Sizhe Wang, Zhengren Wang, Dongsheng Ma, Yongan Yu, Rui Ling, Zhiyu li, Feiyu Xiong, Wentao Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Retrieval-augmented generation (RAG) has emerged as a promising solution to enhance the reliability of large language models (LLMs) with external knowledge. Existing RAG methods operate in explicit representation spaces: in-context methods inject knowledge through text tokens in the input, while parametric methods like Parametric RAG encode documents into model parameters. Although effective, these approaches face inherent limitations. In-context injection suffers from quadratic computational complexity with context length and degraded performance in complex reasoning tasks. Parametric injection, while reducing inference costs, requires substantial storage overhead and computationally expensive offline preprocessing. More fundamentally, both paradigms rely on explicit discrete representations tokens or parameters that may introduce information bottlenecks and hinder seamless knowledge integration. To address these challenges, we introduce Latent RAG, a novel paradigm that performs knowledge injection entirely within the continuous latent space. Our approach encodes documents into ultra-compact latent representations through an offline compression phase, and directly fuses them with the LLM's hidden states via a learned injection mechanism during inference. By operating in the semantic latent space rather than explicit token or parameter spaces, Latent RAG enables more natural knowledge integration while achieving 9,200X storage reduction compared to Parametric RAG. Experimental results on multiple RAG benchmarks demonstrate that Latent RAG substantially enhances both effectiveness and efficiency. Furthermore, it can be seamlessly combined with existing in-context and parametric methods to achieve even better performance.

Large Language Models (LLMs) are known to hallucinate, generating non-factual outputs that undermine user trust. Recent ensemble-based approaches leverage uncertainty estimation to select among multiple LLM responses, achieving promising results in hallucination mitigation. However, these methods treat each model's uncertainty independently, overlooking a crucial signal: cross-model consistency. In this work, we observe that answers agreed upon by multiple models are significantly more likely to be correct-a manifestation of the "wisdom of crowds" principle. Leveraging this insight, we propose Consistency-Calibrated Uncertainty Fusion (CCUF), a framework that calibrates individual model uncertainties using cross-model consistency scores. When multiple models converge on the same answer, CCUF reduces the associated uncertainty estimate; when answers diverge, uncertainty remains elevated. This calibration mechanism enables more reliable answer selection for factoid question answering. Extensive experiments on TruthfulQA, TriviaQA, and FACTOR-news benchmarks demonstrate that CCUF consistently outperforms state-of-the-art hallucination mitigation methods, surpassing the previous best ensemble method UAF by 3.4% in accuracy while exceeding GPT-4 performance on TruthfulQA by 5.2%.

Retrieval-augmented generation (RAG) has emerged as a promising paradigm to enhance Large Language Models (LLMs) with external knowledge, effectively mitigating hallucinations and broadening the model's knowledge coverage. Despite recent advances, existing RAG methods fundamentally assume static query understanding, where the query is interpreted once before retrieval. This assumption proves inadequate for multi-hop questions, where comprehending the query itself often requires retrieval support, creating a chicken-and-egg dilemma between query understanding and information retrieval. To address this challenge, we propose SCORE-RAG Self-COrrecting Exploration-Exploitation REtrieval, a novel framework inspired by the explore-exploit paradigm in decision theory. SCORE-RAG reformulates multi-hop RAG as a two-phase adaptive process: exploration for dynamic query understanding, followed by exploitation for precise evidence gathering. Specifically, SCORE-RAG first performs exploratory retrieval with multi-perspective queries to resolve ambiguities and discover key entities and relations, then conducts targeted exploitation retrieval guided by the refined understanding to construct coherent evidence chains, and finally applies self-correction mechanisms to verify consistency and repair potential errors. Through this integrated approach, SCORE-RAG enables adaptive query comprehension, reduces error accumulation via self-verification, and produces interpretable reasoning chains for accurate answer generation. Extensive experiments on HotPotQA and 2WikiMultihopQA demonstrate that SCORE-RAG significantly outperforms existing state-of-the-art RAG frameworks, achieving substantial improvements particularly on complex multi-hop questions requiring deep reasoning.

