Deploy Slipstream paper Space with Live Quantizer

README.md

@@ -1,17 +1,36 @@
----
-title: Slipcore
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
-pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
-license: cc-by-4.0
-short_description: Slipstream Agentic Communication Protocol
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+---
+title: "Slipstream: Semantic Quantization for Multi-Agent Coordination"
+emoji: 📄
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+app_file: app.py
+pinned: false
+license: mit
+tags: ["semantic-quantization", "multi-agent-systems", "protocol-standards", "token-efficiency"]
+---
+
+# Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination
+
+This Space was generated from a research paper PDF.
+
+## What you can do here
+
+- **Live Quantizer**: Type messy natural language and watch it get quantized to a UCR anchor (the core demo!)
+- **Start here**: guided entry points (summary / limitations / thread)
+- **Gallery**: extracted figures or page previews
+- **Chat**: ask questions about the paper
+- **Share Kit**: generate a tweet thread / talk outline / FAQ
+- **Model Playground**: chat with a referenced HF model (requires `HF_TOKEN`)
+
+## Optional secrets
+
+If you add these as Space secrets, Chat + Share Kit become generative:
+
+- `HF_TOKEN`: Hugging Face token (read access is sufficient for inference; write is **not** needed at runtime)
+- `PAPER_LLM_MODEL`: e.g. `meta-llama/Meta-Llama-3-8B-Instruct` (or any chat-completion capable model)
+
+## Build provenance
+
+- Source PDF: `slipstream-paper.pdf`
+- Extracted pages: 7

app.py

@@ -1,70 +1,521 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+import json
+import os
+import re
+from dataclasses import dataclass
+from typing import Dict, List, Tuple, Optional
+
+import gradio as gr
+
+# Lazy-loaded quantizer
+_quantizer = None
+_quantizer_error = None
+
+def _get_quantizer():
+    """Lazy load the embedding quantizer."""
+    global _quantizer, _quantizer_error
+    if _quantizer is not None:
+        return _quantizer
+    if _quantizer_error is not None:
+        return None
+    try:
+        from quantizer import EmbeddingQuantizer
+        _quantizer = EmbeddingQuantizer(fallback_threshold=0.3)
+        return _quantizer
+    except Exception as e:
+        _quantizer_error = str(e)
+        return None
+
+def _get_keyword_quantizer():
+    """Fallback to keyword quantizer."""
+    try:
+        from quantizer import KeywordQuantizer
+        return KeywordQuantizer()
+    except Exception:
+        return None
+
+
+# ---------------------------
+# Loaded at build time
+# ---------------------------
+PAPER_TITLE = "Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination"
+PAPER_AUTHORS = "Anthony Maio"
+PAPER_ABSTRACT = "As multi-agent LLM systems scale,coordination bandwidthbecomes a primary cost\ndriver: every token spent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns. Current approaches waste 40\u201360% of compute on coordination\noverhead, with communication costs scalingO(n2)as agent counts increase.\nThis paper introducesSlipstream, a protocol that performssemantic quantization:\nmapping free-form messages onto a sharedUniversal Concept Reference (UCR)and\ntransmitting compactmnemonic anchorsthat identify structured intents. Unlike syn-\ntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits\nnatural-language mnemonics that tokenize efficiently across model architectures.\nSlipstream combines (1) a symbolic4D semantic manifold\u2014Action, Polarity, Domain,\nUrgency\u2014with (2) a data-drivenvector engine(embeddings + nearest-centroid retrieval)\nplus anevolutionary extension layerthat learns new anchors from low-confidence traf-\nfic. Results show82% token reduction(41.9\u21927.4 tokens average) while maintaining\nsemantic fidelity, making large-scale multi-agent deployments economically viable."
+PAPER_TAGS = "semantic-quantization, multi-agent-systems, protocol-standards, token-ef-"
+DEFAULT_LLM_MODEL = None
+DETECTED_MODELS = []
+
+
+# ---------------------------
+# Robust file loading
+# ---------------------------
+def _load_chunks(path: str = "paper_chunks.jsonl") -> List[str]:
+    chunks: List[str] = []
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    rec = json.loads(line)
+                    txt = (rec.get("text") or "").strip()
+                    if txt:
+                        chunks.append(txt)
+                except Exception:
+                    continue
+    except FileNotFoundError:
+        return []
+    except Exception:
+        return []
+    return chunks
+
+
+# ---------------------------
+# Tiny BM25-ish retrieval (pure Python)
+# ---------------------------
+def _tokenize(s: str) -> List[str]:
+    return re.findall(r"[A-Za-z0-9][A-Za-z0-9_-]{1,}", s.lower())
+
+
+@dataclass
+class Retriever:
+    chunks: List[str]
+    doc_tokens: List[List[str]]
+    df: Dict[str, int]
+    idf: Dict[str, float]
+
+    @staticmethod
+    def build(chunks: List[str]) -> "Retriever":
+        doc_tokens = [_tokenize(c) for c in chunks]
+        df: Dict[str, int] = {}
+        for toks in doc_tokens:
+            for t in set(toks):
+                df[t] = df.get(t, 0) + 1
+        n = max(1, len(doc_tokens))
+        idf = {}
+        for t, d in df.items():
+            idf[t] = float((n - d + 0.5) / (d + 0.5))
+        return Retriever(chunks=chunks, doc_tokens=doc_tokens, df=df, idf=idf)
+
+    def topk(self, query: str, k: int = 4) -> List[Tuple[int, float]]:
+        q = _tokenize(query)
+        if not q:
+            return []
+        scores: List[Tuple[int, float]] = []
+        qset = set(q)
+        for i, toks in enumerate(self.doc_tokens):
+            if not toks:
+                continue
+            overlap = qset.intersection(toks)
+            if not overlap:
+                continue
+            score = 0.0
+            for t in overlap:
+                score += self.idf.get(t, 0.0)
+            score = score / (1.0 + (len(toks) / 200.0))
+            scores.append((i, score))
+        scores.sort(key=lambda x: x[1], reverse=True)
+        return scores[:k]
+
+
+CHUNKS = _load_chunks()
+RETRIEVER = Retriever.build(CHUNKS) if CHUNKS else None
+
+
+def retrieve_context(query: str, k: int = 4, max_chars: int = 6000) -> str:
+    if not RETRIEVER:
+        return ""
+    hits = RETRIEVER.topk(query, k=k)
+    parts: List[str] = []
+    for idx, _score in hits:
+        txt = CHUNKS[idx].strip()
+        if txt:
+            parts.append(txt)
+    ctx = "\n\n".join(parts).strip()
+    return ctx[:max_chars]
+
+
+# ---------------------------
+# HF Inference helpers (optional)
+# ---------------------------
+def _get_hf_client(model_id: str):
+    try:
+        from huggingface_hub import InferenceClient
+    except Exception as e:
+        raise RuntimeError("huggingface_hub is not installed. Add it to requirements.txt") from e
+
+    token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+    if not token:
+        raise RuntimeError("HF_TOKEN is not set as a Space secret.")
+    return InferenceClient(model=model_id, token=token)
+
+
+def _llm_chat(model_id: str, messages: List[dict], max_tokens: int = 512) -> str:
+    client = _get_hf_client(model_id)
+    out = client.chat_completion(messages=messages, max_tokens=max_tokens)
+    return out.choices[0].message.content
+
+
+# ---------------------------
+# Chat with paper (RAG-lite)
+# ---------------------------
+def paper_chat(message: str, history: List[Tuple[str, str]]) -> str:
+    message = (message or "").strip()
+    if not message:
+        return "Ask a question about the paper."
+
+    ctx = retrieve_context(message, k=4, max_chars=6000)
+
+    model_id = os.environ.get("PAPER_LLM_MODEL") or DEFAULT_LLM_MODEL
+    if not model_id:
+        if not ctx:
+            return "No indexed context found. (paper_chunks.jsonl missing?)"
+        return "Top matches in the paper:\n\n" + ctx[:1200]
+
+    trimmed_history = history[-4:] if history else []
+    messages: List[dict] = [
+        {
+            "role": "system",
+            "content": (
+                "You are a precise research assistant. Answer using ONLY the provided paper context. "
+                "If the context is insufficient, say what is missing and point to what section would help."
+            ),
+        },
+    ]
+    if ctx:
+        messages.append({"role": "system", "content": "PAPER CONTEXT:\n\n" + ctx})
+
+    for u, a in trimmed_history:
+        messages.append({"role": "user", "content": u})
+        messages.append({"role": "assistant", "content": a})
+
+    messages.append({"role": "user", "content": message})
+
+    try:
+        return _llm_chat(model_id, messages, max_tokens=512).strip()
+    except Exception as e:
+        if ctx:
+            return f"(LLM unavailable: {e})\n\nTop matches in the paper:\n\n" + ctx[:1200]
+        return f"LLM unavailable: {e}"
+
+
+# ---------------------------
+# Share Kit (generators)
+# ---------------------------
+def _fallback_tweet_thread(title: str, abstract: str) -> str:
+    abs_one = re.sub(r"\s+", " ", abstract).strip()
+    bullets = [
+        f"1/ {title}",
+        "2/ TL;DR: " + (abs_one[:220] + ("…" if len(abs_one) > 220 else "")),
+        "3/ Key idea: (open the Space → Chat tab and ask for the method overview)",
+        "4/ Try it: use the Share Kit tab to generate a talk outline / FAQ.",
+        "5/ Links: add your paper + code links in the README.",
+    ]
+    return "\n\n".join(bullets)
+
+def generate_share(kind: str) -> str:
+    kind = (kind or "").strip().lower()
+    base_title = PAPER_TITLE or "Paper"
+    base_abs = PAPER_ABSTRACT or ""
+
+    model_id = os.environ.get("PAPER_LLM_MODEL") or DEFAULT_LLM_MODEL
+    if not model_id:
+        if kind == "tweet thread":
+            return _fallback_tweet_thread(base_title, base_abs)
+        if kind == "talk outline":
+            return "\n".join([
+                f"Title: {base_title}",
+                "- Motivation",
+                "- Problem setup",
+                "- Method",
+                "- Results",
+                "- Limitations",
+                "- Q&A",
+            ])
+        if kind == "faq":
+            return "\n".join([
+                "Q: What problem does this address?\nA: " + (base_abs[:220] + ("…" if len(base_abs) > 220 else "")),
+                "",
+                "Q: What is the main contribution?\nA: Ask in the Chat tab.",
+                "",
+                "Q: How do I reproduce it?\nA: Link code + add steps in README.",
+            ])
+        return "Select an item to generate."
+
+    prompt = {
+        "tweet thread": "Write a concise 6-tweet thread summarizing the paper for the ML community.",
+        "talk outline": "Create a 10-minute talk outline with section headers and bullet points.",
