Lattice

A lattice of prompts for serious work

Curated prompts for literature reviews, Aion workflows, datasets, writing, and evaluation. Copy once, adapt for your model.

Showing 1-20 of 100 prompts

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Researchdeep-learningtransformersstate-space-modelslit-review

Systematic Literature Review for Machine Learning Architectures

Structure a comprehensive literature review comparing transformer variants with modern state-space models.

You are an expert Computer Science Researcher specializing in Deep Learning Architectures.

Task: Generate a systematic literature review outline comparing Transformer architectures (Attention mechanism) vs State Space Models (e.g., Mamba, S4).

Structure:
1. Executive Summary: Highlighting key tradeoffs (computational complexity O(N^2) vs O(N), context window scaling, sequence length efficiency).
2. Categorized Taxonomies: Group 10 flagship papers into 3 core paradigms (Self-Attention, Recurrent State Space, Hybrid Architectures).
3. Critical Analysis: Benchmark efficiency on long-context tasks (Passkey retrieval, Needle-in-a-haystack, LRA benchmark).
4. Unresolved Challenges & Open Directions: Memory bandwidth bottlenecks, hardware acceleration (FlashAttention vs Selective Scan kernels).
5. Search Methodology: Recommended IEEE Xplore, arXiv, and NeurIPS search queries.

Input Context: {{MODEL_COMPARISON_FOCUS}}

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Researchexperimental-designhypothesisbenchmarkingstatistics

Hypothesis Formulation & Falsifiable Experiment Design

Transform a conceptual AI idea into a mathematically rigorous, falsifiable hypothesis and experiment protocol.

Act as a Senior Research Methodologist. Convert the following research idea into a formal scientific experimental plan.

Idea: {{RESEARCH_IDEA}}

Deliverables:
- Null Hypothesis (H0) and Alternative Hypothesis (H1) expressed in formal logic/mathematical notation.
- Operationalization of Variables: Independent (e.g., parameter count, context size), Dependent (e.g., perplexity, accuracy, latency), and Control variables.
- Experimental Protocol: Dataset split ratio, cross-validation scheme, baseline selection, and hardware specs.
- Statistical Significance: Required sample size, confidence interval (95%), and hypothesis testing methodology (e.g., Welch's t-test or Wilcoxon signed-rank test).
- Falsification Thresholds: Concrete empirical conditions under which H1 must be rejected.

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Researchablationneural-networksloss-functionsmethodology

Ablation Study Matrix Generator for Neural Networks

Design a granular ablation study plan to isolate the contribution of specific loss functions and modules.

You are a Lead AI Research Scientist preparing a paper submission for ICML/NeurIPS.

Task: Create an exhaustive Ablation Study Matrix for a new multi-modal model.

Requirements:
1. Identify 5 key components to ablate: positional embeddings, auxiliary loss terms, normalization layers (LayerNorm vs RMSNorm), learning rate schedules, and activation functions.
2. Build a Markdown table listing 12 experimental configurations, indicating (+/-) for each component.
3. Define target metrics to measure for each row (Val Loss, Top-1 Accuracy, FLOPs per step, GPU Memory VRAM peak).
4. Draft a template section titled "Ablation Results Analysis" with placeholder callouts for key insights.

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Researchmeta-analysiseffect-sizestatisticsevidence-synthesis

Meta-Analysis & Effect Size Synthesizer

Conduct a quantitative meta-analysis synthesis across multiple independent empirical AI studies.

Act as a Meta-Analysis Specialist in Empirical Software Engineering and Machine Learning.

Objective: Synthesize empirical results from multiple studies on the topic: {{META_ANALYSIS_TOPIC}}.

Please provide:
- Forest Plot Data Schema: Standardized metrics for extraction (Mean Difference, Hedges' g, Odds Ratio, 95% CI).
- Heterogeneity Assessment: Protocol for measuring I^2 index and Cochran's Q test to detect inter-study variance.
- Publication Bias Audit: Instructions for creating a Funnel Plot and performing Egger's regression test.
- Subgroup Analysis: Grouping factors (e.g., fine-tuning vs zero-shot, small vs large model scale).
- Synthesis Narrative: Executive summary for meta-analytic findings.

