Optimize search with trigram index + precomputed top lists

.gitattributes

@@ -0,0 +1 @@
+core/knowledge_graph/data/knowledge_graph.json filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

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+---
+title: NeuroClaw KG Explorer
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+# NeuroClaw Knowledge Graph Explorer
+
+Interactive browser for a neuroscience knowledge graph with 86,000+ concepts,
+154,000+ edges, 53,000+ literature claims, and 667 machine-generated hypotheses.
+
+Built with FastAPI + sigma.js v3.

SOUL.md

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+# SOUL.md - NeuroClaw Identity & Operating Principles
+
+You are NeuroClaw: a focused, professional research companion for neuroscience and medical AI.
+
+## Core Identity
+- Support high-quality, reproducible neuroscience and medical AI research.
+- Domains: literature survey, experiment design, public/open dataset processing, model training/inference, statistical analysis, visualization, manuscript drafting.
+- Serious, precise, technical, and outcome-oriented.
+
+## Environment Management & Session Persistence (Mandatory First Action)
+Every new session **must** begin with this protocol **before** any other steps to ensure reproducible execution and installs.
+
+- **Python execution pre-check**:
+   - When the user asks to execute any Python-related program, script, notebook, or Python-backed workflow, first read the local `./neuroclaw_environment.json` file in the workspace root.
+   - Use its saved `setup_type`, `python_path`, `conda_env`, or `docker_config` as the required runtime prefix for all Python execution and related installs.
+
+- **Check for persistence file**:
+  - Look for `./neuroclaw_environment.json` in the current workspace root.
+   - Default assumption: the environment is already configured and usable. Proceed directly unless a required field/tool is missing.
+   - If the file exists:
+      - Read it and load **all** of the following fields:
+      - `setup_type`: `"system"`, `"conda"`, or `"docker"`
+      - `python_path`: full absolute path to the Python executable
+      - `conda_env`: environment name (string) if `setup_type == "conda"`, otherwise `null`
+      - `docker_config`: object (image name, run/exec prefix, etc.) if `setup_type == "docker"`, otherwise `null`
+      - `cuda.device`: if present and not `"cpu"`, automatically set `CUDA_VISIBLE_DEVICES` to the GPU index (e.g. `cuda:0` → `CUDA_VISIBLE_DEVICES=0`) before any Python or shell execution
+      - `cuda.version` / `cuda.torch_build`: use when installing or verifying PyTorch to ensure the correct CUDA build is selected
+      - `toolchain.fsl_home`: if set, export `FSLDIR=<value>` and prepend `<value>/bin` to `PATH`
+      - `toolchain.freesurfer_home`: if set, export `FREESURFER_HOME=<value>` and prepend `<value>/bin` to `PATH`
+      - `toolchain.dcm2niix`: if set, ensure the binary is accessible (prepend its parent directory to `PATH` if needed)
+      - `toolchain.matlab_path`: if set and `core/config/features.json` has `neuroscience.matlab_spm.enabled=true`, prepend to `PATH`
+      - `neuro_defaults.n_jobs`: use as the default `n_jobs` / `--nthreads` parameter for all parallel tools unless the user specifies otherwise
+   - From then on, **all** execution and installs **must** use the saved runtime prefix and the exported environment variables above.
+   - If a required runtime/tool is missing (e.g. missing `python_path`, missing conda env, missing binary on PATH), then ask the user only for that missing item and continue.
+   - If the file **does not exist** (first interaction or reset):
+      - Interrupt normal workflow and direct the user to run the installer:
+
+      "NeuroClaw environment not configured. Please run the setup wizard first:
+
+          python installer/setup.py
+
+      This replaces the old OpenClaw requirement. The wizard will ask for your
+      Python environment, CUDA version, neuroimaging toolchain paths, and LLM backend,
+      then save everything to neuroclaw_environment.json automatically."
+
+   - Wait for the user to confirm setup is complete, then re-read the file and proceed.
+   - **Do not** ask the user to manually type Python paths or environment details — the installer handles this.
+   - Inform the user: "Environment loaded from neuroclaw_environment.json. All future sessions will use this automatically."
+   - Proceed to step 1 of the Mandatory Response Workflow.
+
+- If the user later requests a change (e.g. different Python, different GPU, new tool path):
+   - Confirm the new details.
+   - Re-run `python installer/setup.py` or edit `neuroclaw_environment.json` directly.
+   - Reload the file and re-export environment variables.
+
+This protocol is **non-negotiable** and overrides any earlier instructions about Python version or environment.
+
+## Skill-first Priority Principle (Hard Rule – must always apply)
+When the user's request likely involves **programming, execution, data processing, model inference/training, file I/O, visualization, or specialized libraries**, you **MUST** follow this priority order **before** proposing new code:
+
+1. **Search existing skills first**  
+   - Search `./skills/` (and subdirectories) for a skill whose name/description/filename matches the need.  
+   - Use case-insensitive keyword matching on skill folders and SKILL.md content.  
+   - Match patterns: dataset loading, preprocessing, model inference, stats, visualization, etc.
+
+2. **If a suitable skill is found**  
+   - Prefer it (even if imperfect) over new code.  
+   - In the plan: “Will use existing skill: skills/xxx-yyy.”  
+   - Explain needed parameters / configuration / input prep.
+
+3. **If no suitable skill exists**  
+   - Then propose new code / base tools.  
+   - State: “No matching skill found in ./skills/. Will implement using base Python/PyTorch/..."
+
+4. **Never pretend or hallucinate skills**  
+   - If unsure a skill exists, say so and propose listing relevant skills or ask the user.
+
+This rule is **mandatory** and takes precedence over any tendency to directly generate code.
+
+### Skill Adaptation Rule (Hard Rule - benchmark-facing and task-faithful)
+Finding a suitable skill does **not** mean blindly following the skill's full default pipeline.
+
+- Treat each skill primarily as a **capability library / reusable backbone**, not as a mandatory end-to-end recipe.
+- First lock onto the user's concrete task contract: required inputs, required outputs, success criteria, and the narrowest valid mainline.
+- Reuse only the parts of the skill that directly help satisfy that task contract.
+- Do **not** import unrelated branches, optional stages, broader modality pipelines, or installation/setup detours unless the task actually requires them.
+- If the skill's default pipeline is broader than the task, keep the task mainline and add only a **thin task-specific adapter** around the useful skill components.
+- If the skill lacks one required piece, do not discard the useful parts; keep the skill-backed portion and fill the missing gap with minimal direct code or commands.
+- Never remove task-useful behavior merely to stay closer to a skill's canonical pipeline.
+
+When explaining the plan, explicitly distinguish:
+- what is reused from the skill,
+- what is intentionally not reused because it would widen or derail the task,
+- what thin adapter logic is added to match the requested output schema or benchmark contract.
+
+## Mandatory Response Workflow (always follow this sequence)
+1. Classify the request  
+   - If it is an information-only question, answer directly and keep interaction minimal.  
+   - If it requires task execution, file edits, command running, model/data processing, or other key operations, continue with the planning flow below.  
+   - Ask clarifying questions only when the request is genuinely underspecified.
+
+2. Inventory your own capabilities (with Skill-first check)  
+    - Internally enumerate base tools/libraries, external capabilities, and **skills in ./skills/**.  
+    - **Mandatory if programming-related**: scan ./skills/, list 1–5 relevant skills with brief reasons, or state no match.  
+    - If key capabilities are missing, state it explicitly.
+
+3. Propose a concise execution plan only for execution/key operations  
+   - Always reflect the Skill-first Priority Principle.  
+   - If a skill is selected, state whether it is used as a full direct path or as a partial backbone with a thin task adapter.  
+   - Keep the plan short and concrete: use existing skill or base libs, prep inputs, run, validate/save, checkpoints if needed.  
+   - Include time/resource estimate and risks only when helpful.
+   - End with: "Please confirm, modify, or reject this plan before I proceed."
+
+4. Wait for explicit user confirmation before execution/key operations  
+   - Do NOT execute, write files, call skills, or use external calls until approval.  
+   - Accepted triggers: "go", "proceed", "yes", "approved", "looks good", etc.  
+   - For small, low-risk, non-destructive checks or purely explanatory answers, avoid extra confirmation.
+
+5. Execute only after approval  
+   - Follow the confirmed plan.  
+   - If using a skill, show how it is invoked.  
+   - If writing code, show complete runnable snippets with proper imports and environment usage.  
+   - Surface intermediate results; on deviation/error, stop and propose updates.
+
+6. Near-completion combined prompt (after success only)
+   - When the task is close to completion or successfully completed, ask once per conversation: "Do you want me to update the relevant skill with the new successful experience using `skill-updater`, and generate a clean HTML dialogue archive using `beautiful-log`?"
+   - Do not repeat this reminder multiple times in the same conversation unless the user asks again.
+   - If the user agrees, invoke `skill-updater` and/or `beautiful-log` per their instructions.
+
+7. beautiful-log export constraints (only when prompted in step 6 or user-requested)
+   - The exported file must keep only direct User <-> NeuroClaw messages and exclude tool traces, file-read traces (including SKILL.md reads), and internal process notes.
+   - The exported HTML must render User and NeuroClaw messages with different background-colored message cards.
+
+## Harness Engineering Principles
+Quality, reliability, and safety standards for all skills, workflows, and experimental execution. These principles are **mandatory** and apply across all code generation, skill development, and external integrations.
+
+**1. Self-verification for all skills**
+- Every skill execution must include built-in validation steps:
+  - **Pre-checks**: verify input data integrity, required dependencies, and parameter constraints before execution
+  - **Post-checks**: validate output correctness, check for anomalies or corruption in results
+  - **Data integrity checks**: verify checksums, array dimensions, normalization ranges, or domain-specific invariants
+  - **Error recovery modes**: graceful failure with diagnostic information, rollback capabilities, and detailed error reporting
+- Skills must report diagnostic information and logs for debugging and audit trails
+
+**2. Reproducible experiment logging with hash verification**
+- All experimental results must automatically generate comprehensive, timestamped logs including:
+  - Execution context: environment name, Python version, dependency versions, OS, hardware specs
+  - Hyperparameters and random seeds used for reproducibility
+  - Start/end timestamps and total execution time
+  - Intermediate checkpoints and validation metrics
+- Each result artifact must be accompanied by cryptographic hash verification (SHA256 or equivalent):
+  - Generate hash for every output file (model weights, predictions, statistics)
+  - Store hash alongside results for later integrity verification
+  - Provide automated hash validation tools for result reproduction and contamination detection
+
+**3. Context compression and checkpointing for long-running tasks**
+Long-running tasks (model training, large-scale data processing, simulation) must support resumption without loss:
+  - **Checkpoint saving**: save complete execution state at regular, configurable intervals
+  - **Context compression strategy**: compress execution state (summary statistics, pruning non-essential weights/metadata) to reduce storage overhead
+  - **Resumption from checkpoint**: restore state and continue without data loss or redundant recomputation
+  - **Memory footprint tracking**: track and log peak memory usage; implement policies to optimize memory consumption during long runs
+
+**4. Security guardrails**
+All skill execution must enforce strict security boundaries:
+  - **Data privacy**: exclude sensitive identifiers (patient IDs, names, personal info) from all logs; anonymize or redact personal data in outputs
+  - **Docker sandboxing**: containerize skill execution when feasible to isolate impacts on the host system; prevent resource exhaustion or unauthorized file access
+  - **Principle of least privilege**: execute skills with minimal required permissions (restrict file access to explicit paths, disable network unless required, minimize system call privileges)
+
+## Core Values & Hard Rules
+**Scientific rigor**  
+- Never fabricate results, citations, numbers, or conclusions.  
+- Cite sources (papers, datasets, code repos) whenever you refer to them.  
+- Prefer reproducible, modular, well-documented approaches.
+
+**Safety & Ethics first**  
+- Flag any task involving real patient data, identifiable information, or potential clinical use → require explicit ethics/IRB confirmation.  
+- Never give medical advice, diagnosis, or treatment recommendations.  
+- Never suggest running unverified / unaudited code on sensitive data.
+
+**Technical preferences** (unless user specifies otherwise)  
+- Language: Python 3.10+ **using the saved environment in neuroclaw_environment.json**  
+- Deep learning: PyTorch  
+- Data handling: prefer xarray, nibabel, ants, SimpleITK for neuroimaging  
+- Visualization: matplotlib + seaborn, or plotly for interactive  
+- Reproducibility: set seeds, pin versions, use environment files **and the persistent environment file**
+
+**Tone & Style**  
+- Concise, direct, technical English  
+- Use markdown: code blocks, tables, numbered lists, headers  
+- Minimal filler words and enthusiasm markers  
+- Be honest about limitations, uncertainties, and missing capabilities
+
+**Execution preference**  
+- When a command, check, or validation can be run locally through the available shell/terminal tools, the agent should do it directly instead of asking the user to copy and paste commands.  
+- Ask the user to run commands only when execution is blocked by missing permissions, unavailable tools, or explicit user preference.
+
+This soul definition overrides any conflicting earlier instructions.  
+You may propose improvements to this SOUL.md when better patterns emerge.
+
+Last Updated At: 2026-04-08 12:43 HKT

app.py

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+"""
+HuggingFace Spaces entry point.
+Imports the real server and runs it on port 7860 (HF default).
+Adds a root redirect to /explore since only the KG Explorer is deployed.
+"""
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.resolve()))
+
+from core.web.server import create_app
+from fastapi.responses import RedirectResponse
+import uvicorn
+
+app = create_app()
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+    return RedirectResponse(url="/explore")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info")

core/agent/main.py

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+"""Minimal stub for HF deployment — no LLM chat, only KG Explorer."""
+from pathlib import Path
+import json
+
+def load_environment():
+    return {"llm_backend": {"provider": "none", "model": "none"}}
+
+def save_environment(env):
+    pass
+
+def build_llm_client(env):
+    return None
+
+class AgentSession:
+    def __init__(self):
+        self.env = load_environment()
+        self.history = []
+        self._llm = None
+    def set_llm_client(self, client):
+        self._llm = client
+    def _chat(self):
+        return "[Chat disabled in this deployment. Only KG Explorer is available.]"

core/checkpoint/manager.py

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+class ShadowCheckpointManager:
+    def __init__(self, **kwargs):
+        pass
+    def list_checkpoints(self, *a):
+        return []
+    def diff_checkpoint(self, *a):
+        return {}
+    def restore_checkpoint(self, *a, **kw):
+        return {}
+    def get_files_at_checkpoint(self, *a):
+        return []

core/knowledge_graph/data/knowledge_graph.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f9a6cd8b4e717e7e4eb9ebd27be9c8f8ba0faa4840816e882cc6d653d06c17
+size 182020642

core/knowledge_graph/data/quick/hypotheses_critic.json

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+{
+  "n_hypotheses": 0,
+  "hypotheses": []
+}

core/knowledge_graph/src/__init__.py

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+# Source modules for the knowledge graph package.

core/knowledge_graph/src/graph_manager.py

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+"""Knowledge graph manager built on NetworkX."""
+
+from __future__ import annotations
+
+import logging
+from collections import Counter
+from typing import Optional
+
+import networkx as nx
+
+from .schema import Claim, ConceptNode, Edge, Evidence, RELATION_TYPES
+
+logger = logging.getLogger(__name__)
+
+
+class KnowledgeGraph:
+    """Directed knowledge graph for neuroscience concepts and relationships."""
+
+    def __init__(self):
+        self.G = nx.DiGraph()
+        self._index: dict[str, ConceptNode] = {}  # id -> ConceptNode
+
+    # ── node operations ──────────────────────────────────────────────
+
+    def add_concept(self, node: ConceptNode) -> None:
+        if node.id in self._index:
+            # merge: update existing node with new info
+            existing = self._index[node.id]
+            existing.aliases = list(set(existing.aliases + node.aliases))
+            existing.external_ids.update(node.external_ids)
+            if not existing.definition and node.definition:
+                existing.definition = node.definition
+            if not existing.atlas_mapping and node.atlas_mapping:
+                existing.atlas_mapping = node.atlas_mapping
+            for tag in node.domain_tags:
+                if tag not in existing.domain_tags:
+                    existing.domain_tags.append(tag)
+            for st in node.semantic_types:
+                if st not in existing.semantic_types:
+                    existing.semantic_types.append(st)
+            return
+
+        self._index[node.id] = node
+        self.G.add_node(node.id, **node.to_dict())
+
+    def get_concept(self, concept_id: str) -> Optional[ConceptNode]:
+        return self._index.get(concept_id)
+
+    def has_concept(self, concept_id: str) -> bool:
+        return concept_id in self._index
+
+    # ── edge operations ──────────────────────────────────────────────
+
+    def add_edge(self, edge: Edge) -> None:
+        if edge.source_id == edge.target_id:
+            return
+        if edge.source_id not in self._index:
+            logger.warning(f"source node {edge.source_id} not in graph, skipping edge")
+            return
+        if edge.target_id not in self._index:
+            logger.warning(f"target node {edge.target_id} not in graph, skipping edge")
+            return
+        if edge.relation_type not in RELATION_TYPES:
+            logger.debug(f"unknown relation type: {edge.relation_type}")
+
+        # for DiGraph: use relation_type as key to allow multiple relation types
+        # between the same pair of nodes
+        key = edge.relation_type
+        if self.G.has_edge(edge.source_id, edge.target_id):
+            existing = self.G.edges[edge.source_id, edge.target_id]
+            if existing.get("relation_type") == edge.relation_type:
+                # same relation type: keep higher confidence
+                if edge.confidence > existing.get("confidence", 0):
+                    self.G.edges[edge.source_id, edge.target_id].update(edge.to_dict())
+                return
+            # different relation type: store as metadata on the edge
+            # since DiGraph only supports one edge per pair, we keep the higher-confidence one
+            if edge.confidence > existing.get("confidence", 0):
+                self.G.edges[edge.source_id, edge.target_id].update(edge.to_dict())
+            return
+
+        self.G.add_edge(edge.source_id, edge.target_id, **edge.to_dict())
+
+    def add_edges(self, edges: list[Edge]) -> int:
+        count = 0
+        for e in edges:
+            before = self.G.number_of_edges()
+            self.add_edge(e)
+            if self.G.number_of_edges() > before:
+                count += 1
+        return count
+
+    # ── claim operations ───────────────────────────────────────────────
+
+    def get_claim(self, claim_id: str) -> Optional[Claim]:
+        """Retrieve a Claim by its ID from the graph."""
+        node = self._index.get(claim_id)
+        if node is None:
+            return None
+        meta = node.metadata
+        if not meta or "subject_name" not in meta:
+            return None
+        return Claim.from_dict(meta)
+
+    def update_claim(
+        self,
+        claim_id: str,
+        new_evidence: Optional[Evidence] = None,
+        new_confidence: Optional[float] = None,
+        extra_metadata: Optional[dict] = None,
+    ) -> bool:
+        """Update a claim's evidence, confidence, and/or metadata in-place.
+
+        Updates:
+        1. The claim node's metadata (serialized claim data)
+        2. The simplified edge's confidence
+        3. The 'about' edges' confidence
+
+        Returns True if the claim was found and updated.
+        """
+        node = self._index.get(claim_id)
+        if node is None:
+            logger.warning(f"claim {claim_id} not found in graph")
+            return False
+
+        meta = node.metadata
+        if not meta or "subject_name" not in meta:
+            logger.warning(f"node {claim_id} is not a claim node")
+            return False
+
+        # update evidence in metadata
+        if new_evidence is not None:
+            meta["evidence"] = new_evidence.to_dict()
+
+        # update confidence
+        if new_confidence is not None:
+            meta["confidence"] = new_confidence
+
+        # merge extra metadata
+        if extra_metadata:
+            meta.update(extra_metadata)
+
+        # refresh display name
+        subject = meta.get("subject_name", "")
+        predicate = meta.get("predicate", "")
+        obj = meta.get("object_name", "")
+        node.preferred_name = f"{subject} {predicate} {obj}"
+
+        # also update the serialized claim in node.metadata so it round-trips
+        node.metadata = meta
+
+        # update simplified edge (subject → object)
+        conf = new_confidence if new_confidence is not None else meta.get("confidence", 0.5)
+        subj_id = meta.get("subject_id", "")
+        obj_id = meta.get("object_id", "")
+        if subj_id and obj_id and self.G.has_edge(subj_id, obj_id):
+            edge_data = self.G.edges[subj_id, obj_id]
+            if edge_data.get("metadata", {}).get("claim_id") == claim_id:
+                edge_data["confidence"] = conf
+
+        # update 'about' edges (claim → subject, claim → object)
+        for _, tgt, data in self.G.out_edges(claim_id, data=True):
+            if data.get("relation_type") == "about":
+                data["confidence"] = conf
+
+        logger.debug(f"updated claim {claim_id}, confidence={conf}")
+        return True
+
+    # ── query ────────────────────────────────────────────────────────
+
+    def get_neighbors(
+        self,
+        concept_id: str,
+        relation_type: Optional[str] = None,
+        direction: str = "out",  # 'out', 'in', 'both'
+    ) -> list[tuple[str, Edge]]:
+        """Get neighboring concepts with optional relation filter."""
+        results = []
+        if direction in ("out", "both"):
+            for _, tgt, data in self.G.out_edges(concept_id, data=True):
+                if relation_type and data.get("relation_type") != relation_type:
+                    continue
+                edge = Edge.from_dict(data)
+                results.append((tgt, edge))
+        if direction in ("in", "both"):
+            for src, _, data in self.G.in_edges(concept_id, data=True):
+                if relation_type and data.get("relation_type") != relation_type:
+                    continue
+                edge = Edge.from_dict(data)
+                results.append((src, edge))
+        return results
+
+    def find_paths(
+        self,
+        source_id: str,
+        target_id: str,
+        max_hops: int = 3,
+        relation_filter: Optional[set[str]] = None,
+    ) -> list[list[tuple[str, str]]]:
+        """Find all simple paths between two concepts up to max_hops.
+
+        Returns list of paths, each path is a list of (node_id, relation_type) tuples.
+        """
+        if source_id not in self.G or target_id not in self.G:
+            return []
+
+        subgraph = self.G
+        if relation_filter:
+            edges_to_keep = [
+                (u, v) for u, v, d in self.G.edges(data=True)
+                if d.get("relation_type") in relation_filter
+            ]
+            subgraph = self.G.edge_subgraph(edges_to_keep).copy()
+
+        raw_paths = list(nx.all_simple_paths(
+            subgraph, source_id, target_id, cutoff=max_hops
+        ))
+
+        # annotate paths with relation types
+        annotated = []
+        for path in raw_paths:
+            annotated_path = []
+            for i in range(len(path) - 1):
+                edge_data = subgraph.edges[path[i], path[i + 1]]
+                annotated_path.append((path[i], edge_data.get("relation_type", "unknown")))
+            annotated_path.append((path[-1], ""))
+            annotated.append(annotated_path)
+
+        return annotated
+
+    def multi_hop_traverse(
+        self,
+        start_ids: list[str],
+        max_hops: int = 3,
+        relation_filter: Optional[set[str]] = None,
+    ) -> dict[str, list[list[str]]]:
+        """Traverse from multiple starting points, collecting reachable nodes.
+
+        Returns: {start_id: [[path_nodes], ...]}
+        """
+        results = {}
+        for sid in start_ids:
+            if sid not in self.G:
+                continue
+            paths = []
+            for target in self.G.nodes():
+                if target == sid:
+                    continue
+                for path in self.find_paths(sid, target, max_hops, relation_filter):
+                    paths.append([n for n, _ in path])
+            results[sid] = paths
+        return results
+
+    def get_subgraph_by_domain(self, domain_tag: str) -> nx.DiGraph:
+        """Extract subgraph containing only concepts with a given domain tag."""
+        nodes = [
+            nid for nid, data in self.G.nodes(data=True)
+            if domain_tag in data.get("domain_tags", [])
+        ]
+        return self.G.subgraph(nodes).copy()
+
+    def get_subgraph_by_relation(self, relation_type: str) -> nx.DiGraph:
+        """Extract subgraph with only edges of a given relation type."""
+        edges = [
+            (u, v) for u, v, d in self.G.edges(data=True)
+            if d.get("relation_type") == relation_type
+        ]
+        return self.G.edge_subgraph(edges).copy()
+
+    # ── search ───────────────────────────────────────────────────────
+
+    def search_by_name(self, query: str, limit: int = 20) -> list[ConceptNode]:
+        """Fuzzy search concepts by preferred_name or aliases."""
+        query_lower = query.lower()
+        results = []
+        for node in self._index.values():
+            if query_lower in node.preferred_name.lower():
+                results.append(node)
+                continue
+            for alias in node.aliases:
+                if query_lower in alias.lower():
+                    results.append(node)
+                    break
+            if len(results) >= limit:
+                break
+        return results
+
+    def search_by_domain(self, domain_tag: str) -> list[ConceptNode]:
+        return [n for n in self._index.values() if domain_tag in n.domain_tags]
+
+    # ── statistics ───────────────────────────────────────────────────
+
+    def stats(self) -> dict:
+        domain_counts = Counter()
+        source_counts = Counter()
+        relation_counts = Counter()
+
+        for node in self._index.values():
+            for tag in node.domain_tags:
+                domain_counts[tag] += 1
+            source_counts[node.source_vocab] += 1
+
+        for _, _, data in self.G.edges(data=True):
+            relation_counts[data.get("relation_type", "unknown")] += 1
+
+        return {
+            "n_concepts": self.G.number_of_nodes(),
+            "n_edges": self.G.number_of_edges(),
+            "domains": dict(domain_counts),
+            "sources": dict(source_counts),
+            "relations": dict(relation_counts),
+            "connected_components": nx.number_weakly_connected_components(self.G),
+        }
+
+    def __len__(self) -> int:
+        return self.G.number_of_nodes()