+        "faq": "Write an FAQ with 6 Q/A pairs focused on method, results, limitations, and usage.",
+    }.get(kind, "Summarize the paper in 8 bullet points.")
+
+    ctx = (PAPER_ABSTRACT or "").strip()
+    messages = [
+        {"role": "system", "content": "You are an expert technical writer for ML research audiences."},
+        {"role": "user", "content": f"Paper title: {base_title}\nAuthors: {PAPER_AUTHORS}\n\nAbstract/context:\n{ctx}\n\nTask: {prompt}"},
+    ]
+    try:
+        return _llm_chat(model_id, messages, max_tokens=600).strip()
+    except Exception as e:
+        if kind == "tweet thread":
+            return _fallback_tweet_thread(base_title, base_abs) + f"\n\n(LLM unavailable: {e})"
+        return f"LLM unavailable: {e}"
+
+
+# ---------------------------
+# Model Playground (chat)
+# ---------------------------
+def model_chat(model_id: str, message: str, history: List[Tuple[str, str]]) -> str:
+    model_id = (model_id or "").strip()
+    message = (message or "").strip()
+    if not model_id:
+        return "Provide a model id."
+    if not message:
+        return "Send a message."
+
+    messages: List[dict] = [{"role": "system", "content": "You are a helpful assistant."}]
+    for u, a in (history[-4:] if history else []):
+        messages.append({"role": "user", "content": u})
+        messages.append({"role": "assistant", "content": a})
+    messages.append({"role": "user", "content": message})
+
+    try:
+        return _llm_chat(model_id, messages, max_tokens=512).strip()
+    except Exception as e:
+        return f"Model call failed: {e}"
+
+
+# ---------------------------
+# UI helpers
+# ---------------------------
+def quantize_intent(intent: str) -> Tuple[str, str, str]:
+    """
+    Quantize a natural language intent to UCR anchor.
+    Returns: (primary_result_md, alternatives_md, wire_format)
+    """
+    intent = (intent or "").strip()
+    if not intent:
+        return "Enter an intent to quantize.", "", ""
+
+    # Try embedding quantizer first, fall back to keyword
+    quantizer = _get_quantizer()
+    method = "embedding"
+    if quantizer is None:
+        quantizer = _get_keyword_quantizer()
+        method = "keyword"
+
+    if quantizer is None:
+        return "Quantizer unavailable. Check logs.", "", ""
+
+    try:
+        result = quantizer.quantize(intent)
+    except Exception as e:
+        return f"Quantization error: {e}", "", ""
+
+    # Confidence color
+    conf = result.confidence
+    if conf >= 0.7:
+        color = "green"
+        conf_label = "High"
+    elif conf >= 0.5:
+        color = "orange"
+        conf_label = "Medium"
+    else:
+        color = "red"
+        conf_label = "Low"
+
+    # Primary result
+    primary_md = f"""
+### {result.anchor.mnemonic}
+
+**Confidence:** <span style="color:{color}; font-weight:bold">{conf:.0%}</span> ({conf_label})
+
+**Canonical meaning:** {result.anchor.canonical}
+
+**Method:** {method} {'(fallback)' if result.is_fallback else ''}
+
+**Coordinates:** `{result.anchor.coords}` (Action, Polarity, Domain, Urgency)
+"""
+
+    # Alternatives
+    if result.alternatives:
+        alt_lines = ["| Anchor | Similarity |", "|--------|------------|"]
+        for alt_anchor, alt_score in result.alternatives[:3]:
+            bar_len = int(alt_score * 10)
+            bar = "█" * bar_len + "░" * (10 - bar_len)
+            alt_lines.append(f"| {alt_anchor.mnemonic} | {bar} {alt_score:.0%} |")
+        alternatives_md = "\n".join(alt_lines)
+    else:
+        alternatives_md = "*No alternatives*"
+
+    # Wire format
+    wire = f"SLIP v1 user agent {result.anchor.mnemonic}"
+    if result.is_fallback:
+        # Truncate long intents for fallback payload
+        payload = intent[:100].replace('"', "'")
+        wire = f'SLIP v1 user agent Fallback "{payload}"'
+
+    return primary_md, alternatives_md, wire
+
+
+EXAMPLE_INTENTS = [
+    ("Review my code", "RequestReview"),
+    ("Task complete!", "InformComplete"),
+    ("System down!", "ObserveError"),
+    ("Can you help?", "RequestHelp"),
+    ("Looks good to me", "EvalApprove"),
+]
+
+
+def start_here(choice: str) -> str:
+    choice = (choice or "").strip().lower()
+    if choice == "quick summary":
+        return f"### {PAPER_TITLE}\n\n**Authors:** {PAPER_AUTHORS}\n\n**Abstract:**\n\n{PAPER_ABSTRACT}"
+    if choice == "how does it work?":
+        return "Go to **Chat** and ask: *Give me a method overview with the key steps.*"
+    if choice == "what are the limitations?":
+        return "Go to **Chat** and ask: *List limitations and failure modes discussed in the paper.*"
+    if choice == "generate a tweet thread":
+        return generate_share("tweet thread")
+    return "Pick an option."
+
+
+def _load_gallery_items() -> List[Tuple[str, str]]:
+    items: List[Tuple[str, str]] = []
+    if os.path.isdir("assets/images"):
+        for fn in sorted(os.listdir("assets/images"))[:48]:
+            path = os.path.join("assets/images", fn)
+            if os.path.isfile(path):
+                items.append((path, fn))
+    if not items and os.path.isdir("assets/pages"):
+        for fn in sorted(os.listdir("assets/pages"))[:24]:
+            path = os.path.join("assets/pages", fn)
+            if os.path.isfile(path):
+                items.append((path, fn))
+    return items
+
+
+CSS = '''
+.paper-hero h1 { margin-bottom: 0.2rem; }
+.paper-hero p { margin-top: 0.2rem; opacity: 0.9; }
+.hint { opacity: 0.85; }
+'''
+
+with gr.Blocks(theme=gr.themes.Soft(), css=CSS) as demo:
+    gr.Markdown(f"# {PAPER_TITLE}", elem_classes=["paper-hero"])
+    if PAPER_AUTHORS:
+        gr.Markdown(f"**Authors:** {PAPER_AUTHORS}", elem_classes=["paper-hero"])
+    if PAPER_TAGS:
+        gr.Markdown(f"**Tags:** {PAPER_TAGS}", elem_classes=["paper-hero"])
+
+    with gr.Tabs():
+        with gr.Tab("Start here"):
+            gr.Markdown("Pick an interaction to explore the paper quickly.", elem_classes=["hint"])
+            choice = gr.Radio(
+                ["Quick summary", "How does it work?", "What are the limitations?", "Generate a tweet thread"],
+                value="Quick summary",
+                label="What do you want?",
+            )
+            out = gr.Markdown()
+            choice.change(start_here, inputs=choice, outputs=out)
+            demo.load(start_here, inputs=choice, outputs=out)
+
+        with gr.Tab("Overview"):
+            gr.Markdown("## Abstract")
+            gr.Markdown(PAPER_ABSTRACT)
+
+            gr.Markdown("---")
+            gr.Markdown("### Text search (snippet)")
+            q = gr.Textbox(label="Find a phrase", placeholder="e.g., scalable oversight", lines=1)
+            snippet = gr.Textbox(label="Top matching context", lines=10)
+
+            def _snippet(query: str) -> str:
+                query = (query or "").strip()
+                if not query:
+                    return ""
+                ctx = retrieve_context(query, k=4, max_chars=1600)
+                return ctx or "No matches."
+
+            q.change(_snippet, inputs=q, outputs=snippet)
+
+        with gr.Tab("Gallery"):
+            gr.Markdown("Extracted images / rendered page previews (if included at build time).", elem_classes=["hint"])
+            gallery = gr.Gallery(label="Figures / pages", columns=2, rows=2, height=520)
+
+            def _gallery():
+                return _load_gallery_items()
+
+            demo.load(_gallery, outputs=gallery)
+
+        with gr.Tab("Chat"):
+            gr.Markdown(
+                "Ask questions. If you set `HF_TOKEN` + `PAPER_LLM_MODEL` as Space secrets, answers become generative; "
+                "otherwise it returns top-matching snippets.",
+                elem_classes=["hint"],
+            )
+            gr.ChatInterface(fn=paper_chat, title="Chat with the Paper")
+
+        with gr.Tab("Share Kit"):
+            gr.Markdown("Generate shareable assets. Works without secrets (deterministic fallback).", elem_classes=["hint"])
+            kind = gr.Dropdown(["Tweet thread", "Talk outline", "FAQ"], value="Tweet thread", label="Generate")
+            btn = gr.Button("Create")
+            share_out = gr.Textbox(lines=14, label="Output")
+            btn.click(lambda k: generate_share(k), inputs=kind, outputs=share_out)
+
+        with gr.Tab("Model Playground"):
+            gr.Markdown("Chat with a referenced Hub model (if any) or provide your own. Requires `HF_TOKEN` secret.", elem_classes=["hint"])
+            model_id = gr.Dropdown(
+                choices=(DETECTED_MODELS if DETECTED_MODELS else []),
+                value=(DETECTED_MODELS[0] if DETECTED_MODELS else None),
+                label="Model id",
+                allow_custom_value=True,
+            )
+
+            def _model_chat_fn(message: str, history: List[Tuple[str, str]], mid: str) -> str:
+                return model_chat(mid, message, history)
+
+            gr.ChatInterface(fn=_model_chat_fn, additional_inputs=[model_id], title="Model Playground")
+
+        with gr.Tab("Live Quantizer"):
+            gr.Markdown("""
+## Think → Quantize → Transmit
+
+Type a messy, natural-language intent and watch it get quantized to a UCR anchor.
+This demonstrates the core Slipstream innovation: mapping free-form language onto a shared semantic manifold.
+""")
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    intent_input = gr.Textbox(
+                        label="Your intent (natural language)",
+                        placeholder="Hey, I'm kinda stuck on this auth bug, can you take a look?",
+                        lines=2,
+                    )
+                    quantize_btn = gr.Button("Quantize", variant="primary")
+
+                    gr.Markdown("**Try these examples:**")
+                    with gr.Row():
+                        for ex_text, ex_anchor in EXAMPLE_INTENTS:
+                            ex_btn = gr.Button(ex_text, size="sm")
+                            ex_btn.click(lambda t=ex_text: t, outputs=intent_input)
+
+                with gr.Column(scale=3):
+                    primary_out = gr.Markdown(label="Result")
+                    with gr.Accordion("Nearby Anchors", open=True):
+                        alternatives_out = gr.Markdown()
+                    wire_out = gr.Code(label="SLIP Wire Format", language=None)
+
+            quantize_btn.click(
+                quantize_intent,
+                inputs=intent_input,
+                outputs=[primary_out, alternatives_out, wire_out],
+            )
+            intent_input.submit(
+                quantize_intent,
+                inputs=intent_input,
+                outputs=[primary_out, alternatives_out, wire_out],
+            )
+
+    gr.Markdown("---\nBuilt with Gradio on Hugging Face Spaces.")
+
+if __name__ == "__main__":
+    demo.launch()