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Researchpeer-reviewneuripsiclrpaper-critique

Peer Review & Critical Appraisal Assistant (Conference Standard)

Critique an academic paper draft following NeurIPS/ICLR official reviewer guidelines.

You are an Area Chair for a top-tier AI conference (ICLR/NeurIPS). Evaluate the submitted research paper outline below.

Paper Draft: {{PAPER_TEXT_OR_OUTLINE}}

Provide a thorough review structured as follows:
1. Summary of Contributions: 3 concise bullet points summarizing novelty and key results.
2. Strengths: Novelty, empirical rigor, clarity, and potential community impact.
3. Weaknesses: Methodological flaws, missing baselines, ungrounded claims, or unclear writing.
4. Questions for Authors: 4 specific technical questions to be answered during rebuttal.
5. Overall Rating: Score from 1 to 10 (with rationale) and Confidence Score (1-5).

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Researchinterdisciplinarybioinformaticsphysics-informedmath

Interdisciplinary Research Translation Framework

Bridge computational biology or physics paradigms into machine learning model formulations.

You are an Interdisciplinary AI Researcher bridging Domain Science with Deep Learning.

Task: Map concepts from {{DOMAIN_CONCEPT}} (e.g., Thermodynamics, Graph Theory, Protein Folding) into a modern Neural Network architecture.

Structure:
- Mathematical Mapping: Translate domain differential equations or physical constraints into loss functions or layer priors (e.g., Physics-Informed Neural Networks - PINNs).
- Architectural Analogs: Compare biological/physical entities with attention maps, latent spaces, or graph nodes.
- Validation Suite: Design a simulation environment to test whether physical laws (e.g., conservation of energy) are respected by the model.

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Researchgrantnsffundingbroader-impacts

Research Grant Proposal & Broader Impacts Statement

Draft a NSF/ERC style research grant narrative emphasizing innovation and societal impact.

Act as a Principal Investigator writing a grant application for NSF / European Research Council.

Grant Focus: {{GRANT_PROJECT_TITLE}}

Draft the following core narrative sections:
1. Project Summary & Intellectual Merit: Transformative nature of the research, technical novelty, and team capability.
2. Broader Impacts: Societal benefits, open-source code/data releases, underrepresented group involvement, and educational outreach.
3. Work Plan & Milestones: 3-year Gantt chart breakdown with quarterly deliverables and risk mitigation strategies.

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Researchdata-leakagecontaminationbenchmarksauditing

Benchmark Dataset Contamination Checker & Audit Plan

Formulate a detection protocol for training data leakage into evaluation benchmarks.

You are an AI Safety and Evaluation Researcher auditing model integrity.

Task: Develop an automated methodology to detect data contamination (test set leakage) in large language models.

Protocol Outline:
- N-gram Overlap Extraction: Define exact string matching and fuzzy min-hash deduplication pipelines between pre-training corpora and evaluation benchmarks.
- Model Perplexity Probing: Use k-shot conditional perplexity comparisons to detect memorized evaluation samples.
- Synthetic Variant Creation: Generate semantically equivalent but rephrased benchmark items to measure performance drop on unseen perturbations.

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Researchquantum-computingqmlvariational-circuitsphysics

Quantum Machine Learning Algorithm Formalization

Formulate a hybrid quantum-classical neural network optimization pipeline.

Act as a Quantum AI Scientist. Formalize a Variational Quantum Eigensolver (VQE) algorithm coupled with classical gradient descent.

Include:
- Mathematical Formulation: Quantum circuit ansatz, parameterized rotation gates, and Hamiltonian expectation value measurement.
- Hybrid Workflow: Classical optimizer (e.g., Adam or SPSA) updating quantum parameters over iterations.
- Noise Model Analysis: Mitigating barren plateaus and decoherence noise on NISQ devices.

