to VIDEO (HLS): pass a captured frame as `image` so reasoning is vision-grounded (`reason(q, image=frame)`).
	- Wire to AUDIO: transcribe first (audio weight) → feed the transcript as the prompt (`reason(transcribe(wav))`).
	- Wire to DATA POINTS: every feed ingested via `ingestDataPoints()` lands in `buildContext().data_points`; `reason()`/`exoReason()` pass that context to the model. Already wired by `FeedAutoRegistrar`.

	### 2. detect · kind: vision
	- Does: 1233-class objects + per-class boxes + RED/GREEN/CAUTION safety tag (safety_lexicon).
	- Train: `integrated_heads.py --head detect` (COCO/LVIS/aircraft).
	- Checkpoint: `integrated_detect.pt`
	- Inference: `exo.detect(img, topk)` → `[(label,score,box,safety)]` · node `POST /detect {image}` · SIM `/api/v1/exo/detect`
	- Wire to VIDEO (HLS): an `<video>`+HLS.js tile registers a frame grabber → the world-model scheduler posts frames to `/api/exo/detect` (browser path), or server-side snapshot via `{imageUrl}`. See "Wiring HLS video" below.
	- Audio / data points: N/A (vision); its detections feed the reasoning/memory context.

	### 3. track · kind: vision
	- Does: appearance embeddings → persistent, unlimited multi-object IDs across frames (re-ID, occlusion re-acquire) via `tracker.py` + entity registry `OBJ-` UINs.
	- Train: `integrated_heads.py --head track` (InfoNCE temporal pairs).
	- Checkpoint: `integrated_track.pt`
	- Inference: `exo.track_frame(img, frame_idx)` / `track_video(frames)` · node `POST /track {image}`
	- Wire to VIDEO (HLS): same frame path as detect; call `/track` per frame to keep IDs.

	### 4. worldmodel · kind: vision/temporal (single-frame)
	- Does: next-latent prediction (CPC) from one frame's ViT tokens — a lightweight pre-act preview.
	- Train: `integrated_heads.py --head worldmodel`.
	- Checkpoint: `integrated_worldmodel.pt` (6.3M MLP)
	- Inference: `exo.worldmodel_next(img)` → latent · (no dedicated node route yet; used internally)
	- Wire to VIDEO (HLS): consumes frames like the other vision heads.

	### 4b. worldmodel_latent · kind: video dynamics
	- Does: multi-step latent dynamics — a causal temporal transformer over the frozen MOTHER T2V 3D-VAE latents predicts future latent frames of a clip.
	- Train: `training/mother_t2v/latent/train_worldmodel_latent.py` (InfoNCE + L2 on real captioned clips). 19.7M params, d=512, depth=6.
	- Checkpoint: `integrated_worldmodel_latent.pt`
	- Eval: next-frame top-1 retrieval — SAME-CLIP (fair dynamics) 0.9983 on 400 held-out clips (the same-clip metric measures true temporal ordering; the prior single-frame head scored 0.167).
	- Inference: `exo.worldmodel_latent_next(frames)` → `(embeds, predicted-next)` — video-native (encodes the clip with the frozen T2V VAE first).

	### 5. face · kind: vision (consent-gated)
	- Does: pseudonymous person UIN; name only for consent-enrolled people (Chris). GDPR/EU-AI-Act; persistent person re-ID off unless `MOTHER_PERSON_UIN=1`.
	- Train: `integrated_heads.py --head face` (reads the recognition store at `/var/lib/mother-exo/training/recognition`).
	- Checkpoint: `integrated_face.pt`
	- Inference: `exo.identify(img)` → `{uin,name,score}` · node `POST /identify {image}` · SIM `/api/v1/exo/identify`
	- Wire to VIDEO (HLS): frame → `/identify`. Enrolment is consent-only.

	### 6. relations · kind: vision
	- Does: spatial relations (on / under / next_to) for scene-graph grounding.
	- Train: `integrated_heads.py --head relations`.
	- Checkpoint: `integrated_relations.pt`
	- Inference: `exo.relation(img)` → `(rel,score)`
	- Wire to VIDEO (HLS): frame → relation.

	### 7. memory (MOTHERrag SPI) · kind: memory
	- Does: Semantic-Pyramid long-term memory — write/recall observations, reasoning, docs, by entity UIN; right-to-erasure.
	- Train: `training/mother_rag.py --train`.
	- Checkpoint: `integrated_memory.pt`
	- Inference: `exo.remember/recall/recall_about/forget` · node `/api/v1/gb10-training/memory*`
	- Wire to DATA POINTS: ingest documents/observations; recall feeds reasoning context. Text memory lazy-loads CORE.

	### 8. speech · kind: speech (voice OUT)
	- Does: 20-language ID + Text-To-Voice — predicts neural-codec tokens → renderer (EnCodec, `codec.py`) decodes to a waveform. MOTHER speaks from her own weights.
	- Train: `training/train_speech.py --train --audio <dir>` (language-ID from text; acoustic from (text,audio) via `build_speech_audio.py`).
	- Checkpoint: `integrated_speech.pt`
	- Inference: `exo.synthesize(text,lang)` → wav · node `POST /speak {text,lang}` → `{audio_b64}` · SIM `/api/v1/exo/speak`
	- Wire to AUDIO (out): play `audio_b64` in the browser, or pipe to a speaker. (This is output; pairs with the audio weight for two-way.)

	### 9. vision (MOTHER DeepVision) · kind: vision foundation
	- Does: self-supervised visual foundation (SimCLR over all on-node imagery) — the shared embedding the vision heads build on. Exportable standalone (`export_deepvision.py` → `mother_deepvision.pt`).
	- Train: `integrated_heads.py --head vision`.
	- Checkpoint: `integrated_vision.pt`
	- Inference: `exo.vision_embed(img)` → 256-d · node `POST /embed {image}`
	- Wire to VIDEO (HLS): frame → embedding (recognition/search/Defence feeds).

	### 10. audio (MOTHER's ears) · kind: audio perception (ASR, audio IN)
	- Does: radio signals + human speech → text (codec tokens → BiGRU encoder → text decoder over CORE vocab). The transcript feeds the reasoning weight. With the speech weight → two-way humanoid conversation (hear → reason → speak).
	- Train: `training/train_audio.py --train --audio <dir>` ((text,audio) pairs — `build_speech_audio.py` bootstrap, or CommonVoice/LibriSpeech + radio comms rendered to `{text,audio}` jsonl).
	- Checkpoint: `integrated_audio.pt`
	- Inference: `exo.transcribe(wav)` → text · `exo.converse(wav,lang)` → `{heard,answer,audio_b64}` · node `POST /transcribe {audio_b64}` · `POST /converse {audio_b64,lang}`
	- Wire to AUDIO (HLS / mic / radio): extract the audio track (HLS audio, mic capture, SDR/radio) → WAV → `/transcribe` or `/converse`.

	---

	## Wiring guide

	### A. Wire a live HLS video feed → the vision weights
	HLS playlists (`.m3u8`) are not single frames, so they must be decoded to frames first.
	1. Browser tile (preferred): mount an `<video>` with HLS.js, draw it to a `<canvas>`, and register a frame grabber with the world model: `registerFrameProvider(id, () => canvas.toDataURL("image/jpeg"))`, then add the feed via `registerDetectableSource(key, [{id,label,kind,lat,lon,streamType:"hls"}])`. The scheduler posts grabbed frames to `/api/exo/detect` (+ `/track`/`/assess`). This is exactly how OverWatch tiles + `GB10ExoEyes` already work.
	2. Server-side snapshot: for MJPEG/JPEG cameras, register with `streamUrl` + `streamType:"jpeg"\|"mjpeg"`; the scheduler server-fetches frames (`{imageUrl}`) — no player needed. (`FeedAutoRegistrar` does this for all TfL JamCams.)
	3. TV broadcast: same as (1) — a broadcast HLS URL in an `<video>` tile. Add the URL to a tile/registrar; frames then flow to detect/track/assess/identify.
	> Node-load: the scheduler caps 8 concurrent + 4 fps for URL feeds; HLS browser tiles target ~15 fps. Add feeds freely — it sweeps fairly.

