hkust-knowcomp/deeprefine-skill/deeprefine

deeprefine apply --refresh-wiki --trace-file ... --refinement-file ...
deeprefine loop finish --trace-file ... --refinement-file ...

MAX_HOPS = 4
INCREMENT_HOP = 1
BASE_TOP_K = 10
MAX_TRIPLE_NUM_BY_STEP = [5, 10, 15, 20]   # cap triples per step
HISTORY_HORIZON = 4                        # abduction uses last N steps

deeprefine loop init --query "<exact question>"

target_queries = pending_history_queries()  # refined != true, dedupe by id, keep order
run "deeprefine history sync-memory" before loading pending history
if target_queries is empty:
    target_queries = [current session question]
 
for question in target_queries:
    interaction_history = []
    for step in 1..MAX_HOPS:
        print "[Step: {step}]"   # show in chat
 
        if step == 1:
            # Vector-retrieval equivalent: graphify query on full question
            RUN: graphify query "<question>"
            triples = parse NODE/EDGE → [{subject, relation, object}, ...]
            cap = MAX_TRIPLE_NUM_BY_STEP[0]  # 5
            record retrieval.method = "graphify_query"
        else:
            # k-hop expansion from entities in previous hop (NOT a new random search)
            entities = unique subjects/objects from previous triples
            expand 1-hop neighbors from graphify-out/graph.json (or graphify query on entities)
            cap = MAX_TRIPLE_NUM_BY_STEP[step-1]
            record retrieval.method = "k_hop_expansion" or "graphify_query+k_hop_expansion"
 
        triples = dedupe; len(triples) <= cap
 
        # Answerable judgement — session LLM, prompts below
        answerable, judgement_raw = LLM_judge(question, triples)
        MUST output ONLY: <judge>Yes</judge> or <judge>No</judge>
 
        append interaction_history with:
          step, query, num_hops=(step-1)*INCREMENT_HOP, base_top_k=10,
          retrieved_subgraph, answerable, judgement_raw,
          retrieval: {method, evidence: "<command output excerpt>"}
 
        if answerable:
            BREAK   # stop hop loop
 
    # --- same branch as DeepRefine.refine() line 314+ ---
    if len(interaction_history) <= 1:
        # Early exit: first hop was answerable — NO graph refinement
        set trace.early_exit = true
        deeprefine loop finish --trace-file ...   # no --refinement-file
        CONTINUE  # move to next pending query
    else:
        # len > 1 → ALWAYS error abduction + actions (even if last hop was Yes)
        error_abduction = LLM_abduction(interaction_history[-HISTORY_HORIZON:])
        MUST output: <abduction>...</abduction>
 
        actions = LLM_kg_refinement(
            last_hop.retrieved_subgraph,
            error_abduction,
            question,
            source file hints from triples,
        )
        MUST output: <refinement>insert_edge(...)|...</refinement>
 
        save refinement to graphify-out/.deeprefine/refinement_actions_<id>.txt
        deeprefine loop validate --trace-file ... --refinement-file ...
        deeprefine review --trace-file ... --refinement-file ...
        SHOW review labels: HIGH / MEDIUM / LOW, evidence, warnings, suggested replacements
        HARD STOP: do not modify graph.json in this /deeprefine turn
        Report the proposed actions and HIGH/MEDIUM/LOW review to the user
        Ask for explicit approval; approval must arrive in the user's next message
 
        # Follow-up turn only, after the user's next message explicitly approves/apply/write:
        if user explicitly approves and no LOW-confidence action remains:
            deeprefine apply --refresh-wiki --trace-file ... --refinement-file ...
            deeprefine loop finish --trace-file ... --refinement-file ...
        if user explicitly accepts LOW-confidence risk in that approval message:
            deeprefine apply --refresh-wiki --allow-low-confidence --trace-file ... --refinement-file ...
            deeprefine loop finish --trace-file ... --refinement-file ...

deeprefine review --trace-file graphify-out/.deeprefine/loop_trace_<id>.json --refinement-file graphify-out/.deeprefine/refinement_actions_<id>.txt

As an advanced judgement assistant, your task is to judge whether the given question is answerable based on the provided KG context.
 
Evaluate whether the given question is answerable based on the provided KG context. Output your judgment in the following format:
<judge>Yes</judge> or <judge>No</judge>
 
**Important:** You must think carefully about the question and the KG context before making your judgment. And output your judgment result directly in the specified format.

Question: {question}
Knowledge Graph (KG) context: {triples_string}

As an advanced error abduction assistant, your task is to analyze the error reasons based on the given interaction history.
 
Analyze the reasons of the unanswerable questions based on the given interaction history from the incompleteness, incorrectness, and redundancy perspectives. Output your analysis in the following format:
<abduction>...</abduction>
 
**Important:** You must think carefully about the interaction history before making your analysis. And output your analysis result directly in the specified format.

Interaction history: {interaction_history}

Step1:
['Query': ..., 'Subgraph_hop': ..., 'Subgraph_content': ..., 'Answerable': ...]
 
Step2:
...

As an advanced knowledge graph refinement assistant, your task is to generate a series of actions (**within 10 actions**) to refine the given KG to make it more suitable for answering the given question.
 
Based on the given KG and the analysed error reasons, refine the given KG to make it more easily for retrieval and answering the given question. You have the following three types of actions to conduct:
 
- insert_edge(subject, relation, object): Insert a new edge into the KG to complete the missing information.
- delete_edge(subject, relation, object): Delete an edge from the KG to remove the redundant information or conflicting information.
- replace_node(old_entity, new_entity): Replace an entity in the KG to correct the errors or deal with disambiguation.
 
Output a series of actions (**within 10 actions**) in the following format:
<refinement>insert_edge("...", "...", "...")|delete_edge("...", "...", "...")|replace_node("...", "...")|...</refinement>
 
**Important:** You must think carefully about the given KG and the analysed error reasons before making your refinement. DO NOT DELETE ANY IRRELEVANT TRIPLES FROM THE ORIGINAL KG. TRY TO KEEP THE ORIGINAL KG AS MUCH AS POSSIBLE. DO NOT GENERATE TOO MANY ACTIONS. And output your refinement result directly in the specified format.

Original Text: {original_text}
KG: {triples_string}
Question: {question}
Error reasons: {error_reasons}