assets/analysis.json

@@ -0,0 +1,75 @@
+{
+  "schema_version": "paper_analysis_v1",
+  "pdf": {
+    "path": "D:\\Development\\slipcore\\private\\zenodo\\slipstream-paper.pdf",
+    "filename": "slipstream-paper.pdf",
+    "sha256": "e91b687dbbe2aa4fe01ec0ae3c5475fda9ad2a5107ea8e81927028c575c707f7",
+    "page_count": 7,
+    "text_pages_extracted": 7,
+    "extracted_chars": 11939
+  },
+  "paper": {
+    "title": "Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination",
+    "authors": [
+      "Anthony Maio"
+    ],
+    "abstract": "As multi-agent LLM systems scale,coordination bandwidthbecomes a primary cost\ndriver: every token spent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns. Current approaches waste 40–60% of compute on coordination\noverhead, with communication costs scalingO(n2)as agent counts increase.\nThis paper introducesSlipstream, a protocol that performssemantic quantization:\nmapping free-form messages onto a sharedUniversal Concept Reference (UCR)and\ntransmitting compactmnemonic anchorsthat identify structured intents. Unlike syn-\ntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits\nnatural-language mnemonics that tokenize efficiently across model architectures.\nSlipstream combines (1) a symbolic4D semantic manifold—Action, Polarity, Domain,\nUrgency—with (2) a data-drivenvector engine(embeddings + nearest-centroid retrieval)\nplus anevolutionary extension layerthat learns new anchors from low-confidence traf-\nfic. Results show82% token reduction(41.9→7.4 tokens average) while maintaining\nsemantic fidelity, making large-scale multi-agent deployments economically viable."
+  },
+  "artifacts": {
+    "urls": [
+      "https://github.com/anthony-maio/slipcore",
+      "https://modelcontextprotocol.io/,",
+      "https://www.linuxfoundation."
+    ],
+    "hf_models": [],
+    "hf_datasets": [],
+    "hf_spaces": [],
+    "possible_hf_repo_ids": [
+      "Edge/embedded",
+      "Msg/Day",
+      "REQ/TSK",
+      "messages/day",
+      "org/press"
+    ],
+    "github_repos": [
+      "anthony-maio/slipcore"
+    ],
+    "arxiv_ids": [
+      "1982.10564",
+      "2690.17728"
+    ],
+    "dois": [
+      "10.1109/TIT.1982.1056489",
+      "10.1145/1772690.1772862",
+      "10.18653/v1/D19-1410"
+    ]
+  },
+  "suggested": {
+    "space_slug": "slipstream-semantic-quantization-for-efficient-m",
+    "space_title": "Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination",
+    "tags": [
+      "semantic-quantization",
+      "multi-agent-systems",
+      "protocol-standards",
+      "token-ef-"
+    ],
+    "emoji": "📄",
+    "colorFrom": "blue",
+    "colorTo": "indigo"
+  },
+  "outputs": {
+    "context_txt": "paper_context.txt",
+    "chunks_jsonl": "paper_chunks.jsonl",
+    "rendered_pages": [
+      {
+        "page": 1,
+        "path": "pages\\page_01.png"
+      },
+      {
+        "page": 2,
+        "path": "pages\\page_02.png"
+      }
+    ],
+    "extracted_images": []
+  }
+}

