Amit Nautiyal, Ayush Bhatt, Gaurav Nautiyal
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We present ChunkRank, an open-source Python library that derives chunk boundaries from a target model's tokenizer and context window, and selects an answer among candidates produced independently per chunk. It ships a validated registry of 90 models across 15 providers and six answer-selection methods, and needs only three core dependencies. For chunking, ChunkRank avoids context-window overflow automatically from the model name, whereas character-based splitters overflow or waste the budget, and a fidelity study across 11 languages shows why token-exact budgets matter beyond English. For answer selection we report a negative result: on NaturalQuestions, TriviaQA and HotpotQA, with extractive and generative readers, no content-based ranker reliably beats taking the first non-empty answer. The reason is reader abstention on chunks that lack the answer, not answer position. A long-context baseline shows that chunking matches single-call reading on single-hop questions, so ChunkRank targets small-window and beyond-window settings. Code, registry and evaluation harness are released.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.29828")
get_code_for_paper("2609.29828")
have("2609.29828")
The run record, dated, one paper per request, free:
curl https://syntology.ai/api/ran/2609.29828.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.29828)
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