MoonshotAI
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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It transforms survey generation from a frozen single-pass pipeline into a recursive hierarchy where parent agents repair completed child subtrees, with shared literature memory and traceable citation provenance.
Who it is for
- Researchers in NLP, LLM agents, and automated scientific writing
- Scientists and scholars who need structured literature surveys
- Developers building research automation or survey-generation tools
- Users of OpenAI-compatible model endpoints seeking reproducible survey workflows
Use cases
- Generating a structured outline and full literature survey for a research topic
- Producing evidence-grounded long-form surveys with citation normalization and LaTeX/PDF export
- Using the bundled Codex skill to run self-contained literature-survey workflows
- Resuming and exporting an existing survey run as LaTeX or PDF
Strengths
- Recursive outline agents use ReAct-style search, browse, propose, critique, and finish loops
- Parent adjudication can promote, demote, move, merge, or remove nodes to resolve hierarchy conflicts
- Leaf agents write disjoint sections in parallel with local evidence sets and citation provenance
- Shared PaperDB and task-scoped context keep knowledge reusable without carrying all intermediate traces
Considerations
- Requires Python 3.10+ and OpenAI-compatible model endpoints with external credentials
- Search adapters and model-specific environment variables must be configured by the user
- The repository intentionally excludes benchmarks, evaluation code, tests, logs, and generated outputs
README quick start
Installation
Description
official codebase: SEER: Automated Survey Generation via Recursive Backtracking Multi-Deep-Research-Agent Systems
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