	### B. Wire a live audio / radio feed → the audio weight
	1. Capture → WAV: mic (`MediaRecorder`), an HLS audio track, or an SDR/radio receiver → 16-bit PCM WAV (any sample rate; the codec resamples to 24 kHz).
	2. Base64 → endpoint: `POST /api/v1/exo/transcribe {audio_b64}` → `{text}` (ASR), or `POST /api/v1/exo/converse {audio_b64,lang}` → `{heard,answer,audio_b64}` (hear→reason→speak).
	3. Two-way humanoid: loop `converse` — mic in, play the returned `audio_b64` out.
	> Radio: feed demodulated audio (voice nets) for transcription; for non-voice RF, feed any metadata as data points (below). Quality scales with the ASR training corpus (bootstrap = espeak; production = CommonVoice/LibriSpeech + radio comms).

	### C. Wire all data points → the reasoning/memory weights
	1. Catalog the feed in `lib/exo/feedCatalog.ts` (`DATA_FEEDS` for OSINT/sensor JSON; `AUDIO_FEEDS` for audio metadata/transcripts).
	2. Ingest on an interval: `FeedAutoRegistrar` pulls each `api` and calls `ingestDataPoints(id, summary)` → merged into `buildContext().data_points`.
	3. Reason over it: `reason()` / `exoReason()` ship `buildContext()` to the model, so every datapoint is in scope. Persist important items via `remember()`.
	> Already wired: ACLED, FIRMS, USGS, CISA, GDELT, ADS-B, AIS, OpenSky, IODA, CelesTrak. Add more by appending to `feedCatalog.ts`.

	---

	## Endpoint reference
	\| Capability \| `load_exo` \| Node (`:8000`) \| SIM (`/api/v1/exo/`) \| MOTHER_AI (`/api/exo/`) \|
	\|---\|---\|---\|---\|---\|
	\| detect \| `detect` \| `/detect` \| `/detect` \| `/detect` \|
	\| assess (scene RED/GREEN) \| `assess` \| `/assess` \| `/assess` \| `/assess` \|
	\| track (unlimited IDs) \| `track_frame` \| `/track` \| `/track` \| `/track` \|
	\| identify (consent) \| `identify` \| `/identify` \| `/identify` \| `/identify` \|
	\| reason \| `reason` \| `/reason` \| `/reason` \| `/reason` \|
	\| speak (voice out) \| `synthesize` \| `/speak` \| `/speak` \| `/speak` \|
	\| transcribe (audio in) \| `transcribe` \| `/transcribe` \| `/transcribe` \| (add route) \|
	\| converse (2-way) \| `converse` \| `/converse` \| `/converse` \| (add route) \|
	\| embed (DeepVision) \| `vision_embed` \| `/embed` \| `/embed` \| — \|

	## Environment
	\| Var \| Where \| Purpose \|
	\|---\|---\|---\|
	\| `MOTHER_EXO_FINAL_MODEL` \| node \| pin the blended `vN` checkpoint \|
	\| `MOTHER_EXO_API_TOKEN` \| node `/etc/default/mother-exo` \| gates `/api/v1/*` (must equal MOTHER_AI `MOTHER_ROBOTICS_TOKEN`) \|
	\| `MOTHER_EXO_VISION_URL` \| node \| SIM → node inference (`http://127.0.0.1:8000`) \|
	\| `MOTHER_ROBOTICS_URL` / `_TOKEN` \| Netlify \| MOTHER_AI → node (funnel + token) \|
	\| `MOTHER_PERSON_UIN` \| node \| enable persistent person re-ID (lawful basis required) \|
	\| `NEXT_PUBLIC_EXO_AUDIO_FEEDS` \| Netlify \| extra audio-metadata feeds for the catalog \|