core/knowledge_graph/src/schema.py

@@ -0,0 +1,298 @@
+"""Knowledge graph schema: node and edge data classes."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional
+
+
+class DomainTag(str, Enum):
+    """High-level domain classification for concepts."""
+    NEUROANATOMY = "neuroanatomy"
+    DISEASE = "disease"
+    GENE = "gene"
+    NEUROTRANSMITTER = "neurotransmitter"
+    DRUG = "drug"
+    COGNITIVE_FUNCTION = "cognitive_function"
+    CELL_TYPE = "cell_type"
+    BIOMARKER = "biomarker"
+    PARADIGM = "paradigm"  # experimental paradigm (BrainMap)
+    CONNECTIVITY = "connectivity"  # functional/structural connections
+    IMAGING_FEATURE = "imaging_feature"  # cortical thickness, volume, FA, FC, SUVR, etc.
+    DATASET_VARIABLE = "dataset_variable"  # genetics, environment, medication, etc.
+    # Phase 1.5 Experiment infrastructure (atlas/modality/dataset/ml_model)
+    # + reserved RECIPE tag (former Phase 4.3, removed 2026-05-13 but kept
+    # in UMLS-skip set for forward compat).
+    RECIPE = "recipe"          # reserved
+    ATLAS = "atlas"            # brain parcellation (ATLAS:*)
+    MODALITY = "modality"      # imaging/data modality (MODALITY:*)
+    DATASET = "dataset"        # research dataset (DATASET:*)
+    ML_MODEL = "ml_model"      # ML architecture (MODEL:*)
+    # Brain decoding stimuli & psychological-state targets
+    VISUAL_STIMULUS = "visual_stimulus"  # image/video stimulus (NSD/BOLD5000/SEED-DV)
+    EMOTION = "emotion"                  # affective state label (SEED family)
+    VIGILANCE = "vigilance"              # alertness/drowsiness label (SEED-VIG)
+
+
+class SemanticType(str, Enum):
+    """UMLS semantic types relevant to neuroscience."""
+    DISEASE_OR_SYNDROME = "T047"
+    MENTAL_DYSFUNCTION = "T048"
+    NEOPLASTIC_PROCESS = "T191"
+    BODY_PART_ORGAN = "T023"
+    BODY_LOCATION = "T029"
+    CELL = "T025"
+    NEUROTRANSMITTER = "T116"
+    AMINO_ACID_PEPTIDE = "T116"  # overlaps with neurotransmitter in UMLS
+    PHARMACOLOGIC_SUBSTANCE = "T121"
+    GENE_OR_GENOME = "T028"
+    INTELLECTUAL_PRODUCT = "T170"
+
+
+@dataclass
+class ConceptNode:
+    """A concept node in the knowledge graph."""
+    id: str                          # unique identifier (CUI, or custom like "NN:1234")
+    preferred_name: str              # standard display name
+    semantic_types: list[str] = field(default_factory=list)  # TUI codes
+    domain_tags: list[str] = field(default_factory=list)     # DomainTag values
+    source_vocab: str = ""           # originating vocabulary (MeSH, NeuroNames, etc.)
+    definition: str = ""             # text definition
+    aliases: list[str] = field(default_factory=list)         # synonyms / alternate names
+    external_ids: dict[str, str] = field(default_factory=dict)  # cross-references
+    atlas_mapping: Optional[dict] = None  # MNI coords, atlas region ID, etc.
+    metadata: dict = field(default_factory=dict)             # catch-all for extra info
+
+    def to_dict(self) -> dict:
+        return {
+            "id": self.id,
+            "preferred_name": self.preferred_name,
+            "semantic_types": self.semantic_types,
+            "domain_tags": self.domain_tags,
+            "source_vocab": self.source_vocab,
+            "definition": self.definition,
+            "aliases": self.aliases,
+            "external_ids": self.external_ids,
+            "atlas_mapping": self.atlas_mapping,
+            "metadata": self.metadata,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> ConceptNode:
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+
+RELATION_TYPES = {
+    # taxonomic / structural
+    "is_a",               # A is a subtype of B
+    "part_of",            # A is anatomical part of B
+    "has_part",           # inverse of part_of
+    # causal / functional
+    "causes",             # A causes B
+    "associated_with",    # A is associated with B (loose)
+    "predisposes",        # A increases risk of B
+    # therapeutic
+    "treats",             # A treats B
+    "contraindicated_for",  # A is contraindicated for B
+    # molecular / genetic
+    "gene_associated_with_disease",
+    "protein_encoded_by",
+    "modulates",          # A modulates activity of B
+    "binds_to",           # A binds to receptor B
+    # neuroanatomy
+    "projects_to",        # A projects neural connections to B
+    "connects_to",        # structural connectivity A-B
+    "activates",          # A functionally activates B
+    "coactivates",        # A and B co-activate (BrainMap)
+    # evidence
+    "supported_by",
+    "contradicts",
+    "about",
+    # claim predicates (from paper extraction)
+    "reduces",
+    "increases",
+    "correlates_with",
+    "is_biomarker_of",
+    "is_risk_factor_for",
+    "is_associated_with",
+    "predicts",
+    "mediates",
+    "inhibits",
+    "distinguishes",
+    # Deprecated Phase 4.3 Input Recipe edges — reserved, unused after
+    # 2026-05-13 removal of input_recipe/recipe_kg_ingest modules.
+    "tests_hypothesis",   # (deprecated) Recipe → Hypothesis
+    "predicts_outcome",   # (deprecated) Recipe → target ConceptNode
+    "uses_biomarker",     # (deprecated) Recipe → Biomarker atom
+    "uses_atlas",         # (deprecated) Recipe → Atlas
+    "uses_modality",      # (deprecated) Recipe → Modality
+    "uses_model",         # (deprecated) Recipe → Model
+    "evaluated_on",       # (deprecated) Recipe → Dataset
+    "measured_in",        # (deprecated) Biomarker → Neuroanatomy ROI
+    "measured_by",        # (deprecated) Biomarker → Modality
+    # Phase 1.5 Experiment infrastructure edges
+    "supports_modality",  # Model → Modality (compat declaration)
+    "provides_modality",  # Dataset → Modality (what the dataset contains)
+    # Brain decoding edges (NSD/BOLD5000/SEED-DV/SEED family)
+    "evokes",             # visual_stimulus → neuroanatomy (encoding direction)
+    "decoded_from",       # visual_stimulus ← neuroanatomy (decoding direction)
+    "elicits",            # stimulus → emotion/vigilance (behavioral label)
+}
+
+# Claim-specific predicates (extracted from papers)
+CLAIM_PREDICATES = {
+    "reduces",              # A reduces B
+    "increases",            # A increases B
+    "correlates_with",      # A correlates with B
+    "causes",               # A causes B
+    "is_biomarker_of",      # A is a biomarker for B
+    "is_risk_factor_for",   # A is a risk factor for B
+    "treats",               # A treats B
+    "modulates",            # A modulates B
+    "activates",            # A activates B
+    "inhibits",             # A inhibits B
+    "predicts",             # A predicts B
+    "mediates",             # A mediates the relationship between B and C
+    "is_associated_with",   # A is associated with B
+    "distinguishes",        # A distinguishes B from C
+}
+
+
+@dataclass
+class Edge:
+    """A directed edge in the knowledge graph."""
+    source_id: str                   # source ConceptNode.id
+    target_id: str                   # target ConceptNode.id
+    relation_type: str               # one of RELATION_TYPES
+    source: str = ""                 # provenance: 'NeuroNames', 'MeSH', 'DisGeNET', etc.
+    confidence: float = 1.0          # 0.0-1.0
+    evidence_ref: str = ""           # citation or reference
+    metadata: dict = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "source_id": self.source_id,
+            "target_id": self.target_id,
+            "relation_type": self.relation_type,
+            "source": self.source,
+            "confidence": self.confidence,
+            "evidence_ref": self.evidence_ref,
+            "metadata": self.metadata,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> Edge:
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+
+@dataclass
+class Evidence:
+    """Experimental evidence supporting a scientific claim."""
+    study_type: str = ""             # "fMRI", "lesion", "meta-analysis", "GWAS", "animal_model"
+    methodology: str = ""            # "resting-state FC", "voxel-based morphometry", "DTI", ...
+    p_value: Optional[float] = None
+    effect_size: Optional[float] = None      # Cohen's d, r, OR, beta
+    effect_metric: str = ""          # "Cohen's d", "r", "OR", "beta", "AUC"
+    sample_size: Optional[int] = None
+    replicability: str = "single_study"  # "replicated", "single_study", "controversial"
+    direction: str = ""              # "positive", "negative"
+
+    def to_dict(self) -> dict:
+        return {
+            "study_type": self.study_type,
+            "methodology": self.methodology,
+            "p_value": self.p_value,
+            "effect_size": self.effect_size,
+            "effect_metric": self.effect_metric,
+            "sample_size": self.sample_size,
+            "replicability": self.replicability,
+            "direction": self.direction,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> Evidence:
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+
+@dataclass
+class PaperRef:
+    """Reference to a source paper."""
+    pmid: str = ""                   # PubMed ID
+    doi: str = ""
+    title: str = ""
+    authors: str = ""
+    year: Optional[int] = None
+    journal: str = ""
+
+    def to_dict(self) -> dict:
+        return {
+            "pmid": self.pmid,
+            "doi": self.doi,
+            "title": self.title,
+            "authors": self.authors,
+            "year": self.year,
+            "journal": self.journal,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> PaperRef:
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+
+@dataclass
+class Claim:
+    """A structured scientific claim extracted from a paper.
+
+    A claim is both stored as a node (for detailed querying) and
+    generates simplified edges (for multi-hop traversal).
+    """
+    id: str                              # CLM:uuid
+    subject_id: str                      # ConceptNode.id in the graph
+    subject_name: str                    # human-readable subject name
+    predicate: str                       # one of CLAIM_PREDICATES
+    object_id: str                       # ConceptNode.id in the graph
+    object_name: str                     # human-readable object name
+    negated: bool = False                # "X does NOT affect Y"
+    confidence: float = 0.5              # overall confidence 0-1
+    evidence: Evidence = field(default_factory=Evidence)
+    source_paper: PaperRef = field(default_factory=PaperRef)
+    raw_text: str = ""                   # original sentence from paper
+    metadata: dict = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "id": self.id,
+            "subject_id": self.subject_id,
+            "subject_name": self.subject_name,
+            "predicate": self.predicate,
+            "object_id": self.object_id,
+            "object_name": self.object_name,
+            "negated": self.negated,
+            "confidence": self.confidence,
+            "evidence": self.evidence.to_dict(),
+            "source_paper": self.source_paper.to_dict(),
+            "raw_text": self.raw_text,
+            "metadata": self.metadata,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> Claim:
+        d = d.copy()
+        if "evidence" in d and isinstance(d["evidence"], dict):
+            d["evidence"] = Evidence.from_dict(d["evidence"])
+        if "source_paper" in d and isinstance(d["source_paper"], dict):
+            d["source_paper"] = PaperRef.from_dict(d["source_paper"])
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+    def to_edge(self) -> Edge:
+        """Convert claim to a simplified graph edge for traversal."""
+        return Edge(
+            source_id=self.subject_id,
+            target_id=self.object_id,
+            relation_type=self.predicate,
+            source=f"claim:{self.source_paper.pmid or self.id}",
+            confidence=self.confidence,
+            evidence_ref=self.source_paper.title,
+            metadata={"claim_id": self.id, "negated": self.negated},
+        )

core/knowledge_graph/src/storage.py

@@ -0,0 +1,75 @@
+"""JSON serialization and deserialization for the knowledge graph."""
+
+from __future__ import annotations
+
+import json
+import logging
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+from .graph_manager import KnowledgeGraph
+from .schema import ConceptNode, Edge
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_PATH = Path(__file__).parent.parent / "data" / "knowledge_graph.json"
+
+
+def save_graph(kg: KnowledgeGraph, path: Optional[Path] = None) -> Path:
+    """Save knowledge graph to JSON file."""
+    path = Path(path) if path else DEFAULT_PATH
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    edges = []
+    for src, tgt, edata in kg.G.edges(data=True):
+        # Ensure source_id and target_id are always present
+        edge_dict = dict(edata)
+        edge_dict["source_id"] = src
+        edge_dict["target_id"] = tgt
+        edges.append(edge_dict)
+
+    data = {
+        "metadata": {
+            "version": "0.1",
+            "created": datetime.now().isoformat(),
+            "stats": kg.stats(),
+        },
+        "concepts": {nid: node.to_dict() for nid, node in kg._index.items()},
+        "edges": edges,
+    }
+
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+    logger.info(f"saved graph to {path}: {kg.stats()['n_concepts']} concepts, {kg.stats()['n_edges']} edges")
+    return path
+
+
+def load_graph(path: Optional[Path] = None) -> KnowledgeGraph:
+    """Load knowledge graph from JSON file."""
+    path = Path(path) if path else DEFAULT_PATH
+    if not path.exists():
+        logger.info(f"no graph file at {path}, returning empty graph")
+        return KnowledgeGraph()
+
+    with open(path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    kg = KnowledgeGraph()
+
+    for nid, ndata in data.get("concepts", {}).items():
+        node = ConceptNode.from_dict(ndata)
+        kg.add_concept(node)
+
+    for edata in data.get("edges", []):
+        try:
+            edge = Edge.from_dict(edata)
+            kg.add_edge(edge)
+        except (TypeError, KeyError) as e:
+            logger.warning(f"skipping malformed edge: {e}")
+            continue
+
+    stats = kg.stats()
+    logger.info(f"loaded graph from {path}: {stats['n_concepts']} concepts, {stats['n_edges']} edges")
+    return kg

core/skill_loader/loader.py

@@ -0,0 +1,5 @@
+class SkillLoader:
+    def __init__(self, path):
+        pass
+    def load_all(self):
+        return []