In this paper, we propose an Audio-Language-Referenced SAM 2 (AL-Ref-SAM 2) pipeline to explore the training-free paradigm for audio and language-referenced video object segmentation, namely AVS and RVOS tasks. The intuitive solution leverages GroundingDINO to identify the target object from a single frame and SAM 2 to segment the identified object throughout the video, which is less robust to spatiotemporal variations due to a lack of video context exploration. Thus, in our AL-Ref-SAM 2 pipeline, we propose a novel GPT-assisted Pivot Selection (GPT-PS) module to instruct GPT-4 to perform two-step temporal-spatial reasoning for sequentially selecting pivot frames and pivot boxes, thereby providing SAM 2 with a high-quality initial object prompt. Within GPT-PS, two task-specific Chain-of-Thought prompts are designed to unleash GPT’s temporal-spatial reasoning capacity by guiding GPT to make selections based on a comprehensive understanding of video and reference information. Furthermore, we propose a Language-Binded Reference Unification (LBRU) module to convert audio signals into language-formatted references, thereby unifying the formats of AVS and RVOS tasks in the same pipeline. Extensive experiments show that our training-free AL-Ref-SAM 2 pipeline achieves performances comparable to or even better than fully-supervised fine-tuning methods.

Audio-Visual Segmentation (AVS) aims to segment sound-producing objects in video frames based on the associated audio signal. Prevailing AVS methods typically adopt an audio-centric Transformer architecture, where object queries are derived from audio features. However, audiocentric Transformers suffer from two limitations: perception ambiguity caused by the mixed nature of audio, and weakened dense prediction ability due to visual detail loss. To address these limitations, we propose a new Vision-Centric Transformer (VCT) framework that leverages vision-derived queries to iteratively fetch corresponding audio and visual information, enabling queries to better distinguish between different sounding objects from mixed audio and accurately delineate their contours. Additionally, we also introduce a Prototype Prompted Query Generation (PPQG) module within our VCT framework to generate vision-derived queries that are both semantically aware and visually rich through audio prototype prompting and pixel context grouping, facilitating audio-visual information aggregation. Extensive experiments demonstrate that our VCT framework achieves new state-of-the-art performances on three subsets of the AVSBench dataset.

Modern code generation has made significant strides in functional correctness and execution efficiency. However, these systems often overlook a critical dimension in real-world software development: \textit{maintainability}. To handle dynamic requirements with minimal rework, we propose \textbf{MaintainCoder} as a pioneering solution. It integrates the Waterfall model, design patterns, and multi-agent collaboration to systematically enhance cohesion, reduce coupling, achieving clear responsibility boundaries and better maintainability. We also introduce \textbf{MaintainBench}, a benchmark comprising requirement changes and novel dynamic metrics on maintenance efforts. Experiments demonstrate that existing code generation methods struggle to meet maintainability standards when requirements evolve. In contrast, MaintainCoder improves dynamic maintainability metrics by more than 60\% with even higher correctness of initial codes. Furthermore, while static metrics fail to accurately reflect maintainability and even contradict each other, our proposed dynamic metrics exhibit high consistency. Our work not only provides the foundation for maintainable code generation, but also highlights the need for more realistic and comprehensive code generation research. Resources: https://github.com/IAAR-Shanghai/MaintainCoder.

This paper presents a second-order sliding-mode (SOSM) control approach for inductively coupled power transfer (ICPT) systems based on zero-current-switching. A second-order dc equivalent model of a series-series (SS) compensated ICPT system is established by replacing its primary side with a dc source and simplifying the rectifier on the secondary side. A SOSM controller implemented by a digital state machine is designed to regulate the output voltage of ICPT systems by controlling the switches on the primary side. Parameters of the controller are derived with a simplified dc equivalent circuit model of the ICPT system. The proposed controller achieves fast dynamic response, robustness against load disturbances and parameter uncertainties without requiring current sensing or any integral terms in the controller. As a result, the output voltage of the system can reach steady state without any overshoots in several switching actions under load disturbances or mutual inductance variations. Simulation results on a 12V/2A system verify the effeteness of the proposed approach.