	## Training all weights
	`bash training/adapters/run_all_weights.sh --full` (vision heads), plus `train_integrated.py` (reasoning), `mother_rag.py` (memory), `train_speech.py` (voice), `train_audio.py` (ears) — then `build_final_model.py` to blend. Validate any blend with `python training/test_all_weights.py --model <vN.pt> --image <frame>` and corpora with `python training/check_dataset.py`.

	---
	license: other
	library_name: pytorch
	pipeline_tag: image-text-to-text
	tags:
	- world-model
	- multimodal
	- robotics
	- computer-vision
	- sovereign
	- mother-exo
	---

	# MOTHER EXO — Model Card

	Model: MOTHER EXO — one sovereign world model (`world_model_exo_final_vN.pt`)
	Architecture: frozen sovereign backbone ⊕ trainable head-only weights
	- CORE-7B (frozen) — sovereign text/reasoning backbone (48 layers, 3072 d)
	- MOTHER DeepVision (frozen) — sovereign SigLIP-SO400M ViT (27 layers, 1152 d, 896 px)
	- cross-attn adapters + per-head projections + task heads — the trained weights
	Posture: non-weapon · observe-and-advise · meaningful human control (HITL) · consent/GDPR/EU-AI-Act · sovereign (only MOTHER's own weights run in the stack; external models are data-only teachers; codecs/vocoders are renderers, not models).
	Build: `training/build_final_model.py` blends the frozen base + every head into one atomic checkpoint (base fingerprint preserved). Load/serve: `training/load_exo.py` (`MotherExo`) + `mother_exo_sim/models/exo_node_server.py` (`:8000`).

	---

	## Modality cheat-sheet — what each weight consumes

	\| Input modality \| Weights that consume it \| How it arrives \|
	\|---\|---\|---\|
	\| Video / image frames \| vision, detect, track, worldmodel, face, relations \| a JPEG/PNG frame (data-URL/base64 or fetchable URL) \|
	\| Audio waveform \| audio (ASR), speech (as TTS output) \| a WAV (base64) \|
	\| Text + world context (data points) \| reasoning, memory \| a query string + `buildContext()` (detections, relations, analytics, data_points) \|

	So: HLS video → decode to frames → the six vision weights. Audio feeds → decode to WAV → the audio weight. All other data points → JSON summaries → the reasoning/memory context.

	---

	## The weights

	### 1. reasoning (adapters) · kind: language
	- Does: vision-grounded language reasoning, threat/HITL advice, tool-call planning, orchestration. CORE-7B ⊕ cross-attn adapters + projection.
	- Train: `training/adapters/train_integrated.py` (streams the `reasoning` manifest: mother_core_v31 + golden + `mother_reason.jsonl` + `mother_cot.jsonl` …). Format: `Question:\n\n…\n\nAnswer:`.
	- Checkpoint: `~/.mother-exo/heads/integrated_adapters.pt`
	- Inference: `exo.reason(prompt, image=None, use_memory=False)` · node `POST /reason {query,image?}` · SIM `/api/v1/exo/reason`
	- Wire to VIDEO (HLS): pass a captured frame as `image` so reasoning is vision-grounded (`reason(q, image=frame)`).
	- Wire to AUDIO: transcribe first (audio weight) → feed the transcript as the prompt (`reason(transcribe(wav))`).
	- Wire to DATA POINTS: every feed ingested via `ingestDataPoints()` lands in `buildContext().data_points`; `reason()`/`exoReason()` pass that context to the model. Already wired by `FeedAutoRegistrar`.