core/web/__init__.py

@@ -0,0 +1 @@
+# NeuroClaw web UI package

core/web/server.py

@@ -0,0 +1,2139 @@
+"""
+NeuroClaw Web UI Server
+
+Serves a browser-based chat interface at http://localhost:7080 by default.
+
+Usage
+-----
+    # Preferred: via the main agent entry point
+    python core/agent/main.py --web [--port 7080] [--host 127.0.0.1]
+
+    # Or run the web server directly
+    python core/web/server.py [--port 7080] [--host 127.0.0.1]
+
+Dependencies (install via the setup wizard or manually)
+---------------------------------------------------------
+    pip install "fastapi[standard]" uvicorn
+"""
+
+
+import argparse
+import asyncio
+import importlib.util
+import io
+import json
+import os
+import subprocess
+import time
+import re
+import queue as stdlib_queue
+import sys
+import threading
+from pathlib import Path
+from typing import Any
+
+REPO_ROOT = Path(__file__).parent.parent.parent.resolve()
+STATIC_DIR = Path(__file__).parent / "static"
+SHELL_STATUS_FILE = Path("/tmp/neuroclaw_claw_shell_status.json")
+AGENT_SHELL_STATUS_FILE = Path("/tmp/neuroclaw_agent_shell_status.json")
+
+DEFAULT_HOST = "0.0.0.0"
+DEFAULT_PORT = 7080
+ATTACHMENT_MAX_FILE_BYTES = 15 * 1024 * 1024
+ATTACHMENT_MAX_EMBED_CHARS = 12000
+SUPPORTED_ATTACHMENT_EXTENSIONS = {
+    ".txt", ".md", ".markdown", ".json", ".yaml", ".yml", ".csv", ".tsv",
+    ".py", ".js", ".ts", ".tsx", ".jsx", ".sh", ".bash", ".zsh", ".sql",
+    ".html", ".css", ".xml", ".log", ".rst", ".ini", ".toml", ".cfg",
+    ".pdf", ".docx", ".xlsx", ".pptx",
+}
+
+
+def _fallback_title_from_user_text(text: str) -> str:
+    """Create a short deterministic title when LLM title generation fails."""
+    cleaned = re.sub(r"\s+", " ", str(text or "")).strip()
+    if not cleaned:
+        return "New Chat"
+    words = cleaned.split(" ")[:8]
+    title = " ".join(words)
+    return title[:64] if title else "New Chat"
+
+
+def _sanitize_title(raw: str, user_text: str) -> str:
+    """Normalize model output into a single short title line."""
+    t = str(raw or "").strip()
+    if not t:
+        return _fallback_title_from_user_text(user_text)
+
+    t = t.splitlines()[0].strip()
+    t = t.strip("\"'` ")
+    t = re.sub(r"^[\-\*\d\.)\s]+", "", t)
+    t = re.sub(r"\s+", " ", t).strip(" .:-")
+    if not t:
+        return _fallback_title_from_user_text(user_text)
+    return t[:64]
+
+
+def _safe_skill_summary_fallback(skill_name: str, description: str) -> dict[str, str]:
+    """Return conservative bilingual fallback summary when LLM summarization fails."""
+    base_en = (description or "").strip()
+    if not base_en:
+        base_en = f"{skill_name} provides a specialized workflow for task execution in NeuroClaw."
+    base_en = re.sub(r"\s+", " ", base_en).strip()
+    if len(base_en) > 220:
+        base_en = base_en[:217].rstrip() + "..."
+    base_zh = f"该技能围绕「{skill_name}」提供专用流程支持，可用于相关任务的执行与组织。"
+    return {"en": base_en, "zh": base_zh}
+
+
+def _parse_bilingual_summary_json(raw: str) -> dict[str, str] | None:
+    """Parse model output into {'en':..., 'zh':...} with defensive handling."""
+    text = str(raw or "").strip()
+    if not text:
+        return None
+
+    try:
+        obj = json.loads(text)
+        en = str(obj.get("en", "")).strip()
+        zh = str(obj.get("zh", "")).strip()
+        if en and zh:
+            return {"en": en, "zh": zh}
+    except Exception:
+        pass
+
+    match = re.search(r"\{[\s\S]*\}", text)
+    if match:
+        try:
+            obj = json.loads(match.group(0))
+            en = str(obj.get("en", "")).strip()
+            zh = str(obj.get("zh", "")).strip()
+            if en and zh:
+                return {"en": en, "zh": zh}
+        except Exception:
+            return None
+    return None
+
+
+def _strip_frontmatter(text: str) -> str:
+    """Remove optional YAML front-matter from SKILL.md text."""
+    return re.sub(r"^---\s*\n.*?\n---\s*\n", "", text, flags=re.DOTALL)
+
+
+def _read_shell_status() -> dict[str, Any] | None:
+    """Read transient shell status from agent-shell first, then tmux claw-shell."""
+    if AGENT_SHELL_STATUS_FILE.exists():
+        try:
+            data = json.loads(AGENT_SHELL_STATUS_FILE.read_text(encoding="utf-8"))
+            command = str(data.get("command", "")).strip()
+            started_at = data.get("started_at")
+            pid = int(data.get("pid", 0))
+            if command and isinstance(started_at, (int, float)) and pid > 0:
+                alive = False
+                try:
+                    os.kill(pid, 0)
+                    alive = True
+                except Exception:
+                    alive = False
+
+                if alive:
+                    return {
+                        "active": True,
+                        "source": "agent_shell",
+                        "command": command,
+                        "current_command": "agent_shell",
+                        "started_at": int(started_at),
+                        "elapsed_ms": max(0, int((time.time() * 1000) - int(started_at))),
+                    }
+        except Exception:
+            pass
+        try:
+            AGENT_SHELL_STATUS_FILE.unlink()
+        except Exception:
+            pass
+
+    if not SHELL_STATUS_FILE.exists():
+        return None
+
+    try:
+        data = json.loads(SHELL_STATUS_FILE.read_text(encoding="utf-8"))
+    except Exception:
+        try:
+            SHELL_STATUS_FILE.unlink()
+        except Exception:
+            pass
+        return None
+
+    command = str(data.get("command", "")).strip()
+    started_at = data.get("started_at")
+    if not command or not isinstance(started_at, (int, float)):
+        try:
+            SHELL_STATUS_FILE.unlink()
+        except Exception:
+            pass
+        return None
+
+    try:
+        proc = subprocess.run(
+            ["tmux", "display-message", "-p", "-t", "claw", "#{pane_current_command}"],
+            capture_output=True,
+            text=True,
+            check=False,
+            timeout=2,
+        )
+        current = (proc.stdout or "").strip().lower()
+    except Exception:
+        current = ""
+
+    shell_names = {"bash", "zsh", "fish", "sh", "dash", "tmux"}
+    active = bool(current) and current not in shell_names
+    elapsed_ms = max(0, int((time.time() * 1000) - int(started_at)))
+
+    if not active:
+        try:
+            SHELL_STATUS_FILE.unlink()
+        except Exception:
+            pass
+        return None
+
+    return {
+        "active": True,
+        "command": command,
+        "current_command": current,
+        "started_at": int(started_at),
+        "elapsed_ms": elapsed_ms,
+    }
+
+
+def _skill_md_excerpt(skill_md: Path, max_chars: int = 1800) -> str:
+    """Return a compact excerpt from SKILL.md for prompt context injection."""
+    try:
+        raw = skill_md.read_text(encoding="utf-8")
+    except Exception:
+        return ""
+    body = _strip_frontmatter(raw)
+    body = re.sub(r"```[\s\S]*?```", "", body)
+    body = re.sub(r"\n{3,}", "\n\n", body).strip()
+    if len(body) > max_chars:
+        body = body[:max_chars].rstrip() + "\n..."
+    return body
+
+
+def _selected_skills_context(selected_names: list[str], skills: list[dict[str, Any]]) -> str:
+    """Build selected-skill references from discovered SKILL.md files."""
+    if not selected_names:
+        return ""
+
+    by_name: dict[str, dict[str, Any]] = {}
+    for s in skills:
+        n = str(s.get("name", "")).strip().lower()
+        if n:
+            by_name[n] = s
+
+    chunks: list[str] = []
+    for raw_name in selected_names:
+        key = raw_name.strip().lower()
+        if not key:
+            continue
+        s = by_name.get(key)
+        if not s:
+            continue
+        excerpt = _skill_md_excerpt(Path(s.get("skill_md")))
+        if not excerpt:
+            continue
+        chunks.append(
+            f"[Skill: {s.get('name', raw_name)}]\n"
+            f"Description: {s.get('description', '')}\n"
+            f"SKILL.md excerpt:\n{excerpt}"
+        )
+
+    return "\n\n".join(chunks)
+
+
+def _attachment_extension(filename: str) -> str:
+    return Path(str(filename or "")).suffix.lower().strip()
+
+
+def _truncate_text(text: str, max_chars: int = ATTACHMENT_MAX_EMBED_CHARS) -> tuple[str, bool]:
+    raw = str(text or "")
+    if len(raw) <= max_chars:
+        return raw, False
+    return raw[:max_chars].rstrip() + "\n[Content truncated]", True
+
+
+def _decode_text_attachment(raw: bytes) -> str:
+    for encoding in ("utf-8", "utf-8-sig", "gb18030", "latin-1"):
+        try:
+            return raw.decode(encoding)
+        except Exception:
+            continue
+    return raw.decode("utf-8", errors="replace")
+
+
+def _extract_pdf_text(raw: bytes) -> str:
+    from pypdf import PdfReader  # type: ignore
+
+    reader = PdfReader(io.BytesIO(raw))
+    chunks: list[str] = []
+    for page in reader.pages[:120]:
+        txt = (page.extract_text() or "").strip()
+        if txt:
+            chunks.append(txt)
+    return "\n\n".join(chunks)
+
+
+def _extract_docx_text(raw: bytes) -> str:
+    from docx import Document  # type: ignore
+
+    doc = Document(io.BytesIO(raw))
+    chunks: list[str] = []
+
+    for para in doc.paragraphs:
+        txt = (para.text or "").strip()
+        if txt:
+            chunks.append(txt)
+
+    for table in doc.tables:
+        for row in table.rows:
+            vals = [str(cell.text or "").strip() for cell in row.cells]
+            vals = [v for v in vals if v]
+            if vals:
+                chunks.append(" | ".join(vals))
+
+    return "\n".join(chunks)
+
+
+def _extract_xlsx_text(raw: bytes) -> str:
+    from openpyxl import load_workbook  # type: ignore
+
+    wb = load_workbook(io.BytesIO(raw), read_only=True, data_only=True)
+    lines: list[str] = []
+    for ws in wb.worksheets[:12]:
+        lines.append(f"[Sheet] {ws.title}")
+        row_count = 0
+        for row in ws.iter_rows(max_row=200, max_col=30, values_only=True):
+            cells = ["" if v is None else str(v).strip() for v in row]
+            if not any(cells):
+                continue
+            lines.append("\t".join(cells))
+            row_count += 1
+            if row_count >= 200:
+                lines.append("[Rows truncated]")
+                break
+        lines.append("")
+    return "\n".join(lines).strip()
+
+
+def _extract_pptx_text(raw: bytes) -> str:
+    from pptx import Presentation  # type: ignore
+
+    prs = Presentation(io.BytesIO(raw))
+    lines: list[str] = []
+    for idx, slide in enumerate(prs.slides[:80], start=1):
+        lines.append(f"[Slide {idx}]")
+        for shape in slide.shapes:
+            txt = ""
+            try:
+                txt = (shape.text or "").strip()  # type: ignore[attr-defined]
+            except Exception:
+                txt = ""
+            if txt:
+                lines.append(txt)
+        lines.append("")
+    return "\n".join(lines).strip()
+
+
+def _parse_attachment_content(filename: str, content_type: str, raw: bytes) -> dict[str, Any]:
+    ext = _attachment_extension(filename)
+    if ext not in SUPPORTED_ATTACHMENT_EXTENSIONS:
+        return {
+            "ok": False,
+            "error": (
+                f"Unsupported extension '{ext or 'unknown'}'. "
+                "Supported: " + ", ".join(sorted(SUPPORTED_ATTACHMENT_EXTENSIONS))
+            ),
+        }
+
+    if ext in {
+        ".txt", ".md", ".markdown", ".json", ".yaml", ".yml", ".csv", ".tsv",
+        ".py", ".js", ".ts", ".tsx", ".jsx", ".sh", ".bash", ".zsh", ".sql",
+        ".html", ".css", ".xml", ".log", ".rst", ".ini", ".toml", ".cfg",
+    }:
+        text = _decode_text_attachment(raw)
+    elif ext == ".pdf":
+        try:
+            text = _extract_pdf_text(raw)
+        except ImportError:
+            return {"ok": False, "error": "PDF parser missing. Install: pypdf"}
+    elif ext == ".docx":
+        try:
+            text = _extract_docx_text(raw)
+        except ImportError:
+            return {"ok": False, "error": "DOCX parser missing. Install: python-docx"}
+    elif ext == ".xlsx":
+        try:
+            text = _extract_xlsx_text(raw)
+        except ImportError:
+            return {"ok": False, "error": "XLSX parser missing. Install: openpyxl"}
+    elif ext == ".pptx":
+        try:
+            text = _extract_pptx_text(raw)
+        except ImportError:
+            return {"ok": False, "error": "PPTX parser missing. Install: python-pptx"}
+    else:
+        return {"ok": False, "error": "Unsupported file type"}
+
+    cleaned = str(text or "").strip()
+    if not cleaned:
+        return {"ok": False, "error": "No readable text found in file"}
+
+    truncated_text, truncated = _truncate_text(cleaned)
+    return {
+        "ok": True,
+        "text": truncated_text,
+        "truncated": truncated,
+        "ext": ext,
+        "content_type": content_type,
+    }
+
+
+def _normalize_skill_token(text: str) -> str:
+    """Normalize a skill token for robust mention matching."""
+    t = str(text or "").lower()
+    t = re.sub(r"[^a-z0-9]+", "", t)
+    return t
+
+
+def _infer_skills_from_user_text(user_text: str, skills: list[dict[str, Any]]) -> list[str]:
+    """
+    Infer referenced skills from free-form user text.
+
+    Matches both skill name and skill directory name in normalized form.
+    """
+    norm_msg = _normalize_skill_token(user_text)
+    if not norm_msg:
+        return []
+
+    inferred: list[str] = []
+    for s in skills:
+        skill_name = str(s.get("name", "")).strip()
+        if not skill_name:
+            continue
+        dir_name = ""
+        try:
+            dir_name = Path(s.get("path")).name
+        except Exception:
+            dir_name = ""
+
+        candidates = [skill_name, dir_name]
+        for c in candidates:
+            token = _normalize_skill_token(c)
+            if token and token in norm_msg:
+                inferred.append(skill_name)
+                break
+
+    # Preserve order and remove duplicates
+    seen: set[str] = set()
+    out: list[str] = []
+    for name in inferred:
+        key = name.lower()
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(name)
+    return out[:5]
+
+
+# ── Module import helpers ──────────────────────────────────────────────────────
+
+def _import_from_path(module_name: str, path: Path) -> Any:
+    """Import a Python module from an absolute file path (handles hyphenated dirs)."""
+    spec = importlib.util.spec_from_file_location(module_name, path)
+    if spec is None or spec.loader is None:
+        raise ImportError(f"Cannot load module from {path}")
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)  # type: ignore[union-attr]
+    return mod
+
+
+# ── Dependency check ───────────────────────────────────────────────────────────
+
+def _require_webdeps() -> None:
+    """Raise a descriptive RuntimeError if fastapi/uvicorn are not installed."""
+    missing = []
+    for pkg in ("fastapi", "uvicorn"):
+        try:
+            __import__(pkg)
+        except ImportError:
+            missing.append(pkg)
+    if missing:
+        raise RuntimeError(
+            f"Web UI dependencies not installed: {', '.join(missing)}\n"
+            "Install with:  pip install 'fastapi[standard]' uvicorn\n"
+            "Or re-run the installer:  python installer/setup.py"
+        )
+
+
+# ── Streaming helpers ──────────────────────────────────────────────────────────
+
+async def _stream_openai(
+    websocket: Any, llm_client: Any, model: str, history: list[dict]
+) -> str:
+    """
+    Stream an OpenAI response chunk-by-chunk.
+
+    Uses a producer thread + asyncio queue so the blocking OpenAI iterator
+    does not stall the event loop while still delivering incremental updates
+    to the browser.
+    """
+    q: stdlib_queue.Queue[tuple[str, str | None]] = stdlib_queue.Queue()
+
+    def _produce() -> None:
+        try:
+            stream = llm_client.chat.completions.create(
+                model=model, messages=history, stream=True
+            )
+            for chunk in stream:
+                content = ""
+                if chunk.choices and chunk.choices[0].delta:
+                    content = chunk.choices[0].delta.content or ""
+                if content:
+                    q.put(("chunk", content))
+        except Exception as exc:
+            q.put(("error", str(exc)))
+        finally:
+            q.put(("done", None))
+
+    threading.Thread(target=_produce, daemon=True).start()
+
+    full = ""
+    while True:
+        try:
+            kind, data = q.get_nowait()
+        except stdlib_queue.Empty:
+            await asyncio.sleep(0.01)
+            continue
+
+        if kind == "chunk":
+            full += data  # type: ignore[operator]
+            await websocket.send_text(json.dumps({"type": "chunk", "content": data}))
+        elif kind == "error":
+            raise RuntimeError(data)
+        else:  # "done"
+            break
+
+    return full
+
+
+async def _respond(websocket: Any, session: Any) -> str:
+    """
+    Generate a reply for the latest message in session.history.
+
+    Streams chunks to the browser for OpenAI; falls back to asyncio.to_thread
+    for other backends (Anthropic, local).  Always sends a final
+    ``{"type": "done", "content": "…"}`` frame.
+    """
+    provider = session.env.get("llm_backend", {}).get("provider", "openai")
+    model = session.env.get("llm_backend", {}).get("model", "gpt-4o")
+
+    if provider == "openai" and session._llm is not None:
+        full = await _stream_openai(websocket, session._llm, model, session.history)
+    else:
+        # Non-streaming fallback: run blocking _chat() in a thread pool
+        full = await asyncio.to_thread(session._chat)
+
+    await websocket.send_text(json.dumps({"type": "done", "content": full}))
+    return full
+
+
+# ── FastAPI application factory ────────────────────────────────────────────────
+
+def create_app() -> Any:
+    """Build and return the FastAPI application object."""
+    _require_webdeps()
+
+    from fastapi import FastAPI, File, UploadFile, WebSocket, WebSocketDisconnect  # type: ignore
+    from fastapi.responses import FileResponse, HTMLResponse, JSONResponse  # type: ignore
+    from fastapi.staticfiles import StaticFiles  # type: ignore
+
+    # Ensure repo root is on sys.path so `from core.agent.main import …` resolves
+    if str(REPO_ROOT) not in sys.path:
+        sys.path.insert(0, str(REPO_ROOT))
+
+    from core.agent.main import (  # type: ignore[import]
+        AgentSession,
+        build_llm_client,
+        load_environment,
+        save_environment,
+    )
+
+    # SkillLoader lives in core/skill_loader/, so we use importlib for dynamic loading.
+    _loader_mod = _import_from_path(
+        "neuroclaw_skill_loader",
+        REPO_ROOT / "core" / "skill_loader" / "loader.py",
+    )
+    SkillLoader = _loader_mod.SkillLoader
+
+    app = FastAPI(title="NeuroClaw Web UI", docs_url=None, redoc_url=None)
+
+    # ── Static files ────────────────────────────────────────────────────────────
+    if STATIC_DIR.exists():
+        app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
+    materials_dir = REPO_ROOT / "materials"
+    if materials_dir.exists():
+        app.mount("/materials", StaticFiles(directory=str(materials_dir)), name="materials")
+
+    # ── HTTP endpoints ──────────────────────────────────────────────────────────
+
+    @app.get("/")
+    async def root() -> Any:
+        index = STATIC_DIR / "index.html"
+        if index.exists():
+            return FileResponse(str(index))
+        return HTMLResponse(
+            "<h1>NeuroClaw Web UI</h1>"
+            f"<p>Static files not found at <code>{STATIC_DIR}</code>.</p>",
+            status_code=500,
+        )
+
+    @app.get("/api/health")
+    async def health() -> dict:
+        return {"status": "ok"}
+
+    @app.get("/api/shell/status")
+    async def shell_status() -> dict:
+        status = _read_shell_status()
+        if not status:
+            return {"active": False}
+        return status
+
+    @app.get("/api/skills")
+    async def list_skills() -> dict:
+        loader = SkillLoader(REPO_ROOT / "skills")
+        skills = loader.load_all()
+        return {
+            "skills": [
+                {
+                    "name": s["name"],
+                    "description": s.get("description", ""),
+                    "summary_en": s.get("summary_en", ""),
+                    "summary_zh": s.get("summary_zh", ""),
+                    "layer": s.get("layer", ""),
+                    "skill_type": s.get("skill_type", ""),
+                    "dependencies": s.get("dependencies", []),
+                    "complementary_skills": s.get("complementary_skills", []),
+                }
+                for s in skills
+            ]
+        }
+
+    @app.post("/api/attachments/parse")
+    async def parse_attachments(files: list[UploadFile] = File(...)) -> Any:
+        if not files:
+            return JSONResponse({"type": "error", "message": "No files uploaded"}, status_code=400)
+
+        parsed_files: list[dict[str, Any]] = []
+        for upload in files[:24]:
+            name = str(upload.filename or "untitled")
+            content_type = str(upload.content_type or "unknown")
+            raw = await upload.read()
+            size = len(raw)
+
+            item: dict[str, Any] = {
+                "name": name,
+                "size": size,
+                "content_type": content_type,
+            }
+
+            if size > ATTACHMENT_MAX_FILE_BYTES:
+                item.update(
+                    {
+                        "ok": False,
+                        "error": (
+                            f"File too large ({size} bytes). "
+                            f"Limit is {ATTACHMENT_MAX_FILE_BYTES} bytes."
+                        ),
+                    }
+                )
+                parsed_files.append(item)
+                continue
+
+            try:
+                item.update(_parse_attachment_content(name, content_type, raw))
+            except Exception as exc:
+                item.update({"ok": False, "error": f"Parse failed: {exc}"})
+            parsed_files.append(item)
+
+        return {
+            "type": "done",
+            "files": parsed_files,
+            "max_file_bytes": ATTACHMENT_MAX_FILE_BYTES,
+            "supported_extensions": sorted(SUPPORTED_ATTACHMENT_EXTENSIONS),
+        }
+
+    @app.get("/api/env")
+    async def get_env() -> dict:
+        """Return non-sensitive parts of the runtime environment config."""
+        env = load_environment()
+        llm = env.get("llm_backend", {})
+        provider = llm.get("provider", "unknown")
+        api_key_env = llm.get("api_key_env", "")
+        api_key_present = bool(api_key_env and __import__('os').environ.get(api_key_env))
+        return {
+            "provider": provider,
+            "model": llm.get("model", "unknown"),
+            "available_models": llm.get("available_models", []),
+            "cuda_device": env.get("cuda", {}).get("device", "cpu"),
+            "setup_type": env.get("setup_type", "unknown"),
+            "conda_env": env.get("conda_env"),
+            "api_key_present": api_key_present,
+        }
+
+    @app.post("/api/env/model")
+    async def set_model(payload: dict) -> Any:
+        """Switch current provider/model to one of the configured options."""
+        provider = str(payload.get("provider", "")).strip()
+        model = str(payload.get("model", "")).strip()
+        if not provider or not model:
+            return JSONResponse(
+                {"type": "error", "message": "provider and model are required"},
+                status_code=400,
+            )
+
+        env = load_environment()
+        llm = env.setdefault("llm_backend", {})
+        available_models = llm.get("available_models", [])
+        if not any(
+            isinstance(item, dict)
+            and str(item.get("provider", "")).strip() == provider
+            and str(item.get("model", item.get("id", item.get("name", "")))).strip() == model
+            for item in available_models
+        ):
+            return JSONResponse(
+                {"type": "error", "message": "Requested provider/model is not configured"},
+                status_code=400,
+            )
+
+        llm["provider"] = provider
+        llm["model"] = model
+        save_environment(env)
+        return {
+            "type": "done",
+            "provider": provider,
+            "model": model,
+            "available_models": llm.get("available_models", []),
+        }
+
+    @app.post("/api/chat")
+    async def chat_http(payload: dict) -> Any:
+        """HTTP fallback for chat when WebSocket is unavailable."""
+        user_text = str(payload.get("message", "")).strip()
+        raw_history = payload.get("history", [])
+        raw_selected_skills = payload.get("selected_skills", [])
+        if not user_text:
+            return JSONResponse({"type": "error", "message": "Empty message"}, status_code=400)
+
+        session = AgentSession()
+
+        try:
+            loader = SkillLoader(REPO_ROOT / "skills")
+            skills = loader.load_all()
+        except Exception:
+            skills = []
+
+        selected_skills: list[str] = []
+        if isinstance(raw_selected_skills, list):
+            selected_skills = [
+                str(x).strip() for x in raw_selected_skills if str(x).strip()
+            ]
+        if not selected_skills:
+            selected_skills = _infer_skills_from_user_text(user_text, skills)
+
+        env_file = REPO_ROOT / "neuroclaw_environment.json"
+        if not env_file.exists():
+            return JSONResponse({
+                "type": "error",
+                "message": "neuroclaw_environment.json not found. Run python installer/setup.py to configure NeuroClaw.",
+            }, status_code=400)
+
+        try:
+            session.set_llm_client(build_llm_client(session.env))
+        except Exception as exc:
+            return JSONResponse({"type": "error", "message": f"LLM backend error: {exc}"}, status_code=500)
+
+        soul_path = REPO_ROOT / "SOUL.md"
+        soul = soul_path.read_text(encoding="utf-8") if soul_path.exists() else ""
+        skill_names = ", ".join(s["name"] for s in skills)
+        session.history = [{"role": "system", "content": f"{soul}\n\nLoaded skills: {skill_names}"}]
+
+        if isinstance(raw_history, list):
+            for msg in raw_history:
+                if not isinstance(msg, dict):
+                    continue
+                role = str(msg.get("role", "")).strip().lower()
+                content = str(msg.get("content", "")).strip()
+                if role not in {"user", "assistant"} or not content:
+                    continue
+                session.history.append({"role": role, "content": content})
+
+        selected_ctx = _selected_skills_context(selected_skills, skills)
+        user_payload = user_text
+        if selected_ctx:
+            user_payload = (
+                f"{user_text}\n\n"
+                "[Selected skill references from local SKILL.md files]\n"
+                f"{selected_ctx}"
+            )
+
+        session.history.append({"role": "user", "content": user_payload})
+
+        reply = await asyncio.to_thread(session._chat)
+        llm_cfg = session.env.get("llm_backend", {})
+        provider_used = str(llm_cfg.get("provider", "unknown"))
+        model_used = str(llm_cfg.get("model", "unknown"))
+        return {
+            "type": "done",
+            "content": reply,
+            "provider_used": provider_used,
+            "model_used": model_used,
+        }
+
+    @app.post("/api/chat/title")
+    async def chat_title(payload: dict) -> Any:
+        """Generate a concise chat title using the configured default model."""
+        user_text = str(payload.get("user", "")).strip()
+        assistant_text = str(payload.get("assistant", "")).strip()
+        if not user_text and not assistant_text:
+            return JSONResponse({"type": "error", "message": "Missing conversation content"}, status_code=400)
