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import json
import re
from pathlib import Path
from typing import Any, Dict, List
from pydantic import BaseModel
SECTION_FILES = [
"abstract.txt",
"introduction.tex",
"related_work.tex",
"tldr.txt",
]
class PaperPackage(BaseModel):
paper_dir: Path
paper_metadata: Dict[str, Any]
extracted_discovery_claim: str
downstream_cluster_evidence: List[Dict[str, Any]]
paper_text: Dict[str, str]
full_processed_text: str
bibliography: List[Dict[str, Any]]
citation_contexts: List[Dict[str, Any]]
def to_prompt_payload(self) -> Dict[str, Any]:
return {
"paper_metadata": self.paper_metadata,
"extracted_discovery_claim": self.extracted_discovery_claim,
"downstream_cluster_evidence": self.downstream_cluster_evidence,
"paper_text": self.paper_text,
"full_processed_text": self.full_processed_text,
"bibliography": self.bibliography,
"citation_contexts": self.citation_contexts,
}
def _load_json(path: Path, default: Any) -> Any:
try:
return json.loads(path.read_text())
except Exception:
return default
def _read_text(path: Path) -> str:
try:
return path.read_text()
except Exception:
return ""
def _normalize_dict_payload(value: Any) -> Dict[str, Any]:
if isinstance(value, dict):
return value
if isinstance(value, list):
for item in value:
if isinstance(item, dict):
return item
return {}
def _collect_sections(paper_dir: Path) -> Dict[str, str]:
sections_dir = paper_dir / "sections"
out: Dict[str, str] = {}
for name in SECTION_FILES:
text = _read_text(sections_dir / name).strip()
if text:
out[name] = text[:12000]
if not out:
processed = _read_text(paper_dir / "processed_main.tex").strip()
if processed:
out["processed_main.tex"] = processed[:24000]
return out
def _collect_full_processed_text(paper_dir: Path) -> str:
processed = _read_text(paper_dir / "processed_main.tex").strip()
if processed:
return processed
sections_dir = paper_dir / "sections"
parts: List[str] = []
if sections_dir.exists():
for path in sorted(sections_dir.iterdir()):
if not path.is_file():
continue
text = _read_text(path).strip()
if text:
parts.append(f"[{path.name}]\n{text}")
return "\n\n".join(parts)
def _extract_year(value: Any) -> Any:
if value:
return value
return None
def _normalise_reference_record(ref: Dict[str, Any]) -> Dict[str, Any]:
cited = ref.get("citedPaper")
source = cited if isinstance(cited, dict) else ref
external_ids = source.get("external_ids") or source.get("externalIds") or {}
return {
"ref_id": (
ref.get("ref_id")
or ref.get("bib_key")
or source.get("ref_id")
or source.get("bib_key")
or source.get("paperId")
or source.get("paper_id")
or external_ids.get("ACL")
or external_ids.get("ArXiv")
or external_ids.get("DOI")
),
"title": source.get("title") or source.get("ref_title"),
"authors": source.get("authors") or source.get("ref_authors"),
"year": _extract_year(source.get("year") or source.get("ref_year")),
"external_ids": external_ids,
}
def _parse_bibtex_entries(text: str, limit: int) -> List[Dict[str, Any]]:
entries: List[Dict[str, Any]] = []
for match in re.finditer(r"@\w+\s*\{\s*([^,]+),(.*?)(?=\n@\w+\s*\{|\Z)", text, re.S):
key = match.group(1).strip()
body = match.group(2)
fields: Dict[str, str] = {}
for field in ("title", "author", "year", "doi", "url", "eprint"):
field_match = re.search(
rf"\b{field}\s*=\s*(\{{(?:[^{{}}]|\{{[^{{}}]*\}})*\}}|\"[^\"]*\"|[^,\n]+)",
body,
re.I | re.S,
)
if field_match:
value = field_match.group(1).strip().strip(",")
if (value.startswith("{") and value.endswith("}")) or (
value.startswith('"') and value.endswith('"')
):
value = value[1:-1]
fields[field] = re.sub(r"\s+", " ", value).strip()
if fields:
external_ids: Dict[str, Any] = {}
if fields.get("doi"):
external_ids["DOI"] = fields["doi"]
if fields.get("eprint"):
external_ids["ArXiv"] = fields["eprint"]
entries.append(
{
"ref_id": key,
"title": fields.get("title"),
"authors": fields.get("author"),
"year": fields.get("year"),
"external_ids": external_ids,
}
)
if len(entries) >= limit:
break
return entries