	### 2. detect · kind: vision
	- Does: 1233-class objects + per-class boxes + RED/GREEN/CAUTION safety tag (safety_lexicon).
	- Train: `integrated_heads.py --head detect` (COCO/LVIS/aircraft).
	- Checkpoint: `integrated_detect.pt`
	- Inference: `exo.detect(img, topk)` → `[(label,score,box,safety)]` · node `POST /detect {image}` · SIM `/api/v1/exo/detect`
	- Wire to VIDEO (HLS): an `<video>`+HLS.js tile registers a frame grabber → the world-model scheduler posts frames to `/api/exo/detect` (browser path), or server-side snapshot via `{imageUrl}`. See "Wiring HLS video" below.
	- Audio / data points: N/A (vision); its detections feed the reasoning/memory context.

	### 3. track · kind: vision
	- Does: appearance embeddings → persistent, unlimited multi-object IDs across frames (re-ID, occlusion re-acquire) via `tracker.py` + entity registry `OBJ-` UINs.
	- Train: `integrated_heads.py --head track` (InfoNCE temporal pairs).
	- Checkpoint: `integrated_track.pt`
	- Inference: `exo.track_frame(img, frame_idx)` / `track_video(frames)` · node `POST /track {image}`
	- Wire to VIDEO (HLS): same frame path as detect; call `/track` per frame to keep IDs.

	### 4. worldmodel · kind: vision/temporal (single-frame)
	- Does: next-latent prediction (CPC) from one frame's ViT tokens — a lightweight pre-act preview.
	- Train: `integrated_heads.py --head worldmodel`.
	- Checkpoint: `integrated_worldmodel.pt` (6.3M MLP)
	- Inference: `exo.worldmodel_next(img)` → latent · (no dedicated node route yet; used internally)
	- Wire to VIDEO (HLS): consumes frames like the other vision heads.

	### 4b. worldmodel_latent · kind: video dynamics
	- Does: multi-step latent dynamics — a causal temporal transformer over the frozen MOTHER T2V 3D-VAE latents predicts future latent frames of a clip.
	- Train: `training/mother_t2v/latent/train_worldmodel_latent.py` (InfoNCE + L2 on real captioned clips). 19.7M params, d=512, depth=6.
	- Checkpoint: `integrated_worldmodel_latent.pt`
	- Eval: next-frame top-1 retrieval — SAME-CLIP (fair dynamics) 0.9983 on 400 held-out clips (the same-clip metric measures true temporal ordering; the prior single-frame head scored 0.167).
	- Inference: `exo.worldmodel_latent_next(frames)` → `(embeds, predicted-next)` — video-native (encodes the clip with the frozen T2V VAE first).

	### 5. face · kind: vision (consent-gated)
	- Does: pseudonymous person UIN; name only for consent-enrolled people (Chris). GDPR/EU-AI-Act; persistent person re-ID off unless `MOTHER_PERSON_UIN=1`.
	- Train: `integrated_heads.py --head face` (reads the recognition store at `/var/lib/mother-exo/training/recognition`).
	- Checkpoint: `integrated_face.pt`
	- Inference: `exo.identify(img)` → `{uin,name,score}` · node `POST /identify {image}` · SIM `/api/v1/exo/identify`
	- Wire to VIDEO (HLS): frame → `/identify`. Enrolment is consent-only.

	### 6. relations · kind: vision
	- Does: spatial relations (on / under / next_to) for scene-graph grounding.
	- Train: `integrated_heads.py --head relations`.
	- Checkpoint: `integrated_relations.pt`
	- Inference: `exo.relation(img)` → `(rel,score)`
	- Wire to VIDEO (HLS): frame → relation.

	### 7. memory (MOTHERrag SPI) · kind: memory
	- Does: Semantic-Pyramid long-term memory — write/recall observations, reasoning, docs, by entity UIN; right-to-erasure.
	- Train: `training/mother_rag.py --train`.
	- Checkpoint: `integrated_memory.pt`
	- Inference: `exo.remember/recall/recall_about/forget` · node `/api/v1/gb10-training/memory*`
	- Wire to DATA POINTS: ingest documents/observations; recall feeds reasoning context. Text memory lazy-loads CORE.