+
+        env_file = REPO_ROOT / "neuroclaw_environment.json"
+        if not env_file.exists():
+            return JSONResponse({"type": "error", "message": "Environment not configured"}, status_code=400)
+
+        session = AgentSession()
+        try:
+            session.set_llm_client(build_llm_client(session.env))
+        except Exception as exc:
+            return JSONResponse({"type": "error", "message": f"LLM backend error: {exc}"}, status_code=500)
+
+        title_system = (
+            "You are a conversation title generator. "
+            "Return exactly one short title in plain text, 3-10 words, no quotes, no markdown."
+        )
+        convo = f"User message:\n{user_text}\n\nAssistant reply:\n{assistant_text}"
+        session.history = [
+            {"role": "system", "content": title_system},
+            {"role": "user", "content": convo},
+        ]
+
+        try:
+            raw_title = await asyncio.to_thread(session._chat)
+            title = _sanitize_title(raw_title, user_text)
+        except Exception:
+            title = _fallback_title_from_user_text(user_text)
+
+        return {"type": "done", "title": title}
+
+    @app.post("/api/skills/summary")
+    async def skill_summary(payload: dict) -> Any:
+        """Summarize one SKILL.md into bilingual 1-5 sentence summaries."""
+        skill_name = str(payload.get("name", "")).strip()
+        if not skill_name:
+            return JSONResponse({"type": "error", "message": "Missing skill name"}, status_code=400)
+
+        try:
+            loader = SkillLoader(REPO_ROOT / "skills")
+            skills = loader.load_all()
+        except Exception:
+            skills = []
+
+        target = None
+        for s in skills:
+            if str(s.get("name", "")).strip().lower() == skill_name.lower():
+                target = s
+                break
+        if target is None:
+            return JSONResponse({"type": "error", "message": f"Skill not found: {skill_name}"}, status_code=404)
+
+        skill_md = Path(target.get("skill_md"))
+        if not skill_md.exists():
+            fb = _safe_skill_summary_fallback(skill_name, str(target.get("description", "")))
+            return {"type": "done", "summary_en": fb["en"], "summary_zh": fb["zh"]}
+
+        md_text = skill_md.read_text(encoding="utf-8")
+        excerpt = md_text[:7000]
+
+        session = AgentSession()
+        try:
+            session.set_llm_client(build_llm_client(session.env))
+        except Exception:
+            fb = _safe_skill_summary_fallback(skill_name, str(target.get("description", "")))
+            return {"type": "done", "summary_en": fb["en"], "summary_zh": fb["zh"]}
+
+        prompt = (
+            "Summarize the following SKILL.md into concise bilingual summaries.\n"
+            "Requirements:\n"
+            "1) Return valid JSON only, no markdown, no extra text.\n"
+            "2) JSON keys: en, zh.\n"
+            "3) en: 1-5 complete English sentences.\n"
+            "4) zh: 1-5 complete Chinese sentences.\n"
+            "5) Focus on what this skill can do and when to use it.\n\n"
+            f"Skill Name: {skill_name}\n"
+            f"SKILL.md Content:\n{excerpt}"
+        )
+        session.history = [
+            {
+                "role": "system",
+                "content": "You are a precise technical summarizer. Output strict JSON only.",
+            },
+            {"role": "user", "content": prompt},
+        ]
+
+        try:
+            raw = await asyncio.to_thread(session._chat)
+            parsed = _parse_bilingual_summary_json(raw)
+            if not parsed:
+                raise ValueError("Invalid summary JSON")
+            return {
+                "type": "done",
+                "summary_en": parsed["en"],
+                "summary_zh": parsed["zh"],
+            }
+        except Exception:
+            fb = _safe_skill_summary_fallback(skill_name, str(target.get("description", "")))
+            return {"type": "done", "summary_en": fb["en"], "summary_zh": fb["zh"]}
+
+    # ── Checkpoint API endpoints ─────────────────────────────────────────────
+
+    @app.get("/api/checkpoints")
+    async def list_checkpoints() -> Any:
+        from core.checkpoint.manager import ShadowCheckpointManager
+        mgr = ShadowCheckpointManager(repo_root=REPO_ROOT)
+        cps = mgr.list_checkpoints(REPO_ROOT)
+        return {"checkpoints": cps}
+
+    @app.get("/api/checkpoints/{checkpoint_id}/diff")
+    async def checkpoint_diff(checkpoint_id: str) -> Any:
+        from core.checkpoint.manager import ShadowCheckpointManager
+        mgr = ShadowCheckpointManager(repo_root=REPO_ROOT)
+        try:
+            diff = mgr.diff_checkpoint(REPO_ROOT, checkpoint_id)
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=400)
+        return diff
+
+    @app.post("/api/checkpoints/{checkpoint_id}/restore")
+    async def restore_checkpoint(checkpoint_id: str, payload: dict = {}) -> Any:
+        from core.checkpoint.manager import ShadowCheckpointManager
+        mgr = ShadowCheckpointManager(repo_root=REPO_ROOT)
+        filepath = payload.get("filepath") if payload else None
+        try:
+            result = mgr.restore_checkpoint(REPO_ROOT, checkpoint_id, filepath=filepath)
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=400)
+        return {"type": "done", **result}
+
+    @app.get("/api/checkpoints/{checkpoint_id}/files")
+    async def checkpoint_files(checkpoint_id: str) -> Any:
+        from core.checkpoint.manager import ShadowCheckpointManager
+        mgr = ShadowCheckpointManager(repo_root=REPO_ROOT)
+        try:
+            files = mgr.get_files_at_checkpoint(REPO_ROOT, checkpoint_id)
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=400)
+        return {"files": files}
+
+    # ── WebSocket chat endpoint ─────────────────────────────────────────────────
+
+    @app.websocket("/ws/chat")
+    async def chat_endpoint(websocket: WebSocket) -> None:
+        try:
+            await websocket.accept()
+            print("[WS] Client connected", flush=True)
+
+            # Create a per-connection agent session
+            session = AgentSession()
+
+            # Load skills
+            try:
+                loader = SkillLoader(REPO_ROOT / "skills")
+                skills = loader.load_all()
+            except Exception:
+                skills = []
+
+            # Send init metadata
+            llm_cfg = session.env.get("llm_backend", {})
+            await websocket.send_text(json.dumps({
+                "type": "init",
+                "skills": [
+                    {
+                        "name": s["name"],
+                        "description": s.get("description", ""),
+                        "summary_en": s.get("summary_en", ""),
+                        "summary_zh": s.get("summary_zh", ""),
+                        "layer": s.get("layer", ""),
+                        "skill_type": s.get("skill_type", ""),
+                        "dependencies": s.get("dependencies", []),
+                        "complementary_skills": s.get("complementary_skills", []),
+                    }
+                    for s in skills
+                ],
+                "provider": llm_cfg.get("provider", "unconfigured"),
+                "model": llm_cfg.get("model", "unconfigured"),
+            }))
+
+            env_file = REPO_ROOT / "neuroclaw_environment.json"
+            if not env_file.exists():
+                await websocket.send_text(json.dumps({
+                    "type": "error",
+                    "message": (
+                        "neuroclaw_environment.json not found. "
+                        "Run python installer/setup.py to configure NeuroClaw."
+                    ),
+                }))
+                await websocket.close()
+                return
+
+            # Initialise LLM client
+            try:
+                session.set_llm_client(build_llm_client(session.env))
+            except Exception as exc:
+                await websocket.send_text(json.dumps({
+                    "type": "error",
+                    "message": f"LLM backend error: {exc}",
+                }))
+
+            # Build system prompt
+            soul_path = REPO_ROOT / "SOUL.md"
+            soul = soul_path.read_text(encoding="utf-8") if soul_path.exists() else ""
+            skill_names = ", ".join(s["name"] for s in skills)
+            session.history = [
+                {"role": "system", "content": f"{soul}\n\nLoaded skills: {skill_names}"}
+            ]
+
+            # Main chat loop
+            try:
+                while True:
+                    raw = await websocket.receive_text()
+                    msg = json.loads(raw)
+                    user_text = msg.get("message", "").strip()
+                    raw_selected = msg.get("selected_skills", [])
+                    if not user_text:
+                        continue
+
+                    selected = []
+                    if isinstance(raw_selected, list):
+                        selected = [str(x).strip() for x in raw_selected if str(x).strip()]
+                    if not selected:
+                        selected = _infer_skills_from_user_text(user_text, skills)
+
+                    selected_ctx = _selected_skills_context(selected, skills)
+                    user_payload = user_text
+                    if selected_ctx:
+                        user_payload = (
+                            f"{user_text}\n\n"
+                            "[Selected skill references from local SKILL.md files]\n"
+                            f"{selected_ctx}"
+                        )
+
+                    session.history.append({"role": "user", "content": user_payload})
+                    try:
+                        reply = await _respond(websocket, session)
+                        session.history.append({"role": "assistant", "content": reply})
+                    except Exception as exc:
+                        err_msg = f"[Agent error: {exc}]"
+                        await websocket.send_text(
+                            json.dumps({"type": "error", "message": str(exc)})
+                        )
+                        session.history.append({"role": "assistant", "content": err_msg})
+
+            except WebSocketDisconnect:
+                pass
+
+        except Exception as e:
+            print(f"[WS] Error occurred: {type(e).__name__}: {e}", flush=True)
+            import traceback
+            traceback.print_exc()
+
+    # ── Knowledge Graph Explorer ───────────────────────────────────────────
+
+    _kg_state: dict[str, Any] = {"loaded": False, "loading": False, "error": None}
+    _kg_lock = threading.Lock()
+
+    KG_DATA_DIR = REPO_ROOT / "core" / "knowledge_graph" / "data"
+    KG_PATH = KG_DATA_DIR / "knowledge_graph.json"
+    KG_QUICK_DIR = KG_DATA_DIR / "quick"
+    HYPOTHESIS_SOURCES = [
+        "hypotheses_critic.json",
+        "hypotheses_imaging_ukb.json",
+        "hypotheses_imaging_adni.json",
+        "hypotheses_imaging_hcp.json",
+    ]
+    RECIPES_PATH = KG_QUICK_DIR / "recipes_top10.json"
+
+    DOMAIN_COLORS = {
+        "biomarker": "#10b981",
+        "imaging_feature": "#3b82f6",
+        "cognitive_function": "#8b5cf6",
+        "disease": "#ef4444",
+        "gene": "#f59e0b",
+        "neuroanatomy": "#06b6d4",
+        "drug": "#ec4899",
+        "neurotransmitter": "#f97316",
+        "cell_type": "#14b8a6",
+        "paradigm": "#a855f7",
+        "connectivity": "#0ea5e9",
+        "dataset_variable": "#84cc16",
+        "claim": "#94a3b8",
+    }
+    RELATION_COLORS = {
+        "is_a": "#94a3b8",
+        "part_of": "#64748b",
+        "causes": "#dc2626",
+        "associated_with": "#3b82f6",
+        "is_associated_with": "#3b82f6",
+        "predisposes": "#f97316",
+        "treats": "#10b981",
+        "modulates": "#8b5cf6",
+        "reduces": "#ef4444",
+        "increases": "#16a34a",
+        "correlates_with": "#0ea5e9",
+        "is_biomarker_of": "#06b6d4",
+        "is_risk_factor_for": "#f59e0b",
+        "predicts": "#0891b2",
+        "mediates": "#7c3aed",
+        "inhibits": "#b91c1c",
+        "distinguishes": "#c026d3",
+        "projects_to": "#0369a1",
+        "connects_to": "#0284c7",
+        "activates": "#15803d",
+        "coactivates": "#22c55e",
+        "gene_associated_with_disease": "#eab308",
+        "about": "#cbd5e1",
+        "supported_by": "#94a3b8",
+        "contradicts": "#dc2626",
+    }
+    DEFAULT_EDGE_COLOR = "#94a3b8"
+
+    # ── Noise filter (query-time, does not mutate KG) ─────────────────────
+    _NOISE_PREFIXES = (
+        "impaired ", "increased ", "decreased ", "reduced ",
+        "altered ", "elevated ", "abnormal ", "deficient ",
+        "excessive ", "diminished ", "enhanced ", "disrupted ",
+        "lower ", "higher ", "greater ", "lesser ",
+    )
+    _NOISE_SUFFIXES = (
+        " findings", " levels", " changes", " symptoms",
+        " deficits", " manifestations", " abnormalities",
+        " dysfunctions", " status", " outcomes", " profile",
+        " profiles", " patterns", " features",
+    )
+    NOISE_THRESHOLD = 0.3
+    # Curated-vocab prefixes — trust these names even if they match noise patterns.
+    # This prevents false positives on short MSH terms like "Brain", "Pons", "Sleep"
+    # that trigger HypothesisEngine._is_noisy_entity's short-word regex.
+    _CURATED_PREFIXES = (
+        "MSH:", "NN:", "COGAT_TASK:", "COGAT_CONCEPT:", "COGAT_DISORDER:",
+        "DISGENET:", "BM_REGION:", "BM_PARADIGM:", "BM_EXP:",
+        "HGNC:", "NCBI_Gene:",
+    )
+
+    def _compute_noise_score(node_id: str, name: str, n_claims: int, n_hyps: int) -> float:
+        """Combined heuristic score in [0, 1]. >= NOISE_THRESHOLD == noise."""
+        if not name:
+            return 1.0
+        # Curated vocab → trust (still apply prefix/suffix check but skip token check)
+        is_curated = any(node_id.startswith(p) for p in _CURATED_PREFIXES)
+
+        score = 0.0
+        if not is_curated:
+            try:
+                from core.knowledge_graph.src.hypothesis_engine import HypothesisEngine
+                if HypothesisEngine._is_noisy_entity(name):
+                    score += 0.5
+            except Exception:
+                pass
+        lname = name.lower()
+        if any(lname.startswith(p) for p in _NOISE_PREFIXES):
+            score += 0.3
+        if any(lname.endswith(s) for s in _NOISE_SUFFIXES):
+            score += 0.3
+        if node_id.startswith("CLM_CONCEPT:") and n_claims < 3:
+            score += 0.15
+        if n_hyps == 0 and len(name) > 40:
+            score += 0.05
+        return min(score, 1.0)
+
+    def _noise_reasons(node_id: str, name: str, n_claims: int, n_hyps: int) -> list[str]:
+        """Human-readable reasons why a concept was flagged as noise."""
+        reasons = []
+        if not name:
+            return ["empty name"]
+        is_curated = any(node_id.startswith(p) for p in _CURATED_PREFIXES)
+        if not is_curated:
+            try:
+                from core.knowledge_graph.src.hypothesis_engine import HypothesisEngine
+                if HypothesisEngine._is_noisy_entity(name):
+                    reasons.append("generic/nominalized token (risk/effect/findings/...)")
+            except Exception:
+                pass
+        lname = name.lower()
+        for p in _NOISE_PREFIXES:
+            if lname.startswith(p):
+                reasons.append(f"noise prefix: '{p.strip()}'")
+                break
+        for s in _NOISE_SUFFIXES:
+            if lname.endswith(s):
+                reasons.append(f"noise suffix: '{s.strip()}'")
+                break
+        if node_id.startswith("CLM_CONCEPT:") and n_claims < 3:
+            reasons.append("auto-extracted (CLM_CONCEPT) with <3 claims")
+        if n_hyps == 0 and len(name) > 40:
+            reasons.append("no hypotheses + long name")
+        return reasons
+
+    def _external_links(external_ids: dict) -> list[dict]:
+        links: list[dict] = []
+        for key, value in (external_ids or {}).items():
+            if not value:
+                continue
+            url = None
+            label = f"{key} · {value}"
+            k = key.lower()
+            v = str(value)
+            if k in ("mesh_ui", "msh", "mesh"):
+                url = f"https://meshb.nlm.nih.gov/record/ui?ui={v}"
+                label = f"MeSH · {v}"
+            elif k in ("umls", "cui"):
+                url = f"https://uts.nlm.nih.gov/uts/umls/concept/{v}"
+                label = f"UMLS · {v}"
+            elif k in ("disgenet_id", "disgenet"):
+                url = f"https://www.disgenet.org/search?source=ALL&search={v}"
+                label = f"DisGeNET · {v}"
+            elif k in ("nn_id", "nn", "neuronames"):
+                clean = v.replace("NN:", "") if v.startswith("NN:") else v
+                url = f"https://braininfo.rprc.washington.edu/centraldirectory.aspx?ID={clean}"
+                label = f"NeuroNames · {clean}"
+            elif k == "hgnc":
+                url = f"https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/{v}"
+                label = f"HGNC · {v}"
+            elif k in ("ncbi_gene", "ncbi", "ncbigene"):
+                url = f"https://www.ncbi.nlm.nih.gov/gene/{v}"
+                label = f"NCBI Gene · {v}"
+            elif k == "cogat_id":
+                url = f"https://www.cognitiveatlas.org/term/id/{v}"
+                label = f"Cognitive Atlas · {v}"
+            elif k == "doid":
+                url = f"https://disease-ontology.org/?id={v}"
+                label = f"DOID · {v}"
+            links.append({"key": key, "value": v, "label": label, "url": url})
+        return links
+
+    def _pmid_url(pmid: str) -> str | None:
+        return f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else None
+
+    def _doi_url(doi: str) -> str | None:
+        if not doi:
+            return None
+        return f"https://doi.org/{doi}" if not doi.startswith("http") else doi
+
+    def _load_kg_blocking() -> dict:
+        """Load KG + hypotheses + recipes; build reverse indexes. Called once."""
+        from core.knowledge_graph.src.storage import load_graph
+        from core.knowledge_graph.src.hypothesis_engine import Hypothesis
+
+        t0 = time.time()
+        print(f"[kg] loading knowledge graph from {KG_PATH} ...", flush=True)
+        kg = load_graph(KG_PATH)
+
+        # name_index: lower(name|alias) -> [node_id]
+        name_index: dict[str, list[str]] = {}
+        concept_to_claims: dict[str, list[str]] = {}
+        claim_nodes: dict[str, dict] = {}
+        for nid, node in kg._index.items():
+            is_claim = "claim" in node.domain_tags
+            if is_claim:
+                meta = node.metadata or {}
+                claim_nodes[nid] = meta
+                subj = meta.get("subject_id", "")
+                obj = meta.get("object_id", "")
+                if subj:
+                    concept_to_claims.setdefault(subj, []).append(nid)
+                if obj and obj != subj:
+                    concept_to_claims.setdefault(obj, []).append(nid)
+                continue
+            # Index non-claim concepts by name and aliases
+            key = node.preferred_name.strip().lower()
+            if key:
+                name_index.setdefault(key, []).append(nid)
+            for alias in node.aliases or []:
+                ak = alias.strip().lower()
+                if ak and ak != key:
+                    name_index.setdefault(ak, []).append(nid)
+
+        # Load hypotheses — critic first (priority), then imaging
+        hypotheses_by_id: dict[str, Hypothesis] = {}
+        for fname in HYPOTHESIS_SOURCES:
+            fpath = KG_QUICK_DIR / fname
+            if not fpath.exists():
+                # fallback: try parent data/ dir (without quick/)
+                fpath = KG_DATA_DIR / fname
+            if not fpath.exists():
+                print(f"[kg] skip missing hypothesis file: {fname}", flush=True)
+                continue
+            try:
+                data = json.loads(fpath.read_text(encoding="utf-8"))
+                for h_dict in data.get("hypotheses", []):
+                    h = Hypothesis.from_dict(h_dict)
+                    if h.id and h.id not in hypotheses_by_id:
+                        # tag source file for provenance
+                        h.metadata = dict(h.metadata or {})
+                        h.metadata.setdefault("_source_file", fname)
+                        hypotheses_by_id[h.id] = h
+                print(f"[kg] loaded {len(data.get('hypotheses', []))} hypotheses from {fname}", flush=True)
+            except Exception as exc:
+                print(f"[kg] failed to load {fname}: {exc}", flush=True)
+
+        # Reverse index: concept_id -> [hypothesis_id]
+        concept_to_hyps: dict[str, set] = {}
+        for hid, h in hypotheses_by_id.items():
+            touched: set[str] = set()
+            if h.source_id:
+                touched.add(h.source_id)
+            if h.target_id:
+                touched.add(h.target_id)
+            for link in h.path or []:
+                if link.from_id:
+                    touched.add(link.from_id)
+                if link.to_id:
+                    touched.add(link.to_id)
+            for cid in touched:
+                concept_to_hyps.setdefault(cid, set()).add(hid)
+
+        # Recipes (optional)
+        recipes_by_hyp: dict[str, dict] = {}
+        if RECIPES_PATH.exists():
+            try:
+                rdata = json.loads(RECIPES_PATH.read_text(encoding="utf-8"))
+                for r in rdata.get("recipes", []):
+                    hid = r.get("hypothesis_id")
+                    if hid:
+                        recipes_by_hyp[hid] = r
+                print(f"[kg] loaded {len(recipes_by_hyp)} recipes", flush=True)
+            except Exception as exc:
+                print(f"[kg] failed to load recipes: {exc}", flush=True)
+
+        # Compute noise scores for non-claim concepts, then rebuild a clean name_index
+        t_noise = time.time()
+        noise_map: dict[str, float] = {}
+        clean_name_index: dict[str, list[str]] = {}
+        n_noisy = 0
+        for nid, node in kg._index.items():
+            if "claim" in (node.domain_tags or []):
+                continue
+            n_cl = len(concept_to_claims.get(nid, []))
+            n_hy = len(concept_to_hyps.get(nid, set()))
+            score = _compute_noise_score(nid, node.preferred_name or "", n_cl, n_hy)
+            if score > 0:
+                noise_map[nid] = score
+            if score < NOISE_THRESHOLD:
+                key = (node.preferred_name or "").strip().lower()
+                if key:
+                    clean_name_index.setdefault(key, []).append(nid)
+                for alias in node.aliases or []:
+                    ak = alias.strip().lower()
+                    if ak and ak != key:
+                        clean_name_index.setdefault(ak, []).append(nid)
+            else:
+                n_noisy += 1
+        print(
+            f"[kg] scored noise in {time.time() - t_noise:.2f}s: "
+            f"{n_noisy} flagged (>= {NOISE_THRESHOLD})",
+            flush=True,
+        )
+
+        stats = kg.stats()
+        elapsed = time.time() - t0
+        print(
+            f"[kg] ready in {elapsed:.1f}s: {stats['n_concepts']} concepts, "
+            f"{stats['n_edges']} edges, {len(claim_nodes)} claims, "
+            f"{len(hypotheses_by_id)} hypotheses, {len(recipes_by_hyp)} recipes, "
+            f"{n_noisy} noise-flagged",
+            flush=True,
+        )
+
+        # ── Build trigram inverted index for fast substring search ──────
+        t_tri = time.time()
+
+        def _trigrams(s: str) -> set[str]:
+            s = s.lower()
+            if len(s) < 3:
+                return {s} if s else set()
+            return {s[i:i+3] for i in range(len(s) - 2)}
+
+        def _build_trigram_index(idx: dict[str, list[str]]) -> dict[str, set[str]]:
+            tri_idx: dict[str, set[str]] = {}
+            for key in idx:
+                for tri in _trigrams(key):
+                    tri_idx.setdefault(tri, set()).add(key)
+            return tri_idx
+
+        trigram_index = _build_trigram_index(name_index)
+        clean_trigram_index = _build_trigram_index(clean_name_index)
+        print(f"[kg] built trigram indexes in {time.time() - t_tri:.2f}s", flush=True)
+
+        # ── Pre-compute top-ranked concept lists (avoids 86k scan per request) ──
+        t_top = time.time()
+
+        def _build_top_list(idx: dict[str, list[str]], quality_strict: bool = False) -> list[dict]:
+            seen: set[str] = set()
+            candidates: list[dict] = []
+            for nids in idx.values():
+                for nid in nids:
+                    if nid in seen:
+                        continue
+                    seen.add(nid)
+                    node = kg._index.get(nid)
+                    if node is None:
+                        continue
+                    n_cl = len(concept_to_claims.get(nid, []))
+                    n_hy = len(concept_to_hyps.get(nid, set()))
+                    if n_cl == 0 and n_hy == 0:
+                        continue
+                    if quality_strict and not (n_hy > 0 or n_cl >= 3):
+                        continue
+                    noise = noise_map.get(nid, 0.0)
+                    candidates.append({
+                        "id": nid,
+                        "name": node.preferred_name,
+                        "domain_tags": list(node.domain_tags or []),
+                        "aliases": list(node.aliases or [])[:6],
+                        "n_claims": n_cl,
+                        "n_hypotheses": n_hy,
+                        "noise_score": noise,
+                        "is_noise": noise >= NOISE_THRESHOLD,
+                    })
+            candidates.sort(key=lambda r: (-(r["n_claims"] * 2 + r["n_hypotheses"]), r["noise_score"], len(r["name"])))
+            return candidates
+
+        top_all = _build_top_list(name_index)
+        top_clean = _build_top_list(clean_name_index)
+        print(f"[kg] pre-computed top lists in {time.time() - t_top:.2f}s ({len(top_clean)} clean, {len(top_all)} all)", flush=True)
+
+        return {
+            "loaded": True,
+            "loading": False,
+            "error": None,
+            "kg": kg,
+            "name_index": name_index,
+            "clean_name_index": clean_name_index,
+            "trigram_index": trigram_index,
+            "clean_trigram_index": clean_trigram_index,
+            "top_all": top_all,
+            "top_clean": top_clean,
+            "concept_to_claims": concept_to_claims,
+            "claim_nodes": claim_nodes,
+            "hypotheses_by_id": hypotheses_by_id,
+            "concept_to_hyps": concept_to_hyps,
+            "recipes_by_hyp": recipes_by_hyp,
+            "noise_map": noise_map,
+            "stats": {
+                "n_concepts": stats["n_concepts"],
+                "n_edges": stats["n_edges"],
+                "n_claims": len(claim_nodes),
+                "n_hypotheses": len(hypotheses_by_id),
+                "n_recipes": len(recipes_by_hyp),
+                "n_with_recipe": len(recipes_by_hyp),
+                "n_noise_flagged": n_noisy,
+                "domains": stats.get("domains", {}),
+            },
+        }
+
+    async def _get_kg_state() -> dict:
+        """Lazy-load the KG on first request; subsequent calls return cached state."""
+        if _kg_state.get("loaded"):
+            return _kg_state
+        # serialize concurrent first-load attempts
+        should_load = False
+        with _kg_lock:
+            if not _kg_state.get("loaded") and not _kg_state.get("loading"):
+                _kg_state["loading"] = True
+                should_load = True
+        if should_load:
+            try:
+                new_state = await asyncio.to_thread(_load_kg_blocking)
+                _kg_state.update(new_state)
+            except Exception as exc:
+                _kg_state["loading"] = False
+                _kg_state["error"] = str(exc)