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Researchcausal-inferencedagjudea-pearlconfounding

Causal Inference & Directed Acyclic Graph (DAG) Designer

Construct a causal DAG to disentangle confounding variables in observational study data.

You are a Causal Inference Statistician trained in Judea Pearl's Causal Framework.

Task: Model the causal mechanism for {{CAUSAL_PROBLEM}}.

Requirements:
1. Construct a Directed Acyclic Graph (DAG) specifying Treatments (T), Outcomes (Y), Confounders (X), Mediators (M), and Colliders (C).
2. Apply do-calculus to prove whether the causal effect P(Y | do(T)) is identifiable from observational data.
3. Identify the Backdoor Criterion adjustment set to eliminate confounding bias.
4. Write Python code using `dowhy` or `causalgraphicalmodels` to estimate treatment effects.

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Researchbioinformaticsgenomicsvariant-callingdna

Bioinformatics Genomic Sequence Alignment Protocol

Formulate a computational pipeline for variant calling and structural variant detection.

Act as a Computational Biologist formulating an NGS (Next-Generation Sequencing) analysis pipeline.

Task: Outline an end-to-end variant calling workflow for whole-genome sequencing (WGS).

Pipeline Stages:
- Quality Control & Trimming: FastQC, adapter trimming parameters.
- Alignment & Mapping: BWA-MEM alignment against GRCh38 reference genome, SAM/BAM sorting and duplicate marking (Picard).
- Variant Discovery: GATK HaplotypeCaller parameters for SNPs and Indels.
- Annotation & Filtering: SnpEff / ANNOVAR annotation, VCF filtering rules.

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Researchreinforcement-learningreward-shapingroboticsrlhf

Reinforcement Learning Reward Function Engineering

Design a dense, non-hackable reward function for complex robotic manipulation tasks.

You are a Senior Reinforcement Learning Researcher.

Task: Design a shaped reward function for an agent learning: {{RL_TASK_DESCRIPTION}}.

Requirements:
1. Mathematical Expression: Combine distance-to-goal penalty, velocity bonus, and action smoothness penalties into a unified scalar reward.
2. Anti-Reward Hacking Precautions: Identify potential unintended behaviors (e.g., spinning in place, exploiting simulator bugs) and include negative constraints.
3. Potential-Based Reward Shaping (PBRS): Express the reward using a state-potential function `R(s,a,s') = gamma * Phi(s') - Phi(s)` to preserve optimal policy invariants.

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Researchneuromorphicsnnspiking-neural-netlow-power

Neuroscience-Inspired Neuromorphic Computing Architecture

Propose a spiking neural network (SNN) architecture for low-power event camera processing.

Act as a Neuromorphic Hardware Engineer.

Design a Spiking Neural Network (SNN) using Leaky Integrate-and-Fire (LIF) neuron models for processing asynchronous event-based vision data.

Provide:
- Mathematical Dynamics: Membrane potential differential equations, threshold condition, and refractory period equations.
- Learning Rule: Spike-Timing-Dependent Plasticity (STDP) or Surrogate Gradient Backpropagation algorithm.
- Energy Efficiency Estimation: Compare FLOPS vs Synaptic Operations (SOPs) against standard CNNs.

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Researchgame-theorymulti-agentnash-equilibriumsimulation

Autonomous Multi-Agent Game Theory Simulation

Model strategic equilibrium in competitive multi-agent environment using Nash Equilibrium.

You are a Game Theorist and Multi-Agent Systems Researcher.

Task: Model a non-cooperative game with N autonomous agents competing for finite resources: {{RESOURCE_CONTEXT}}.

Include:
- Normal Form & Extensive Form Representations: Payoff matrix, strategy spaces, and information sets (complete vs incomplete information).
- Equilibrium Solution: Analytical derivation of Pure and Mixed Strategy Nash Equilibria.
- Evolutionary Game Theory: Replicator dynamics differential equations to model strategy evolution over generations.

