Recent advances in Large Language Models (LLMs) - particularly model scaling and test-time techniques - have greatly enhanced the reasoning capabilities of language models at the expense of higher inference costs. To lower inference costs, prior works train router models or deferral mechanisms that allocate easy queries to a small, efficient model, while forwarding harder queries to larger, more expensive models. However, these trained router models often lack robustness under domain shifts and require expensive data synthesis techniques such as Monte Carlo rollouts to obtain sufficient ground-truth routing labels for training. In this work, we propose Confidence-Guided Stepwise Model Routing for Cost-Efficient Reasoning (STEER), a domain-agnostic, framework that performs fine-grained, step-level routing between smaller and larger LLMs without utilizing external models. STEER leverages confidence scores from the smaller model’s logits prior to generating a reasoning step, so that the large model is invoked only when necessary. Extensive evaluations using different LLMs on a diverse set of challenging benchmarks across multiple domains such as Mathematical Reasoning, Multi-Hop QA, and Planning tasks indicate that STEER achieves competitive or enhanced accuracy while reducing inference costs (up to +20% accuracy with 48% less FLOPs compared to solely using the larger model on AIME), outperforming baselines that rely on trained external modules. Our results establish model-internal confidence as a robust, domain-agnostic signal for model routing, offering a scalable pathway for efficient LLM deployment.

Recent advances in LLM agents have largely built on reasoning backbones like ReAct (Yao et al., 2023), which interleaves thought and action in complex environments.However, Re-Act often produces ungrounded or incoherent reasoning steps, leading to misalignment between the agent's actual state and goal.Our analysis finds that this stems from ReAct's inability to maintain consistent internal beliefs and goal alignment, causing compounding errors and hallucinations.To address this, we introduce ReflAct, a novel backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent's state relative to its goal.By explicitly grounding decisions in states and enforcing ongoing goal alignment, ReflAct dramatically improves strategic reliability.This design delivers substantial empirical gains: ReflAct surpasses Re-Act by 27.7% on average, achieving a 93.3% success rate in ALFWorld.Notably, ReflAct even outperforms ReAct with added enhancement modules (e.g., Reflexion, WKM), showing that strengthening the core reasoning backbone is key to reliable agent performance.Observation: You pick up the apple 1 from the countertop 1. Thought: Now I take an apple 1.Next, I need to go to a microwave 1 and heat it.Action: go to microwave 1 Observation: The microwave 1 is closed.Thought: The microwave 1 is closed, so I need to open it first.Action: open microwave 1 Observation: You open the microwave 1.The microwave 1 is open.In it, you see nothing.

Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability.Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation.To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field.In doing so, we propose entropy-adaptive Gibbs sampling and entropybased noise scheduling to counterbalance each model's shortcomings.Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in nonautoregressive text generation.Context Jake was playing with his toys.He accidentally broke his favorite one.He cried a lot over it.His parents decided to replace it for him.Keyword not stop Jake just could not stop crying.Jake feel It made Jake feel So much better.would enjoy Jake said he would enjoy the new toy Context Neil was in Sofia, Bulgaria.He was enjoying a trip backpacking through Europe....He thought the food and culture in