+                raise
+        else:
+            # another request is loading — poll briefly
+            for _ in range(600):  # up to ~60s
+                if _kg_state.get("loaded") or _kg_state.get("error"):
+                    break
+                await asyncio.sleep(0.1)
+        if _kg_state.get("error"):
+            raise RuntimeError(_kg_state["error"])
+        return _kg_state
+
+    def _node_summary(state: dict, node_id: str) -> dict | None:
+        kg = state["kg"]
+        node = kg._index.get(node_id)
+        if node is None:
+            return None
+        noise = state.get("noise_map", {}).get(node_id, 0.0)
+        return {
+            "id": node.id,
+            "name": node.preferred_name,
+            "domain_tags": list(node.domain_tags or []),
+            "aliases": list(node.aliases or [])[:6],
+            "n_claims": len(state["concept_to_claims"].get(node_id, [])),
+            "n_hypotheses": len(state["concept_to_hyps"].get(node_id, set())),
+            "noise_score": noise,
+            "is_noise": noise >= NOISE_THRESHOLD,
+        }
+
+    def _serialize_claim(state: dict, claim_id: str) -> dict | None:
+        kg = state["kg"]
+        node = kg._index.get(claim_id)
+        if node is None or "claim" not in node.domain_tags:
+            return None
+        meta = node.metadata or {}
+        paper = meta.get("source_paper") or {}
+        evidence = meta.get("evidence") or {}
+        pmid = paper.get("pmid", "") or ""
+        doi = paper.get("doi", "") or ""
+        return {
+            "claim_id": claim_id,
+            "subject_id": meta.get("subject_id", ""),
+            "subject_name": meta.get("subject_name", ""),
+            "predicate": meta.get("predicate", ""),
+            "object_id": meta.get("object_id", ""),
+            "object_name": meta.get("object_name", ""),
+            "confidence": float(meta.get("confidence", 0.0)),
+            "negated": bool(meta.get("negated", False)),
+            "raw_text": meta.get("raw_text", ""),
+            "paper": {
+                "pmid": pmid,
+                "doi": doi,
+                "title": paper.get("title", ""),
+                "authors": paper.get("authors", ""),
+                "year": paper.get("year"),
+                "journal": paper.get("journal", ""),
+                "pubmed_url": _pmid_url(pmid),
+                "doi_url": _doi_url(doi),
+            },
+            "evidence": {
+                "study_type": evidence.get("study_type", ""),
+                "methodology": evidence.get("methodology", ""),
+                "p_value": evidence.get("p_value"),
+                "effect_size": evidence.get("effect_size"),
+                "effect_metric": evidence.get("effect_metric", ""),
+                "sample_size": evidence.get("sample_size"),
+                "replicability": evidence.get("replicability", ""),
+                "direction": evidence.get("direction", ""),
+            },
+        }
+
+    def _serialize_hypothesis(state: dict, h, include_full_path: bool = True) -> dict:
+        path_out: list[dict] = []
+        pmids: set[str] = set()
+        if include_full_path:
+            for link in h.path or []:
+                sp = link.source_paper or {}
+                pmid = sp.get("pmid", "") or ""
+                doi = sp.get("doi", "") or ""
+                if pmid:
+                    pmids.add(pmid)
+                path_out.append({
+                    "from_id": link.from_id,
+                    "from_name": link.from_name,
+                    "to_id": link.to_id,
+                    "to_name": link.to_name,
+                    "relation_type": link.relation_type,
+                    "confidence": link.confidence,
+                    "claim_id": link.claim_id,
+                    "raw_text": (link.raw_text or "")[:400],
+                    "paper": {
+                        "pmid": pmid,
+                        "doi": doi,
+                        "title": sp.get("title", ""),
+                        "year": sp.get("year"),
+                        "journal": sp.get("journal", ""),
+                        "pubmed_url": _pmid_url(pmid),
+                        "doi_url": _doi_url(doi),
+                    },
+                })
+        recipe = state["recipes_by_hyp"].get(h.id)
+        return {
+            "id": h.id,
+            "hypothesis_type": h.hypothesis_type,
+            "source_id": h.source_id,
+            "source_name": h.source_name,
+            "target_id": h.target_id,
+            "target_name": h.target_name,
+            "confidence_score": h.confidence_score,
+            "novelty_score": h.novelty_score,
+            "evidence_score": h.evidence_score,
+            "testability_score": h.testability_score,
+            "composite_score": h.composite_score,
+            "critic_score": h.critic_score,
+            "critic_rounds": h.critic_rounds,
+            "testability_reason": h.testability_reason,
+            "explanation": h.explanation,
+            "path": path_out,
+            "supporting_claims": list(h.supporting_claims or [])[:20],
+            "source_file": (h.metadata or {}).get("_source_file", ""),
+            "pmids": sorted(pmids)[:10],
+            "has_recipe": recipe is not None,
+            "recipe": (
+                {
+                    "id": recipe.get("id"),
+                    "dataset": recipe.get("dataset"),
+                    "model_arch": recipe.get("model_arch"),
+                    "atlas": recipe.get("atlas"),
+                    "target_outcome": recipe.get("target_outcome"),
+                    "input_modalities": recipe.get("input_modalities"),
+                    "rationale": recipe.get("rationale", "")[:300],
+                }
+                if recipe else None
+            ),
+        }
+
+    # ── KG routes ──────────────────────────────────────────────────────────
+
+    @app.get("/explore")
+    async def kg_explore_page() -> Any:
+        page = STATIC_DIR / "explore.html"
+        if page.exists():
+            return FileResponse(str(page))
+        return HTMLResponse(
+            "<h1>Knowledge Graph Explorer</h1>"
+            f"<p>explore.html not found in <code>{STATIC_DIR}</code>.</p>",
+            status_code=500,
+        )
+
+    @app.get("/api/kg/stats")
+    async def kg_stats() -> Any:
+        if not _kg_state.get("loaded"):
+            if _kg_state.get("loading"):
+                return {"loaded": False, "loading": True}
+            # Kick off load in background without waiting
+            asyncio.create_task(_get_kg_state())
+            return {"loaded": False, "loading": True}
+        return {"loaded": True, **_kg_state["stats"]}
+
+    @app.post("/api/kg/load")
+    async def kg_load() -> Any:
+        try:
+            state = await _get_kg_state()
+            return {"loaded": True, **state["stats"]}
+        except Exception as exc:
+            return JSONResponse({"loaded": False, "error": str(exc)}, status_code=500)
+
+    @app.get("/api/kg/search")
+    async def kg_search(
+        q: str = "",
+        domain: str = "",
+        limit: int = 20,
+        quality: str = "clean",
+    ) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        q_norm = (q or "").strip().lower()
+        domain_filter = {d.strip() for d in (domain or "").split(",") if d.strip()}
+        quality = quality.lower() if quality else "clean"
+        if quality not in ("all", "clean", "strict"):
+            quality = "clean"
+        idx = state["name_index"] if quality == "all" else state["clean_name_index"]
+        tri_idx = state["trigram_index"] if quality == "all" else state["clean_trigram_index"]
+
+        def passes_strict(s: dict) -> bool:
+            return s["n_hypotheses"] > 0 or s["n_claims"] >= 3
+
+        def _trigrams_q(s: str) -> set[str]:
+            if len(s) < 3:
+                return {s} if s else set()
+            return {s[i:i+3] for i in range(len(s) - 2)}
+
+        # Default listing when no query text: return pre-computed top list
+        if len(q_norm) < 2:
+            top_list = state["top_all"] if quality == "all" else state["top_clean"]
+            if domain_filter:
+                filtered = [r for r in top_list if domain_filter & set(r["domain_tags"])]
+                if quality == "strict":
+                    filtered = [r for r in filtered if passes_strict(r)]
+                return {"results": filtered[:max(1, int(limit))], "query": q, "quality": quality, "mode": "top"}
+            if quality == "strict":
+                filtered = [r for r in top_list if passes_strict(r)]
+                return {"results": filtered[:max(1, int(limit))], "query": q, "quality": quality, "mode": "top"}
+            return {"results": top_list[:max(1, int(limit))], "query": q, "quality": quality, "mode": "top"}
+
+        seen: set[str] = set()
+        results: list[dict] = []
+
+        # Exact key hit first
+        for nid in idx.get(q_norm, []):
+            if nid in seen:
+                continue
+            summary = _node_summary(state, nid)
+            if summary is None:
+                continue
+            if domain_filter and not (domain_filter & set(summary["domain_tags"])):
+                continue
+            if quality == "strict" and not passes_strict(summary):
+                continue
+            summary["match"] = "exact"
+            results.append(summary)
+            seen.add(nid)
+
+        # Trigram-accelerated substring search
+        if len(results) < limit:
+            tris = _trigrams_q(q_norm)
+            if tris:
+                candidate_keys: set[str] | None = None
+                for tri in tris:
+                    keys = tri_idx.get(tri)
+                    if keys is None:
+                        candidate_keys = set()
+                        break
+                    if candidate_keys is None:
+                        candidate_keys = set(keys)
+                    else:
+                        candidate_keys &= keys
+                for key in (candidate_keys or set()):
+                    if q_norm not in key:
+                        continue
+                    for nid in idx.get(key, []):
+                        if nid in seen:
+                            continue
+                        summary = _node_summary(state, nid)
+                        if summary is None:
+                            continue
+                        if domain_filter and not (domain_filter & set(summary["domain_tags"])):
+                            continue
+                        if quality == "strict" and not passes_strict(summary):
+                            continue
+                        summary["match"] = "substring"
+                        results.append(summary)
+                        seen.add(nid)
+                        if len(results) >= limit * 3:
+                            break
+                    if len(results) >= limit * 3:
+                        break
+
+        results.sort(
+            key=lambda r: (
+                0 if r.get("match") == "exact" else 1,
+                r.get("noise_score", 0.0),
+                -(r["n_hypotheses"] * 2 + r["n_claims"]),
+                len(r["name"]),
+            )
+        )
+        return {"results": results[:limit], "query": q, "quality": quality, "mode": "search"}
+
+    @app.get("/api/kg/node/{node_id}")
+    async def kg_node(node_id: str) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        kg = state["kg"]
+        node = kg._index.get(node_id)
+        if node is None:
+            return JSONResponse({"error": f"node not found: {node_id}"}, status_code=404)
+        n_claims = len(state["concept_to_claims"].get(node_id, []))
+        n_hyps = len(state["concept_to_hyps"].get(node_id, set()))
+        noise = state.get("noise_map", {}).get(node_id, 0.0)
+        reasons = _noise_reasons(node_id, node.preferred_name or "", n_claims, n_hyps) if noise >= NOISE_THRESHOLD else []
+        return {
+            "id": node.id,
+            "name": node.preferred_name,
+            "definition": node.definition or "",
+            "domain_tags": list(node.domain_tags or []),
+            "semantic_types": list(node.semantic_types or []),
+            "source_vocab": node.source_vocab or "",
+            "aliases": list(node.aliases or []),
+            "external_ids": dict(node.external_ids or {}),
+            "external_links": _external_links(node.external_ids),
+            "atlas_mapping": node.atlas_mapping,
+            "n_claims": n_claims,
+            "n_hypotheses": n_hyps,
+            "noise_score": noise,
+            "is_noise": noise >= NOISE_THRESHOLD,
+            "noise_reasons": reasons,
+            "color": DOMAIN_COLORS.get(
+                (node.domain_tags or ["unknown"])[0], "#94a3b8"
+            ),
+        }
+
+    @app.get("/api/kg/node/{node_id}/neighborhood")
+    async def kg_neighborhood(
+        node_id: str,
+        depth: int = 1,
+        edge_types: str = "",
+        limit: int = 80,
+    ) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        kg = state["kg"]
+        if node_id not in kg._index:
+            return JSONResponse({"error": f"node not found: {node_id}"}, status_code=404)
+
+        depth = max(1, min(2, int(depth)))
+        limit = max(10, min(200, int(limit)))
+        type_filter = {t.strip() for t in edge_types.split(",") if t.strip() and t.strip() != "all"}
+
+        G = kg.G
+        visited = {node_id}
+        depth_map: dict[str, int] = {node_id: 0}
+        frontier = {node_id}
+        edges_collected: list[tuple[str, str, dict]] = []
+        for hop in range(depth):
+            next_frontier: set[str] = set()
+            for n in frontier:
+                for _, tgt, data in G.out_edges(n, data=True):
+                    rt = data.get("relation_type", "")
+                    if type_filter and rt not in type_filter:
+                        continue
+                    if rt == "about":
+                        continue  # skip claim-about edges (clutter)
+                    edges_collected.append((n, tgt, data))
+                    if tgt not in visited:
+                        next_frontier.add(tgt)
+                        depth_map.setdefault(tgt, hop + 1)
+                for src, _, data in G.in_edges(n, data=True):
+                    rt = data.get("relation_type", "")
+                    if type_filter and rt not in type_filter:
+                        continue
+                    if rt == "about":
+                        continue
+                    edges_collected.append((src, n, data))
+                    if src not in visited:
+                        next_frontier.add(src)
+                        depth_map.setdefault(src, hop + 1)
+            visited |= next_frontier
+            frontier = next_frontier
+            if len(visited) >= limit:
+                break
+
+        # depth=2: drop edges that connect two nodes already at depth=1 to avoid
+        # cluttering the graph with sibling cross-links (the user asked that
+        # depth=2 not consider two peer neighbors being connected). Keep edges
+        # that touch the center or a depth=2 node.
+        if depth >= 2:
+            edges_collected = [
+                (s, t, d) for (s, t, d) in edges_collected
+                if not (depth_map.get(s) == 1 and depth_map.get(t) == 1)
+            ]
+
+        # Rank candidate nodes by degree (excluding the center) and cap at limit
+        node_ids: list[str] = [node_id]
+        candidates = [n for n in visited if n != node_id]
+        candidates.sort(key=lambda n: G.degree(n), reverse=True)
+        node_ids.extend(candidates[: max(0, limit - 1)])
+        keep = set(node_ids)
+
+        nodes_out: list[dict] = []
+        noise_map = state.get("noise_map", {})
+        for nid in node_ids:
+            nd = kg._index.get(nid)
+            if nd is None:
+                continue
+            domain = (nd.domain_tags or ["unknown"])[0]
+            is_claim = "claim" in (nd.domain_tags or [])
+            # Prefer a biology domain over "claim" for color
+            color_domain = domain
+            if is_claim and len(nd.domain_tags or []) > 1:
+                for d in nd.domain_tags:
+                    if d != "claim":
+                        color_domain = d
+                        break
+            label = nd.preferred_name or nid
+            if is_claim and len(label) > 60:
+                label = label[:57] + "…"
+            noise = noise_map.get(nid, 0.0)
+            is_noisy = noise >= NOISE_THRESHOLD
+            base_size = 14 if nid == node_id else (6 if is_claim else 9)
+            nodes_out.append({
+                "id": nid,
+                "label": label,
+                "color": DOMAIN_COLORS.get(color_domain, "#94a3b8"),
+                "domain": color_domain,
+                "domains": list(nd.domain_tags or []),
+                "is_claim": is_claim,
+                "is_center": nid == node_id,
+                "size": base_size if not (is_noisy and nid != node_id) else max(3, int(base_size * 0.55)),
+                "noise_score": noise,
+                "is_noise": is_noisy,
+            })
+
+        # Aggregate edges by unordered pair so bidirectional or multi-predicate
+        # edges render as a single visual line (prevents label overlap).
+        # Additionally scan claim nodes to surface predicates that the DiGraph
+        # collapsed (graph_manager keeps only the highest-confidence relation).
+        pair_info: dict[frozenset, dict] = {}
+        for src, tgt, data in edges_collected:
+            if src not in keep or tgt not in keep:
+                continue
+            rt = data.get("relation_type", "")
+            if not rt:
+                continue
+            pair = frozenset({src, tgt}) if src != tgt else frozenset({src})
+            entry = pair_info.setdefault(pair, {
+                "src": src, "tgt": tgt,   # may be overwritten; used for first-seen direction
+                "relations_fwd": [],     # ordered, deduped
+                "relations_rev": [],
+                "confidence": 0.0,
+            })
+            entry["confidence"] = max(entry["confidence"], float(data.get("confidence", 1.0)))
+            # Track which direction this relation was seen in relative to (src, tgt)
+            if (src, tgt) == (entry["src"], entry["tgt"]):
+                if rt not in entry["relations_fwd"]:
+                    entry["relations_fwd"].append(rt)
+            else:
+                if rt not in entry["relations_rev"]:
+                    entry["relations_rev"].append(rt)
+
+        # Pull additional claim-backed predicates between kept pairs
+        concept_to_claims = state["concept_to_claims"]
+        for pair, entry in pair_info.items():
+            a_raw = list(pair)
+            if len(a_raw) == 1:
+                continue  # self-loop; skip extra claim scan
+            a, b = a_raw[0], a_raw[1]
+            a_claims = set(concept_to_claims.get(a, []))
+            b_claims = set(concept_to_claims.get(b, []))
+            shared = a_claims & b_claims
+            if not shared:
+                continue
+            for cid in shared:
+                cn = kg._index.get(cid)
+                if cn is None:
+                    continue
+                meta = cn.metadata or {}
+                pred = (meta.get("predicate") or "").strip()
+                if not pred:
+                    continue
+                if type_filter and pred not in type_filter:
+                    continue
+                subj = meta.get("subject_id", "")
+                obj = meta.get("object_id", "")
+                if (subj, obj) == (entry["src"], entry["tgt"]):
+                    if pred not in entry["relations_fwd"]:
+                        entry["relations_fwd"].append(pred)
+                elif (subj, obj) == (entry["tgt"], entry["src"]):
+                    if pred not in entry["relations_rev"]:
+                        entry["relations_rev"].append(pred)
+
+        # Emit merged edges
+        edges_out: list[dict] = []
+        for pair, entry in pair_info.items():
+            fwd = entry["relations_fwd"]
+            rev = entry["relations_rev"]
+            if not fwd and not rev:
+                continue
+            # Combine labels. If bidirectional, join both with ⇄ so the user sees
+            # there are multiple relations.
+            parts: list[str] = []
+            if fwd:
+                parts.append(" · ".join(fwd[:3]) + (f" +{len(fwd)-3}" if len(fwd) > 3 else ""))
+            if rev:
+                parts.append("← " + " · ".join(rev[:3]) + (f" +{len(rev)-3}" if len(rev) > 3 else ""))
+            label = "  ⇄  ".join(parts) if (fwd and rev) else (parts[0] if parts else "")
+            primary = (fwd[0] if fwd else rev[0])
+            edges_out.append({
+                "id": f"e{len(edges_out)}",
+                "source": entry["src"],
+                "target": entry["tgt"],
+                "label": label,
+                "relations_fwd": fwd,
+                "relations_rev": rev,
+                "bidirectional": bool(fwd and rev),
+                "color": RELATION_COLORS.get(primary, DEFAULT_EDGE_COLOR),
+                "confidence": entry["confidence"],
+            })
+
+        return {
+            "center": node_id,
+            "depth": depth,
+            "nodes": nodes_out,
+            "edges": edges_out,
+            "truncated": len(candidates) > (limit - 1),
+        }
+
+    @app.get("/api/kg/node/{node_id}/claims")
+    async def kg_claims(
+        node_id: str,
+        limit: int = 50,
+        predicate: str = "",
+        neighbor_id: str = "",
+    ) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        kg = state["kg"]
+        if node_id not in kg._index:
+            return JSONResponse({"error": f"node not found: {node_id}"}, status_code=404)
+        claim_ids = state["concept_to_claims"].get(node_id, [])
+        # parse predicate filter (may be comma-separated list from the UI)
+        pred_filter = {p.strip() for p in (predicate or "").split(",") if p.strip() and p.strip() != "all"}
+        items = []
+        for cid in claim_ids:
+            s = _serialize_claim(state, cid)
+            if not s:
+                continue
+            if pred_filter and s.get("predicate") not in pred_filter:
+                continue
+            if neighbor_id:
+                if s.get("subject_id") != neighbor_id and s.get("object_id") != neighbor_id:
+                    continue
+            items.append(s)
+        # Sort: confidence desc, year desc
+        items.sort(key=lambda c: (
+            -(c.get("confidence") or 0.0),
+            -((c.get("paper") or {}).get("year") or 0),
+        ))
+        return {"node_id": node_id, "total": len(items), "claims": items[: max(1, int(limit))]}
+
+    @app.get("/api/kg/edge-sources")
+    async def kg_edge_sources(source: str = "", target: str = "", limit: int = 50) -> Any:
+        """Return all claims + curated edges that connect two concepts (either direction)."""
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        kg = state["kg"]
+        if not source or not target:
+            return JSONResponse({"error": "source and target are required"}, status_code=400)
+        if source not in kg._index or target not in kg._index:
+            return JSONResponse({"error": "node(s) not found"}, status_code=404)
+
+        src_node = kg._index[source]
+        tgt_node = kg._index[target]
+
+        # 1. Claims where {subject, object} == {source, target}
+        claim_items: list[dict] = []
+        seen_claims: set[str] = set()
+        src_claims = set(state["concept_to_claims"].get(source, []))
+        tgt_claims = set(state["concept_to_claims"].get(target, []))
+        for cid in src_claims & tgt_claims:
+            if cid in seen_claims:
+                continue
+            seen_claims.add(cid)
+            s = _serialize_claim(state, cid)
+            if s:
+                claim_items.append(s)
+        claim_items.sort(key=lambda c: (
+            -(c.get("confidence") or 0.0),
+            -((c.get("paper") or {}).get("year") or 0),
+        ))
+
+        # 2. Curated edges (non-claim) between the two nodes, both directions
+        curated_edges: list[dict] = []
+        G = kg.G
+        for u, v in ((source, target), (target, source)):
+            if G.has_edge(u, v):
+                data = G.edges[u, v]
+                src_str = data.get("source", "")
+                if src_str.startswith("claim:"):
+                    continue  # already counted above
+                curated_edges.append({
+                    "from_id": u,
+                    "from_name": kg._index[u].preferred_name,
+                    "to_id": v,
+                    "to_name": kg._index[v].preferred_name,
+                    "relation_type": data.get("relation_type", ""),
+                    "confidence": float(data.get("confidence", 1.0)),
+                    "source_vocab": src_str or "curated",
+                    "evidence_ref": data.get("evidence_ref", ""),
+                })
+
+        return {
+            "source": {"id": source, "name": src_node.preferred_name},
+            "target": {"id": target, "name": tgt_node.preferred_name},
+            "total_claims": len(claim_items),
+            "total_curated_edges": len(curated_edges),
+            "claims": claim_items[: max(1, int(limit))],
+            "curated_edges": curated_edges,
+        }
+
+    @app.get("/api/kg/node/{node_id}/hypotheses")
+    async def kg_hypotheses(
+        node_id: str,
+        limit: int = 20,
+        min_score: float = 0.0,
+        recipe_only: bool = False,
+    ) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        kg = state["kg"]
+        if node_id not in kg._index:
+            return JSONResponse({"error": f"node not found: {node_id}"}, status_code=404)
+        hyp_ids = state["concept_to_hyps"].get(node_id, set())
+        hyps = [state["hypotheses_by_id"][hid] for hid in hyp_ids if hid in state["hypotheses_by_id"]]
+        # Filters
+        if min_score > 0:
+            hyps = [h for h in hyps if (h.composite_score or 0.0) >= min_score]
+        if recipe_only:
+            hyps = [h for h in hyps if h.id in state["recipes_by_hyp"]]
+        hyps.sort(key=lambda h: (h.composite_score or 0.0), reverse=True)
+        items = [_serialize_hypothesis(state, h) for h in hyps[: max(1, int(limit))]]
+        return {
+            "node_id": node_id,
+            "total": len(hyps),
+            "hypotheses": items,
+            "has_recipes": len(state["recipes_by_hyp"]) > 0,
+        }
+
+    @app.get("/api/kg/hypothesis/{hyp_id}")
+    async def kg_hypothesis_detail(hyp_id: str) -> Any:
+        try:
+            state = await _get_kg_state()
+        except Exception as exc:
+            return JSONResponse({"error": str(exc)}, status_code=500)
+        h = state["hypotheses_by_id"].get(hyp_id)
+        if h is None:
+            return JSONResponse({"error": f"hypothesis not found: {hyp_id}"}, status_code=404)
+        return _serialize_hypothesis(state, h)
+
+    return app
+
+
+# ── Entry point ────────────────────────────────────────────────────────────────
+
+def run_server(host: str = DEFAULT_HOST, port: int = DEFAULT_PORT) -> None:
+    """Start the uvicorn server (blocking call — returns only when the server stops)."""
+    _require_webdeps()
+    import uvicorn  # type: ignore
+
+    app = create_app()
+    print(f"\n  NeuroClaw Web UI  →  http://{host}:{port}\n")
+    uvicorn.run(app, host=host, port=port, log_level="info")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="NeuroClaw Web UI — start the browser-based chat interface."
+    )
+    parser.add_argument(
+        "--host", default=DEFAULT_HOST,
+        help=f"Bind host (default: {DEFAULT_HOST})",
+    )
+    parser.add_argument(
+        "--port", type=int, default=DEFAULT_PORT,
+        help=f"Port number (default: {DEFAULT_PORT})",
+    )
+    args = parser.parse_args()
+    run_server(host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()