	### 8. speech · kind: speech (voice OUT)
	- Does: 20-language ID + Text-To-Voice — predicts neural-codec tokens → renderer (EnCodec, `codec.py`) decodes to a waveform. MOTHER speaks from her own weights.
	- Train: `training/train_speech.py --train --audio <dir>` (language-ID from text; acoustic from (text,audio) via `build_speech_audio.py`).
	- Checkpoint: `integrated_speech.pt`
	- Inference: `exo.synthesize(text,lang)` → wav · node `POST /speak {text,lang}` → `{audio_b64}` · SIM `/api/v1/exo/speak`
	- Wire to AUDIO (out): play `audio_b64` in the browser, or pipe to a speaker. (This is output; pairs with the audio weight for two-way.)

	### 9. vision (MOTHER DeepVision) · kind: vision foundation
	- Does: self-supervised visual foundation (SimCLR over all on-node imagery) — the shared embedding the vision heads build on. Exportable standalone (`export_deepvision.py` → `mother_deepvision.pt`).
	- Train: `integrated_heads.py --head vision`.
	- Checkpoint: `integrated_vision.pt`
	- Inference: `exo.vision_embed(img)` → 256-d · node `POST /embed {image}`
	- Wire to VIDEO (HLS): frame → embedding (recognition/search/Defence feeds).

	### 10. audio (MOTHER's ears) · kind: audio perception (ASR, audio IN)
	- Does: radio signals + human speech → text (codec tokens → BiGRU encoder → text decoder over CORE vocab). The transcript feeds the reasoning weight. With the speech weight → two-way humanoid conversation (hear → reason → speak).
	- Train: `training/train_audio.py --train --audio <dir>` ((text,audio) pairs — `build_speech_audio.py` bootstrap, or CommonVoice/LibriSpeech + radio comms rendered to `{text,audio}` jsonl).
	- Checkpoint: `integrated_audio.pt`
	- Inference: `exo.transcribe(wav)` → text · `exo.converse(wav,lang)` → `{heard,answer,audio_b64}` · node `POST /transcribe {audio_b64}` · `POST /converse {audio_b64,lang}`
	- Wire to AUDIO (HLS / mic / radio): extract the audio track (HLS audio, mic capture, SDR/radio) → WAV → `/transcribe` or `/converse`.

	---

	## Wiring guide

	### A. Wire a live HLS video feed → the vision weights
	HLS playlists (`.m3u8`) are not single frames, so they must be decoded to frames first.
	1. Browser tile (preferred): mount an `<video>` with HLS.js, draw it to a `<canvas>`, and register a frame grabber with the world model: `registerFrameProvider(id, () => canvas.toDataURL("image/jpeg"))`, then add the feed via `registerDetectableSource(key, [{id,label,kind,lat,lon,streamType:"hls"}])`. The scheduler posts grabbed frames to `/api/exo/detect` (+ `/track`/`/assess`). This is exactly how OverWatch tiles + `GB10ExoEyes` already work.
	2. Server-side snapshot: for MJPEG/JPEG cameras, register with `streamUrl` + `streamType:"jpeg"\|"mjpeg"`; the scheduler server-fetches frames (`{imageUrl}`) — no player needed. (`FeedAutoRegistrar` does this for all TfL JamCams.)
	3. TV broadcast: same as (1) — a broadcast HLS URL in an `<video>` tile. Add the URL to a tile/registrar; frames then flow to detect/track/assess/identify.
	> Node-load: the scheduler caps 8 concurrent + 4 fps for URL feeds; HLS browser tiles target ~15 fps. Add feeds freely — it sweeps fairly.