def _collect_bibtex_citation_contexts(paper_dir: Path, limit: int = 60) -> List[Dict[str, Any]]:
bibtex = _read_text(paper_dir / "references.bib")
processed = _read_text(paper_dir / "processed_main.tex")
if not bibtex or not processed:
return []
refs = _parse_bibtex_entries(bibtex, limit=500)
out: List[Dict[str, Any]] = []
seen: set[tuple[str, int]] = set()
for ref in refs:
ref_id = ref.get("ref_id")
if not ref_id:
continue
for match in re.finditer(rf"\\cite\w*\s*(?:\[[^\]]*\]\s*)*\{{[^}}]*\b{re.escape(str(ref_id))}\b[^}}]*\}}", processed):
key = (str(ref_id), match.start())
if key in seen:
continue
seen.add(key)
start = max(0, match.start() - 350)
end = min(len(processed), match.end() + 350)
snippet = re.sub(r"\s+", " ", processed[start:end]).strip()
out.append(
{
"ref_id": ref_id,
"citation_marker": ref.get("title") or ref_id,
"text": snippet,
"section": None,
"intents": [],
}
)
if len(out) >= limit:
return out
return out
def _collect_bibliography(paper_dir: Path, limit: int = 80) -> List[Dict[str, Any]]:
refs = _load_json(paper_dir / "references_metadata.json", [])
if isinstance(refs, list) and refs:
return [_normalise_reference_record(ref) for ref in refs[:limit] if isinstance(ref, dict)]
bibtex = _read_text(paper_dir / "references.bib")
if bibtex:
return _parse_bibtex_entries(bibtex, limit)
return []
def _collect_citation_contexts(paper_dir: Path, limit: int = 60) -> List[Dict[str, Any]]:
refs = _load_json(paper_dir / "references_metadata.json", [])
out = []
if isinstance(refs, list):
for ref in refs:
if not isinstance(ref, dict):
continue
ref_record = _normalise_reference_record(ref)
for context in ref.get("contextsWithIntent") or []:
if not isinstance(context, dict):
continue
text = context.get("context") or context.get("text") or ""
if not text:
continue
out.append(
{
"ref_id": ref_record.get("ref_id"),
"citation_marker": ref_record.get("title"),
"text": text,
"section": context.get("section"),
"intents": context.get("intents", []),
}
)
if len(out) >= limit:
return out
contexts = _load_json(paper_dir / "usage_contexts.json", [])
if isinstance(contexts, list):
for item in contexts:
entry = {
"ref_id": item.get("ref_id") or item.get("bib_key"),
"citation_marker": item.get("citation_marker"),
"text": item.get("text") or item.get("text_raw") or "",
"section": item.get("section"),
}
if entry["text"]:
out.append(entry)
if len(out) >= limit:
break
if not out:
out = _collect_bibtex_citation_contexts(paper_dir, limit=limit)
return out
def _collect_downstream_cluster_evidence(paper_dir: Path) -> List[Dict[str, Any]]:
discovery = _normalize_dict_payload(_load_json(paper_dir / "usage_discovery_from_contributions.json", {}))
clusters = discovery.get("clusters", [])
out = []
for cluster in clusters:
out.append(
{
"cluster_id": cluster.get("cluster_id"),
"representative_claim": cluster.get("representative_claim") or cluster.get("cluster_title"),
"cluster_title": cluster.get("cluster_title"),
"count": cluster.get("count"),
"merge_rationale": cluster.get("merge_rationale"),
}
)
return out
def load_paper_package(paper_dir: str | Path, extracted_claim_override: str | None = None) -> PaperPackage:
paper_dir = Path(paper_dir)
discovery = _normalize_dict_payload(_load_json(paper_dir / "usage_discovery_from_contributions.json", {}))
paper_metadata = _normalize_dict_payload(_load_json(paper_dir / "paper_metadata.json", {}))
claim = extracted_claim_override or (
discovery.get("most_impactful_contribution_self_contained")
or discovery.get("most_impactful_contribution")
or ""
)
return PaperPackage(
paper_dir=paper_dir,
paper_metadata=paper_metadata,
extracted_discovery_claim=claim,
downstream_cluster_evidence=_collect_downstream_cluster_evidence(paper_dir),
paper_text=_collect_sections(paper_dir),
full_processed_text=_collect_full_processed_text(paper_dir),
bibliography=_collect_bibliography(paper_dir),
citation_contexts=_collect_citation_contexts(paper_dir),
)
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