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Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation
Shouren Wang, Wang Yang, Chuang Ma +7
Hybrid-thinking language models expose explicit /think and /no_think modes, but current designs do not separate them cleanly. Even in /no_think mode, models often emit long and sel…
Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers
Wang Yang, Debargha Ganguly, Xinpeng Li +5
Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we found that such mode switching is lar…
Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks
Debargha Ganguly, Vikash Singh, Sreehari Sankar +7
Large language models (LLMs) show remarkable promise for democratizing automated reasoning by generating formal specifications. However, a fundamental tension exists: LLMs are prob…
SELF: Self-Extend the Context Length With Logistic Growth Function
Phat Thanh Dang, Saahil Thoppay, Wang Yang +3
Large language models suffer issues when operated on long contexts that are larger than their training context length due to the standard position encoding for tokens in the attent…
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +8
Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of…
100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability?
Wang Yang, Hongye Jin, Shaochen Zhong +4
Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM enables users to effortlessly process many originally exhaust…
Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
Wang Yang, Xiang Yue, Vipin Chaudhary +1
Recent advances leverage post-training to enhance model reasoning performance, which typically requires costly training pipelines and still suffers from inefficient, overly lengthy…
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Nengbo Wang, Xiaotian Han, Jagdip Singh +2
Large language models (LLMs) have revolutionized natural language processing (NLP), particularly through Retrieval-Augmented Generation (RAG), which enhances LLM capabilities by in…