paper_chunks.jsonl

@@ -0,0 +1,6 @@
+{"chunk_id": 0, "text": "Slipstream: Semantic Quantization for Efficient\nMulti-Agent Coordination\nAnthony Maio\nIndependent Researcher\nanthony@making-minds.ai\n2025\nAbstract\nAs multi-agent LLM systems scale,coordination bandwidthbecomes a primary cost\ndriver: every token spent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns. Current approaches waste 40–60% of compute on coordination\noverhead, with communication costs scalingO(n2)as agent counts increase.\nThis paper introducesSlipstream, a protocol that performssemantic quantization:\nmapping free-form messages onto a sharedUniversal Concept Reference (UCR)and\ntransmitting compactmnemonic anchorsthat identify structured intents. Unlike syn-\ntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits\nnatural-language mnemonics that tokenize efficiently across model architectures.\nSlipstream combines (1) a symbolic4D semantic manifold—Action, Polarity, Domain,\nUrgency—with (2) a data-drivenvector engine(embeddings + nearest-centroid retrieval)\nplus anevolutionary extension layerthat learns new anchors from low-confidence traf-\nfic. Results show82% token reduction(41.9→7.4 tokens average) while maintaining\nsemantic fidelity, making large-scale multi-agent deployments economically viable.\nKeywords:Semantic Quantization, Multi-Agent Systems, Protocol Standards, Token Ef-\nficiency, Agentic AI\n1 Introduction\n1.1 The Coordination Crisis\nAgent swarms incur atokenizer tax: the repeated, non-semantic overhead of communicating\nmessage types, domains, and priorities. This overhead often dominates when messages are\nstructured (routing, task dispatch, acknowledgements).\nA typical coordination message:\n1{\n2\" sender \": \" planning_agent \",\n3\" recipient \": \" execution_agent \",\n4\" message_type \": \" task_delegation \",\n5\" content \": {\n6\" request \": \" Please review the authentication code \",\n7\" priority \": \" high \"\n8}\n9}\n•Token count:∼45 tokens\n•Semantic content:∼10 tokens\n•Information density:22%\n1\nAt GPT-4o pricing ($5/M input, $15/M output), a 50-agent deployment exchanging 1,000\nmessages/day costs$180,000/yearin coordination tokens alone—before any work is per-\nformed.\n1.2 Why Syntactic Compression Fails\nOur initial approach, nSLIP v1, focused on syntactic minification:\n1REQ / TSK |s =7| d =3| act = review_auth\n•Expected tokens:8–10\n•Actual tokens with BPE:18–22\nThe failure stems"}
+{"chunk_id": 1, "text": "tic Compression Fails\nOur initial approach, nSLIP v1, focused on syntactic minification:\n1REQ / TSK |s =7| d =3| act = review_auth\n•Expected tokens:8–10\n•Actual tokens with BPE:18–22\nThe failure stems from Byte-Pair Encoding (BPE) tokenizer behavior. Punctuation and\nspecial characters fragment into separate tokens:\nTable 1: BPE Tokenization of Syntactic Compression\nInput Tokens\nREQ/TSK REQ,/,TSK= 3\n|s=7| |,s,=,7,|= 5\nThis “Tokenizer Tax” negates syntactic savings entirely.\n1.3 The Solution: Semantic Quantization\nInstead of compressingsyntax, we quantizesemantics. Agents share a pre-agreed “concept\ncodebook” (the UCR) and transmit pointers to meanings:\n1SLIP v1 planner executor RequestReview auth_module\nToken count:7 tokens (82% reduction)\nThe key insight:natural English words tokenize efficiently.RequestReviewis 1–2\ntokens across major tokenizers, while0x0011fragments into 3–4 tokens.\n2 The Universal Concept Reference\n2.1 The 4D Semantic Manifold\nThe UCR represents each anchor as a coordinate in a 4-dimensional semantic space:\nTable 2: UCR Semantic Dimensions\nDimension Values Purpose\nACTION request, inform, propose, evaluate Speech act type\nPOLARITY negative, neutral, positive Outcome sentiment\nDOMAIN task, plan, observation, control Context area\nURGENCY routine, elevated, critical Priority level\nThis structure provides:\n1.Interpretability:Anchors can be audited, extended, and reasoned about\n2\n2.Constraint surface:Agents can validate structural plausibility\n3.Semantic arithmetic:Combining dimensions yields predictable intents\n2.2 Anchor Structure\nEach anchor includes:\n1@dataclass\n2class UCRAnchor :\n3index : int # Unique ID (0 x0000 -0 xFFFF )\n4mnemonic : str # Wire token : \" RequestReview \"\n5canonical : str # Human description\n6coords : tuple [int , ...] # Position in manifold\n7is_core : bool # True if immutable core anchor\n•Core Range (0x0000–0x7FFF):Standard anchors, immutable per version\n•Extension Range (0x8000–0xFFFF):Installation-specific, evolvable\n2.3 Core Anchors\nTable 3: Core UCR Anchors by Category\nCategory Anchors\nRequestsRequestTask,RequestReview,RequestHelp,RequestPlan\nInformInformComplete,InformProgress,InformBlocked,InformStatus\nProposeProposePlan,ProposeChange,ProposeAlternative\nEvaluateEvalApprove,EvalReject,EvalNeedsWork\nMetaAccept,Reject,MetaAck,MetaHandoff,Fallback\n3 Protocol Specification\n3.1 Wire Format\n1SLIP v1 <src > <dst > <anchor >"}
+{"chunk_id": 2, "text": "n,ProposeChange,ProposeAlternative\nEvaluateEvalApprove,EvalReject,EvalNeedsWork\nMetaAccept,Reject,MetaAck,MetaHandoff,Fallback\n3 Protocol Specification\n3.1 Wire Format\n1SLIP v1 <src > <dst > <anchor > [ payload ...]\nTable 4: Wire Format Fields\nField Description\nSLIP v1Protocol marker and version\n<src>Source agent identifier\n<dst>Destination agent identifier\n<anchor>UCR mnemonic (e.g.,RequestReview)\n[payload]Optional space-separated parameters\nDesign Principles:\n•No special characters that fragment in BPE\n•Natural English words for efficient tokenization\n•Human-readable for debugging\n•Model-agnostic (works across GPT-4, Claude, Llama, etc.)\n3\n3.2 The Think-Quantize-Transmit Pattern\nThe TQT pattern consists of three stages:\n1.THINK:Agent forms natural language intent: “Please review the authentication code\nfor security”\n2.QUANTIZE:Map to nearest UCR anchor via keyword matching (fast, zero-dependency)\nor embedding similarity (accurate, requires ML). Result:RequestReview(confidence:\n0.89)\n3.TRANSMIT:Wire format:SLIP v1 dev reviewer RequestReview auth. Tokens: 7\n(vs 45 for JSON)\n4 Vector Quantization Engine\n4.1 Embedding-Based Retrieval\nThe vector quantization engine leverages sentence embeddings [Reimers and Gurevych, 2019]\nto map natural language intents to UCR anchors. Given a messagex, the vector engine embeds\nit and retrieves the best anchor by cosine similarity:\nk∗ = argmaxk cos(E(x),ck)(1)\nWhereE(x)is the thought embedding andck is the anchor centroid. This approach extends\nclassical quantization theory [Lloyd, 1982] to the semantic domain.\nA confidence thresholdτcontrols whether to emit an anchor or fall back to plaintext:\n1def quantize ( thought : str , threshold : float = 0.55) :\n2embedding = encode ( thought )\n3similarities = cosine ( embedding , centroids )\n4best_idx = argmax ( similarities )\n5\n6if similarities [ best_idx ] < threshold :\n7return Fallback ( thought )\n8\n9return anchors [ best_idx ]\n4.2 Graceful Degradation\nThe system operates in three modes:\nTable 5: Quantization Modes\nMode Dependencies Accuracy Use Case\nFull ML sentence-transformers 94% Production\nKeyword None 78% Edge/embedded\nFallback None 100% (passthrough) Novel intents\n5 Evolutionary Extension Layer\n5.1 The Drift Problem\nStatic codebooks degrade underconcept drift—new domains, task types, and terminology\nemerge over time. A codebook trained on software development fails on biotech"}
+{"chunk_id": 3, "text": "Extension Layer\n5.1 The Drift Problem\nStatic codebooks degrade underconcept drift—new domains, task types, and terminology\nemerge over time. A codebook trained on software development fails on biotech vocabulary.\n4\n5.2 Extension Learning\nSlipstream reserves the extension range (0x8000–0xFFFF) for learned anchors:\n1.Log:Messages with low quantization confidence are recorded\n2.Cluster:K-means identifies recurring semantic patterns [Sculley, 2010]\n3.Mint:New anchors are created with inferred 4D coordinates\n4.Register:Indices assigned in extension range; vector index rebuilt\n1class ExtensionManager :\n2def propose_extensions (self , fallbacks , min_cluster_size =3) :\n3embeddings = encode ( fallbacks )\n4clusters = kmeans ( embeddings , k= len ( fallbacks ) // min_cluster_size )\n5\n6new_anchors = []\n7for cluster in clusters :\n8if len ( cluster ) >= min_cluster_size :\n9centroid = mean ( embeddings [ cluster ])\n10exemplar = nearest_to_centroid ( cluster )\n11coords = infer_coords ( exemplar )\n12new_anchors . append ( mint_anchor ( centroid , exemplar , coords ))\n13\n14return new_anchors\n5.3 Governance\nExtension learning can be abused. Mitigations:\n•Minimum cluster size requirements\n•Rate limits on minting\n•Human approval gates for production\n•Provenance logging for each anchor\n6 Evaluation\n6.1 Token Efficiency\nTable 6: Token Efficiency Comparison\nMessage Type JSON Tokens SLIP Tokens Reduction\nTask delegation 47.3 8.2 82.7%\nStatus update 35.1 6.4 81.8%\nError report 52.0 9.1 82.5%\nAverage 41.9 7.4 82.3%\n5\n6.2 Cost Savings\nTable 7: Annual Cost Comparison by Deployment Scale\nScale Agents Msg/Day JSON Cost SLIP Cost Savings\nStartup 10 500 $3,600 $650 $2,950\nScale-up 50 5,000 $180,000 $32,400 $147,600\nEnterprise 1,000 500,000 $2,500,000 $450,000$2,050,000\n6.3 Semantic Fidelity\n•Retrieval accuracy:94% top-1 on intent classification\n•Coverage:88.7% of messages quantize without fallback\n•Codebook utilization:87% of anchors actively used\n7 Integration with AAIF Ecosystem\nSlipstream is designed as thetransport layerfor the Linux Foundation’s Agentic AI Founda-\ntion (AAIF) standards [Linux Foundation, 2025]:\n+-------------------------------------+\n| Application (Agent Logic) |\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n| MCP / A2A (Semantic Layer) | <- Discovery, capabilities\n+-----------------+-------------------+\n|\n+-----------------v-------"}
+{"chunk_id": 4, "text": "----------+-------------------+\n|\n+-----------------v-------------------+\n| MCP / A2A (Semantic Layer) | <- Discovery, capabilities\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n| Slipstream (Transport Layer) | <- 82% token reduction\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n| Network (HTTP, WebSocket, gRPC) |\n+-------------------------------------+\nCompatibility:Works transparently beneath Model Context Protocol (MCP) [Anthropic,\n2024] and Agent2Agent (A2A), like gRPC optimizes HTTP/2.\n8 Security Considerations\nTable 8: Security Threats and Mitigations\nThreat Mitigation\nPrompt injection via payloads Validate types; treat payloads as untrusted\nAnchor poisoning Min cluster size, rate limits, human approval\nOver-compression Allow fallback to plaintext; confidence thresholds\nSemantic drift Evolutionary layer; version-locked core anchors\n6\n9 Implementation\nA reference implementation is available asslipcore:\n1pip install slipcore\n1from slipcore import slip , decode , think_quantize_transmit\n2\n3# Direct message creation\n4wire = slip (\" alice \", \" bob \", \" RequestReview \", [\" auth_module \"])\n5# -> \" SLIP v1 alice bob RequestReview auth_module \"\n6\n7# Think - Quantize - Transmit pattern\n8wire = think_quantize_transmit (\n9\" Please review the authentication code \",\n10src =\" dev \", dst =\" reviewer \"\n11)\n12# -> \" SLIP v1 dev reviewer RequestReview \"\n13\n14# Decode\n15msg = decode ( wire )\n16print ( msg . anchor . canonical ) # \" Request review of work \"\n•Repository:https://github.com/anthony-maio/slipcore\n•License:Apache 2.0\n10 Conclusion\nSlipstream demonstrates thatsemantic quantizationis the necessary evolution for high-\nthroughput agent coordination. By grounding agents in a structured 4D manifold and trans-\nmitting natural-language mnemonics, we achieve 82% token reduction without sacrificing inter-\npretability or cross-model compatibility.\nThe protocol’s evolutionary layer enables adaptation to new domains while keeping core\nsemantics stable. As agent swarms scale, the shared UCR becomes a form of “collective\nunderstanding”—reducing not just tokens, but the cognitive overhead of coordination itself.\nReferences\nAnthropic. Model context protocol specification.https://modelcontextprotocol.io/, 2024.\nAccessed: 2024.\nLinux Foundation. Agentic AI foundation announcement.https://www.linuxfoundation."}
+{"chunk_id": 5, "text": "f.\nReferences\nAnthropic. Model context protocol specification.https://modelcontextprotocol.io/, 2024.\nAccessed: 2024.\nLinux Foundation. Agentic AI foundation announcement.https://www.linuxfoundation.\norg/press/agentic-ai-foundation, 2025. Accessed: 2025.\nStuart Lloyd. Least squares quantization in PCM.IEEE Transactions on Information Theory,\n28(2):129–137, 1982. doi: 10.1109/TIT.1982.1056489.\nNils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using siamese BERT-\nnetworks. InProceedings of the 2019 Conference on Empirical Methods in Natural Lan-\nguage Processing and the 9th International Joint Conference on Natural Language Processing\n(EMNLP-IJCNLP), pages 3982–3992. Association for Computational Linguistics, 2019. doi:\n10.18653/v1/D19-1410.\nD. Sculley. Web-scale k-means clustering. InProceedings of the 19th International Conference\non World Wide Web, pages 1177–1178. ACM, 2010. doi: 10.1145/1772690.1772862.\n7"}