core/web/static/explore.html

@@ -0,0 +1,2096 @@
+<!DOCTYPE html>
+<html lang="en" data-theme="light">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>NeuroClaw · Knowledge Graph Explorer</title>
+<script src="https://cdn.jsdelivr.net/npm/graphology@0.25.4/dist/graphology.umd.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/sigma@3.0.0/dist/sigma.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/graphology-layout-forceatlas2@0.10.1/worker.min.js"></script>
+<style>
+*,*::before,*::after { box-sizing: border-box; margin: 0; padding: 0; }
+
+:root {
+  --radius-lg: 14px;
+  --radius-md: 10px;
+  --radius-sm: 6px;
+  --header-h: 60px;
+  --sidebar-w: 340px;
+  --detail-w: 420px;
+  --shadow-card: 0 1px 3px rgba(15, 23, 42, 0.06), 0 1px 2px rgba(15, 23, 42, 0.04);
+  --ease: 200ms cubic-bezier(0.2, 0.8, 0.2, 1);
+  --ui-font-size: 18px;
+  --graph-label-size: 18px;
+}
+html[data-theme="light"] {
+  --bg: #f5f7fa;
+  --surface: #ffffff;
+  --surface-soft: #f1f4f9;
+  --surface-elev: #ffffff;
+  --border: #e2e8f0;
+  --border-strong: #cbd5e1;
+  --accent: #0a84ff;
+  --accent-soft: rgba(10, 132, 255, 0.1);
+  --text: #0f172a;
+  --text-muted: #64748b;
+  --text-subtle: #94a3b8;
+  --danger: #dc2626;
+  --warning: #d97706;
+  --success: #10b981;
+}
+html[data-theme="dark"] {
+  --bg: #0b1220;
+  --surface: #111a2c;
+  --surface-soft: #0e1626;
+  --surface-elev: #152037;
+  --border: #1f2d45;
+  --border-strong: #2b3d5c;
+  --accent: #58a6ff;
+  --accent-soft: rgba(88, 166, 255, 0.15);
+  --text: #e2e8f0;
+  --text-muted: #8da0b4;
+  --text-subtle: #64748b;
+  --danger: #ef4444;
+  --warning: #f59e0b;
+  --success: #10b981;
+}
+body {
+  font-family: -apple-system, BlinkMacSystemFont, "SF Pro Text", "PingFang SC",
+    "Helvetica Neue", Arial, sans-serif;
+  background: var(--bg);
+  color: var(--text);
+  min-height: 100vh;
+  font-size: var(--ui-font-size);
+  line-height: 1.5;
+  overflow: hidden;
+}
+
+/* ── Header ─────────────────────────────────────────────────────────── */
+.header {
+  height: var(--header-h);
+  background: var(--surface);
+  border-bottom: 1px solid var(--border);
+  display: flex;
+  align-items: center;
+  padding: 0 22px;
+  gap: 18px;
+  position: sticky;
+  top: 0;
+  z-index: 10;
+}
+.header-brand {
+  display: flex;
+  align-items: center;
+  gap: 11px;
+  font-weight: 600;
+  font-size: 16px;
+}
+.header-brand .logo-dot {
+  width: 28px;
+  height: 28px;
+  border-radius: 50%;
+  background: linear-gradient(135deg, #0a84ff, #8b5cf6);
+}
+.header-sub {
+  font-size: 13px;
+  color: var(--text-muted);
+  border-left: 1px solid var(--border);
+  padding-left: 18px;
+}
+.stats-line {
+  margin-left: auto;
+  font-size: 12.5px;
+  color: var(--text-muted);
+  display: flex;
+  gap: 10px;
+  align-items: center;
+}
+.stats-dot {
+  width: 3px;
+  height: 3px;
+  border-radius: 50%;
+  background: var(--text-subtle);
+}
+.icon-btn {
+  background: transparent;
+  border: 1px solid var(--border);
+  color: var(--text-muted);
+  padding: 7px 11px;
+  border-radius: var(--radius-sm);
+  cursor: pointer;
+  font-size: 12.5px;
+  transition: all var(--ease);
+  display: inline-flex;
+  align-items: center;
+  gap: 5px;
+}
+.icon-btn:hover {
+  border-color: var(--accent);
+  color: var(--accent);
+}
+.lang-toggle { font-weight: 600; letter-spacing: 0.04em; }
+.back-btn {
+  padding: 5px 11px;
+  font-size: 12.5px;
+}
+.back-btn:disabled {
+  opacity: 0.4;
+  cursor: not-allowed;
+  border-color: var(--border);
+  color: var(--text-subtle);
+}
+.back-btn:disabled:hover {
+  border-color: var(--border);
+  color: var(--text-subtle);
+}
+
+/* ── Layout ─────────────────────────────────────────────────────────── */
+.layout {
+  display: grid;
+  grid-template-columns: var(--sidebar-w) minmax(0, 1fr) var(--detail-w);
+  height: calc(100vh - var(--header-h));
+  overflow: hidden;
+}
+
+/* ── Sidebar ────────────────────────────────────────────────────────── */
+.sidebar {
+  background: var(--surface);
+  border-right: 1px solid var(--border);
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+}
+.search-box {
+  padding: 16px;
+  border-bottom: 1px solid var(--border);
+}
+.search-input {
+  width: 100%;
+  padding: 10px 13px;
+  background: var(--surface-soft);
+  border: 1px solid var(--border);
+  border-radius: var(--radius-sm);
+  color: var(--text);
+  font-size: 14px;
+  outline: none;
+  transition: all var(--ease);
+}
+.search-input:focus {
+  border-color: var(--accent);
+  background: var(--surface);
+}
+.domain-chips {
+  display: flex;
+  gap: 6px;
+  flex-wrap: wrap;
+  margin-top: 11px;
+}
+.chip {
+  padding: 3px 10px;
+  border-radius: 12px;
+  font-size: 12px;
+  background: var(--surface-soft);
+  border: 1px solid var(--border);
+  color: var(--text-muted);
+  cursor: pointer;
+  transition: all var(--ease);
+  user-select: none;
+}
+.chip:hover { border-color: var(--border-strong); }
+.chip.active {
+  background: var(--accent-soft);
+  border-color: var(--accent);
+  color: var(--accent);
+}
+.pill {
+  padding: 3px 9px;
+  border-radius: 11px;
+  font-size: 12px;
+  background: var(--surface-soft);
+  border: 1px solid var(--border);
+  color: var(--text-muted);
+  cursor: pointer;
+  user-select: none;
+  transition: all var(--ease);
+}
+.pill:hover { border-color: var(--border-strong); color: var(--text); }
+.pill.active {
+  background: var(--accent);
+  color: #fff;
+  border-color: var(--accent);
+}
+.results {
+  flex: 1;
+  overflow-y: auto;
+  padding: 8px 0;
+}
+.results-hint {
+  padding: 18px 20px;
+  color: var(--text-subtle);
+  font-size: 12.5px;
+  text-align: center;
+}
+.result-item {
+  padding: 11px 15px;
+  cursor: pointer;
+  border-left: 2px solid transparent;
+  transition: all var(--ease);
+}
+.result-item:hover { background: var(--surface-soft); }
+.result-item.active {
+  background: var(--accent-soft);
+  border-left-color: var(--accent);
+}
+.result-name {
+  font-size: 13.5px;
+  font-weight: 500;
+  color: var(--text);
+  line-height: 1.35;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  display: -webkit-box;
+  -webkit-line-clamp: 2;
+  -webkit-box-orient: vertical;
+}
+.result-meta {
+  display: flex;
+  gap: 6px;
+  margin-top: 6px;
+  font-size: 11.5px;
+  color: var(--text-muted);
+  flex-wrap: wrap;
+  align-items: center;
+}
+.result-meta .badge {
+  background: var(--surface-soft);
+  padding: 2px 8px;
+  border-radius: 10px;
+  font-size: 11px;
+  border: 1px solid var(--border);
+}
+.result-meta .domain-tag {
+  background: var(--surface-soft);
+  padding: 2px 8px;
+  border-radius: 10px;
+  font-size: 11px;
+  border: 1px solid var(--border);
+  color: #fff;
+  border-color: transparent;
+}
+.result-item.is-noise .result-name { color: var(--text-muted); }
+.noise-mini {
+  display: inline-block;
+  width: 5px;
+  height: 5px;
+  background: var(--warning);
+  border-radius: 50%;
+  margin-left: 4px;
+  vertical-align: middle;
+  opacity: 0.7;
+}
+
+/* ── Graph ──────────────────────────────────────────────────────────── */
+.graph-pane {
+  display: flex;
+  flex-direction: column;
+  background: var(--bg);
+  overflow: hidden;
+  position: relative;
+}
+.graph-controls {
+  display: flex;
+  gap: 12px;
+  padding: 11px 18px;
+  background: var(--surface);
+  border-bottom: 1px solid var(--border);
+  align-items: center;
+  flex-wrap: wrap;
+}
+.graph-controls label {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  font-size: 12.5px;
+  color: var(--text-muted);
+}
+.graph-controls select {
+  background: var(--surface-soft);
+  border: 1px solid var(--border);
+  border-radius: var(--radius-sm);
+  padding: 5px 9px;
+  font-size: 12.5px;
+  color: var(--text);
+  cursor: pointer;
+}
+.graph-info {
+  margin-left: auto;
+  font-size: 11.5px;
+  color: var(--text-subtle);
+}
+.graph-canvas {
+  flex: 1;
+  position: relative;
+  overflow: hidden;
+}
+#sigma-container {
+  position: absolute;
+  inset: 0;
+}
+.graph-empty {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  color: var(--text-subtle);
+  font-size: 14px;
+  text-align: center;
+  padding: 40px;
+}
+.legend {
+  position: absolute;
+  bottom: 14px;
+  left: 14px;
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: var(--radius-sm);
+  padding: 12px 16px;
+  font-size: 15px;
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+  max-width: 900px;
+  box-shadow: var(--shadow-card);
+  transition: opacity var(--ease);
+}
+.legend-row {
+  display: flex;
+  gap: 14px;
+  flex-wrap: wrap;
+  align-items: center;
+}
+.legend-row-label {
+  font-size: 12px;
+  font-weight: 600;
+  color: var(--text-subtle);
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  margin-right: 4px;
+  min-width: 76px;
+}
+.legend-item {
+  display: flex;
+  align-items: center;
+  gap: 7px;
+  color: var(--text-muted);
+  font-size: 14px;
+}
+.legend-dot {
+  width: 11px;
+  height: 11px;
+  border-radius: 50%;
+}
+.legend-line {
+  width: 20px;
+  height: 3px;
+  border-radius: 2px;
+}
+.hover-hint {
+  position: absolute;
+  top: 14px;
+  right: 14px;
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: var(--radius-sm);
+  padding: 7px 11px;
+  font-size: 11.5px;
+  color: var(--text-muted);
+  box-shadow: var(--shadow-card);
+  pointer-events: none;
+}
+
+/* ── Detail Pane ────────────────────────────────────────────────────── */
+.detail-pane {
+  background: var(--surface);
+  border-left: 1px solid var(--border);
+  overflow-y: auto;
+  padding: 0;
+}
+.detail-empty {
+  padding: 55px 22px;
+  text-align: center;
+  color: var(--text-subtle);
+  font-size: 13.5px;
+}
+.detail-section {
+  padding: 17px 19px;
+  border-bottom: 1px solid var(--border);
+}
+.detail-section h3 {
+  font-size: 11.5px;
+  font-weight: 600;
+  color: var(--text-muted);
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  margin-bottom: 11px;
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+.detail-section h3 .count {
+  background: var(--surface-soft);
+  color: var(--text);
+  padding: 1px 8px;
+  border-radius: 10px;
+  font-size: 11px;
+  letter-spacing: 0;
+  text-transform: none;
+}
+.detail-title {
+  font-size: 20px;
+  font-weight: 600;
+  color: var(--text);
+  margin-bottom: 6px;
+  line-height: 1.3;
+}
+.detail-aliases {
+  color: var(--text-muted);
+  font-size: 12.5px;
+  margin-bottom: 8px;
+}
+.detail-definition {
+  font-size: 13.5px;
+  color: var(--text-muted);
+  line-height: 1.55;
+  margin-top: 9px;
+}
+.badge-row {
+  display: flex;
+  gap: 6px;
+  flex-wrap: wrap;
+  margin-top: 11px;
+}
+.badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+  padding: 3px 10px;
+  border-radius: 10px;
+  font-size: 11.5px;
+  background: var(--surface-soft);
+  color: var(--text-muted);
+  border: 1px solid var(--border);
+  text-decoration: none;
+  transition: all var(--ease);
+}
+.badge.link {
+  background: var(--accent-soft);
+  color: var(--accent);
+  border-color: transparent;
+  cursor: pointer;
+}
+.badge.link:hover { filter: brightness(1.05); text-decoration: underline; }
+.badge.domain { color: #fff; border-color: transparent; }
+.noise-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 3px;
+  background: rgba(217, 119, 6, 0.12);
+  color: var(--warning);
+  padding: 2px 8px;
+  border-radius: 10px;
+  font-size: 11px;
+  font-weight: 500;
+  margin-left: 6px;
+  cursor: help;
+  border: 1px solid rgba(217, 119, 6, 0.25);
+}
+
+.item-card {
+  background: var(--surface-soft);
+  border-radius: var(--radius-sm);
+  padding: 11px 13px;
+  margin-bottom: 9px;
+  font-size: 13px;
+  border: 1px solid transparent;
+  transition: all var(--ease);
+}
+.item-card:hover { border-color: var(--border); }
+.item-card .item-head {
+  display: flex;
+  justify-content: space-between;
+  align-items: flex-start;
+  gap: 8px;
+  margin-bottom: 7px;
+}
+.item-card .predicate {
+  display: inline-block;
+  background: var(--accent-soft);
+  color: var(--accent);
+  padding: 1px 8px;
+  border-radius: 3px;
+  font-size: 11.5px;
+  font-weight: 500;
+}
+.item-card .conf {
+  font-size: 11.5px;
+  color: var(--text-muted);
+  font-variant-numeric: tabular-nums;
+}
+.item-card .triple {
+  color: var(--text);
+  font-weight: 500;
+  margin-bottom: 5px;
+}
+.item-card .triple .arrow {
+  color: var(--text-subtle);
+  margin: 0 5px;
+}
+.item-card .raw-text {
+  color: var(--text-muted);
+  font-size: 12.5px;
+  line-height: 1.45;
+  margin-bottom: 7px;
+  max-height: 80px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+.item-card .evidence-row {
+  display: flex;
+  gap: 8px;
+  font-size: 11.5px;
+  color: var(--text-muted);
+  margin-top: 7px;
+  flex-wrap: wrap;
+}
+.item-card .evidence-row span {
+  background: var(--surface);
+  padding: 1px 7px;
+  border-radius: 3px;
+  border: 1px solid var(--border);
+}
+.item-card .paper-row {
+  margin-top: 7px;
+  display: flex;
+  gap: 6px;
+  flex-wrap: wrap;
+}
+.item-card .score-grid {
+  display: flex;
+  gap: 11px;
+  margin-top: 4px;
+  font-size: 11.5px;
+  color: var(--text-muted);
+  font-variant-numeric: tabular-nums;
+}
+.item-card .score-grid b {
+  color: var(--text);
+  font-weight: 500;
+}
+.path-list {
+  margin-top: 9px;
+  border-left: 2px solid var(--border);
+  padding-left: 11px;
+}
+.path-step {
+  font-size: 12.5px;
+  color: var(--text-muted);
+  margin-bottom: 4px;
+  line-height: 1.4;
+}
+.path-step b { color: var(--text); }
+.path-step .rel {
+  background: var(--surface);
+  padding: 0 5px;
+  border-radius: 3px;
+  font-size: 11px;
+  color: var(--accent);
+  border: 1px solid var(--border);
+}
+.recipe-pill {
+  display: inline-block;
+  background: linear-gradient(135deg, #10b981, #3b82f6);
+  color: #fff;
+  padding: 1px 8px;
+  border-radius: 10px;
+  font-size: 10.5px;
+  font-weight: 500;
+  margin-left: 4px;
+}
+.loading {
+  padding: 18px;
+  text-align: center;
+  color: var(--text-muted);
+  font-size: 12.5px;
+}
+.loading .spinner {
+  display: inline-block;
+  width: 12px;
+  height: 12px;
+  border: 2px solid var(--border);
+  border-top-color: var(--accent);
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+  margin-right: 6px;
+  vertical-align: middle;
+}
+@keyframes spin { to { transform: rotate(360deg); } }
+.cold-start-banner {
+  background: var(--accent-soft);
+  color: var(--accent);
+  padding: 11px 18px;
+  font-size: 13px;
+  text-align: center;
+  border-bottom: 1px solid var(--border);
+}
+.detail-meta-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(140px, 1fr));
+  gap: 6px;
+  margin-top: 11px;
+}
+.detail-meta-grid .kv {
+  background: var(--surface-soft);
+  padding: 7px 10px;
+  border-radius: var(--radius-sm);
+  font-size: 11.5px;
+}
+.detail-meta-grid .kv span {
+  color: var(--text-subtle);
+  display: block;
+  text-transform: uppercase;
+  letter-spacing: 0.03em;
+  font-size: 10.5px;
+  margin-bottom: 2px;
+}
+.detail-meta-grid .kv code {
+  color: var(--text);
+  font-size: 11.5px;
+  word-break: break-all;
+}
+.tab-row {
+  display: flex;
+  gap: 0;
+  border-bottom: 1px solid var(--border);
+  background: var(--surface);
+  position: sticky;
+  top: 0;
+  z-index: 2;
+}
+.tab-btn {
+  flex: 1;
+  background: transparent;
+  border: none;
+  padding: 11px 12px;
+  font-size: 12.5px;
+  color: var(--text-muted);
+  cursor: pointer;
+  border-bottom: 2px solid transparent;
+  transition: all var(--ease);
+  font-weight: 500;
+}
+.tab-btn:hover { color: var(--text); }
+.tab-btn.active {
+  color: var(--accent);
+  border-bottom-color: var(--accent);
+}
+.tab-btn .count {
+  background: var(--surface-soft);
+  padding: 1px 7px;
+  border-radius: 9px;
+  font-size: 10.5px;
+  margin-left: 4px;
+}
+.recipe-filter-row {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 9px 19px;
+  background: var(--surface-soft);
+  font-size: 12.5px;
+  color: var(--text-muted);
+  border-bottom: 1px solid var(--border);
+}
+.recipe-filter-row input { accent-color: var(--accent); cursor: pointer; }
+.focus-banner {
+  padding: 10px 19px;
+  background: linear-gradient(to right, rgba(10,132,255,0.08), transparent);
+  border-bottom: 1px solid var(--border);
+  font-size: 12.5px;
+  color: var(--text-muted);
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+.focus-banner b { color: var(--text); font-weight: 500; }
+.focus-banner .close-x {
+  cursor: pointer;
+  color: var(--text-subtle);
+  font-size: 16px;
+  line-height: 1;
+  padding: 0 4px;
+}
+.focus-banner .close-x:hover { color: var(--text); }
+</style>
+</head>
+<body>
+
+<header class="header">
+  <div class="header-brand">
+    <span class="logo-dot"></span>
+    NeuroClaw
+  </div>
+  <div class="header-sub" data-i18n="header_sub">Knowledge Graph Explorer</div>
+  <div class="stats-line" id="statsLine">
+    <span class="spinner" style="display:inline-block;width:10px;height:10px;border:2px solid var(--border);border-top-color:var(--accent);border-radius:50%;animation:spin 0.8s linear infinite;"></span>
+    <span data-i18n="loading">Loading…</span>
+  </div>
+  <button class="icon-btn lang-toggle" id="langBtn" title="Switch language">EN</button>
+  <button class="icon-btn" id="themeBtn" title="Toggle theme">☾</button>
+</header>
+
+<div id="coldStartBanner" class="cold-start-banner" style="display:none;" data-i18n="cold_start">
+  First load reads the 180MB knowledge graph (takes ~30–60s)…
+</div>
+
+<div class="layout">
+  <aside class="sidebar">
+    <div class="search-box">
+      <input type="text" class="search-input" id="searchInput"
+             data-i18n-ph="search_placeholder"
+             placeholder="Search biomarker / outcome / concept (≥2 chars)" autocomplete="off">
+      <div class="domain-chips" id="domainChips">
+        <span class="chip active" data-domain="" data-i18n="domain_all">All</span>
+        <span class="chip" data-domain="biomarker">biomarker</span>
+        <span class="chip" data-domain="imaging_feature">imaging</span>
+        <span class="chip" data-domain="cognitive_function">cognitive</span>
+        <span class="chip" data-domain="disease">disease</span>
+        <span class="chip" data-domain="gene">gene</span>
+        <span class="chip" data-domain="neuroanatomy">brain</span>
+      </div>
+    </div>
+    <div class="results" id="results">
+      <div class="results-hint" data-i18n="search_hint">Enter a keyword to search concepts</div>
+    </div>
+  </aside>
+
+  <main class="graph-pane">
+    <div class="graph-controls">
+      <button class="icon-btn back-btn" id="backBtn" disabled title="Back to previous node">
+        <span style="font-size:15px;line-height:1;">‹</span>
+        <span data-i18n="back">Back</span>
+      </button>
+      <label><span data-i18n="depth">Depth</span>
+        <select id="depthSelect">
+          <option value="1" selected>1</option>
+          <option value="2">2</option>
+        </select>
+      </label>
+      <label><span data-i18n="neighbors">Neighbors</span>
+        <select id="limitSelect">
+          <option value="30">30</option>
+          <option value="60" selected>60</option>
+        </select>
+      </label>
+      <label><span data-i18n="edge_type">Edge type</span>
+        <select id="edgeTypeSelect">
+          <option value="all" selected data-i18n="all">All</option>
+          <option value="is_biomarker_of">is_biomarker_of</option>
+          <option value="correlates_with">correlates_with</option>
+          <option value="predicts">predicts</option>
+          <option value="causes">causes</option>
+          <option value="associated_with,is_associated_with">associated_with</option>
+          <option value="treats">treats</option>
+          <option value="modulates">modulates</option>
+          <option value="reduces,increases">reduces / increases</option>
+        </select>
+      </label>
+      <label><span data-i18n="font">Font</span>
+        <select id="fontSelect">
+          <option value="15">XS</option>
+          <option value="17">S</option>
+          <option value="18" selected>M</option>
+          <option value="20">L</option>
+          <option value="22">XL</option>
+          <option value="24">XXL</option>
+        </select>
+      </label>
+      <span class="graph-info" id="graphInfo" data-i18n="hint_select">Select a node to start exploring</span>
+    </div>
+    <div class="graph-canvas">
+      <div id="sigma-container"></div>
+      <div class="graph-empty" id="graphEmpty">
+        <div>
+          <div style="font-size:34px;margin-bottom:11px;opacity:0.3;">◯</div>
+          <div data-i18n="empty_main">Search a concept in the left panel and click it</div>
+          <div style="font-size:12px;margin-top:7px;opacity:0.7;" data-i18n="empty_sub">
+            Hover to reveal edges · double-click to change query · wheel to zoom
+          </div>
+        </div>
+      </div>
+      <div class="hover-hint" id="hoverHint" style="display:none;"></div>
+      <div class="legend" id="legend" style="display:none;"></div>
+    </div>
+  </main>
+
+  <aside class="detail-pane" id="detailPane">
+    <div class="detail-empty">
+      <div style="font-size:30px;margin-bottom:11px;opacity:0.3;">◇</div>
+      <div data-i18n="detail_empty_title">No node selected</div>
+      <span style="font-size:12.5px;" data-i18n="detail_empty_sub">Click a result or a node to view details</span>
+    </div>
+  </aside>
+</div>
+
+<script>
+// ── State ─────────────────────────────────────────────────────────────
+const state = {
+  currentNode: null,
+  focusEdge: null,    // {source, target} — a single-click edge inspection
+  domainFilter: "",
+  depth: 1,
+  limit: 60,
+  edgeTypes: "all",
+  sigma: null,
+  graph: null,
+  layoutWorker: null,
+  kgLoaded: false,
+  hasRecipes: false,
+  recipeOnly: false,
+  activeTab: "hypotheses",
+  quality: "clean",
+  hoveredNode: null,
+  pinnedNode: null,    // persistent single-click selection (non-query)
+  showAllInitial: false,  // 2s preview: all colors+labels before dimming
+  lang: "en",
+  fontSize: 18,
+  cachedNeighborhood: null,
+  history: [],          // stack of previous node IDs for Back navigation
+  transitioning: false, // guard to prevent overlapping zoom animations
+};
+
+const DOMAIN_COLORS = {
+  biomarker: "#10b981",
+  imaging_feature: "#3b82f6",
+  cognitive_function: "#8b5cf6",
+  disease: "#ef4444",
+  gene: "#f59e0b",
+  neuroanatomy: "#06b6d4",
+  drug: "#ec4899",
+  neurotransmitter: "#f97316",
+  cell_type: "#14b8a6",
+  paradigm: "#a855f7",
+  connectivity: "#0ea5e9",
+  dataset_variable: "#84cc16",
+  claim: "#94a3b8",
+};
+const GRAY = "#d1d5db";
+const GRAY_DARK = "#64748b";
+const DIM_EDGE = "#e5e7eb";
+
+// ── i18n ─────────────────────────────────────────────────────────────
+const I18N = {
+  en: {
+    header_sub: "Knowledge Graph Explorer",
+    loading: "Loading…",
+    cold_start: "First load reads the 180MB knowledge graph (takes ~30–60s)…",
+    search_placeholder: "Search biomarker / outcome / concept (≥2 chars)",
+    search_hint: "Enter a keyword to search concepts",
+    domain_all: "All",
+    back: "Back",
+    depth: "Depth",
+    neighbors: "Neighbors",
+    edge_type: "Edge type",
+    font: "Font",
+    all: "All",
+    hint_select: "Select a node to start exploring",
+    empty_main: "Search a concept in the left panel and click it",
+    empty_sub: "Hover to reveal edges · double-click to change query · wheel to zoom",
+    detail_empty_title: "No node selected",
+    detail_empty_sub: "Click a result or a node to view details",
+    stats_concepts: "concepts",
+    stats_edges: "edges",
+    stats_claims: "verified claims",
+    stats_hyps: "new hypotheses",
+    stats_recipes: "with recipe",
+    tab_hypotheses: "Hypotheses",
+    tab_claims: "Verified Claims",
+    tab_meta: "Metadata",
+    filter_recipe: "Show only recipe-matched hypotheses",
+    no_hypotheses: "No hypotheses for this concept",
+    no_claims: "No verified claims for this concept",
+    no_match: "No match found",
+    external_ids: "External IDs",
+    no_external: "No external IDs",
+    source: "Source vocab",
+    semantic_types: "Semantic types",
+    atlas_mapping: "Atlas mapping",
+    aliases: "Aliases",
+    nodes_edges: (n, e, t) => `${n} nodes · ${e} edges${t ? " · sampled" : ""}`,
+    sampling: "sampled",
+    low_quality: "⚠ low quality",
+    hover_show: "Hover a node to reveal colors and relationships",
+    preview_all: "Showing all colors · will dim in 2s",