	### B. Wire a live audio / radio feed → the audio weight
	1. Capture → WAV: mic (`MediaRecorder`), an HLS audio track, or an SDR/radio receiver → 16-bit PCM WAV (any sample rate; the codec resamples to 24 kHz).
	2. Base64 → endpoint: `POST /api/v1/exo/transcribe {audio_b64}` → `{text}` (ASR), or `POST /api/v1/exo/converse {audio_b64,lang}` → `{heard,answer,audio_b64}` (hear→reason→speak).
	3. Two-way humanoid: loop `converse` — mic in, play the returned `audio_b64` out.
	> Radio: feed demodulated audio (voice nets) for transcription; for non-voice RF, feed any metadata as data points (below). Quality scales with the ASR training corpus (bootstrap = espeak; production = CommonVoice/LibriSpeech + radio comms).

	### C. Wire all data points → the reasoning/memory weights
	1. Catalog the feed in `lib/exo/feedCatalog.ts` (`DATA_FEEDS` for OSINT/sensor JSON; `AUDIO_FEEDS` for audio metadata/transcripts).
	2. Ingest on an interval: `FeedAutoRegistrar` pulls each `api` and calls `ingestDataPoints(id, summary)` → merged into `buildContext().data_points`.
	3. Reason over it: `reason()` / `exoReason()` ship `buildContext()` to the model, so every datapoint is in scope. Persist important items via `remember()`.
	> Already wired: ACLED, FIRMS, USGS, CISA, GDELT, ADS-B, AIS, OpenSky, IODA, CelesTrak. Add more by appending to `feedCatalog.ts`.

	---

	## Endpoint reference
	\| Capability \| `load_exo` \| Node (`:8000`) \| SIM (`/api/v1/exo/`) \| MOTHER_AI (`/api/exo/`) \|
	\|---\|---\|---\|---\|---\|
	\| detect \| `detect` \| `/detect` \| `/detect` \| `/detect` \|
	\| assess (scene RED/GREEN) \| `assess` \| `/assess` \| `/assess` \| `/assess` \|
	\| track (unlimited IDs) \| `track_frame` \| `/track` \| `/track` \| `/track` \|
	\| identify (consent) \| `identify` \| `/identify` \| `/identify` \| `/identify` \|
	\| reason \| `reason` \| `/reason` \| `/reason` \| `/reason` \|
	\| speak (voice out) \| `synthesize` \| `/speak` \| `/speak` \| `/speak` \|
	\| transcribe (audio in) \| `transcribe` \| `/transcribe` \| `/transcribe` \| (add route) \|
	\| converse (2-way) \| `converse` \| `/converse` \| `/converse` \| (add route) \|
	\| embed (DeepVision) \| `vision_embed` \| `/embed` \| `/embed` \| — \|

	## Environment
	\| Var \| Where \| Purpose \|
	\|---\|---\|---\|
	\| `MOTHER_EXO_FINAL_MODEL` \| node \| pin the blended `vN` checkpoint \|
	\| `MOTHER_EXO_API_TOKEN` \| node `/etc/default/mother-exo` \| gates `/api/v1/*` (must equal MOTHER_AI `MOTHER_ROBOTICS_TOKEN`) \|
	\| `MOTHER_EXO_VISION_URL` \| node \| SIM → node inference (`http://127.0.0.1:8000`) \|
	\| `MOTHER_ROBOTICS_URL` / `_TOKEN` \| Netlify \| MOTHER_AI → node (funnel + token) \|
	\| `MOTHER_PERSON_UIN` \| node \| enable persistent person re-ID (lawful basis required) \|
	\| `NEXT_PUBLIC_EXO_AUDIO_FEEDS` \| Netlify \| extra audio-metadata feeds for the catalog \|

	## Training all weights
	`bash training/adapters/run_all_weights.sh --full` (vision heads), plus `train_integrated.py` (reasoning), `mother_rag.py` (memory), `train_speech.py` (voice), `train_audio.py` (ears) — then `build_final_model.py` to blend. Validate any blend with `python training/test_all_weights.py --model <vN.pt> --image <frame>` and corpora with `python training/check_dataset.py`.