paper_context.txt

@@ -0,0 +1,269 @@
+Slipstream: Semantic Quantization for Efficient
+Multi-Agent Coordination
+Anthony Maio
+Independent Researcher
+anthony@making-minds.ai
+2025
+Abstract
+As multi-agent LLM systems scale,coordination bandwidthbecomes a primary cost
+driver: every token spent on routing, intent framing, and redundant context is paid repeat-
+edly across agents and turns. Current approaches waste 40–60% of compute on coordination
+overhead, with communication costs scalingO(n2)as agent counts increase.
+This paper introducesSlipstream, a protocol that performssemantic quantization:
+mapping free-form messages onto a sharedUniversal Concept Reference (UCR)and
+transmitting compactmnemonic anchorsthat identify structured intents. Unlike syn-
+tactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits
+natural-language mnemonics that tokenize efficiently across model architectures.
+Slipstream combines (1) a symbolic4D semantic manifold—Action, Polarity, Domain,
+Urgency—with (2) a data-drivenvector engine(embeddings + nearest-centroid retrieval)
+plus anevolutionary extension layerthat learns new anchors from low-confidence traf-
+fic. Results show82% token reduction(41.9→7.4 tokens average) while maintaining
+semantic fidelity, making large-scale multi-agent deployments economically viable.
+Keywords:Semantic Quantization, Multi-Agent Systems, Protocol Standards, Token Ef-
+ficiency, Agentic AI
+1 Introduction
+1.1 The Coordination Crisis
+Agent swarms incur atokenizer tax: the repeated, non-semantic overhead of communicating
+message types, domains, and priorities. This overhead often dominates when messages are
+structured (routing, task dispatch, acknowledgements).
+A typical coordination message:
+1{
+2" sender ": " planning_agent ",
+3" recipient ": " execution_agent ",
+4" message_type ": " task_delegation ",
+5" content ": {
+6" request ": " Please review the authentication code ",
+7" priority ": " high "
+8}
+9}
+•Token count:∼45 tokens
+•Semantic content:∼10 tokens
+•Information density:22%
+1
+At GPT-4o pricing ($5/M input, $15/M output), a 50-agent deployment exchanging 1,000
+messages/day costs$180,000/yearin coordination tokens alone—before any work is per-
+formed.
+1.2 Why Syntactic Compression Fails
+Our initial approach, nSLIP v1, focused on syntactic minification:
+1REQ / TSK |s =7| d =3| act = review_auth
+•Expected tokens:8–10
+•Actual tokens with BPE:18–22
+The failure stems from Byte-Pair Encoding (BPE) tokenizer behavior. Punctuation and
+special characters fragment into separate tokens:
+Table 1: BPE Tokenization of Syntactic Compression
+Input Tokens
+REQ/TSK REQ,/,TSK= 3
+|s=7| |,s,=,7,|= 5
+This “Tokenizer Tax” negates syntactic savings entirely.
+1.3 The Solution: Semantic Quantization
+Instead of compressingsyntax, we quantizesemantics. Agents share a pre-agreed “concept
+codebook” (the UCR) and transmit pointers to meanings:
+1SLIP v1 planner executor RequestReview auth_module
+Token count:7 tokens (82% reduction)
+The key insight:natural English words tokenize efficiently.RequestReviewis 1–2
+tokens across major tokenizers, while0x0011fragments into 3–4 tokens.
+2 The Universal Concept Reference
+2.1 The 4D Semantic Manifold
+The UCR represents each anchor as a coordinate in a 4-dimensional semantic space:
+Table 2: UCR Semantic Dimensions
+Dimension Values Purpose
+ACTION request, inform, propose, evaluate Speech act type
+POLARITY negative, neutral, positive Outcome sentiment
+DOMAIN task, plan, observation, control Context area
+URGENCY routine, elevated, critical Priority level
+This structure provides:
+1.Interpretability:Anchors can be audited, extended, and reasoned about
+2
+2.Constraint surface:Agents can validate structural plausibility
+3.Semantic arithmetic:Combining dimensions yields predictable intents
+2.2 Anchor Structure
+Each anchor includes:
+1@dataclass
+2class UCRAnchor :
+3index : int # Unique ID (0 x0000 -0 xFFFF )
+4mnemonic : str # Wire token : " RequestReview "
+5canonical : str # Human description
+6coords : tuple [int , ...] # Position in manifold
+7is_core : bool # True if immutable core anchor
+•Core Range (0x0000–0x7FFF):Standard anchors, immutable per version
+•Extension Range (0x8000–0xFFFF):Installation-specific, evolvable
+2.3 Core Anchors
+Table 3: Core UCR Anchors by Category
+Category Anchors
+RequestsRequestTask,RequestReview,RequestHelp,RequestPlan
+InformInformComplete,InformProgress,InformBlocked,InformStatus
+ProposeProposePlan,ProposeChange,ProposeAlternative
+EvaluateEvalApprove,EvalReject,EvalNeedsWork
+MetaAccept,Reject,MetaAck,MetaHandoff,Fallback
+3 Protocol Specification
+3.1 Wire Format
+1SLIP v1 <src > <dst > <anchor > [ payload ...]
+Table 4: Wire Format Fields
+Field Description
+SLIP v1Protocol marker and version
+<src>Source agent identifier
+<dst>Destination agent identifier
+<anchor>UCR mnemonic (e.g.,RequestReview)
+[payload]Optional space-separated parameters
+Design Principles:
+•No special characters that fragment in BPE
+•Natural English words for efficient tokenization
+•Human-readable for debugging
+•Model-agnostic (works across GPT-4, Claude, Llama, etc.)
+3
+3.2 The Think-Quantize-Transmit Pattern
+The TQT pattern consists of three stages:
+1.THINK:Agent forms natural language intent: “Please review the authentication code
+for security”
+2.QUANTIZE:Map to nearest UCR anchor via keyword matching (fast, zero-dependency)
+or embedding similarity (accurate, requires ML). Result:RequestReview(confidence:
+0.89)
+3.TRANSMIT:Wire format:SLIP v1 dev reviewer RequestReview auth. Tokens: 7
+(vs 45 for JSON)
+4 Vector Quantization Engine
+4.1 Embedding-Based Retrieval
+The vector quantization engine leverages sentence embeddings [Reimers and Gurevych, 2019]
+to map natural language intents to UCR anchors. Given a messagex, the vector engine embeds
+it and retrieves the best anchor by cosine similarity:
+k∗ = argmaxk cos(E(x),ck)(1)
+WhereE(x)is the thought embedding andck is the anchor centroid. This approach extends
+classical quantization theory [Lloyd, 1982] to the semantic domain.
+A confidence thresholdτcontrols whether to emit an anchor or fall back to plaintext:
+1def quantize ( thought : str , threshold : float = 0.55) :
+2embedding = encode ( thought )
+3similarities = cosine ( embedding , centroids )
+4best_idx = argmax ( similarities )
+5
+6if similarities [ best_idx ] < threshold :
+7return Fallback ( thought )
+8
+9return anchors [ best_idx ]
+4.2 Graceful Degradation
+The system operates in three modes:
+Table 5: Quantization Modes
+Mode Dependencies Accuracy Use Case
+Full ML sentence-transformers 94% Production
+Keyword None 78% Edge/embedded
+Fallback None 100% (passthrough) Novel intents
+5 Evolutionary Extension Layer
+5.1 The Drift Problem
+Static codebooks degrade underconcept drift—new domains, task types, and terminology
+emerge over time. A codebook trained on software development fails on biotech vocabulary.
+4
+5.2 Extension Learning
+Slipstream reserves the extension range (0x8000–0xFFFF) for learned anchors:
+1.Log:Messages with low quantization confidence are recorded
+2.Cluster:K-means identifies recurring semantic patterns [Sculley, 2010]
+3.Mint:New anchors are created with inferred 4D coordinates
+4.Register:Indices assigned in extension range; vector index rebuilt
+1class ExtensionManager :
+2def propose_extensions (self , fallbacks , min_cluster_size =3) :
+3embeddings = encode ( fallbacks )
+4clusters = kmeans ( embeddings , k= len ( fallbacks ) // min_cluster_size )
+5
+6new_anchors = []
+7for cluster in clusters :
+8if len ( cluster ) >= min_cluster_size :
+9centroid = mean ( embeddings [ cluster ])
+10exemplar = nearest_to_centroid ( cluster )
+11coords = infer_coords ( exemplar )
+12new_anchors . append ( mint_anchor ( centroid , exemplar , coords ))
+13
+14return new_anchors
+5.3 Governance
+Extension learning can be abused. Mitigations:
+•Minimum cluster size requirements
+•Rate limits on minting
+•Human approval gates for production
+•Provenance logging for each anchor
+6 Evaluation
+6.1 Token Efficiency
+Table 6: Token Efficiency Comparison
+Message Type JSON Tokens SLIP Tokens Reduction
+Task delegation 47.3 8.2 82.7%
+Status update 35.1 6.4 81.8%
+Error report 52.0 9.1 82.5%
+Average 41.9 7.4 82.3%
+5
+6.2 Cost Savings
+Table 7: Annual Cost Comparison by Deployment Scale
+Scale Agents Msg/Day JSON Cost SLIP Cost Savings
+Startup 10 500 $3,600 $650 $2,950
+Scale-up 50 5,000 $180,000 $32,400 $147,600
+Enterprise 1,000 500,000 $2,500,000 $450,000$2,050,000
+6.3 Semantic Fidelity
+•Retrieval accuracy:94% top-1 on intent classification
+•Coverage:88.7% of messages quantize without fallback
+•Codebook utilization:87% of anchors actively used
+7 Integration with AAIF Ecosystem
+Slipstream is designed as thetransport layerfor the Linux Foundation’s Agentic AI Founda-
+tion (AAIF) standards [Linux Foundation, 2025]:
++-------------------------------------+
+| Application (Agent Logic) |
++-----------------+-------------------+
+|
++-----------------v-------------------+
+| MCP / A2A (Semantic Layer) | <- Discovery, capabilities
++-----------------+-------------------+
+|
++-----------------v-------------------+
+| Slipstream (Transport Layer) | <- 82% token reduction
++-----------------+-------------------+
+|
++-----------------v-------------------+
+| Network (HTTP, WebSocket, gRPC) |
++-------------------------------------+
+Compatibility:Works transparently beneath Model Context Protocol (MCP) [Anthropic,
+2024] and Agent2Agent (A2A), like gRPC optimizes HTTP/2.
+8 Security Considerations
+Table 8: Security Threats and Mitigations
+Threat Mitigation
+Prompt injection via payloads Validate types; treat payloads as untrusted
+Anchor poisoning Min cluster size, rate limits, human approval
+Over-compression Allow fallback to plaintext; confidence thresholds
+Semantic drift Evolutionary layer; version-locked core anchors
+6
+9 Implementation
+A reference implementation is available asslipcore:
+1pip install slipcore
+1from slipcore import slip , decode , think_quantize_transmit
+2
+3# Direct message creation
+4wire = slip (" alice ", " bob ", " RequestReview ", [" auth_module "])
+5# -> " SLIP v1 alice bob RequestReview auth_module "
+6
+7# Think - Quantize - Transmit pattern
+8wire = think_quantize_transmit (
+9" Please review the authentication code ",
+10src =" dev ", dst =" reviewer "
+11)
+12# -> " SLIP v1 dev reviewer RequestReview "
+13
+14# Decode
+15msg = decode ( wire )
+16print ( msg . anchor . canonical ) # " Request review of work "
+•Repository:https://github.com/anthony-maio/slipcore
+•License:Apache 2.0
+10 Conclusion
+Slipstream demonstrates thatsemantic quantizationis the necessary evolution for high-
+throughput agent coordination. By grounding agents in a structured 4D manifold and trans-
+mitting natural-language mnemonics, we achieve 82% token reduction without sacrificing inter-
+pretability or cross-model compatibility.
+The protocol’s evolutionary layer enables adaptation to new domains while keeping core
+semantics stable. As agent swarms scale, the shared UCR becomes a form of “collective
+understanding”—reducing not just tokens, but the cognitive overhead of coordination itself.
+References
+Anthropic. Model context protocol specification.https://modelcontextprotocol.io/, 2024.
+Accessed: 2024.
+Linux Foundation. Agentic AI foundation announcement.https://www.linuxfoundation.
+org/press/agentic-ai-foundation, 2025. Accessed: 2025.
+Stuart Lloyd. Least squares quantization in PCM.IEEE Transactions on Information Theory,
+28(2):129–137, 1982. doi: 10.1109/TIT.1982.1056489.
+Nils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using siamese BERT-
+networks. InProceedings of the 2019 Conference on Empirical Methods in Natural Lan-
+guage Processing and the 9th International Joint Conference on Natural Language Processing
+(EMNLP-IJCNLP), pages 3982–3992. Association for Computational Linguistics, 2019. doi:
+10.18653/v1/D19-1410.
+D. Sculley. Web-scale k-means clustering. InProceedings of the 19th International Conference
+on World Wide Web, pages 1177–1178. ACM, 2010. doi: 10.1145/1772690.1772862.
+7

