Conformal Selective Acting (CSA) fills a gap in conformal methods by providing per-round, pathwise-valid selective risk bounds for adaptive RLVR LLM streams under predictable updates and isotonic calibration.
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9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9representative citing papers
Synthetic training data designed to break the correlation between semantic and preferential signals in text embeddings provably improves preference prediction across 11 online deliberation datasets.
GraSP-VL turns frozen VLM embedding length into a controllable semantic granularity interface via a learned shared prefix transform that creates a Semantic Matryoshka structure.
PopuLoRA shows that co-evolving populations of LoRA adapters through cross-evaluated self-play can outperform compute-matched single-agent baselines on multiple code and math reasoning benchmarks.
SCALE-LoRA proposes a post-retrieval audit framework using sparse residual composition and disagreement-based reliability signals to improve open-pool LoRA adapter reuse on tasks like BIG-Bench Hard.
Authors call for contamination-resistant LLM benchmarks that exploit Transformer training-inference asymmetry and require new mathematical methods for cross-architecture interoperability.
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.
An ensemble of per-language fine-tuned Gemma 3 models with three synthetic data strategies and per-language threshold tuning achieves 2nd place overall in SemEval-2026 Task 9 with mean macro-F1 of 0.811.
citing papers explorer
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Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs
Conformal Selective Acting (CSA) fills a gap in conformal methods by providing per-round, pathwise-valid selective risk bounds for adaptive RLVR LLM streams under predictable updates and isotonic calibration.
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Embeddings for Preferences, Not Semantics
Synthetic training data designed to break the correlation between semantic and preferential signals in text embeddings provably improves preference prediction across 11 online deliberation datasets.
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GraSP-VL: Length as a Semantic Granularity Interface for Vision-Language Representations
GraSP-VL turns frozen VLM embedding length into a controllable semantic granularity interface via a learned shared prefix transform that creates a Semantic Matryoshka structure.
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PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play
PopuLoRA shows that co-evolving populations of LoRA adapters through cross-evaluated self-play can outperform compute-matched single-agent baselines on multiple code and math reasoning benchmarks.
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SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability
SCALE-LoRA proposes a post-retrieval audit framework using sparse residual composition and disagreement-based reliability signals to improve open-pool LoRA adapter reuse on tasks like BIG-Bench Hard.
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LLM Benchmark Datasets Should Be Contamination-Resistant
Authors call for contamination-resistant LLM benchmarks that exploit Transformer training-inference asymmetry and require new mathematical methods for cross-architecture interoperability.
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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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PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation
An ensemble of per-language fine-tuned Gemma 3 models with three synthetic data strategies and per-language threshold tuning achieves 2nd place overall in SemEval-2026 Task 9 with mean macro-F1 of 0.811.
- PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning