TwinRouterBench supplies 970 execution-verified router prefixes across five datasets plus a live harness for 100 held-out SWE-bench cases, scoring routers on tier accuracy, trajectory success, and realized token cost without LLM judges.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A pipeline with LoRA-fine-tuned query rewriting, BM25+dense hybrid retrieval via RRF, and cross-encoder reranking reaches nDCG@5 of 0.531 on multi-turn retrieval across four domains.
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.
citing papers explorer
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TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
TwinRouterBench supplies 970 execution-verified router prefixes across five datasets plus a live harness for 100 held-out SWE-bench cases, scoring routers on tier accuracy, trajectory success, and realized token cost without LLM judges.
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Caraman at SemEval-2026 Task 8: Three-Stage Multi-Turn Retrieval with Query Rewriting, Hybrid Search, and Cross-Encoder Reranking
A pipeline with LoRA-fine-tuned query rewriting, BM25+dense hybrid retrieval via RRF, and cross-encoder reranking reaches nDCG@5 of 0.531 on multi-turn retrieval across four domains.
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H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.