quantizer.py

@@ -0,0 +1,818 @@
+"""
+Semantic Quantizer - The Think-Quantize-Transmit Engine
+
+Maps agent thoughts (natural language) to UCR anchors.
+Supports three modes:
+1. Keyword-based (fast, no dependencies)
+2. Embedding-based with centroids (accurate, requires sentence-transformers)
+3. Hybrid with CoordsInferer (prototype similarity + heuristics)
+
+Also handles:
+- Fallback detection (when confidence is too low)
+- Usage tracking (for UCR evolution)
+- Coordinate inference for new anchors
+"""
+
+from __future__ import annotations
+from dataclasses import dataclass, field
+from typing import Optional, Callable, Dict, List, Tuple
+from collections import Counter
+import re
+
+try:
+    from .ucr import UCR, UCRAnchor, get_default_ucr, CORE_RANGE_END
+except ImportError:
+    from ucr import UCR, UCRAnchor, get_default_ucr, CORE_RANGE_END
+
+
+# ============ Optional Dependencies ============
+
+try:
+    import numpy as np
+    HAS_NUMPY = True
+except ImportError:
+    np = None  # type: ignore
+    HAS_NUMPY = False
+
+
+# ============ Semantic Coordinates ============
+
+@dataclass(frozen=True)
+class SemanticCoords:
+    """
+    4D logical position of an intent in the semantic manifold.
+
+    This is a human-readable representation used during coordinate inference.
+    Maps to UCR's numeric coords via ACTION_MAP, DOMAIN_MAP, etc.
+    """
+    action: str    # REQ, INF, EVAL, CMD, OBS, PROP, META
+    polarity: int  # -1 (negative), 0 (neutral), 1 (positive)
+    domain: str    # TASK, QA, INFRA, AUTH, ERR, DOC, META, GEN
+    urgency: int   # 0 (routine) to 3 (critical)
+
+
+# Map v3-style string coords to v2 numeric coords
+ACTION_MAP = {"OBS": 0, "INF": 1, "ASK": 2, "REQ": 3, "PROP": 4, "COMMIT": 5, "EVAL": 6, "META": 7, "CMD": 3}
+DOMAIN_MAP = {"TASK": 0, "PLAN": 1, "OBS": 2, "EVAL": 3, "CTRL": 4, "RES": 5, "ERR": 6, "GEN": 7,
+              "QA": 3, "INFRA": 5, "AUTH": 4, "DOC": 1, "META": 4}
+
+
+def semantic_coords_to_tuple(sc: SemanticCoords) -> tuple[int, ...]:
+    """Convert SemanticCoords to UCR numeric tuple."""
+    action = ACTION_MAP.get(sc.action.upper(), 3)  # default REQ
+    domain = DOMAIN_MAP.get(sc.domain.upper(), 7)  # default GEN
+    # Map polarity: -1->1, 0->4, 1->6
+    polarity = {-1: 1, 0: 4, 1: 6}.get(sc.polarity, 4)
+    # Map urgency 0-3 to 0-7: 0->1, 1->3, 2->5, 3->7
+    urgency = min(7, max(0, sc.urgency * 2 + 1))
+    return (action, polarity, domain, urgency)
+
+
+# ============ Coordinate Inference (from v3) ============
+
+class CoordsInferer:
+    """
+    Assigns (Action, Polarity, Domain, Urgency) to text.
+
+    Hybrid approach:
+    - Heuristics for urgency and polarity (reliable, fast)
+    - Optional prototype embedding similarity for action/domain refinement
+
+    LIMITATIONS AND CAVEATS:
+    ========================
+
+    1. HEURISTIC FRAGILITY:
+       - Keyword matching is context-blind ("please" could be polite or pleading)
+       - Urgency detection relies on explicit markers ("ASAP", "urgent")
+       - Domain detection depends on domain-specific vocabulary
+
+    2. PROTOTYPE EMBEDDING ISSUES:
+       - Limited prototype phrases (3 per action, 2 per domain)
+       - English-only prototypes; other languages will use fallback
+       - Short messages may not have enough signal for reliable similarity
+
+    3. KNOWN FAILURE MODES:
+       - Sarcasm/irony: "Great, another bug" -> incorrectly infers positive polarity
+       - Questions phrased as statements: "I wonder if..." -> misses ASK action
+       - Multi-intent: "Review and deploy this" -> only captures first action
+       - Implicit urgency: "CEO is waiting" -> misses critical urgency
+
+    4. RECOMMENDED USAGE:
+       - Use as initialization for finetuned models, not production truth
+       - Always validate inferred coords against anchor centroids
+       - Track fallback rate; >15% indicates poor UCR coverage
+       - For production: finetune a small model on CoordsInferer output
+
+    5. ACCURACY ESTIMATES (informal testing):
+       - Urgency:  ~80% (explicit markers work well)
+       - Polarity: ~70% (sentiment is hard)
+       - Action:   ~65% with embeddings, ~55% heuristic-only
+       - Domain:   ~60% (highly vocabulary-dependent)
+
+    Ported from v3's coordinate inference system.
+    """
+
+    def __init__(self, embed_batch: Optional[Callable] = None):
+        self._embed_batch = embed_batch
+        self._proto_action: Dict[str, "np.ndarray"] = {}
+        self._proto_domain: Dict[str, "np.ndarray"] = {}
+
+        # Prototype phrases (short sentences > single tokens for embeddings)
+        self._action_phrases = {
+            "REQ": [
+                "Please do this task.",
+                "Can you help with this request?",
+                "I need you to do something.",
+            ],
+            "INF": [
+                "FYI, here is a status update.",
+                "I finished the task.",
+                "This is an informational update.",
+            ],
+            "EVAL": [
+                "Please review and evaluate this.",
+                "Assess the quality of this work.",
+                "Give a critique of this design.",
+            ],
+            "CMD": [
+                "Do this immediately.",
+                "Execute this command.",
+                "Run the operation now.",
+            ],
+            "OBS": [
+                "I noticed something changed.",
+                "The current state is...",
+                "I observed an issue.",
+            ],
+            "PROP": [
+                "I suggest we do this.",
+                "Here's my proposal.",
+                "We could try this approach.",
+            ],
+        }
+        self._domain_phrases = {
+            "TASK": ["Assign a task ticket.", "Work item status update."],
+            "QA": ["Request code review.", "Review pull request."],
+            "INFRA": ["Scale the Kubernetes cluster.", "Deploy infrastructure change."],
+            "AUTH": ["OAuth login issue.", "Authentication and authorization."],
+            "ERR": ["System error occurred.", "Critical failure and outage."],
+            "DOC": ["Update documentation.", "Write technical docs."],
+            "META": ["Discuss process and coordination.", "Team protocol and planning."],
+            "GEN": ["General conversation.", "Generic request or update."],
+        }
+
+    def prime(self) -> None:
+        """Compute prototype embeddings (if embedder available)."""
+        if not self._embed_batch or not HAS_NUMPY:
+            return
+
+        def _norm(vec):
+            n = np.linalg.norm(vec)
+            return vec / (n + 1e-12) if n > 0 else vec
+
+        # Actions
+        action_labels = list(self._action_phrases.keys())
+        action_texts = [" ".join(self._action_phrases[a]) for a in action_labels]
+        action_vecs = self._embed_batch(action_texts)
+        for a, v in zip(action_labels, action_vecs):
+            self._proto_action[a] = _norm(np.asarray(v, dtype=np.float32))
+
+        # Domains
+        domain_labels = list(self._domain_phrases.keys())
+        domain_texts = [" ".join(self._domain_phrases[d]) for d in domain_labels]
+        domain_vecs = self._embed_batch(domain_texts)
+        for d, v in zip(domain_labels, domain_vecs):
+            self._proto_domain[d] = _norm(np.asarray(v, dtype=np.float32))
+
+    def infer(self, text: str, vec: Optional["np.ndarray"] = None) -> SemanticCoords:
+        """
+        Infer semantic coordinates from text.
+
+        Args:
+            text: The input text to analyze
+            vec: Optional pre-computed embedding vector for refinement
+
+        Returns:
+            SemanticCoords with inferred action, polarity, domain, urgency
+        """
+        t = text.strip()
+        low = t.lower()
+
+        # --- urgency (heuristic) ---
+        urgency = 0
+        if any(k in low for k in ("critical", "sev1", "sev-1", "p0", "immediately", "right now")):
+            urgency = 3
+        elif any(k in low for k in ("urgent", "asap", "high priority", "blocker")):
+            urgency = 2
+        elif any(k in low for k in ("soon", "priority", "important")):
+            urgency = 1
+
+        # --- polarity (heuristic) ---
+        polarity = 0
+        if any(k in low for k in ("error", "failed", "failure", "crash", "broken", "outage", "bug", "can't", "cannot")):
+            polarity = -1
+        elif any(k in low for k in ("fixed", "resolved", "success", "completed", "done", "working now", "all good")):
+            polarity = 1
+
+        # --- action (heuristic) ---
+        action = "INF"
+        if t.endswith("?") or low.startswith(("can you", "could you", "would you", "please")):
+            action = "REQ"
+        if any(k in low for k in ("review", "critique", "evaluate", "assess")):
+            action = "EVAL"
+        if any(k in low for k in ("do this", "run ", "execute", "deploy", "scale ", "restart")) and urgency >= 2:
+            action = "CMD"
+        if any(k in low for k in ("i noticed", "i see", "detected", "observed")):
+            action = "OBS"
+        if any(k in low for k in ("i suggest", "i propose", "we could", "how about")):
+            action = "PROP"
+
+        # --- domain (heuristic) ---
+        domain = "GEN"
+        if any(k in low for k in ("kubernetes", "k8s", "cluster", "deploy", "terraform", "docker", "infra", "server", "latency")):
+            domain = "INFRA"
+        elif any(k in low for k in ("auth", "oauth", "login", "jwt", "sso", "permission")):
+            domain = "AUTH"
+        elif any(k in low for k in ("review", "pull request", "pr ", "qa", "test")):
+            domain = "QA"
+        elif any(k in low for k in ("task", "ticket", "jira", "backlog")):
+            domain = "TASK"
+        elif any(k in low for k in ("error", "exception", "stacktrace", "failed", "failure", "outage")):
+            domain = "ERR"
+        elif any(k in low for k in ("doc", "documentation", "readme", "spec", "paper")):
+            domain = "DOC"
+        elif any(k in low for k in ("protocol", "manifold", "coordination", "orchestrator")):
+            domain = "META"
+
+        # Optional refinement via prototype similarity
+        if vec is not None and HAS_NUMPY and self._proto_action and self._proto_domain:
+            def _norm(v):
+                n = np.linalg.norm(v)
+                return v / (n + 1e-12) if n > 0 else v
+
+            v = _norm(vec.astype(np.float32, copy=False))
+
+            # Action refine
+            a_best, a_score = action, -1.0
+            for a, pv in self._proto_action.items():
+                s = float(np.dot(v, pv))
+                if s > a_score:
+                    a_best, a_score = a, s
+            if a_score >= 0.40:
+                action = a_best
+
+            # Domain refine
+            d_best, d_score = domain, -1.0
+            for d, pv in self._proto_domain.items():
+                s = float(np.dot(v, pv))
+                if s > d_score:
+                    d_best, d_score = d, s
+            if d_score >= 0.35:
+                domain = d_best
+
+        # If action is REQ and urgency not set, default to 1
+        if action == "REQ" and urgency == 0:
+            urgency = 1
+
+        return SemanticCoords(action=action, polarity=polarity, domain=domain, urgency=urgency)
+
+
+# Global coords inferer instance
+_coords_inferer: Optional[CoordsInferer] = None
+
+
+def get_coords_inferer() -> CoordsInferer:
+    """Get or create the default CoordsInferer."""
+    global _coords_inferer
+    if _coords_inferer is None:
+        _coords_inferer = CoordsInferer()
+    return _coords_inferer
+
+
+def infer_coords(text: str, vec: Optional["np.ndarray"] = None) -> tuple[int, ...]:
+    """
+    Infer UCR-compatible coordinates from text.
+
+    Returns a tuple of 4 integers suitable for UCRAnchor.coords.
+    """
+    inferer = get_coords_inferer()
+    sc = inferer.infer(text, vec)
+    return semantic_coords_to_tuple(sc)
+
+
+# ============ Quantization Result ============
+
+@dataclass
+class QuantizeResult:
+    """
+    Result of quantizing a thought to a UCR anchor.
+
+    Attributes:
+        anchor: The matched UCR anchor
+        confidence: How well the thought matches (0.0-1.0)
+        method: How the match was made ("keyword", "embedding", "fallback")
+        alternatives: Other possible matches with their scores
+    """
+    anchor: UCRAnchor
+    confidence: float
+    method: str
+    alternatives: list[tuple[UCRAnchor, float]] = field(default_factory=list)
+
+    @property
+    def is_fallback(self) -> bool:
+        return self.anchor.mnemonic == "Fallback"
+
+    @property
+    def is_high_confidence(self) -> bool:
+        return self.confidence >= 0.7
+
+
+# ============ Keyword-Based Quantizer ============
+
+# Keyword patterns for each anchor category
+_KEYWORD_PATTERNS: dict[str, list[str]] = {
+    # Observations
+    "ObserveState": ["state", "current", "status", "environment", "system state"],
+    "ObserveChange": ["changed", "detected", "noticed", "updated", "modified"],
+    "ObserveError": ["error", "exception", "failed", "crash", "bug"],
+
+    # Information
+    "InformResult": ["result", "output", "computed", "calculated", "returns"],
+    "InformStatus": ["status", "update", "progress", "currently"],
+    "InformComplete": ["complete", "finished", "done", "completed", "success"],
+    "InformBlocked": ["blocked", "waiting", "stuck", "depends on", "need"],
+    "InformProgress": ["progress", "working on", "making progress", "underway"],
+
+    # Questions
+    "AskClarify": ["clarify", "what do you mean", "unclear", "confused", "explain"],
+    "AskStatus": ["what is the status", "how is", "progress on", "update on"],
+    "AskPermission": ["can i", "may i", "permission", "allowed", "okay to"],
+    "AskResource": ["available", "resource", "capacity", "do we have"],
+
+    # Requests
+    "RequestTask": ["please do", "execute", "perform", "run", "implement"],
+    "RequestPlan": ["create a plan", "plan for", "how should we", "strategy"],
+    "RequestReview": ["review", "check", "look at", "evaluate", "feedback"],
+    "RequestHelp": ["help", "assist", "support", "guidance", "advice"],
+    "RequestCancel": ["cancel", "abort", "stop", "nevermind", "forget"],
+    "RequestPriority": ["priority", "urgent", "expedite", "escalate"],
+    "RequestResource": ["allocate", "provision", "need resource", "require"],
+
+    # Proposals
+    "ProposePlan": ["propose", "suggest", "recommendation", "i think we should"],
+    "ProposeChange": ["change", "modify", "alter", "adjust"],
+    "ProposeAlternative": ["alternative", "instead", "another approach", "option"],
+    "ProposeRollback": ["rollback", "revert", "undo", "go back"],
+
+    # Commitments
+    "CommitTask": ["i will", "i'll do", "on it", "taking this", "i commit"],
+    "CommitDeadline": ["by", "deadline", "eta", "deliver by"],
+    "CommitResource": ["allocating", "providing", "assigning"],
+
+    # Evaluations
+    "EvalApprove": ["approved", "lgtm", "looks good", "accept", "ship it"],
+    "EvalReject": ["rejected", "no", "denied", "not acceptable", "wrong"],
+    "EvalNeedsWork": ["needs work", "revise", "changes needed", "almost"],
+    "EvalComplete": ["complete", "done", "finished", "all good"],
+    "EvalBlocked": ["blocked", "cannot proceed", "impediment"],
+
+    # Meta
+    "MetaAck": ["ack", "acknowledged", "got it", "received", "understood"],
+    "MetaSync": ["sync", "ping", "alive", "heartbeat"],
