Overview
Y2's differentiator with cutting edge RLM techniques.
Y2 Recursive Research: Overview
The Problem with Traditional AI Research
Most AI-powered research tools operate in a single pass: you ask a question, the AI searches, and returns an answer. This approach has fundamental limitations:
Surface-Level Coverage
Single-pass systems can only explore topics at one depth. They're like reading the first page of Google results—you get an overview, but miss the nuanced details buried in specialized sources.
No Follow-up Questions
Human researchers naturally ask follow-up questions. When you discover something interesting, you dig deeper. Traditional AI tools don't have this capability—they can't pursue promising leads or explore subtopics that emerge during research.
Source Quantity vs Quality Trade-off
You're forced to choose: either retrieve many shallow sources, or a few deep sources. You can't do both efficiently in a single pass.
Fixed Context Window
Every search happens in isolation. There's no memory of what was already discovered, leading to redundant searches and wasted API costs.
Introducing Recursive Language Models (RLM)
Y2's recursive research system is inspired by Recursive Language Models, an emerging AI research paradigm that mirrors how humans actually conduct research:
Start Broad - Get the lay of the land
Identify Gaps - Notice what's missing or needs clarification
Dive Deep - Explore specific subtopics with targeted searches
Synthesize - Combine findings from all layers into coherent insights
The Key Insight
Just as recursion in programming solves complex problems by breaking them into smaller subproblems, recursive research breaks down complex topics into manageable subtopics, each investigated independently and then synthesized.
Why Y2's Approach is Superior
1. Multi-Layer Intelligence Gathering
Layer 0: Reconnaissance
Broad scan of the topic landscape (10 sources)
Identifies key themes, recent developments, and knowledge gaps
Purpose: "What's out there?"
Layer 1: Deep Dive
Focused exploration of 3-5 subtopics (5 sources each)
Targeted searches for specific aspects discovered in Layer 0
Purpose: "What are the details?"
Layer 2: Verification (optional)
Cross-reference findings and check for recent updates (3 sources per subtopic)
Validate facts across multiple sources
Purpose: "Is this accurate and current?"
2. Intelligent Subtopic Decomposition
After each layer, GLM-4.5 analyzes the findings and generates specific subtopics for deeper investigation. This is fundamentally different from pre-defined queries:
Traditional Approach:
Recursive Approach:
The system discovers what to research based on what it finds, not what you pre-program.
3. Source Freshness Validation
Not all sources are created equal. Y2 implements sophisticated freshness scoring:
Age-Based Scoring
Exponential decay formula: newer sources score higher
Topic-specific thresholds:
Crypto prices: 24 hours
Security threats: 30 days
General topics: 90 days
Content-Based Staleness Detection
Scans for outdated year references (2020, 2021, etc.)
Detects phrases like "last year" or "archived"
Flags deprecated information
Aggregate Metrics
Average source age across all layers
Freshness score (0-1, higher = better)
Stale source count and warnings
4. Link Health Checking
Sources are useless if they're dead. Y2 validates every link:
HTTP Status Validation
Asynchronous HEAD requests (doesn't block report delivery)
5-second timeout per link with parallel execution
Circuit breaker for repeatedly failing domains
Web Archive Fallback
If a link is dead, automatically fetch Wayback Machine snapshot
Preserves historical sources that would otherwise be lost
Metadata includes archive URL and timestamp
5. Parallel Execution Strategy
Y2 implements a hybrid execution strategy:
Breadth-First (Parallel)
All subtopics at a layer execute simultaneously
3x faster than sequential execution
Optimal for time-sensitive reports
Depth-First (Sequential)
Each subtopic completes fully before next starts
Lower peak memory usage
Better for extremely deep research (depth 3+)
Hybrid (Default)
Parallel at Layer 0 and Layer 1
Sequential at Layer 2 for verification
Best balance of speed and resource efficiency
Real-World Impact
Traditional Single-Pass Report
Y2 Recursive Research (Depth 1)
When to Use Each Depth
Depth 0: Quick Intelligence
Use Cases:
Daily market briefs
Price updates
News summaries
Quick fact-checking
Characteristics:
30 seconds
3-5 sources
Broad overview
Depth 1: Standard Research
Use Cases:
Weekly intelligence reports
Competitive analysis
Industry trend reports
Investment research
Characteristics:
90 seconds
12-20 sources
Balanced depth and breadth
Depth 2: Deep Investigation
Use Cases:
Strategic planning documents
Due diligence reports
Comprehensive threat assessments
Academic-quality research
Characteristics:
4 minutes
40-60 sources
Maximum depth and verification
The Recursive Advantage
Y2's recursive research system doesn't just search more—it searches smarter:
Adaptive: Discovers what to research based on findings
Thorough: Multiple layers ensure nothing is missed
Fresh: Validates source recency and accessibility
Efficient: Parallel execution maximizes speed
Cost-Effective: GLM-4.5 delivers quality at 10% the cost
Verifiable: Cross-references findings across layers
This isn't just an incremental improvement over single-pass systems. It's a fundamental rethinking of how AI conducts research—modeled after human intelligence gathering, not simple keyword
Last updated