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Decompilation is foundational to binary analysis, yet conventional tools prioritize human readability over strict recompilability and verifiable runtime correctness. While recent LLM-based approaches ...
LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignm...
We present Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation, a two-phase pipeline for translating real-world C projects to safe Rust. Existing approac...
The advent of large language models (LLMs) has ushered in a new era in automated code translation across programming languages. Since most code-specific LLMs are pretrained on well-commented code from...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID code-translation | Route /topic/code-translation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/code-translationMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Code Translation",
"cluster": "Code Translation"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Code Translation",
"normalized_query": "code-translation",
"route": "/topic/code-translation",
"paper_ref": null,
"topic_slug": "code-translation",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.