Zhinan Gou | Searcharxiv

arXiv2026

Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition

Multimodal Intent Recognition (MIR) aims to understand complex user intentions by leveraging text, video, and audio signals. However, existing approaches face two key challenges: (1) overlooking intricate cross-modal interactions for distinguishing consistent and inconsistent cues, and (2) ineffectively modeling multimodal conflicts, leading to semantic cancellation. To address these, we propose a novel Cognitive Dual-Pathway Reasoning (CDPR) framework, which constructs a stable semantic foundation via the intuition pathway and mitigates high-level semantic conflicts through the reasoning pathway, cooperatively establishing deep semantic relations. Specifically, we first employ a representation disentanglement strategy to extract modality-invariant and specific features. Subsequently, the intuition pathway aggregates cross-modal consensus using shared features for solid global representations. The reasoning pathway introduces an inconsistency perception mechanism, combining semantic prototype matching with statistical probability calibration to precisely quantify conflict severity, and dynamically adjusting the weights between both pathways. Furthermore, a multi-view loss function is adopted to alleviate modality laziness and learn structured features at different stages. Extensive experiments on two benchmarks show that CDPR achieves SOTA performance and superior robustness in mitigating multimodal inconsistency. The code is available at https://github.com/Hebust-NLP/CDPR.

cs.MM↗

arXiv2025

UR$^2$: Unify RAG and Reasoning through Reinforcement Learning

Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Learning from Verifiable Rewards (RLVR) for complex reasoning. However, existing attempts to unify these paradigms remain narrow in scope, typically limited to open-domain QA with fixed retrieval settings, which constrains generalization to broader domains. To address this limitation, we propose UR$^2$ (Unified RAG and Reasoning)), a general reinforcement learning framework that dynamically coordinates retrieval and reasoning. UR$^2$ introduces two key designs: a difficulty-aware curriculum that selectively invokes retrieval only for challenging instances, and a hybrid knowledge access strategy that combines domain-specific offline corpora with on-the-fly LLM-generated summaries. Together, these components mitigate the imbalance between retrieval and reasoning and improve robustness to noisy information. Experiments on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks show that UR$^2$, built on Qwen-2.5-3/7B and LLaMA-3.1-8B, consistently outperforms existing RAG and RL baselines, and achieves performance comparable to GPT-4o-mini and GPT-4.1-mini on several benchmarks. Our code is available at https://github.com/Tsinghua-dhy/UR2.

cs.CL↗