Joint Consistency casts test-time aggregation as Ising-type energy minimization with pairwise LLM-judge interactions, subsuming voting methods and outperforming baselines across reasoning tasks.
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5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
SOMA estimates a local response manifold from early turns and adapts a small surrogate model via divergence-maximizing prompts and localized LoRA fine-tuning for efficient multi-turn serving.
Systematic testing of prompt engineering for LLM equational reasoning finds a performance ceiling of 60-79% accuracy that extensive engineering cannot exceed, driven by undecidability and model capacity limits.
JoyAI-LLM Flash delivers a 48B MoE LLM with 2.7B active parameters per token via FiberPO RL and dense multi-token prediction, released with checkpoints on Hugging Face.
citing papers explorer
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Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization
Joint Consistency casts test-time aggregation as Ising-type energy minimization with pairwise LLM-judge interactions, subsuming voting methods and outperforming baselines across reasoning tasks.
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SOMA: Efficient Multi-turn LLM Serving via Small Language Model
SOMA estimates a local response manifold from early turns and adapts a small surrogate model via divergence-maximizing prompts and localized LoRA fine-tuning for efficient multi-turn serving.
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Less Is More: Cognitive Load and the Single-Prompt Ceiling in LLM Mathematical Reasoning
Systematic testing of prompt engineering for LLM equational reasoning finds a performance ceiling of 60-79% accuracy that extensive engineering cannot exceed, driven by undecidability and model capacity limits.
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JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
JoyAI-LLM Flash delivers a 48B MoE LLM with 2.7B active parameters per token via FiberPO RL and dense multi-token prediction, released with checkpoints on Hugging Face.
- Measuring Maximum Activations in Open Large Language Models