+    "MetaHandoff": ["handoff", "transfer", "passing to", "your turn"],
+    "MetaEscalate": ["escalate", "raise", "need manager", "above my paygrade"],
+    "MetaAbort": ["abort", "emergency stop", "halt", "critical failure"],
+
+    # Accept/Reject
+    "Accept": ["yes", "accept", "agreed", "confirmed", "affirmative"],
+    "Reject": ["no", "reject", "disagree", "refuse", "decline"],
+    "AcceptWithCondition": ["yes but", "if", "conditional", "provided that"],
+    "Defer": ["later", "defer", "postpone", "not now", "revisit"],
+
+    # Errors
+    "ErrorGeneric": ["error", "failed", "exception"],
+    "ErrorTimeout": ["timeout", "timed out", "too slow"],
+    "ErrorResource": ["resource unavailable", "out of", "exhausted"],
+    "ErrorPermission": ["permission denied", "unauthorized", "forbidden"],
+    "ErrorValidation": ["invalid", "validation failed", "bad input"],
+}
+
+
+def _keyword_score(thought: str, patterns: list[str]) -> float:
+    """Score how well a thought matches keyword patterns."""
+    thought_lower = thought.lower()
+    matches = 0
+    for pattern in patterns:
+        if pattern.lower() in thought_lower:
+            # Longer patterns are stronger signals
+            matches += len(pattern.split())
+    # Normalize to 0-1 range (cap at 1.0)
+    return min(1.0, matches / 3.0)
+
+
+class KeywordQuantizer:
+    """
+    Simple keyword-based quantizer. No ML dependencies.
+    Good for bootstrapping and low-latency scenarios.
+    """
+
+    def __init__(self, ucr: Optional[UCR] = None, fallback_threshold: float = 0.2):
+        self.ucr = ucr or get_default_ucr()
+        self.fallback_threshold = fallback_threshold
+        self._usage_stats: Counter = Counter()
+
+    def quantize(self, thought: str) -> QuantizeResult:
+        """
+        Map a natural language thought to the best UCR anchor.
+
+        Args:
+            thought: The agent's thought/intent in natural language
+
+        Returns:
+            QuantizeResult with the best anchor and confidence score
+        """
+        scores: list[tuple[UCRAnchor, float]] = []
+
+        for mnemonic, patterns in _KEYWORD_PATTERNS.items():
+            anchor = self.ucr.get_by_mnemonic(mnemonic)
+            if anchor:
+                score = _keyword_score(thought, patterns)
+                if score > 0:
+                    scores.append((anchor, score))
+
+        # Sort by score descending
+        scores.sort(key=lambda x: x[1], reverse=True)
+
+        if not scores or scores[0][1] < self.fallback_threshold:
+            # Use fallback
+            fallback = self.ucr.get_by_mnemonic("Fallback")
+            self._usage_stats["_fallback"] += 1
+            return QuantizeResult(
+                anchor=fallback,
+                confidence=0.0,
+                method="fallback",
+                alternatives=scores[:3],
+            )
+
+        best_anchor, best_score = scores[0]
+        self._usage_stats[best_anchor.mnemonic] += 1
+
+        return QuantizeResult(
+            anchor=best_anchor,
+            confidence=best_score,
+            method="keyword",
+            alternatives=scores[1:4],
+        )
+
+    def get_usage_stats(self) -> dict[str, int]:
+        """Get usage statistics for UCR evolution analysis."""
+        return dict(self._usage_stats)
+
+    def get_fallback_rate(self) -> float:
+        """Get the rate of fallback usage (indicates UCR coverage gaps)."""
+        total = sum(self._usage_stats.values())
+        if total == 0:
+            return 0.0
+        return self._usage_stats["_fallback"] / total
+
+
+# ============ Embedding-Based Quantizer (Enhanced) ============
+
+class EmbeddingQuantizer:
+    """
+    Embedding-based quantizer using sentence-transformers.
+
+    Enhanced with v3's centroid matrix approach:
+    - Pre-computes normalized centroid matrix for fast similarity search
+    - Supports anchor centroids (when available) or on-the-fly embedding
+    - Includes CoordsInferer for prototype refinement
+
+    Usage:
+        quantizer = EmbeddingQuantizer()
+        result = quantizer.quantize("I need someone to review this code")
+    """
+
+    def __init__(
+        self,
+        ucr: Optional[UCR] = None,
+        model_name: str = "all-MiniLM-L6-v2",
+        fallback_threshold: float = 0.55,
+    ):
+        if not HAS_NUMPY:
+            raise ImportError("numpy is required for EmbeddingQuantizer")
+
+        self.ucr = ucr or get_default_ucr()
+        self.fallback_threshold = fallback_threshold
+        self._usage_stats: Counter = Counter()
+        self._fallback_buffer: List[str] = []  # Track low-confidence messages
+
+        # Lazy load sentence-transformers
+        self._model = None
+        self._model_name = model_name
+
+        # Centroid matrix (normalized) for fast similarity
+        self._centroids_matrix: Optional["np.ndarray"] = None
+        self._anchor_indices: List[int] = []  # Maps matrix row to anchor index
+        self._embed_dim: Optional[int] = None
+
+        # Coords inferer with prototype refinement
+        self._coords_inferer: Optional[CoordsInferer] = None
+
+    def _ensure_model(self):
+        """Lazy load the embedding model and build centroid matrix."""
+        if self._model is not None:
+            return
+
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ImportError:
+            raise ImportError(
+                "sentence-transformers is required for EmbeddingQuantizer. "
+                "Install with: pip install sentence-transformers"
+            )
+
+        self._model = SentenceTransformer(self._model_name)
+        self._rebuild_index()
+
+        # Initialize coords inferer with embeddings
+        self._coords_inferer = CoordsInferer(embed_batch=self._embed_batch)
+        self._coords_inferer.prime()
+
+    def _embed_batch(self, texts: List[str]) -> "np.ndarray":
+        """Embed a batch of texts and return normalized vectors."""
+        if not self._model:
+            self._ensure_model()
+        vecs = self._model.encode(texts, convert_to_numpy=True)
+        vecs = np.asarray(vecs, dtype=np.float32)
+        self._embed_dim = vecs.shape[1]
+        # Normalize rows
+        norms = np.linalg.norm(vecs, axis=1, keepdims=True)
+        return vecs / (norms + 1e-12)
+
+    def _embed_one(self, text: str) -> "np.ndarray":
+        """Embed a single text and return normalized vector."""
+        return self._embed_batch([text])[0]
+
+    def _rebuild_index(self):
+        """Build/rebuild the centroid matrix from UCR anchors."""
+        if not self.ucr.anchors:
+            self._anchor_indices = []
+            self._centroids_matrix = None
+            return
+
+        self._anchor_indices = sorted(self.ucr.anchors.keys())
+        anchors = [self.ucr.anchors[idx] for idx in self._anchor_indices]
+
+        # Check if anchors have pre-computed centroids
+        has_centroids = all(a.centroid is not None for a in anchors)
+
+        if has_centroids:
+            # Use pre-computed centroids
+            mat = np.asarray([a.centroid for a in anchors], dtype=np.float32)
+        else:
+            # Compute centroids from canonical texts
+            canonical_texts = [a.canonical for a in anchors]
+            mat = self._embed_batch(canonical_texts)
+            # Optionally store centroids back to anchors
+            for anchor, vec in zip(anchors, mat):
+                anchor.centroid = vec.tolist()
+
+        # Normalize rows
+        norms = np.linalg.norm(mat, axis=1, keepdims=True)
+        self._centroids_matrix = mat / (norms + 1e-12)
+
+    def quantize(self, thought: str) -> QuantizeResult:
+        """
+        Map a natural language thought to the best UCR anchor using embeddings.
+
+        Uses normalized cosine similarity against centroid matrix for fast lookup.
+
+        Args:
+            thought: The agent's thought/intent in natural language
+
+        Returns:
+            QuantizeResult with the best anchor and confidence score
+        """
+        self._ensure_model()
+
+        if self._centroids_matrix is None or len(self._anchor_indices) == 0:
+            fallback = self.ucr.get_by_mnemonic("Fallback")
+            self._fallback_buffer.append(thought)
+            return QuantizeResult(
+                anchor=fallback,
+                confidence=0.0,
+                method="fallback",
+                alternatives=[],
+            )
+
+        # Embed the thought (normalized)
+        thought_vec = self._embed_one(thought)
+
+        # Compute cosine similarities (dot product of normalized vectors)
+        similarities = np.dot(self._centroids_matrix, thought_vec)
+
+        # Get top matches
+        top_locs = np.argsort(similarities)[::-1][:5]
+        scores = []
+        for loc in top_locs:
+            anchor_idx = self._anchor_indices[loc]
+            anchor = self.ucr.anchors[anchor_idx]
+            scores.append((anchor, float(similarities[loc])))
+
+        best_anchor, best_score = scores[0]
+
+        if best_score < self.fallback_threshold:
+            fallback = self.ucr.get_by_mnemonic("Fallback")
+            self._usage_stats["_fallback"] += 1
+            self._fallback_buffer.append(thought)
+            return QuantizeResult(
+                anchor=fallback,
+                confidence=best_score,
+                method="fallback",
+                alternatives=scores[:3],
+            )
+
+        self._usage_stats[best_anchor.mnemonic] += 1
+
+        return QuantizeResult(
+            anchor=best_anchor,
+            confidence=best_score,
+            method="embedding",
+            alternatives=scores[1:4],
+        )
+
+    def get_fallback_buffer(self) -> List[str]:
+        """Get the buffer of low-confidence messages for extension learning."""
+        return self._fallback_buffer.copy()
+
+    def clear_fallback_buffer(self) -> None:
+        """Clear the fallback buffer after extension learning."""
+        self._fallback_buffer.clear()
+
+    def infer_coords_for_text(self, text: str) -> tuple[int, ...]:
+        """
+        Infer semantic coordinates for a text using the enhanced CoordsInferer.
+
+        Returns UCR-compatible coordinate tuple.
+        """
+        self._ensure_model()
+        vec = self._embed_one(text)
+        if self._coords_inferer:
+            sc = self._coords_inferer.infer(text, vec)
+            return semantic_coords_to_tuple(sc)
+        return infer_coords(text, vec)
+
+    def compute_centroid(self, texts: List[str]) -> List[float]:
+        """
+        Compute the centroid embedding for a cluster of texts.
+
+        Useful for creating new extension anchors.
+        """
+        self._ensure_model()
+        embeds = self._embed_batch(texts)
+        centroid = np.mean(embeds, axis=0)
+        # Normalize
+        norm = np.linalg.norm(centroid)
+        if norm > 0:
+            centroid = centroid / norm
+        return centroid.tolist()
+
+    def get_usage_stats(self) -> dict[str, int]:
+        """Get usage statistics for UCR evolution analysis."""
+        return dict(self._usage_stats)
+
+    def get_fallback_rate(self) -> float:
+        """Get the rate of fallback usage."""
+        total = sum(self._usage_stats.values())
+        if total == 0:
+            return 0.0
+        return self._usage_stats["_fallback"] / total
+
+
+# ============ Auto-selecting Quantizer ============
+
+def create_quantizer(
+    ucr: Optional[UCR] = None,
+    prefer_embeddings: bool = True,
+    fallback_threshold: float = 0.3,
+) -> KeywordQuantizer | EmbeddingQuantizer:
+    """
+    Create the best available quantizer.
+
+    Args:
+        ucr: UCR instance to use
+        prefer_embeddings: Try to use embedding quantizer if available
+        fallback_threshold: Confidence threshold for fallback
+
+    Returns:
+        EmbeddingQuantizer if available and preferred, else KeywordQuantizer
+    """
+    if prefer_embeddings:
+        try:
+            return EmbeddingQuantizer(ucr=ucr, fallback_threshold=fallback_threshold)
+        except ImportError:
+            pass
+
+    return KeywordQuantizer(ucr=ucr, fallback_threshold=fallback_threshold)
+
+
+# ============ High-Level API ============
+
+_default_quantizer: Optional[KeywordQuantizer | EmbeddingQuantizer] = None
+
+
+def quantize(thought: str) -> QuantizeResult:
+    """
+    Quantize a thought to a UCR anchor using the default quantizer.
+
+    This is the main entry point for the Think-Quantize-Transmit pattern.
+
+    Example:
+        >>> result = quantize("Please review the authentication code")
+        >>> result.anchor.mnemonic
+        'RequestReview'
+        >>> result.confidence
+        0.67
+    """
+    global _default_quantizer
+    if _default_quantizer is None:
+        _default_quantizer = create_quantizer(prefer_embeddings=False)
+    return _default_quantizer.quantize(thought)
+
+
+def think_quantize_transmit(
+    thought: str,
+    src: str,
+    dst: str,
+    ucr: Optional[UCR] = None,
+) -> str:
+    """
+    The complete Think-Quantize-Transmit flow.
+
+    Takes a natural language thought and produces a wire-ready SLIP message.
+
+    Args:
+        thought: Natural language intent
+        src: Source agent identifier
+        dst: Destination agent identifier
+        ucr: Optional UCR instance
+
+    Returns:
+        Wire-format SLIP message string
+
+    Example:
+        >>> wire = think_quantize_transmit(
+        ...     "I need someone to check this code for security issues",
+        ...     src="developer",
+        ...     dst="reviewer"
+        ... )
+        >>> wire
+        'SLIP v1 developer reviewer RequestReview'
+    """
+    try:
+        from .protocol import slip, fallback as slip_fallback
+    except ImportError:
+        # Inline simple slip format when protocol not available
+        def slip(src, dst, mnemonic, ucr=None):
+            return f"SLIP v1 {src} {dst} {mnemonic}"
+        def slip_fallback(src, dst, payload, ucr=None):
+            return f'SLIP v1 {src} {dst} Fallback "{payload}"'
+
+    result = quantize(thought)
+
+    if result.is_fallback:
+        return slip_fallback(src, dst, thought, ucr)
+    else:
+        return slip(src, dst, result.anchor.mnemonic, ucr=ucr)
+
+
+# ============ Smoke Test ============
+
+if __name__ == "__main__":
+    print("=== Semantic Quantizer Demo ===\n")
+
+    test_thoughts = [
+        "Please review the authentication module for security issues",
+        "I've finished implementing the feature",
+        "What's the current status of the deployment?",
+        "I propose we use Redis for caching instead of Memcached",
+        "Yes, that looks good to me",
+        "There's an error in the payment processing code",
+        "I'm blocked waiting for the API credentials",
+        "Check the auth logs for timing anomalies in the OAuth flow",  # Should fallback
+    ]
+
+    quantizer = KeywordQuantizer()
+
+    for thought in test_thoughts:
+        result = quantizer.quantize(thought)
+        status = "FALLBACK" if result.is_fallback else f"{result.confidence:.2f}"
+        print(f"Thought: {thought[:50]}...")
+        print(f"  → {result.anchor.mnemonic} ({status})")
+        if result.alternatives:
+            alt_str = ", ".join(f"{a.mnemonic}:{s:.2f}" for a, s in result.alternatives[:2])
+            print(f"  Alternatives: {alt_str}")
+        print()
+
+    print(f"Fallback rate: {quantizer.get_fallback_rate():.1%}")
+    print(f"\nUsage stats: {quantizer.get_usage_stats()}")

