| name | open-researcher |
| title | OpenResearcher: Offline Open-Domain Reasoning with Local Search |
| version | 0.0.3 |
| engine | skillxiv-v0.0.3-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2603.20278 |
| keywords | ["Research Infrastructure","Reasoning","Search","Offline","Tool Use"] |
| description | Replace live web API-dependent research with offline corpus-based trajectory synthesis. Decouples answer-guided document retrieval (10K gold + 15M FineWeb) from synthesis via local search engine, eliminating $5,760 Serper costs while enabling reproducible, analyzable reasoning chains through three primitives: Search (ranked retrieval), Open (full document fetch), Find (intra-document verification). |
Capability Gap
Existing deep research systems (like those using live web APIs) face three critical limitations:
- Cost at scale: $5,760+ for comprehensive search synthesis limits research automation
- Reproducibility: Dynamic web content changes make trajectories non-deterministic and difficult to audit
- Analysis opacity: Live API calls obscure failure modes—difficult to diagnose why reasoning chains fail
OpenResearcher eliminates these constraints through offline infrastructure.
Core Abstractions
Three explicit browser primitives model human research behavior while enabling systematic analysis:
1. Search
Returns top-K ranked results with snippets from corpus.
- Executed against merged document pool (10K gold + 15M FineWeb)
- Deterministic ranking via local search engine
- Enables corpus-level evidence discovery
2. Open
Fetches full document content from URLs in corpus.
- Returns complete text for selected documents
- Supports multi-document integration
- Enables deep context understanding
3. Find
Locates exact string matches within opened documents.
- Precise evidence localization
- Supports substring search and regex
- Enables verification of claims
This progression from broad retrieval → content fetching → precise localization mirrors human research iterating from literature survey to evidence collection.
Design Decisions
Two-Phase Synthesis Pipeline:
Phase 1: Answer-Guided Corpus Bootstrapping
- Initial query → Serper API gathers 10K gold documents
- Merged with 15M FineWeb base corpus
- One-time cost to construct domain-specific corpus
Phase 2: Offline Trajectory Synthesis
- Teacher model (GPT-OSS-120B) generates 97K+ trajectories
- No further API calls required
- Deterministic, reproducible reasoning chains
This separation decouples expensive retrieval from synthesis, making reasoning scalable once corpus is established.
Integration Patterns
API Layer Design:
┌─────────────────────────────┐
│ Reasoning Agent │
│ (queries research tasks) │
└──────────────┬──────────────┘
│
┌──────────────▼──────────────┐
│ Three-Tool Abstraction │
│ Search / Open / Find │
└──────────────┬──────────────┘
│
┌──────────────▼──────────────┐
│ Local Search Engine │
│ (Merged corpus: 10K+15M) │
└─────────────────────────────┘
