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The process unfolds in five core stages: data input, graph construction, feature processing, core computation, and output prediction, emphasizing the key roles of heterogeneous graph characteristics, meta-path index construction, and node-level attention mechanisms. The following is a detailed process description: Model Overall Process Overview The THAN model, based on a heterogeneous graph (containing users, cascade nodes, and various relationships), captures semantic associations between nodes through meta-path indexing, combines a time decay mechanism and multi-head attention to compute node embeddings, and is ultimately used for single-step prediction of cascade propagation. The process can be divided into 5 core stages, as follows: 1. Data Input and Initialization Input Data: Heterogeneous graph data (graph): Includes nodes (user, cascade), edges (social, interaction, diffusion), and edge attributes (such as timestamp). Initial features: User features (user_initial_features) and cascade features (cascade_initial_features), stored in dictionary form (id_to_idx maps node ID to feature index). Configuration parameters: Meta-path types (e.g., U-U-social, U-U-interact, C-U-C), number of attention heads, time decay coefficient lambda_time, etc. Initialization Operations: Device configuration (CPU/GPU) and memory optimization (cache clearing, asynchronous data transfer). Model component initialization: Feature projection layer (type_transform), multi-head attention parameters (att_params), etc. 2. Heterogeneous Graph Meta-Path Index Construction The build_metapath_index method is used to pre-compute the meta-path index, capturing the associations between different types of nodes and time decay features, to speed up subsequent attention calculations: Meta-Path Definition: U-U-social: User - Social - User (no timestamp, only stores neighbor relationships). U-U-interact: User - Interact - User (stores interaction timestamp, time decay value, and prefix sum). C-U-C: Cascade - User - Cascade (cascade relationship connected through intermediate users, stores diffusion timestamp, time decay value, and prefix sum). Index Content: For meta-paths containing time information (U-U-interact, C-U-C), store by node pair: Sorted list of timestamps (ts). Time decay value (decay, calculated based on 1 - exp(-lambda*(t-T_earliest))). Prefix sum of decay values (prefix, accelerates cumulative weight calculation). Index Cache: The calculation results are saved locally (save_metapath_full_cache) to avoid repeated calculations. 3. Feature Projection and Node Embedding Initialization Feature Projection: Through type_tr
This document outlines a mathematical modeling approach for ...
![Report Introduction: 'Colleagues, today I will report on the technical integration plan of R3-RAG and Open AI Co-Scientist. The core challenge currently facing scientific research assistance systems is that traditional retrieval-augmented generation methods rely on manually designed fixed workflows and lack adaptive optimization capabilities.'
Problem Analysis:
Existing Limitations: Although Co-Scientist has the advantage of multi-agent collaboration, its retrieval strategy is relatively static.
Technical Opportunities: R3-RAG achieves autonomous reasoning-retrieval strategy learning through reinforcement learning.
Integration Value: Injecting R3-RAG's intelligent retrieval capabilities into the Co-Scientist framework to improve the quality of scientific hypothesis generation.
1.2 Report Structure Overview
This report will be divided into four parts:
Architecture Design: Overall integration plan and technical roadmap
Core Implementation: Key components and algorithm details
Implementation Plan: Phased advancement strategy
Expected Results: Performance evaluation and risk control
2. Technical Architecture Deep Dive
2.1 Overall Architecture Design Philosophy
Layered Fusion Strategy: A three-layer architecture of 'retrieval layer enhancement - agent layer transformation - training layer integration' is adopted.
Architecture Diagram Description:
User Input
↓
[R3-RAG Enhanced Retrieval Layer] - Dynamic Routing + Multi-Hop Retrieval
↓
[Co-Scientist Agent Layer] - Generation, Reflection, Ranking Agent Enhancement
↓
[Reinforcement Learning Training Layer] - OpenRLHF Policy Optimization
↓
Optimized Output + Policy Update
2.2 Data Flow and Control Flow Design
Workflow:
Intelligent Retrieval: Dynamic routing based on query complexity (single-hop/multi-hop)
Hypothesis Generation: Retrieval-augmented context-aware generation
Dual Evaluation: Comprehensive scoring of result rewards + process rewards
Policy Optimization: Periodic reinforcement learning training
3. Core Component Technical Implementation
3.1 Intelligent Retrieval Service (Technical Highlights)
Dynamic Retrieval Routing Mechanism:
def dynamic_retrieval(query, complexity_threshold=0.7):
complexity = assess_complexity(query) # Semantic complexity analysis
if complexity > threshold:
return multi_hop_retrieval(query) # Complex problem: 3-hop recursive retrieval
else:
return single_retrieval(query) # Simple problem: single retrieval
Multi-Hop Retrieval Implementation:
Hop Count Control: Maximum 3 hops to prevent infinite recursion
Hybrid Retrieval: BGE dense retrieval + BM25 sparse retrieval
Context Evolution: Deepen query semantics based on previous hop results
3.2 Generation Agent Enhancement (Innovation Points)
Retrieval-augmented prompts](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2F0VLFF0qOpp8HSo4kGxZ6WaTGh6OocIiK%2Fc309356e-8747-43df-9f8c-2f46cafd7571%2Fcf1a310b-650c-4a01-87bf-5da6d141e794.png&w=3840&q=75)
