Give it a research topic. Get back a novel, experiment-ready research idea — complete with paper chains, trend analysis, and next steps.
Pipeline Phases
+ quality reviewer
Literature Branches
parallel chain builders
Future Prediction Modes
Reflection · Analogy · Deep Dive · Imitate
Idea Evaluation Axes
Novelty · Significance · Clarity · Feasibility · Effectiveness
Chain Depth
papers per branch
Paper Reference
DAMO-NLP-SG, 2024
Each phase builds on the last — chain construction informs trend analysis, which informs future prediction, which informs idea generation, which feeds the ELO selector.
Map the research landscape, identify sub-fields and open problems, generate three distinct search perspectives (Recent Advances, Cross-Domain, Foundational)
Three parallel agents each build a 3–5 paper lineage chain from their assigned perspective using forward (citations) + backward (references) literature search
Synthesize how each chain evolved paper-by-paper, identify the dominant research momentum, and surface cross-chain patterns and contradictions
Forecast future directions using 4 modes: Reflection (limitations of latest work), Analogy (cross-domain transfer), Deep Dive (bottleneck attack), Imitate (replicate impact structure)
Generate one research idea per chain branch — motivation, novelty statement, and method — then score each on 5 axes: Novelty, Significance, Clarity, Feasibility, Effectiveness
Verify each candidate idea against existing literature via search — confirm no published paper already covers the same motivation × method × improvement combination
Run a pairwise ELO tournament (A vs B, B vs C, A vs C) across all 5 Idea Arena axes to select the single best research idea from the three branches
Design a rigorous experimental plan: hypothesis, datasets/benchmarks, strong baselines, evaluation metrics, implementation steps, ablation studies, failure modes
Peer-review the experiment plan for clarity (unambiguous steps), feasibility (PhD-student executable), and technical quality (appropriate baselines and metrics)
Assemble the final Research Brief: chain narrative, trend analysis, winning idea, experiment outline, key technical entities, and concrete next steps for the next 1–3 months
🚀 Branch A — Recent Advances
Anchors to the most impactful work published in the last 2 years; traces the latest breakthrough directions forward and backward
🔀 Branch B — Cross-Domain Transfer
Searches adjacent fields for techniques applicable to this topic; builds a chain showing how borrowed methods evolved in their home domain
🏛️ Branch C — Foundational Depth
Traces seminal/theoretical papers; shows how the mathematical or conceptual core of the field was established and refined over time
K=3 branches · Chain length 3–5 papers · Forward + backward search · ELO tournament selects winning idea
A complete Research Brief — not just a list of papers, but a narrative arc from the field's history to your novel contribution.
Topic: “Efficient fine-tuning of large language models with limited compute”
1. Chain Narrative
Chain A (Recent Advances): LoRA (2021) → QLoRA (2023) → GaLore (2024). Chain B (Cross-Domain): Low-rank matrix adaptation from signal processing → PEFT → Adapter layers. Chain C (Foundational): Full fine-tuning → Prompt tuning → Prefix tuning → LoRA.
2. Research Trend
All three chains converge on a single momentum: reducing the trainable parameter count while maintaining task-specific expressiveness. The bottleneck has shifted from memory to gradient quality — most recent work still suffers from suboptimal gradient signal in the low-rank subspace.
3. Future Directions
Mode 1 (Reflection): QLoRA acknowledges rank selection is heuristic. Mode 2 (Analogy): Compressed sensing theory uses adaptive basis selection — could dynamically choose LoRA rank per layer. Mode 3 (Deep Dive): Gradient subspace alignment through training is poorly understood. Mode 4 (Imitate): GaLore's full-rank gradient projection idea applied earlier in training.
4. Winning Idea (ELO)
Title: "Adaptive Rank LoRA via Compressed Sensing". Motivation: Fixed LoRA rank wastes capacity on saturated layers and under-fits dynamic layers. Novelty: Apply compressed sensing signal recovery to adaptively assign rank per layer per training step. Method: Monitor gradient energy distribution per layer; increase rank where energy spills outside subspace, decrease where it doesn't. Score: 7.8/10.
5. Experiment Outline
Baselines: LoRA (r=8,16,32), QLoRA, AdaLoRA. Datasets: GLUE, MMLU, GSM8K. Metrics: Task accuracy, trainable params (M), peak GPU memory (GB), convergence steps. Ablations: (1) fixed vs adaptive rank, (2) per-layer vs global threshold, (3) gradient energy window size.
6. Key Entities & Next Steps
Entities: LoRA, QLoRA, GaLore, PEFT, AdaLoRA, GLUE, MMLU, LLaMA-3. Next Steps: (1) Implement rank monitor on LLaMA-3-8B fine-tuning, (2) Run AdaLoRA baseline for comparison, (3) Submit to arXiv by end of month.
Most AI research tools retrieve a flat list of related papers. CoI organises literature into a progressive chain — so the agent reads the field's developmental trajectory, not just its current snapshot.
Based on: Li et al. (2024), “Chain of Ideas: Revolutionizing Research via Novel Idea Development with LLM Agents” — arXiv:2410.13185. Original implementation: DAMO-NLP-SG/CoI-Agent. Adapted for Google ADK with GoogleSearchTool (no Semantic Scholar API required).
Chain of Ideas vs. other research-ideation agents on the platform.
| Dimension | CHAIN OF IDEAS | SCIENCE | VIRSCI | FACET |
|---|---|---|---|---|
| Primary Focus | Research idea generation | Literature survey + hypothesis | Collaborative ideation panel | Cross-domain facet recombination |
| Input | Research topic (text) | Research question + domain | Research topic + team size | Research problem |
| Core Mechanic | Progressive paper chains + ELO | Literature scan + peer review | Multi-agent team discussion | Facet extraction + recombination |
| Output | Research brief + experiment plan | Research paper draft | Idea with team consensus | Novel cross-domain idea |
| Chain/Structure | K=3 branches, 3-5 papers each | Flat literature list | Discussion rounds | Facet matrix |
| Paper | Li et al., arXiv:2410.13185 | ResearchAgent / SciAgent | VirSci (AAAI 2025) | Scideator (CHI 2024) |
Enter any research topic and get a complete Research Brief — paper chains, trend analysis, novel idea, experiment design, and next steps — in minutes.