Yang Ruofeng

Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore research topics in manipulation or navigation
• Plan simulation studies before using physical robots
• Check novelty and feasibility of robotics ideas

Create pixel art illustrations as SVG files, a scalable image format, for READMEs, documentation, and slides. Build character portraits, conversations, or group scenes, then refine colors, speech bubbles, arrows, and spacing to fit the requested layout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a pixel art hero image for a README
• Illustrate a conversation with characters and speech bubbles
• Make a character illustration in project colors

Run model training, fine-tuning, inference, and batch jobs on Modal’s cloud GPUs. Estimate hardware needs and cost, generate Python launch scripts, run or deploy workloads, monitor logs, and retrieve results without managing remote servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test CUDA code without a local GPU
• Run model training or LoRA fine-tuning on cloud GPUs
• Deploy a model inference API or vLLM service

Plan a 10–12 page systems research paper for conferences such as OSDI and SOSP. Get section page budgets, paragraph roles, and writing patterns that connect the research problem, design decisions, and evaluation evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning sections within a systems conference page limit
• Structuring an abstract and introduction
• Explaining design choices alongside alternatives

Turns research reports and experimental results into a paper through outline planning, figure creation, LaTeX drafting, PDF compilation, and review. Produces editable source files and a compiled PDF, with checks connecting claims to evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a research report into a paper draft
• Creating paper figures and tables from experiment data
• Preparing a manuscript for a target venue

Turn ARIS research reports in Markdown or JSON into a single HTML file for reading. Choose a report or dashboard layout, retain source version details, and optionally review whether the conversion preserves content and structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reading long research reports in a browser
• Viewing review logs alongside their JSON state
• Creating a research-wiki dashboard view

Draft a research paper section by section in LaTeX, a document typesetting format, using an outline and research materials. Apply venue formatting, incorporate figures, verify bibliography records, and check that repeated theoretical statements agree.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting paper sections from an outline and research findings
• Applying conference or IEEE paper formatting
• Checking bibliography metadata and removing uncited entries

Find related papers across local PDFs, research notes, and web sources, then summarize their key contributions and relevance. Use existing collections as a starting point, remove duplicates, and search for literature that fills gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find related work for a research topic
• Summarize relevant papers already stored locally
• Combine Zotero highlights with Obsidian research notes

Build a lasting research knowledge base linking papers, ideas, experiments, and claims. Keep failed approaches and unresolved gaps available for future work, generate compact summaries, and check records for missing connections or conflicting evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping papers and research ideas in a project knowledge base
• Recording failed approaches and their reasons
• Reviewing links between experiments and claims

Improve a compiled LaTeX paper through two rounds of Claude review, edits, and recompilation. The workflow addresses inconsistent reasoning, unsupported claims, unclear writing, and visual presentation, while keeping a cumulative log of changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Polish a generated paper before submission
• Correct inconsistencies and unsupported claims
• Improve figures, tables, and page layout

Organize paper reviews into individual concerns and draft responses within venue limits. Track evidence, unanswered issues, and approved commitments, then produce reviewer replies and a revision checklist that can be updated during follow-up rounds.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning responses to multiple reviewers
• Drafting a rebuttal within a character limit
• Preparing separate replies for reviewer threads

Compare an invention with prior art to assess novelty and inventive step, meaning whether its differences would be obvious. Produce claim comparison tables, suggested claim amendments, and a report identifying potential grounds for rejection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing an invention before filing a patent application
• Comparing claim elements with prior art
• Considering claim amendments to address rejection risks

Develop mathematical proofs for machine learning and AI theory by checking assumptions, intermediate results, and edge cases. Produce a complete proof, a corrected claim with its proof, or a report explaining the unresolved gap.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a proof of a theorem or lemma
• Expand a proof sketch into justified steps
• Check for hidden assumptions and counterexamples

Improve an existing LaTeX paper through two rounds of Gemini review, targeted edits, and PDF rebuilding. Check whether claims match the evidence, resolve notation and presentation issues, and retain review logs and PDF versions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review and revise a completed paper draft
• Align claims with supporting evidence
• Resolve notation and explanation inconsistencies

Get critical feedback from Claude on research ideas, papers, and experimental results. Through multiple review rounds, identify unsupported claims, missing experiments, and narrative weaknesses, then document agreed conclusions and a prioritized improvement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding logical gaps in a research idea
• Planning additional experiments after review
• Checking whether paper claims match the evidence

Turn a feature request into a working prototype, starting with a minimal path from input to output and adding features through individual checks. Record consequential assumptions before coding, then use a separate model to find decisions missing from that record.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a working prototype from a feature description
• Extending a prototype one checked feature at a time
• Recording assumptions made during implementation

Launch machine learning experiments on local GPUs, remote servers, Vast.ai, or Modal. It checks the configured environment, deploys code, saves logs, and verifies startup, with optional W&B metric logging and automatic Vast.ai result collection and instance cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run training jobs on a local GPU
• Deploy experiments to an SSH server
• Run experiments on Vast.ai and collect results

Explore computer architecture and circuit design settings by repeatedly running programs, measuring results, and adjusting parameters. Compare configurations against a baseline, respect design constraints, and receive a report with the best configuration found and the search history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tune cache sizes and processor settings
• Reduce circuit area while meeting timing constraints
• Compare compiler flags for runtime or code size

Fill unfinished research-wiki paper pages using summaries and abstracts from external sources. Organize each paper’s problem, method, results, limitations, and reusable ideas while preserving existing content by default and recording which source supplied the information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fill paper pages left unfinished after a literature import
• Summarize one paper’s methods, results, and limitations
• Prepare reusable research ideas for later brainstorming

Measure scripts, running processes, or whole systems to find performance bottlenecks. Select suitable tools, add measurement code when needed, and report CPU, GPU, memory, and communication findings with prioritized recommendations and a record of file changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding slow functions in a Python script
• Investigating CPU and GPU memory usage
• Measuring communication overhead across multiple GPUs

Get critical feedback on research ideas, papers, and experimental results from an external AI reviewer. Repeated discussions help identify unsupported claims, plan missing experiments, and document agreed improvements with priorities and estimated computing costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find gaps in research claims and evidence
• Plan additional experiments within a computing budget
• Review a paper's structure before submission

Turn a vague research direction into a focused proposal and a detailed experiment roadmap. This workflow connects method refinement with validation planning, producing review records, an experiment tracker, and a summary of what to run next.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a rough research idea into a focused proposal
• Plan experiments that test the paper's claims
• Organize experiment order, budget, and decision points

Investigate software errors, version conflicts, and API questions across official documentation, GitHub, technical Q&A, and Chinese developer communities. Results distinguish matching errors, source authority, and reported environments, giving you documented leads and next checks for debugging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find existing reports of the same error
• Investigate package and runtime version conflicts
• Look up API usage in documentation and Q&A

Manage GPU compute jobs on Qizhi through a command-line tool. Find available nodes, submit distributed training runs, launch batches with different parameter combinations, and inspect, monitor, or stop jobs across workspaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding available GPU nodes across workspaces
• Submitting distributed training jobs
• Launching evaluation batches with different parameter combinations