grounded research ideation
Generate falsifiable, literature-grounded research proposals using cross-domain mechanism transfer.
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Grok Bot+18 agentsgrounded research ideation
Example session with this skill installed
Find a novel method to reduce inference latency in LLMs on edge devices where memory bandwidth is the bottleneck. Focus on a cross-domain mechanism.
- Read your context and instructions
- Compiled the grounded research ideation
The ideation run for the edge-LLM memory bottleneck is complete. I have identified a method-level gap in current quantization approaches and borrowed a dynamic scheduling mechanism from high-speed networking to address it.
Research Proposal: Dynamic Flow-Control Quantization (DFQ)
| Component | Detail |
|---|---|
| Problem | High inference latency in LLMs on edge devices. |
| Challenge Axis | Memory bandwidth bottleneck during KV-cache access. |
| Gap | Static quantization (e.g., 4-bit) reduces size but fails to adapt to variable throughput needs of different attention heads. |
| Source Setting | Network Traffic Shaping (Active Queue Management). |
| Shared Structure | Bursty demand on a shared, narrow-bandwidth channel with varying priority levels. |
| Mechanism | Borrowing Weighted Fair Queuing (WFQ) to dynamically allocate bit-width budget to attention heads based on activation importance. |
| Falsified If | Latency does not decrease when compared to static 4-bit quantization at equivalent perplexity levels on a Jetson Orin Nano. |
Next steps
- Review the full proposal in
runs/edge-llm-latency/proposal.md. - Execute the "cheapest experiment" outlined in Stage 6: a simulated bandwidth test on the attention head priority weights.
- Verify the source mechanism papers in
runs/edge-llm-latency/papers/to ensure the adaptation holds for your specific hardware.
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About this skill
Academic and industrial R&D projects often stall because LLMs generate "novel" ideas that are either routine extensions of existing work or complete hallucinations. Grounded Research Ideation implements the IdeaScientist framework to solve this by enforcing a strict, multi-stage workflow that connects structural challenges with cross-domain mechanisms. It eliminates "novelty by jargon" by requiring domain-neutral abstractions and verified, read-before-cite literature grounding.
What it does
- Gap Identification finding method-level limitations in same-problem prior art through systematic keyword regimes.
- Cross-Domain Innovation borrowing mechanisms from structurally analogous problems in unrelated fields to solve target challenges.
- Strict Grounding enforcing a read-before-cite gate where papers must be registered and summarized with text anchors before inclusion.
- Falsifiable Proposals generating results-masked research plans that define specific observations that would prove the idea wrong.
- Automated Validation running schema and validity checks to ensure citations are real and novelty claims are non-trivial.
How it works
- Intake Define the problem, challenge axes, and domain-neutral restatements in
problem.md. - Search & Gap Find Run same-problem searches to identify mechanisms in current literature and pinpoint unresolved method-level gaps.
- Innovate Search cross-setting regimes for analogous mechanisms, performing a transfer self-test to verify non-obviousness.
- Review & Report Pass a falsification review to survive reject/revise loops, then generate the final grounded proposal.
Frameworks & tools
This skill utilizes a specialized Python CLI tool ideasci.py for state management and validation. It integrates with arXiv and OpenAlex for literature retrieval and requires only the Python standard library.
Why this beats prompting it yourself
Generic prompts produce plausible-sounding but ungrounded research ideas that often turn out to be existing work. This skill forces a structured abstraction layer that prevents the agent from simply suggesting the first obvious extension, backed by a mechanical gate that blocks unread citations.
Use cases
- Proposing a novel machine learning architecture for a specific hardware constraint.
- Finding transferable signal processing mechanisms to solve a biological data noise problem.
- Developing a falsifiable research proposal for a grant application or thesis direction.
- Identifying why existing state-of-the-art methods fail at a specific, observable edge case.
Known limitations
Does not support code implementation, experiment execution, or paper summarization/surveys. Proposals are results-masked and unvalidated by physical experiments. Retrieval requires network access.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Requires
Arxiv
Openalex
W3
Python
macOS