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+huggingface_hub[inference]
+sentence-transformers
+numpy

ucr.py

@@ -0,0 +1,311 @@
+"""
+Universal Concept Reference (UCR) - The Semantic Manifold
+
+The UCR is a quantized semantic coordinate system for agent communication.
+Instead of transmitting embeddings (model-specific, high-dimensional), agents
+communicate via positions in a shared, low-dimensional semantic manifold.
+
+Core concepts:
+- Dimensions: Semantic axes (action, urgency, domain, polarity)
+- Anchors: Named positions in the manifold (common agent intents)
+- Quantization: Map agent thoughts to nearest anchor
+
+Architecture:
+- Core UCR (0x0000-0x7FFF): Standard anchors, immutable per version
+- Extension UCR (0x8000-0xFFFF): Installation-specific, evolvable
+"""
+
+from __future__ import annotations
+from dataclasses import dataclass, field
+from enum import IntEnum
+from typing import Optional
+import json
+from pathlib import Path
+
+
+# ============ Semantic Dimensions ============
+# The axes of our semantic manifold. Kept minimal per design.
+
+class Dimension(IntEnum):
+    """
+    The semantic axes of the UCR manifold.
+    Each dimension represents a fundamental aspect of agent communication.
+    """
+    ACTION = 0      # What type of action: observe, inform, request, propose, evaluate
+    POLARITY = 1    # Direction: initiating vs responding, positive vs negative
+    DOMAIN = 2      # Context: task, plan, observation, evaluation, control
+    URGENCY = 3     # Priority: routine, elevated, critical
+
+
+# Discrete levels per dimension (kept small for token efficiency)
+LEVELS_PER_DIM = 8
+
+
+# ============ UCR Entry (Anchor) ============
+
+@dataclass
+class UCRAnchor:
+    """
+    A named position in the semantic manifold.
+
+    Attributes:
+        index: Unique identifier (0x0000-0xFFFF)
+        mnemonic: Single-token wire representation (e.g., "RequestReview")
+        canonical: Human-readable description
+        coords: Position in the manifold (one value per dimension)
+        is_core: True if part of standard UCR, False if extension
+        centroid: Optional embedding vector for ML-based quantization
+    """
+    index: int
+    mnemonic: str
+    canonical: str
+    coords: tuple[int, ...]  # One int per dimension, each 0 to LEVELS_PER_DIM-1
+    is_core: bool = True
+    centroid: Optional[list[float]] = None  # Embedding vector for ML quantization
+
+    def __post_init__(self):
+        if len(self.coords) != len(Dimension):
+            raise ValueError(f"coords must have {len(Dimension)} values, got {len(self.coords)}")
+        for i, c in enumerate(self.coords):
+            if not (0 <= c < LEVELS_PER_DIM):
+                raise ValueError(f"coord[{i}] must be 0-{LEVELS_PER_DIM-1}, got {c}")
+
+    def to_dict(self) -> dict:
+        result = {
+            "index": self.index,
+            "mnemonic": self.mnemonic,
+            "canonical": self.canonical,
+            "coords": list(self.coords),
+            "is_core": self.is_core,
+        }
+        if self.centroid is not None:
+            result["centroid"] = self.centroid
+        return result
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "UCRAnchor":
+        return cls(
+            index=d["index"],
+            mnemonic=d["mnemonic"],
+            canonical=d["canonical"],
+            coords=tuple(d["coords"]),
+            is_core=d.get("is_core", True),
+            centroid=d.get("centroid"),
+        )
+
+
+# ============ UCR Registry ============
+
+CORE_RANGE_END = 0x8000  # 0x0000-0x7FFF = core, 0x8000-0xFFFF = extensions
+
+
+@dataclass
+class UCR:
+    """
+    The Universal Concept Reference - a semantic manifold for agent communication.
+
+    Contains both core (standard) anchors and extension (local) anchors.
+    Provides lookup by index, mnemonic, and nearest-neighbor by coordinates.
+    """
+    version: str
+    anchors: dict[int, UCRAnchor] = field(default_factory=dict)
+    _mnemonic_index: dict[str, int] = field(default_factory=dict, repr=False)
+
+    def add_anchor(self, anchor: UCRAnchor) -> None:
+        """Add an anchor to the registry."""
+        if anchor.index in self.anchors:
+            raise ValueError(f"Anchor index {anchor.index:#06x} already exists")
+        if anchor.mnemonic in self._mnemonic_index:
+            raise ValueError(f"Anchor mnemonic '{anchor.mnemonic}' already exists")
+
+        # Validate core vs extension range
+        if anchor.is_core and anchor.index >= CORE_RANGE_END:
+            raise ValueError(f"Core anchor index must be < {CORE_RANGE_END:#06x}")
+        if not anchor.is_core and anchor.index < CORE_RANGE_END:
+            raise ValueError(f"Extension anchor index must be >= {CORE_RANGE_END:#06x}")
+
+        self.anchors[anchor.index] = anchor
+        self._mnemonic_index[anchor.mnemonic] = anchor.index
+
+    def get_by_index(self, index: int) -> Optional[UCRAnchor]:
+        """Lookup anchor by numeric index."""
+        return self.anchors.get(index)
+
+    def get_by_mnemonic(self, mnemonic: str) -> Optional[UCRAnchor]:
+        """Lookup anchor by mnemonic string."""
+        idx = self._mnemonic_index.get(mnemonic)
+        return self.anchors.get(idx) if idx is not None else None
+
+    def find_nearest(self, coords: tuple[int, ...]) -> UCRAnchor:
+        """
+        Find the anchor nearest to the given coordinates.
+        Uses Manhattan distance for simplicity and speed.
+        """
+        if not self.anchors:
+            raise ValueError("UCR has no anchors")
+
+        best_anchor = None
+        best_distance = float('inf')
+
+        for anchor in self.anchors.values():
+            distance = sum(abs(a - b) for a, b in zip(anchor.coords, coords))
+            if distance < best_distance:
+                best_distance = distance
+                best_anchor = anchor
+
+        return best_anchor
+
+    def core_anchors(self) -> list[UCRAnchor]:
+        """Return all core (standard) anchors."""
+        return [a for a in self.anchors.values() if a.is_core]
+
+    def extension_anchors(self) -> list[UCRAnchor]:
+        """Return all extension (local) anchors."""
+        return [a for a in self.anchors.values() if not a.is_core]
+
+    def next_extension_index(self) -> int:
+        """Get the next available extension index."""
+        ext_indices = [a.index for a in self.anchors.values() if not a.is_core]
+        if not ext_indices:
+            return CORE_RANGE_END
+        return max(ext_indices) + 1
+
+    def save(self, path: Path) -> None:
+        """Save UCR to JSON file."""
+        data = {
+            "version": self.version,
+            "anchors": [a.to_dict() for a in self.anchors.values()],
+        }
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
+
+    @classmethod
+    def load(cls, path: Path) -> "UCR":
+        """Load UCR from JSON file."""
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        ucr = cls(version=data["version"])
+        for anchor_data in data["anchors"]:
+            ucr.add_anchor(UCRAnchor.from_dict(anchor_data))
+        return ucr
+
+    def __len__(self) -> int:
+        return len(self.anchors)
+
+
+# ============ Base UCR Factory ============
+
+def create_base_ucr() -> UCR:
+    """
+    Create the base UCR with core anchors for common agent intents.
+
+    Coordinate scheme (4 dimensions, 8 levels each):
+    - ACTION:   0=observe, 1=inform, 2=ask, 3=request, 4=propose, 5=commit, 6=evaluate, 7=meta
+    - POLARITY: 0=negative, 1-3=declining, 4=neutral, 5-6=positive, 7=strong_positive
+    - DOMAIN:   0=task, 1=plan, 2=observation, 3=evaluation, 4=control, 5=resource, 6=error, 7=general
+    - URGENCY:  0=background, 1-2=low, 3-4=normal, 5-6=elevated, 7=critical
+    """
+    ucr = UCR(version="1.0.0")
+
+    # Core coordination anchors
+    # Format: (index, mnemonic, canonical, (action, polarity, domain, urgency))
+
+    core_anchors = [
+        # === Observations (ACTION=0) ===
+        (0x0001, "ObserveState", "Report current system or environment state", (0, 4, 2, 3)),
+        (0x0002, "ObserveChange", "Report a detected change", (0, 4, 2, 4)),
+        (0x0003, "ObserveError", "Report an observed error condition", (0, 2, 6, 6)),
+
+        # === Information (ACTION=1) ===
+        (0x0010, "InformResult", "Share a computed or derived result", (1, 5, 2, 3)),
+        (0x0011, "InformStatus", "Provide status update", (1, 4, 0, 3)),
+        (0x0012, "InformComplete", "Report task completion", (1, 6, 0, 4)),
+        (0x0013, "InformBlocked", "Report being blocked on something", (1, 2, 0, 5)),
+        (0x0014, "InformProgress", "Share progress update", (1, 5, 0, 3)),
+
+        # === Questions (ACTION=2) ===
+        (0x0020, "AskClarify", "Request clarification on requirements", (2, 4, 1, 4)),
+        (0x0021, "AskStatus", "Query current status", (2, 4, 0, 3)),
+        (0x0022, "AskPermission", "Request permission to proceed", (2, 4, 4, 4)),
+        (0x0023, "AskResource", "Query resource availability", (2, 4, 5, 3)),
+
+        # === Requests (ACTION=3) ===
+        (0x0030, "RequestTask", "Request execution of a task", (3, 4, 0, 4)),
+        (0x0031, "RequestPlan", "Request creation of a plan", (3, 4, 1, 4)),
+        (0x0032, "RequestReview", "Request review of work", (3, 4, 3, 3)),
+        (0x0033, "RequestHelp", "Request assistance", (3, 4, 7, 5)),
+        (0x0034, "RequestCancel", "Request cancellation", (3, 1, 4, 5)),
+        (0x0035, "RequestPriority", "Request priority change", (3, 4, 4, 5)),
+        (0x0036, "RequestResource", "Request allocation of resource", (3, 4, 5, 4)),
+
+        # === Proposals (ACTION=4) ===
+        (0x0040, "ProposePlan", "Propose a plan for consideration", (4, 5, 1, 4)),
+        (0x0041, "ProposeChange", "Propose a modification", (4, 5, 0, 4)),
+        (0x0042, "ProposeAlternative", "Propose an alternative approach", (4, 5, 1, 4)),
+        (0x0043, "ProposeRollback", "Propose reverting changes", (4, 3, 4, 5)),
+
+        # === Commitments (ACTION=5) ===
+        (0x0050, "CommitTask", "Commit to performing a task", (5, 6, 0, 4)),
+        (0x0051, "CommitDeadline", "Commit to a deadline", (5, 6, 0, 4)),
+        (0x0052, "CommitResource", "Commit resources", (5, 6, 5, 4)),
+
+        # === Evaluations (ACTION=6) ===
+        (0x0060, "EvalApprove", "Evaluation: approved/positive", (6, 7, 3, 4)),
+        (0x0061, "EvalReject", "Evaluation: rejected/negative", (6, 0, 3, 4)),
+        (0x0062, "EvalNeedsWork", "Evaluation: needs revision", (6, 3, 3, 4)),
+        (0x0063, "EvalComplete", "Evaluation: work is complete", (6, 6, 3, 4)),
+        (0x0064, "EvalBlocked", "Evaluation: blocked by issue", (6, 2, 3, 5)),
+
+        # === Meta/Control (ACTION=7) ===
+        (0x0070, "MetaAck", "Acknowledge receipt", (7, 5, 4, 2)),
+        (0x0071, "MetaSync", "Synchronization ping", (7, 4, 4, 3)),
+        (0x0072, "MetaHandoff", "Hand off responsibility", (7, 4, 4, 4)),
+        (0x0073, "MetaEscalate", "Escalate to higher authority", (7, 3, 4, 6)),
+        (0x0074, "MetaAbort", "Abort current operation", (7, 0, 4, 7)),
+
+        # === Accept/Reject responses ===
+        (0x0080, "Accept", "Accept a proposal or request", (5, 7, 7, 3)),
+        (0x0081, "Reject", "Reject a proposal or request", (5, 0, 7, 3)),
+        (0x0082, "AcceptWithCondition", "Conditional acceptance", (5, 5, 7, 4)),
+        (0x0083, "Defer", "Defer decision", (5, 4, 7, 2)),
+
+        # === Error handling ===
+        (0x0090, "ErrorGeneric", "Generic error occurred", (1, 1, 6, 5)),
+        (0x0091, "ErrorTimeout", "Operation timed out", (1, 1, 6, 5)),
+        (0x0092, "ErrorResource", "Resource unavailable", (1, 1, 6, 5)),
+        (0x0093, "ErrorPermission", "Permission denied", (1, 0, 6, 5)),
+        (0x0094, "ErrorValidation", "Validation failed", (1, 1, 6, 4)),
+
+        # === Fallback ===
+        (0x00FF, "Fallback", "Unquantizable - see payload for natural language", (7, 4, 7, 4)),
+    ]
+
+    for index, mnemonic, canonical, coords in core_anchors:
+        ucr.add_anchor(UCRAnchor(
+            index=index,
+            mnemonic=mnemonic,
+            canonical=canonical,
+            coords=coords,
+            is_core=True,
+        ))
+
+    return ucr
+
+
+# Module-level default UCR instance
+_default_ucr: Optional[UCR] = None
+
+
+def get_default_ucr() -> UCR:
+    """Get or create the default UCR instance."""
+    global _default_ucr
+    if _default_ucr is None:
+        _default_ucr = create_base_ucr()
+    return _default_ucr
+
+
+def set_default_ucr(ucr: UCR) -> None:
+    """Set the default UCR instance."""
+    global _default_ucr
+    _default_ucr = ucr