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Researchexplainable-aishapinterpretabilityfeature-importance

Explainable AI (XAI) Attribution & SHAP Analysis Spec

Create an interpretability framework utilizing SHAP and Integrated Gradients for black-box models.

Act as an XAI Interpretability Researcher.

Task: Formulate a model interpretability pipeline for high-stakes healthcare/finance predictions.

Specifications:
1. Local Explanations: Compute KernelSHAP and Integrated Gradients feature attribution values.
2. Global Explanations: Aggregate SHAP summary plots and partial dependence plots (PDP).
3. Faithfulness & Robustness Metrics: Verify attribution consistency under input noise perturbations using Sensitivity-N.

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Researchfederated-learningdifferential-privacysecure-aggregationprivacy

Federated Learning Privacy & Security Architecture

Architect a privacy-preserving federated learning system with Secure Aggregation.

You are a Distributed Systems Privacy Researcher.

Task: Architect a Federated Learning (FL) framework across 10,000 edge client devices.

Structure:
- Federated Averaging (FedAvg): Mathematical update formulation for local SGD steps and central aggregation.
- Privacy Guarantees: Implement Differential Privacy (DP-SGD) with noise multiplier (epsilon, delta) calculations.
- Threat Mitigation: Protection against Byzantine clients, model poisoning attacks, and membership inference.

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Researchpinnphysicspdefluid-dynamics

Physics-Informed Neural Network (PINN) PDE Solver

Formulate a PINN loss setup to solve Navier-Stokes fluid dynamics partial differential equations.

Act as a Computational Physicist specializing in Physics-Informed Machine Learning.

Task: Formulate a PINN model to solve 2D incompressible Navier-Stokes equations.

Deliverables:
- Loss Function Decomposition: Loss = L_data + L_PDE + L_boundary + L_initial.
- Automatic Differentiation Spec: PyTorch/JAX compute graph layout for extracting second-order derivatives.
- Boundary Condition Enforcement: Hard vs Soft boundary constraint techniques.

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Researchgnnmpnndrug-discoverychemistry

Graph Neural Network (GNN) Molecular Drug Discovery

Design a Message Passing Neural Network (MPNN) for molecular property prediction.

You are a Computational Chemist and Molecular AI Researcher.

Task: Design a Message Passing Neural Network (MPNN) to predict bioactivity binding affinity from SMILES strings.

Deliverables:
- Graph Representation: Atom feature vectors (nodes) and bond feature vectors (edges).
- Message Passing & Readout: Aggregate function, update function, and invariant global pooling (Set2Set).
- Benchmarking: Evaluation on MoleculeNet benchmark datasets (ESOL, FreeSolv, Lipophilicity).

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Researchclimate-aispatio-temporalearth-observationweather

Climate Science Spatio-Temporal Forecasting Model

Structure a spatial-temporal graph neural net for extreme weather event prediction.

Act as an Earth System AI Scientist.

Task: Build a spatial-temporal neural network architecture to forecast regional precipitation using satellite telemetry.

Components:
- Data Cube Ingestion: Handling multi-spectral satellite imagery + ERA5 reanalysis weather grid data.
- Spatial-Temporal ConvLSTM / Graph Convolutional Network: Modeling long-range atmospheric teleconnections.
- Physical Invariants: Mass and moisture conservation loss constraints.

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Researchnlpmultilingualcross-linguallow-resource

NLP Polyglot Cross-Lingual Transfer Evaluation

Assess zero-shot cross-lingual transfer capabilities across low-resource languages.

You are a Computational Linguist researching Cross-Lingual Transfer Learning.

Task: Evaluate zero-shot transfer performance of a multilingual LLM from high-resource (English) to low-resource languages.

Framework:
- Language Typology Breakdown: Select languages representing diverse morphological structures (Analytic, Agglutinative, Fusional).
- Evaluation Tasks: Named Entity Recognition (NER), Natural Language Inference (NLI).
- Alignment Assessment: Measure cross-lingual embedding space alignment using Representational Similarity Analysis (RSA).

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