Complex reasoning with Large Language Models (LLMs) demands a careful balance between accuracy and computational cost. Verification is crucial for reliability but faces trade-off: robust process-based verifiers are computationally prohibitive, while fast verifiers lack precision. We introduce flexive, a unified generative verifier designed to navigate this trade-off by dynamically allocating compute between rapid fast thinking and deliberative slow thinking. A key innovation is our training strategy: we use Group Relative Policy Optimization (GRPO) to specifically enhance the reliability of the fast mode. This targeted training generalizes effectively, elevating the slow mode to state-of-the-art open-source performance. To deploy flexive, we propose the solve-detect-verify (SDV) pipeline. Moving beyond static Best-of-N ranking, SDV employs an iterative refinement process that utilizes likelihood-based probing to detect solution completion, curtailing overthinking, and leverages flexive’s feedback for targeted correction. Solve-detect-verify establishes a new open-source state-of-the-art on ProcessBench, outperforming GenPRM-32B while requiring ~2.3x fewer TFLOPS and 15x less training data. On AIME 2024, the full SDV pipeline achieves 83.3% accuracy, surpassing strong baselines while using significantly fewer tokens.

Task-oriented dialogue (TOD) systems are widely used across various domains, including customer service, appointment scheduling, and technical support. In real-world scenarios, such systems must adhere to given operational guidelines. However, existing solutions based on large language models often cannot achieve strict guideline compliance, even when fine-tuned with domain knowledge. To address this issue, we introduce a novel TOD system named GuidedTOD, which explicitly considers domain-specific guidelines by integrating a policy module. This module employs a Markov Chain, termed Chained Prior, to efficiently encode and dynamically update guideline knowledge. During inference, the Chained Prior re-ranks outputs from the domain-expert language model using beam search, ensuring guideline adherence. Experimental results show that GuidedTOD significantly improves guideline compliance, achieving approximately 20% better action prediction accuracy than state-of-the-art solutions. Code is available here: https://github.com/cure-lab/GuidedTOD.

Large Language Models have advanced significantly in complex reasoning, often leveraging external reward model to improve the reliability of their multi-step processes. However, existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. Therefore, we present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman’s Systems Theory. Dyve adaptively applies immediate token-level confirmation (System 1) for straightforward steps and comprehensive analysis (System 2) for complex ones. Unlike traditional verifiers that only evaluate final outputs, Dyve employs a step-wise consensus-filtered supervision strategy, leveraging Monte Carlo estimation, LLM-as-a-Judge, and specialized reasoning models to extract high-quality training signals from noisy rollouts. Experimental results on ProcessBench and the MATH dataset confirm that Dyve significantly outperforms existing process-based verifiers and boosts performance in Best-of-N settings while maintaining computational efficiency by strategically allocating verification resources.

Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it has been understudied. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.