|
44 | 44 | 'warnings': ['w'], 'errors': [] |
45 | 45 | }) |
46 | 46 | assert compact['summary_source'] == 'arxiv_abstract' |
| 47 | + brief = mod.as_brief({ |
| 48 | + 'paper_id': 'x', |
| 49 | + 'title': 'Test Paper', |
| 50 | + 'status': 'ok', |
| 51 | + 'source_used': 'alphaxiv+arxiv', |
| 52 | + 'summary_source': 'alphaxiv_report', |
| 53 | + 'best_summary': 'This paper introduces a retrieval-augmented planner for long-horizon tasks. It addresses brittle multi-step planning in dynamic environments. The method combines a planner, a verifier, and a memory cache. It uses iterative self-correction with retrieved demonstrations.', |
| 54 | + 'alphaxiv_status': 'available', |
| 55 | + 'arxiv_status': 'available', |
| 56 | + 'resolved_alphaxiv_url': 'u1', |
| 57 | + 'resolved_arxiv_url': 'u2', |
| 58 | + 'alphaxiv_report': 'This paper introduces a retrieval-augmented planner for long-horizon tasks. It addresses brittle multi-step planning in dynamic environments. The method combines a planner, a verifier, and a memory cache. It uses iterative self-correction with retrieved demonstrations.', |
| 59 | + 'alphaxiv_description': '', |
| 60 | + 'arxiv_abstract': 'This paper introduces a retrieval-augmented planner for long-horizon tasks.', |
| 61 | + 'warnings': [], |
| 62 | + 'errors': [], |
| 63 | + }) |
| 64 | + assert 'Paper: Test Paper (x)' in brief |
| 65 | + assert 'Problem solved:' in brief |
| 66 | + assert 'Core method:' in brief |
| 67 | + assert 'Source: alphaXiv report. Confidence: higher.' in brief |
| 68 | + brief_fallback = mod.as_brief({ |
| 69 | + 'paper_id': 'y', |
| 70 | + 'title': 'Fallback Paper', |
| 71 | + 'status': 'ok', |
| 72 | + 'source_used': 'arxiv', |
| 73 | + 'summary_source': 'arxiv_abstract', |
| 74 | + 'best_summary': 'We study efficient adaptation for vision models. The problem is updating large models under tight compute budgets. Our method trains a low-rank adapter and freezes the backbone.', |
| 75 | + 'alphaxiv_status': 'thin', |
| 76 | + 'arxiv_status': 'available', |
| 77 | + 'resolved_alphaxiv_url': 'u1', |
| 78 | + 'resolved_arxiv_url': 'u2', |
| 79 | + 'alphaxiv_report': '', |
| 80 | + 'alphaxiv_description': 'Short overview only.', |
| 81 | + 'arxiv_abstract': 'We study efficient adaptation for vision models. The problem is updating large models under tight compute budgets. Our method trains a low-rank adapter and freezes the backbone.', |
| 82 | + 'warnings': ['AlphaXiv page fetched, but only a short overview was available.'], |
| 83 | + 'errors': [], |
| 84 | + }) |
| 85 | + assert 'Source: arXiv abstract fallback. Confidence: basic (alphaXiv: thin).' in brief_fallback |
| 86 | + assert 'Worth reading? Abstract-first; alphaXiv was thin, so this brief relies on arXiv.' in brief_fallback |
47 | 87 | print('offline normalization smoke test ok') |
48 | 88 | PY |
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