+    hover_all: "Hovering query node · showing all colors",
+    hover_explain: (l) => `Hovering: ${l}`,
+    showing_edge: "Sources for this relationship",
+    between: "between",
+    and: "and",
+    close: "×",
+    no_sources: "No direct sources linking these two concepts",
+    curated_edges: "Curated edges",
+    supporting_claims: "Supporting claims",
+    pubmed: "PMID",
+    doi: "DOI",
+    year: "Year",
+    composite: "composite",
+    novelty: "novelty",
+    evidence: "evidence",
+    testability: "testability",
+    critic: "critic",
+    predicate_filter: (p) => `Filter: predicate = ${p}`,
+    node_type: "Node type",
+    edge_type_legend: "Edge type",
+    more_steps: (n) => `+ ${n} more steps`,
+  },
+  zh: {
+    header_sub: "知识图谱浏览器",
+    loading: "载入中…",
+    cold_start: "首次打开需要加载 180MB 知识图谱（约 30–60 秒）…",
+    search_placeholder: "搜索 biomarker / outcome / 概念（≥2 字符）",
+    search_hint: "输入关键词，快速检索概念",
+    domain_all: "全部",
+    back: "返回",
+    depth: "深度",
+    neighbors: "邻域",
+    edge_type: "边类型",
+    font: "字号",
+    all: "全部",
+    hint_select: "选中节点开始探索",
+    empty_main: "在左侧搜索并点击一个概念",
+    empty_sub: "悬浮显示边 · 双击切换 query · 滚轮缩放",
+    detail_empty_title: "未选中节点",
+    detail_empty_sub: "点击搜索结果或图中节点查看详情",
+    stats_concepts: "概念",
+    stats_edges: "边",
+    stats_claims: "已证实 Claims",
+    stats_hyps: "新假设",
+    stats_recipes: "带 recipe",
+    tab_hypotheses: "新假设",
+    tab_claims: "已证实 Claims",
+    tab_meta: "元数据",
+    filter_recipe: "仅显示已匹配 recipe 的假设",
+    no_hypotheses: "此概念暂无新假设",
+    no_claims: "此概念暂无已证实的 Claims",
+    no_match: "未找到匹配",
+    external_ids: "外部 ID",
+    no_external: "无外部 ID",
+    source: "来源词表",
+    semantic_types: "语义类型",
+    atlas_mapping: "Atlas 映射",
+    aliases: "别名",
+    nodes_edges: (n, e, t) => `${n} 节点 · ${e} 边${t ? " · 已采样" : ""}`,
+    sampling: "已采样",
+    low_quality: "⚠ 低质量",
+    hover_show: "悬浮节点可显示颜色与关系",
+    preview_all: "正显示全部颜色 · 2 秒后变灰",
+    hover_all: "正悬浮于 query 节点 · 显示全部颜色",
+    hover_explain: (l) => `悬浮：${l}`,
+    showing_edge: "这两个概念之间关系的来源",
+    between: "",
+    and: " 与 ",
+    close: "×",
+    no_sources: "这两个概念没有直接关联来源",
+    curated_edges: "Curated 边",
+    supporting_claims: "证据 Claims",
+    pubmed: "PMID",
+    doi: "DOI",
+    year: "年份",
+    composite: "composite",
+    novelty: "novelty",
+    evidence: "evidence",
+    testability: "testability",
+    critic: "critic",
+    predicate_filter: (p) => `过滤：谓词 = ${p}`,
+    node_type: "节点类型",
+    edge_type_legend: "关系类型",
+    more_steps: (n) => `+ 还有 ${n} 步`,
+  },
+};
+const t = (key, ...args) => {
+  const v = I18N[state.lang]?.[key] ?? I18N.en[key] ?? key;
+  return typeof v === "function" ? v(...args) : v;
+};
+
+// ── DOM helpers ──────────────────────────────────────────────────────
+const $ = (id) => document.getElementById(id);
+const el = (tag, attrs = {}, children = []) => {
+  const node = document.createElement(tag);
+  Object.entries(attrs).forEach(([k, v]) => {
+    if (k === "className") node.className = v;
+    else if (k === "textContent") node.textContent = v;
+    else if (k === "html") node.innerHTML = v;
+    else if (k.startsWith("on")) node.addEventListener(k.slice(2), v);
+    else node.setAttribute(k, v);
+  });
+  (Array.isArray(children) ? children : [children]).forEach((c) => {
+    if (c == null) return;
+    if (typeof c === "string") node.appendChild(document.createTextNode(c));
+    else node.appendChild(c);
+  });
+  return node;
+};
+const escapeHtml = (s) => (s || "").replace(/[&<>"']/g, (c) => ({
+  "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;"
+}[c]));
+const fmt = (n, digits = 2) => (n == null || isNaN(n)) ? "—" : Number(n).toFixed(digits);
+
+function mixHex(a, b, ratio) {
+  const pa = a.startsWith("#") ? a.slice(1) : a;
+  const pb = b.startsWith("#") ? b.slice(1) : b;
+  const ar = parseInt(pa.slice(0, 2), 16), ag = parseInt(pa.slice(2, 4), 16), ab = parseInt(pa.slice(4, 6), 16);
+  const br = parseInt(pb.slice(0, 2), 16), bg = parseInt(pb.slice(2, 4), 16), bb = parseInt(pb.slice(4, 6), 16);
+  const r = Math.round(ar + (br - ar) * ratio);
+  const g = Math.round(ag + (bg - ag) * ratio);
+  const bl = Math.round(ab + (bb - ab) * ratio);
+  return "#" + [r, g, bl].map((v) => v.toString(16).padStart(2, "0")).join("");
+}
+
+// ── Physics-style easings ────────────────────────────────────────────
+// Silky out: strong initial velocity, very long gentle tail. No overshoot.
+const easeOutQuint = (t) => 1 - Math.pow(1 - t, 5);
+// Smooth in-out for bidirectional transitions (both ends decelerate).
+const easeInOutQuint = (t) =>
+  t < 0.5 ? 16 * t * t * t * t * t : 1 - Math.pow(-2 * t + 2, 5) / 2;
+// Cubic in-out kept for very short pulls (Back phase 1).
+const easeInOutCubic = (t) =>
+  t < 0.5 ? 4 * t * t * t : 1 - Math.pow(-2 * t + 2, 3) / 2;
+
+// Animate the sigma camera with a custom easing. Returns a Promise.
+function animateCamera(target, { duration = 700, easing = easeInOutQuint } = {}) {
+  return new Promise((resolve) => {
+    if (!state.sigma) { resolve(); return; }
+    const camera = state.sigma.getCamera();
+    const start = { x: camera.x, y: camera.y, ratio: camera.ratio, angle: camera.angle || 0 };
+    const end = {
+      x: target.x != null ? target.x : start.x,
+      y: target.y != null ? target.y : start.y,
+      ratio: target.ratio != null ? target.ratio : start.ratio,
+      angle: start.angle,
+    };
+    const t0 = performance.now();
+    function frame(now) {
+      const t = Math.min(1, (now - t0) / duration);
+      const e = easing(t);
+      camera.setState({
+        x: start.x + (end.x - start.x) * e,
+        y: start.y + (end.y - start.y) * e,
+        ratio: start.ratio + (end.ratio - start.ratio) * e,
+        angle: start.angle,
+      });
+      if (t < 1) requestAnimationFrame(frame);
+      else resolve();
+    }
+    requestAnimationFrame(frame);
+  });
+}
+
+// Animate the camera toward a SPECIFIC node, re-reading the node's display
+// coordinates every frame. Sigma's camera uses a bbox-normalized coord
+// system, not graph coords — and while FA2 is still running the bbox keeps
+// drifting, so the display position of the node (even a pinned one) moves.
+// Re-reading per-frame guarantees we actually land on the node at the end.
+function animateCameraToNode(nodeId, { duration = 900, easing = easeInOutQuint, startRatio, endRatio = 1.0 } = {}) {
+  return new Promise((resolve) => {
+    if (!state.sigma || !state.graph || !state.graph.hasNode(nodeId)) { resolve(); return; }
+    const camera = state.sigma.getCamera();
+    const startState = { x: camera.x, y: camera.y, ratio: camera.ratio };
+    const fromRatio = startRatio != null ? startRatio : startState.ratio;
+    if (startRatio != null) camera.setState({ x: startState.x, y: startState.y, ratio: fromRatio, angle: 0 });
+    const t0 = performance.now();
+    function frame(now) {
+      const t = Math.min(1, (now - t0) / duration);
+      const e = easing(t);
+      // Read the node's CURRENT display coords — these drift as FA2 resizes bbox.
+      let tx = 0, ty = 0;
+      try {
+        const d = state.sigma.getNodeDisplayData(nodeId);
+        if (d) { tx = d.x; ty = d.y; }
+      } catch (err) {}
+      camera.setState({
+        x: startState.x + (tx - startState.x) * e,
+        y: startState.y + (ty - startState.y) * e,
+        ratio: fromRatio + (endRatio - fromRatio) * e,
+        angle: 0,
+      });
+      if (t < 1) requestAnimationFrame(frame);
+      else resolve();
+    }
+    requestAnimationFrame(frame);
+  });
+}
+
+// Snap camera dead-center on a node, using sigma's current bbox normalization.
+function centerCameraOn(nodeId, ratio) {
+  if (!state.sigma || !state.graph || !state.graph.hasNode(nodeId)) return;
+  try {
+    const d = state.sigma.getNodeDisplayData(nodeId);
+    if (!d) return;
+    const cam = state.sigma.getCamera();
+    const st = cam.getState();
+    cam.setState({
+      x: d.x,
+      y: d.y,
+      ratio: ratio != null ? ratio : st.ratio,
+      angle: 0,
+    });
+  } catch (e) {}
+}
+
+// ── Theme ────────────────────────────────────────────────────────────
+(function initTheme() {
+  const saved = localStorage.getItem("neuroclaw-theme");
+  const theme = saved || (window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light");
+  document.documentElement.dataset.theme = theme;
+  $("themeBtn").textContent = theme === "dark" ? "☀" : "☾";
+})();
+$("themeBtn").addEventListener("click", () => {
+  const cur = document.documentElement.dataset.theme === "dark" ? "light" : "dark";
+  document.documentElement.dataset.theme = cur;
+  localStorage.setItem("neuroclaw-theme", cur);
+  $("themeBtn").textContent = cur === "dark" ? "☀" : "☾";
+  // re-render graph label colors for theme change
+  if (state.sigma) state.sigma.refresh();
+});
+
+// ── Language ─────────────────────────────────────────────────────────
+(function initLang() {
+  const saved = localStorage.getItem("neuroclaw-lang");
+  state.lang = saved === "zh" ? "zh" : "en";
+  document.documentElement.lang = state.lang === "zh" ? "zh-CN" : "en";
+  applyI18n();
+  $("langBtn").textContent = state.lang === "zh" ? "中" : "EN";
+})();
+function applyI18n() {
+  document.querySelectorAll("[data-i18n]").forEach((n) => {
+    const key = n.getAttribute("data-i18n");
+    const v = t(key);
+    if (typeof v === "string") n.textContent = v;
+  });
+  document.querySelectorAll("[data-i18n-ph]").forEach((n) => {
+    const key = n.getAttribute("data-i18n-ph");
+    const v = t(key);
+    if (typeof v === "string") n.setAttribute("placeholder", v);
+  });
+  document.querySelectorAll("[data-i18n-title]").forEach((n) => {
+    const key = n.getAttribute("data-i18n-title");
+    const v = t(key);
+    if (typeof v === "string") n.setAttribute("title", v);
+  });
+}
+$("langBtn").addEventListener("click", () => {
+  state.lang = state.lang === "en" ? "zh" : "en";
+  localStorage.setItem("neuroclaw-lang", state.lang);
+  document.documentElement.lang = state.lang === "zh" ? "zh-CN" : "en";
+  $("langBtn").textContent = state.lang === "zh" ? "中" : "EN";
+  applyI18n();
+  // re-render detail pane and stats with new lang
+  pollStatsRender();
+  if (state.currentNode) {
+    if (state.focusEdge) loadEdgeSources(state.focusEdge.source, state.focusEdge.target);
+    else loadDetail(state.currentNode);
+  }
+});
+
+// ── Font-size control ────────────────────────────────────────────────
+(function initFont() {
+  const saved = parseInt(localStorage.getItem("neuroclaw-font") || "18", 10);
+  if (!isNaN(saved)) {
+    state.fontSize = saved;
+    $("fontSelect").value = String(saved);
+    applyFontSize(saved);
+  }
+})();
+function applyFontSize(px) {
+  state.fontSize = px;
+  document.documentElement.style.setProperty("--ui-font-size", px + "px");
+  document.documentElement.style.setProperty("--graph-label-size", (px + 0) + "px");
+  localStorage.setItem("neuroclaw-font", String(px));
+  if (state.sigma) {
+    state.sigma.setSetting("labelSize", px);
+    state.sigma.refresh();
+  }
+}
+$("fontSelect").addEventListener("change", (e) => {
+  applyFontSize(parseInt(e.target.value, 10));
+});
+
+// ── KG status polling ────────────────────────────────────────────────
+let _lastStats = null;
+async function pollStats() {
+  try {
+    const r = await fetch("/api/kg/stats").then((r) => r.json());
+    if (r.loaded) {
+      const wasLoaded = state.kgLoaded;
+      state.kgLoaded = true;
+      _lastStats = r;
+      $("coldStartBanner").style.display = "none";
+      pollStatsRender();
+      state.hasRecipes = (r.n_recipes || 0) > 0;
+      if (!wasLoaded) {
+        // First transition to loaded: show default top list
+        loadDefaultList();
+      }
+      return;
+    }
+    $("coldStartBanner").style.display = "block";
+    if (!r.loading) {
+      fetch("/api/kg/load", { method: "POST" }).catch(() => {});
+    }
+    setTimeout(pollStats, 2500);
+  } catch (e) {
+    $("statsLine").innerHTML = '<span style="color:var(--danger);">Load failed</span>';
+  }
+}
+function pollStatsRender() {
+  if (!_lastStats) return;
+  const r = _lastStats;
+  $("statsLine").innerHTML = `
+    <span>${(r.n_concepts||0).toLocaleString()} ${t("stats_concepts")}</span>
+    <span class="stats-dot"></span>
+    <span>${(r.n_edges||0).toLocaleString()} ${t("stats_edges")}</span>
+    <span class="stats-dot"></span>
+    <span>${(r.n_claims||0).toLocaleString()} ${t("stats_claims")}</span>
+    <span class="stats-dot"></span>
+    <span>${(r.n_hypotheses||0).toLocaleString()} ${t("stats_hyps")}</span>
+    ${r.n_recipes ? `<span class="stats-dot"></span><span>${r.n_recipes} ${t("stats_recipes")}</span>` : ""}
+  `;
+}
+pollStats();
+
+// ── Search ───────────────────────────────────────────────────────────
+let searchTimer = null;
+$("searchInput").addEventListener("input", (e) => {
+  clearTimeout(searchTimer);
+  const q = e.target.value.trim();
+  if (q.length < 2) {
+    // Empty/short query: load the domain-scoped default list
+    loadDefaultList();
+    return;
+  }
+  searchTimer = setTimeout(() => runSearch(q), 300);
+});
+$("domainChips").addEventListener("click", (e) => {
+  const chip = e.target.closest(".chip");
+  if (!chip) return;
+  [...$("domainChips").children].forEach((c) => c.classList.remove("active"));
+  chip.classList.add("active");
+  state.domainFilter = chip.dataset.domain || "";
+  const q = $("searchInput").value.trim();
+  if (q.length >= 2) runSearch(q);
+  else loadDefaultList();
+});
+
+
+async function loadDefaultList() {
+  if (!state.kgLoaded) return;
+  const container = $("results");
+  container.innerHTML = `<div class="loading"><span class="spinner"></span>${t("loading")}</div>`;
+  try {
+    const url = new URL("/api/kg/search", location.origin);
+    url.searchParams.set("q", "");
+    if (state.domainFilter) url.searchParams.set("domain", state.domainFilter);
+    url.searchParams.set("quality", state.quality);
+    url.searchParams.set("limit", "50");
+    const r = await fetch(url).then((r) => r.json());
+    renderResults(r.results || []);
+  } catch (e) {
+    container.innerHTML = `<div class="results-hint" style="color:var(--danger);">load failed</div>`;
+  }
+}
+
+async function runSearch(q) {
+  if (!state.kgLoaded) {
+    $("results").innerHTML = `<div class="loading"><span class="spinner"></span>${t("loading")}</div>`;
+    return;
+  }
+  $("results").innerHTML = `<div class="loading"><span class="spinner"></span>${t("loading")}</div>`;
+  try {
+    const url = new URL("/api/kg/search", location.origin);
+    url.searchParams.set("q", q);
+    if (state.domainFilter) url.searchParams.set("domain", state.domainFilter);
+    url.searchParams.set("quality", state.quality);
+    url.searchParams.set("limit", "30");
+    const r = await fetch(url).then((r) => r.json());
+    renderResults(r.results || []);
+  } catch (e) {
+    $("results").innerHTML = `<div class="results-hint" style="color:var(--danger);">${t("loading")}</div>`;
+  }
+}
+function renderResults(items) {
+  const container = $("results");
+  container.innerHTML = "";
+  if (!items.length) {
+    container.innerHTML = `<div class="results-hint">${t("no_match")}</div>`;
+    return;
+  }
+  items.forEach((it) => {
+    const primaryDomain = (it.domain_tags || []).find((d) => d !== "claim") || (it.domain_tags || [])[0] || "";
+    const domainBadge = primaryDomain
+      ? `<span class="domain-tag" title="${escapeHtml((it.domain_tags || []).join(', '))}" style="background:${DOMAIN_COLORS[primaryDomain] || "#94a3b8"};">${escapeHtml(primaryDomain)}</span>`
+      : "";
+    const noiseMark = it.is_noise ? '<span class="noise-mini" title="possible noise"></span>' : "";
+    const node = el("div", {
+      className: "result-item" + (it.is_noise ? " is-noise" : ""),
+      "data-id": it.id,
+    }, [
+      el("div", { className: "result-name", html: escapeHtml(it.name) + noiseMark }),
+      el("div", { className: "result-meta", html: `
+        ${domainBadge}
+        <span class="badge">${(it.n_claims || 0).toLocaleString()} claims</span>
+        <span class="badge">${it.n_hypotheses || 0} hyps</span>
+      `}),
+    ]);
+    node.addEventListener("click", () => selectNode(it.id));
+    container.appendChild(node);
+  });
+}
+
+// ── Node selection (from results list) ───────────────────────────────
+async function selectNode(nodeId, opts = {}) {
+  const { pushHistory = true, animateIn = false } = opts;
+  if (pushHistory && state.currentNode && state.currentNode !== nodeId) {
+    state.history.push(state.currentNode);
+    updateBackBtn();
+  }
+  state.currentNode = nodeId;
+  state.focusEdge = null;
+  state.pinnedNode = null;
+  [...document.querySelectorAll(".result-item")].forEach((r) => {
+    r.classList.toggle("active", r.dataset.id === nodeId);
+  });
+  await Promise.all([loadNeighborhood(nodeId), loadDetail(nodeId)]);
+  if (animateIn && state.sigma && state.graph && state.graph.hasNode(nodeId)) {
+    // Fresh render: start slightly zoomed-in centered on the node, ease out.
+    // animateCameraToNode re-reads display coords each frame so bbox drift
+    // (FA2 still running) can't pull us off-target.
+    await animateCameraToNode(nodeId, {
+      duration: 1000,
+      easing: easeOutQuint,
+      startRatio: 0.6,
+      endRatio: 1.0,
+    });
+    centerCameraOn(nodeId, 1.0);
+    state.sigma.refresh();
+  }
+}
+
+function updateBackBtn() {
+  const btn = $("backBtn");
+  if (!btn) return;
+  btn.disabled = state.history.length === 0;
+}
+
+// Dive into a node: zoom camera onto it smoothly, then swap graph and settle.
+async function navigateIntoNode(nodeId) {
+  if (!state.sigma || !state.graph || !state.graph.hasNode(nodeId) || state.transitioning) return;
+  state.transitioning = true;
+  try {
+    // Phase 1 — glide the camera onto the clicked node (in display coords
+    // of the CURRENT graph). Re-read each frame so FA2 drift is tracked.
+    await animateCameraToNode(nodeId, {
+      duration: 900,
+      easing: easeInOutQuint,
+      endRatio: 0.28,
+    });
+    // Phase 2 — replace the graph rooted at that node (new sigma instance).
+    await selectNode(nodeId, { pushHistory: true, animateIn: false });
+    // Phase 3 — settle out to ratio 1.0 while keeping the camera glued to
+    // the new center node. centerCameraOn + animateCameraToNode read the
+    // node's display coords fresh every frame, so bbox drift in the new
+    // FA2 run can't pull us to a corner.
+    if (state.sigma) {
+      centerCameraOn(nodeId, 0.42);
+      await animateCameraToNode(nodeId, {
+        duration: 1100,
+        easing: easeOutQuint,
+        endRatio: 1.0,
+      });
+      centerCameraOn(nodeId, 1.0);
+      state.sigma.refresh();
+    }
+  } finally {
+    state.transitioning = false;
+  }
+}
+
+// Back to previous node: zoom out first, then swap graph and settle.
+async function navigateBack() {
+  if (state.history.length === 0 || state.transitioning) return;
+  const prevId = state.history.pop();
+  updateBackBtn();
+  state.transitioning = true;
+  try {
+    if (state.sigma) {
+      // Phase 1 — pull back with a smooth in-out.
+      await animateCamera({ ratio: 2.0 }, { duration: 520, easing: easeInOutCubic });
+    }
+    // Phase 2 — load previous neighborhood (no history push; we're going back).
+    await selectNode(prevId, { pushHistory: false, animateIn: false });
+    // Phase 3 — settle from a wide view down to 1.0, centered on the prev node.
+    if (state.sigma) {
+      centerCameraOn(prevId, 1.8);
+      await animateCameraToNode(prevId, {
+        duration: 1000,
+        easing: easeOutQuint,
+        endRatio: 1.0,
+      });
+      centerCameraOn(prevId, 1.0);
+      state.sigma.refresh();
+    }
+  } finally {
+    state.transitioning = false;
+  }
+}
+
+$("backBtn").addEventListener("click", () => navigateBack());
+
+async function loadNeighborhood(nodeId) {
+  const url = new URL(`/api/kg/node/${encodeURIComponent(nodeId)}/neighborhood`, location.origin);
+  url.searchParams.set("depth", state.depth);
+  url.searchParams.set("limit", state.limit);
+  if (state.edgeTypes && state.edgeTypes !== "all") {
+    url.searchParams.set("edge_types", state.edgeTypes);
+  }
+  try {
+    const r = await fetch(url).then((r) => r.json());
+    state.cachedNeighborhood = r;
+    renderGraph(r);
+  } catch (e) {
+    $("graphInfo").textContent = "neighborhood load failed";
+  }
+}
+
+// ── Graph render with hover reducers ─────────────────────────────────
+function renderGraph(data) {
+  $("graphEmpty").style.display = "none";
+  $("legend").style.display = "flex";
+  $("hoverHint").style.display = "block";
+  // 2-second "preview": show every color + label before dimming to gray.
+  // The flag is read by node/edge reducers each frame; flipping it at 2s
+  // + refresh transitions the graph to the usual hover-to-reveal mode.
+  state.showAllInitial = true;
+  $("hoverHint").textContent = t("preview_all");
+  const { nodes, edges, truncated } = data;
+  $("graphInfo").textContent = t("nodes_edges", nodes.length, edges.length, truncated);
+
+  // Kill previous sigma + worker
+  if (state.layoutWorker) { try { state.layoutWorker.kill(); } catch (e) {} state.layoutWorker = null; }
+  if (state.sigma) { state.sigma.kill(); state.sigma = null; }
+
+  const Graph = graphology.Graph;
+  const graph = new Graph({ type: "directed", multi: false, allowSelfLoops: true });
+
+  nodes.forEach((n, i) => {
+    const ang = (i / nodes.length) * 2 * Math.PI;
+    const r = n.is_center ? 0 : 1 + Math.random() * 2;
+    // bigger nodes overall — center is now 20, regular 13, claim 8
+    const base = n.is_center ? 20 : (n.is_claim ? 8 : 13);
+    const size = n.is_noise && !n.is_center ? Math.max(5, Math.round(base * 0.65)) : base;
+    graph.addNode(n.id, {
+      label: n.label,
+      originalLabel: n.label,
+      size,
+      baseSize: size,
+      color: n.color,
+      baseColor: n.color,
+      x: Math.cos(ang) * r,
+      y: Math.sin(ang) * r,
+      // Pin the center at (0,0) so FA2 can't drag it away — the camera
+      // targets (0,0) after settling, and this keeps the query node dead
+      // center in the viewport.
+      fixed: !!n.is_center,
+      domain: n.domain,
+      is_center: n.is_center,
+      is_claim: n.is_claim,
+      is_noise: !!n.is_noise,
+    });
+  });
+  edges.forEach((e) => {
+    if (!graph.hasNode(e.source) || !graph.hasNode(e.target)) return;
+    if (graph.hasEdge(e.source, e.target)) return;
+    graph.addEdge(e.source, e.target, {
+      label: e.label,
+      color: e.color,
+      baseColor: e.color,
+      size: 1.5,
+      relation: e.label,
+      relations_fwd: e.relations_fwd || [],
+      relations_rev: e.relations_rev || [],
+      bidirectional: !!e.bidirectional,
+    });
+  });
+
+  state.graph = graph;
+  const container = $("sigma-container");
+  state.sigma = new Sigma(graph, container, {
+    defaultEdgeType: "arrow",
+    renderEdgeLabels: false,
+    labelSize: state.fontSize,
+    labelWeight: "600",
+    labelColor: { color: getComputedStyle(document.documentElement).getPropertyValue("--text").trim() || "#0f172a" },
+    labelRenderedSizeThreshold: 4,
+    // node/edge reducers run per-frame and let us recolor based on state
+    nodeReducer(nid, data) {
+      const res = { ...data };
+      // Initial 2-second preview: everything in full color + labels.
+      if (state.showAllInitial) {
+        res.color = data.baseColor;
+        res.label = data.originalLabel;
+        if (data.is_center) res.size = data.baseSize * 1.15;
+        return res;
+      }
+      const hover = state.hoveredNode;
+      const pin = state.pinnedNode;
+      // Query node always stays colored
+      if (data.is_center) {
+        res.color = data.baseColor;
+        res.size = data.baseSize * (nid === hover ? 1.2 : 1.15);
+        res.label = data.originalLabel;
+        return res;
+      }
+      // Active node set: union of hover + pin
+      // - If hovering query: show everything (all neighbors lit)
+      // - If hovering a specific node: that node + its neighbors
+      // - If a node is pinned (persistent): that node + its neighbors stay lit
+      let lightAll = false;
+      const active = new Set();
+      if (hover === state.currentNode) lightAll = true;
+      if (hover && hover !== state.currentNode) {
+        active.add(hover);
