The radar's query was built from dozens of raw claims (procedural-heavy noise), so
matches were thematic but imprecise and gave no reason WHY a lead is relevant. Now:
- Prefer the analyst's distilled legal_arguments (argument_title + legal_topic — one
crisp CREAC issue per row) over raw claims.
- Search EACH issue separately and MERGE, so every lead is attributed to the case
issue(s) it answers (`matched_issues`) — the chair sees "this ruling is for your
'זכות עמידה' issue", not just a blended score.
- Fall back to the raw-claims blended query pre-aggregation; `source` reports the path.
- Shared `_radar_enrich` helper (gap status + action + matched_issues), bounded to 25
issues to cap the per-issue fan-out.
Validated: 8124-09-24 (32 args → per-issue) surfaces betterment rulings each tagged to
its issue (היעדר השבחה / זהות הנישום / סעיף 7(ב)); 1044-03-26 (0 args) falls back to
claims unchanged. No tool/endpoint signature change (new fields pass through the dict).
Invariants: G2 (reuses the one digest search + arg accessor), INV-DIG1 (radar only).
No schema change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Chaim
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