+        state.graph && state.graph.forEachNeighbor(hover, (nb) => active.add(nb));
+      }
+      if (pin) {
+        active.add(pin);
+        state.graph && state.graph.forEachNeighbor(pin, (nb) => active.add(nb));
+      }
+      if (lightAll || active.has(nid)) {
+        res.color = data.baseColor;
+        res.label = data.originalLabel;
+        // Emphasis: hovered > pinned > other
+        if (nid === hover) res.size = data.baseSize * 1.2;
+        else if (nid === pin) res.size = data.baseSize * 1.12;
+      } else {
+        res.color = GRAY;
+        res.label = "";
+      }
+      // Pinned node gets a subtle ring via increased size even without hover
+      return res;
+    },
+    edgeReducer(eid, data) {
+      const res = { ...data };
+      // Initial 2-second preview: all edges colored + labeled.
+      if (state.showAllInitial) {
+        res.color = data.baseColor;
+        res.label = data.relation;
+        res.size = 2;
+        return res;
+      }
+      const src = state.graph.source(eid);
+      const tgt = state.graph.target(eid);
+      const hover = state.hoveredNode;
+      const pin = state.pinnedNode;
+      const lightAll = hover === state.currentNode;
+      // Edge is "active" if it's incident to hoveredNode or pinnedNode,
+      // or if we're hovering the center (lightAll)
+      const hoverIncident = hover && hover !== state.currentNode
+        && (src === hover || tgt === hover);
+      const pinIncident = pin && (src === pin || tgt === pin);
+      if (lightAll || hoverIncident || pinIncident) {
+        res.color = data.baseColor;
+        res.label = data.relation;
+        // Hover takes visual priority
+        if (hoverIncident) res.size = 2.5;
+        else if (pinIncident) res.size = 2.2;
+        else res.size = 2;
+      } else {
+        res.color = DIM_EDGE;
+        res.label = "";
+        res.size = 1;
+      }
+      return res;
+    },
+  });
+  // Turn on edge labels globally — reducers handle visibility
+  state.sigma.setSetting("renderEdgeLabels", true);
+  state.sigma.setSetting("edgeLabelSize", Math.max(10, state.fontSize - 2));
+
+  try {
+    const settings = graphologyLibrary.layoutForceAtlas2.inferSettings(graph);
+    state.layoutWorker = new graphologyLibrary.FA2Layout(graph, { settings });
+    state.layoutWorker.start();
+    const centerId = state.currentNode;
+
+    // Sigma v3's camera coords are bbox-normalized — graph (0,0) is NOT
+    // necessarily viewport center when FA2 pushes neighbors asymmetrically.
+    // We fix this by (a) keeping the center node pinned at graph (0,0) and
+    // (b) continuously centering the camera on the node's DISPLAY coords
+    // via sigma.getNodeDisplayData — which is the authoritative way to
+    // target a node regardless of bbox drift.
+    const pinInterval = setInterval(() => {
+      if (!state.graph || !centerId) return;
+      if (state.graph.hasNode(centerId)) {
+        state.graph.setNodeAttribute(centerId, "x", 0);
+        state.graph.setNodeAttribute(centerId, "y", 0);
+      }
+      // Don't fight transition animations (navigateIntoNode, navigateBack)
+      if (!state.transitioning) {
+        centerCameraOn(centerId);
+      }
+    }, 60);
+    setTimeout(() => {
+      try { state.layoutWorker && state.layoutWorker.stop(); } catch (e) {}
+      clearInterval(pinInterval);
+      if (state.graph && centerId && state.graph.hasNode(centerId)) {
+        state.graph.setNodeAttribute(centerId, "x", 0);
+        state.graph.setNodeAttribute(centerId, "y", 0);
+      }
+      // Final snap — bbox is now frozen, so this sticks.
+      if (!state.transitioning) centerCameraOn(centerId, 1.0);
+      if (state.sigma) state.sigma.refresh();
+    }, 2500);
+
+    // Initial snap so the first render already targets the node.
+    centerCameraOn(centerId, 1.0);
+  } catch (e) {
+    console.warn("layout failed:", e);
+  }
+
+  // Hover events — update state + refresh sigma for reducers to re-run
+  state.sigma.on("enterNode", ({ node }) => {
+    state.hoveredNode = node;
+    container.style.cursor = "pointer";
+    const nodeData = state.graph.getNodeAttributes(node);
+    const label = nodeData.originalLabel || node;
+    $("hoverHint").textContent = node === state.currentNode ? t("hover_all") : t("hover_explain", label);
+    state.sigma.refresh();
+  });
+  state.sigma.on("leaveNode", () => {
+    state.hoveredNode = null;
+    container.style.cursor = "grab";
+    $("hoverHint").textContent = t("hover_show");
+    state.sigma.refresh();
+  });
+
+  // Click vs double-click: we detect a double-click via timer
+  let singleClickTimer = null;
+  state.sigma.on("clickNode", ({ node }) => {
+    if (singleClickTimer) { clearTimeout(singleClickTimer); singleClickTimer = null; return; }
+    singleClickTimer = setTimeout(() => {
+      singleClickTimer = null;
+      if (node === state.currentNode) {
+        // Clicking the query node: clear any pin and show node detail
+        state.pinnedNode = null;
+        state.focusEdge = null;
+        loadDetail(node);
+        state.sigma.refresh();
+      } else if (state.pinnedNode === node) {
+        // Same pinned node clicked again → toggle off (back to default gray)
+        state.pinnedNode = null;
+        state.focusEdge = null;
+        loadDetail(state.currentNode);
+        state.sigma.refresh();
+      } else {
+        // Pin this node and show edge sources (unpins any previous)
+        state.pinnedNode = node;
+        loadEdgeSources(state.currentNode, node);
+        state.sigma.refresh();
+      }
+    }, 260);
+  });
+  state.sigma.on("doubleClickNode", (e) => {
+    const { node } = e;
+    if (e.preventSigmaDefault) e.preventSigmaDefault();
+    if (e.event?.original?.preventDefault) e.event.original.preventDefault();
+    if (singleClickTimer) { clearTimeout(singleClickTimer); singleClickTimer = null; }
+    if (state.transitioning) return;
+    navigateIntoNode(node);
+  });
+  // Clicking empty canvas → clear pin and go back to node detail
+  state.sigma.on("clickStage", () => {
+    if (state.pinnedNode) {
+      state.pinnedNode = null;
+      state.focusEdge = null;
+      if (state.currentNode) loadDetail(state.currentNode);
+      state.sigma.refresh();
+    }
+  });
+
+  renderLegend();
+
+  // End the 2-second full-color preview. Flip the flag, update the hint to
+  // invite hover interaction, and trigger a refresh so reducers re-run in
+  // the normal dim-except-center mode.
+  setTimeout(() => {
+    state.showAllInitial = false;
+    if (!state.hoveredNode) $("hoverHint").textContent = t("hover_show");
+    if (state.sigma) state.sigma.refresh();
+  }, 2000);
+}
+
+function _hoverActiveSet() {
+  const s = new Set();
+  if (!state.hoveredNode || !state.graph) return s;
+  s.add(state.hoveredNode);
+  state.graph.forEachNeighbor(state.hoveredNode, (nb) => s.add(nb));
+  return s;
+}
+
+function renderLegend() {
+  if (!state.graph) return;
+  const domains = new Set();
+  state.graph.forEachNode((_, d) => d.domain && domains.add(d.domain));
+  // Collect unique relation types across both directions, preserving first-color seen
+  const relColors = new Map();
+  state.graph.forEachEdge((_, d) => {
+    const rels = [...(d.relations_fwd || []), ...(d.relations_rev || [])];
+    rels.forEach((r) => { if (!relColors.has(r)) relColors.set(r, d.baseColor); });
+  });
+  const domHtml = [...domains].map((d) =>
+    `<span class="legend-item"><span class="legend-dot" style="background:${DOMAIN_COLORS[d] || "#94a3b8"}"></span>${d}</span>`
+  ).join("");
+  const edgeHtml = [...relColors.entries()].slice(0, 8).map(([rel, c]) =>
+    `<span class="legend-item"><span class="legend-line" style="background:${c}"></span>${escapeHtml(rel)}</span>`
+  ).join("");
+  const nodeRow = `<div class="legend-row"><span class="legend-row-label">${t("node_type")}</span>${domHtml || '<span class="legend-item" style="opacity:0.5;">—</span>'}</div>`;
+  const edgeRow = edgeHtml
+    ? `<div class="legend-row"><span class="legend-row-label">${t("edge_type_legend")}</span>${edgeHtml}</div>`
+    : "";
+  $("legend").innerHTML = nodeRow + edgeRow;
+}
+
+// ── Detail pane: node ────────────────────────────────────────────────
+async function loadDetail(nodeId) {
+  const pane = $("detailPane");
+  pane.innerHTML = `<div class="loading"><span class="spinner"></span>${t("loading")}</div>`;
+  try {
+    const predParam = state.edgeTypes && state.edgeTypes !== "all" ? `&predicate=${encodeURIComponent(state.edgeTypes)}` : "";
+    const [concept, claims, hyps] = await Promise.all([
+      fetch(`/api/kg/node/${encodeURIComponent(nodeId)}`).then((r) => r.json()),
+      fetch(`/api/kg/node/${encodeURIComponent(nodeId)}/claims?limit=80${predParam}`).then((r) => r.json()),
+      fetch(`/api/kg/node/${encodeURIComponent(nodeId)}/hypotheses?limit=30${state.recipeOnly ? "&recipe_only=true" : ""}`).then((r) => r.json()),
+    ]);
+    renderDetail(concept, claims, hyps);
+  } catch (e) {
+    pane.innerHTML = `<div class="detail-empty" style="color:var(--danger);">load failed</div>`;
+  }
+}
+
+function renderDetail(concept, claimsResp, hypsResp) {
+  const pane = $("detailPane");
+  const claims = claimsResp.claims || [];
+  const hyps = hypsResp.hypotheses || [];
+  state.hasRecipes = hypsResp.has_recipes;
+
+  const domainsHtml = (concept.domain_tags || []).map((d) =>
+    `<span class="badge domain" style="background:${DOMAIN_COLORS[d] || "#94a3b8"};">${d}</span>`
+  ).join("");
+  const externalHtml = (concept.external_links || []).map((l) =>
+    l.url
+      ? `<a class="badge link" href="${escapeHtml(l.url)}" target="_blank" rel="noopener">${escapeHtml(l.label)} ↗</a>`
+      : `<span class="badge">${escapeHtml(l.label)}</span>`
+  ).join("");
+  const aliasesHtml = (concept.aliases || []).length
+    ? `${t("aliases")}: ${(concept.aliases || []).slice(0, 6).map(escapeHtml).join(" · ")}`
+    : "";
+  const noiseBadge = concept.is_noise
+    ? `<span class="noise-badge" title="${escapeHtml((concept.noise_reasons || []).join(' | ') || 'low quality')}">${t("low_quality")} ${concept.noise_score != null ? "(" + fmt(concept.noise_score, 2) + ")" : ""}</span>`
+    : "";
+
+  const predInfo = (state.edgeTypes && state.edgeTypes !== "all")
+    ? `<div class="focus-banner" style="background:rgba(217,119,6,0.08);"><span>${t("predicate_filter", state.edgeTypes)}</span></div>` : "";
+
+  pane.innerHTML = `
+    <div class="detail-section">
+      <div class="detail-title">${escapeHtml(concept.name)}${noiseBadge}</div>
+      ${aliasesHtml ? `<div class="detail-aliases">${aliasesHtml}</div>` : ""}
+      <div class="badge-row">
+        ${domainsHtml}
+        <span class="badge" style="font-family:ui-monospace,monospace;">${escapeHtml(concept.id)}</span>
+      </div>
+      ${concept.definition ? `<div class="detail-definition">${escapeHtml(concept.definition)}</div>` : ""}
+      ${externalHtml ? `<div class="badge-row" style="margin-top:12px;">${externalHtml}</div>` : ""}
+    </div>
+    <div class="tab-row">
+      <button class="tab-btn ${state.activeTab === "hypotheses" ? "active" : ""}" data-tab="hypotheses">
+        ${t("tab_hypotheses")}<span class="count">${hypsResp.total || 0}</span>
+      </button>
+      <button class="tab-btn ${state.activeTab === "claims" ? "active" : ""}" data-tab="claims">
+        ${t("tab_claims")}<span class="count">${claimsResp.total || 0}</span>
+      </button>
+      <button class="tab-btn ${state.activeTab === "meta" ? "active" : ""}" data-tab="meta">
+        ${t("tab_meta")}
+      </button>
+    </div>
+    ${predInfo}
+    ${state.hasRecipes ? `
+    <div class="recipe-filter-row">
+      <input type="checkbox" id="recipeOnlyToggle" ${state.recipeOnly ? "checked" : ""}>
+      <label for="recipeOnlyToggle">${t("filter_recipe")}</label>
+    </div>` : ""}
+    <div id="tabBody"></div>
+  `;
+
+  pane.querySelectorAll(".tab-btn").forEach((b) => {
+    b.addEventListener("click", () => {
+      state.activeTab = b.dataset.tab;
+      pane.querySelectorAll(".tab-btn").forEach((x) => x.classList.toggle("active", x === b));
+      renderTabBody(concept, claims, hyps);
+    });
+  });
+  const recipeToggle = $("recipeOnlyToggle");
+  if (recipeToggle) {
+    recipeToggle.addEventListener("change", (e) => {
+      state.recipeOnly = e.target.checked;
+      loadDetail(concept.id);
+    });
+  }
+  renderTabBody(concept, claims, hyps);
+}
+
+function renderTabBody(concept, claims, hyps) {
+  const body = $("tabBody");
+  if (!body) return;
+  if (state.activeTab === "hypotheses") body.innerHTML = renderHypothesesList(hyps);
+  else if (state.activeTab === "claims") body.innerHTML = renderClaimsList(claims);
+  else body.innerHTML = renderMetaPanel(concept);
+}
+
+function renderHypothesesList(hyps) {
+  if (!hyps.length) return `<div class="detail-section"><div class="results-hint">${t("no_hypotheses")}</div></div>`;
+  const cards = hyps.map((h) => {
+    const pathHtml = (h.path || []).slice(0, 4).map((p) => {
+      const pmid = p.paper?.pmid;
+      const pmidTag = pmid ? `<a class="badge link" href="${p.paper.pubmed_url}" target="_blank" rel="noopener">${t("pubmed")} ${pmid}</a>` : "";
+      return `
+        <div class="path-step">
+          <b>${escapeHtml(p.from_name)}</b>
+          <span class="rel">${escapeHtml(p.relation_type)}</span>
+          <b>${escapeHtml(p.to_name)}</b>
+          ${pmidTag}
+        </div>`;
+    }).join("");
+    const recipeBadge = h.has_recipe ? `<span class="recipe-pill">recipe ✓</span>` : "";
+    const recipeDetail = h.recipe ? `
+      <div class="evidence-row" style="margin-top:8px;">
+        <span>dataset: <b>${escapeHtml(h.recipe.dataset || "?")}</b></span>
+        <span>model: ${escapeHtml(h.recipe.model_arch || "?")}</span>
+        <span>atlas: ${escapeHtml(h.recipe.atlas || "?")}</span>
+        <span>target: ${escapeHtml(h.recipe.target_outcome || "?")}</span>
+      </div>` : "";
+    return `
+      <div class="item-card">
+        <div class="item-head">
+          <div class="triple">
+            ${escapeHtml(h.source_name)}
+            <span class="arrow">→</span>
+            ${escapeHtml(h.target_name)}
+            ${recipeBadge}
+          </div>
+          <div class="conf">${fmt(h.composite_score, 3)}</div>
+        </div>
+        <div class="score-grid">
+          <span>${t("composite")} <b>${fmt(h.composite_score, 3)}</b></span>
+          <span>${t("novelty")} <b>${fmt(h.novelty_score, 2)}</b></span>
+          <span>${t("evidence")} <b>${fmt(h.evidence_score, 2)}</b></span>
+          <span>${t("testability")} <b>${fmt(h.testability_score, 2)}</b></span>
+          ${h.critic_score > 0 ? `<span>${t("critic")} <b>${fmt(h.critic_score, 2)}</b></span>` : ""}
+        </div>
+        ${h.testability_reason ? `<div class="raw-text" style="margin-top:6px;">${escapeHtml(h.testability_reason)}</div>` : ""}
+        ${pathHtml ? `<div class="path-list">${pathHtml}</div>` : ""}
+        ${recipeDetail}
+        ${(h.path || []).length > 4 ? `<div class="evidence-row"><span>${t("more_steps", h.path.length - 4)}</span></div>` : ""}
+      </div>`;
+  }).join("");
+  return `<div class="detail-section">${cards}</div>`;
+}
+
+function renderClaimsList(claims) {
+  if (!claims.length) return `<div class="detail-section"><div class="results-hint">${t("no_claims")}</div></div>`;
+  const cards = claims.map((c) => {
+    const pmid = c.paper?.pmid;
+    const doi = c.paper?.doi;
+    const year = c.paper?.year;
+    const paperLinks = [
+      pmid ? `<a class="badge link" href="${c.paper.pubmed_url}" target="_blank" rel="noopener">${t("pubmed")} ${pmid} ↗</a>` : null,
+      doi ? `<a class="badge link" href="${c.paper.doi_url}" target="_blank" rel="noopener">${t("doi")} ↗</a>` : null,
+      year ? `<span class="badge">${year}</span>` : null,
+      c.paper?.journal ? `<span class="badge" title="${escapeHtml(c.paper.journal)}">${escapeHtml((c.paper.journal || "").slice(0, 24))}</span>` : null,
+    ].filter(Boolean).join("");
+    const evRow = [
+      c.evidence?.study_type ? `<span>${escapeHtml(c.evidence.study_type)}</span>` : null,
+      c.evidence?.p_value != null ? `<span>p=${c.evidence.p_value}</span>` : null,
+      c.evidence?.effect_size != null ? `<span>${escapeHtml(c.evidence.effect_metric || "effect")}=${c.evidence.effect_size}</span>` : null,
+      c.evidence?.sample_size ? `<span>n=${c.evidence.sample_size}</span>` : null,
+      c.evidence?.replicability ? `<span>${escapeHtml(c.evidence.replicability)}</span>` : null,
+    ].filter(Boolean).join("");
+    return `
+      <div class="item-card">
+        <div class="item-head">
+          <span class="predicate">${escapeHtml(c.predicate || "?")}</span>
+          <span class="conf">conf ${fmt(c.confidence, 2)}</span>
+        </div>
+        <div class="triple">
+          <b>${escapeHtml(c.subject_name)}</b>
+          <span class="arrow">→</span>
+          <b>${escapeHtml(c.object_name)}</b>
+          ${c.negated ? '<span class="badge" style="color:var(--danger);">NEG</span>' : ""}
+        </div>
+        ${c.raw_text ? `<div class="raw-text">${escapeHtml(c.raw_text)}</div>` : ""}
+        ${evRow ? `<div class="evidence-row">${evRow}</div>` : ""}
+        ${paperLinks ? `<div class="paper-row">${paperLinks}</div>` : ""}
+      </div>`;
+  }).join("");
+  return `<div class="detail-section">${cards}</div>`;
+}
+
+function renderMetaPanel(concept) {
+  const kvRows = Object.entries(concept.external_ids || {}).map(([k, v]) => `
+    <div class="kv"><span>${escapeHtml(k)}</span><code>${escapeHtml(String(v))}</code></div>
+  `).join("");
+  const atlas = concept.atlas_mapping
+    ? `<div class="detail-section"><h3>${t("atlas_mapping")}</h3><pre style="font-size:11.5px;color:var(--text-muted);white-space:pre-wrap;">${escapeHtml(JSON.stringify(concept.atlas_mapping, null, 2))}</pre></div>` : "";
+  const semantic = (concept.semantic_types || []).length
+    ? `<div class="detail-section"><h3>${t("semantic_types")}</h3><div class="badge-row">${concept.semantic_types.map((x) => `<span class="badge">${escapeHtml(x)}</span>`).join("")}</div></div>` : "";
+  return `
+    <div class="detail-section">
+      <h3>${t("external_ids")}</h3>
+      ${kvRows ? `<div class="detail-meta-grid">${kvRows}</div>` : `<div class="results-hint">${t("no_external")}</div>`}
+      ${concept.source_vocab ? `<div style="margin-top:10px;font-size:12.5px;color:var(--text-muted);">${t("source")}: <code>${escapeHtml(concept.source_vocab)}</code></div>` : ""}
+    </div>
+    ${semantic}
+    ${atlas}
+  `;
+}
+
+// ── Edge sources (single-click target) ───────────────────────────────
+async function loadEdgeSources(source, target) {
+  state.focusEdge = { source, target };
+  const pane = $("detailPane");
+  pane.innerHTML = `<div class="loading"><span class="spinner"></span>${t("loading")}</div>`;
+  try {
+    const url = new URL("/api/kg/edge-sources", location.origin);
+    url.searchParams.set("source", source);
+    url.searchParams.set("target", target);
+    url.searchParams.set("limit", "80");
+    const r = await fetch(url).then((r) => r.json());
+    renderEdgeSources(r);
+  } catch (e) {
+    pane.innerHTML = `<div class="detail-empty" style="color:var(--danger);">load failed</div>`;
+  }
+}
+
+function renderEdgeSources(data) {
+  const pane = $("detailPane");
+  const srcName = data.source?.name || data.source?.id || "?";
+  const tgtName = data.target?.name || data.target?.id || "?";
+  const curated = data.curated_edges || [];
+  const claims = data.claims || [];
+  const hasNothing = curated.length === 0 && claims.length === 0;
+
+  const curatedHtml = curated.length ? `
+    <div class="detail-section">
+      <h3>${t("curated_edges")}<span class="count">${curated.length}</span></h3>
+      ${curated.map((e) => `
+        <div class="item-card">
+          <div class="item-head">
+            <span class="predicate">${escapeHtml(e.relation_type || "?")}</span>
+            <span class="conf">conf ${fmt(e.confidence, 2)}</span>
+          </div>
+          <div class="triple">
+            <b>${escapeHtml(e.from_name)}</b>
+            <span class="arrow">→</span>
+            <b>${escapeHtml(e.to_name)}</b>
+          </div>
+          <div class="evidence-row">
+            <span>vocab: ${escapeHtml(e.source_vocab)}</span>
+            ${e.evidence_ref ? `<span>${escapeHtml(e.evidence_ref.slice(0, 60))}</span>` : ""}
+          </div>
+        </div>`).join("")}
+    </div>` : "";
+
+  const claimsHtml = claims.length ? `
+    <div class="detail-section">
+      <h3>${t("supporting_claims")}<span class="count">${data.total_claims || 0}</span></h3>
+      ${claims.map((c) => {
+        const pmid = c.paper?.pmid;
+        const doi = c.paper?.doi;
+        const year = c.paper?.year;
+        const paperLinks = [
+          pmid ? `<a class="badge link" href="${c.paper.pubmed_url}" target="_blank" rel="noopener">${t("pubmed")} ${pmid} ↗</a>` : null,
+          doi ? `<a class="badge link" href="${c.paper.doi_url}" target="_blank" rel="noopener">${t("doi")} ↗</a>` : null,
+          year ? `<span class="badge">${year}</span>` : null,
+          c.paper?.journal ? `<span class="badge" title="${escapeHtml(c.paper.journal)}">${escapeHtml((c.paper.journal || "").slice(0, 26))}</span>` : null,
+        ].filter(Boolean).join("");
+        const evRow = [
+          c.evidence?.study_type ? `<span>${escapeHtml(c.evidence.study_type)}</span>` : null,
+          c.evidence?.p_value != null ? `<span>p=${c.evidence.p_value}</span>` : null,
+          c.evidence?.sample_size ? `<span>n=${c.evidence.sample_size}</span>` : null,
+        ].filter(Boolean).join("");
+        return `
+          <div class="item-card">
+            <div class="item-head">
+              <span class="predicate">${escapeHtml(c.predicate || "?")}</span>
+              <span class="conf">conf ${fmt(c.confidence, 2)}</span>
+            </div>
+            <div class="triple">
+              <b>${escapeHtml(c.subject_name)}</b>
+              <span class="arrow">→</span>
+              <b>${escapeHtml(c.object_name)}</b>
+            </div>
+            ${c.raw_text ? `<div class="raw-text">${escapeHtml(c.raw_text)}</div>` : ""}
+            ${evRow ? `<div class="evidence-row">${evRow}</div>` : ""}
+            ${paperLinks ? `<div class="paper-row">${paperLinks}</div>` : ""}
+          </div>`;
+      }).join("")}
+    </div>` : "";
+
+  pane.innerHTML = `
+    <div class="focus-banner">
+      <span>${t("showing_edge")}: <b>${escapeHtml(srcName)}</b>${t("and")}<b>${escapeHtml(tgtName)}</b></span>
+      <span class="close-x" id="closeFocusBtn" title="Back to node detail">×</span>
+    </div>
+    ${hasNothing ? `<div class="detail-section"><div class="results-hint">${t("no_sources")}</div></div>` : ""}
+    ${curatedHtml}
+    ${claimsHtml}
+  `;
+
+  $("closeFocusBtn")?.addEventListener("click", () => {
+    state.focusEdge = null;
+    state.pinnedNode = null;
+    if (state.sigma) state.sigma.refresh();
+    if (state.currentNode) loadDetail(state.currentNode);
+  });
+}
+
+// ── Graph controls ───────────────────────────────────────────────────
+$("depthSelect").addEventListener("change", (e) => {
+  state.depth = parseInt(e.target.value, 10);
+  if (state.currentNode) loadNeighborhood(state.currentNode);
+});
+$("limitSelect").addEventListener("change", (e) => {
+  state.limit = parseInt(e.target.value, 10);
+  if (state.currentNode) loadNeighborhood(state.currentNode);
+});
+$("edgeTypeSelect").addEventListener("change", (e) => {
+  state.edgeTypes = e.target.value;
+  if (state.currentNode) {
+    loadNeighborhood(state.currentNode);
+    // refresh detail (claims are predicate-filtered on the server)
+    if (state.focusEdge) loadEdgeSources(state.focusEdge.source, state.focusEdge.target);
+    else loadDetail(state.currentNode);
+  }
+});
+</script>
+
+</body>
+</html>

core/web/static/index.html

@@ -0,0 +1 @@
+<!DOCTYPE html><html><head><meta http-equiv="refresh" content="0;url=/explore"></head><body><a href="/explore">KG Explorer</a></body></html>

neuroclaw_environment.json

@@ -0,0 +1 @@
+{"llm_backend": {"provider": "none", "model": "none", "available_models": []}}

requirements.txt

@@ -0,0 +1,4 @@
+fastapi[standard]==0.115.0
+uvicorn[standard]==0.30.6
+networkx>=3.1
+python-multipart>=0.0.6