GenAI4ELab/papercli-papers huggingface dataset:
AudioTrust: Benchmarking The Multifaceted Trustworthiness of Audio Large Language Models
Authors: Kai Li; Can Shen; Yile Liu; Jirui Han; Kelong zheng; Xuechao Zou; Lionel Z. WANG; Shun Zhang; Xingjian Du; Hanjun Luo; Yingbin Jin; Xinxin Xing; Ziyang Ma; Yue Liu; YiFan Zhang; Junfeng Fang; Kun Wang; Yibo Yan; Gelei Deng; Haoyang LI; Yiming Li; Xiaobin Zhuang; Tianlong Chen; Qingsong Wen; Tianwei Zhang; Yang Liu; Haibo Hu; Zhizheng Wu; Xiaolin Hu; Eng Siong Chng; Wenyuan Xu; XiaoFeng Wang; Wei Dong; Xinfeng Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid development and widespread adoption of Audio Large Language Models (ALLMs) require a rigorous assessment of their trustworthiness. However, existing evaluation frameworks, primarily designed for text, are not equipped to handle the unique vulnerabilities introduced by audio’s acoustic properties. We find that significant trustworthiness risks in ALLMs arise from non-semantic acoustic cues, such as timbre, accent, and background noise, which can be used to manipulate model behavior. To address this gap, we propose AudioTrust, the first framework for large-scale and systematic evaluation of ALLM trustworthiness concerning these audio-specific risks. AudioTrust spans six key dimensions: fairness, hallucination, safety, privacy, robustness, and authenticition. It is implemented through 26 distinct sub-tasks and a curated dataset of over 4,420 audio samples collected from real-world scenarios (e.g., daily conversations, emergency calls, and voice assistant interactions), purposefully constructed to probe the trustworthiness of ALLMs across multiple dimensions. Our comprehensive evaluation includes 18 distinct experimental configurations and employs human-validated automated pipelines to objectively and scalably quantify model outputs. Experimental results reveal the boundaries and limitations of 14 state-of-the-art (SOTA) open-source and closed-source ALLMs when confronted with diverse high-risk audio scenarios, thereby offering critical insights into the secure and trustworthy deployment of future audio models. Our platform and benchmark are publicly available at https://github.com/JusperLee/AudioTrust.
On the Convergence Direction of Gradient Descent
Authors: Shuo Chen; Xiaolong Li; Jiaying Peng; Yao Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Gradient descent (GD) is a fundamental optimization method in deep learning, yet its asymptotic directional properties remain less understood. In this paper, we prove that if GD converges, its trajectory either aligns toward a fixed direction or oscillates along a specific line. The fixed-direction convergence occurs under small learning rates, while the oscillatory convergence behavior emerges for large learning rates. This result offers a new lens for understanding long-term GD dynamics. Experimentally, we find that this directional convergence behavior also appears in stochastic gradient descent (SGD) and Adam. Furthermore, we discuss how these theoretical findings regarding oscillatory convergence might offer a perspective on the sharpness dynamics observed in the Edge of Stability (EoS) regime. Our work provides both theoretical clarity and practical insight into the behavior of dynamics for multiple optimization methods.
Think in Parallel, Answer as One: Logit Averaging for Open-Ended Reasoning
Authors: Haonan Wang; Chao Du; Kenji Kawaguchi; Tianyu Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Majority voting has proven effective for close-ended question answering by aggregating parallel reasoning traces. However, it is not directly applicable to open-ended reasoning, such as code generation and web-based deep research, where a “majority” over complete solutions is ill-defined. We introduce THINKMERGE, a training-free, plug-and-play decoding strategy that runs K parallel reasoning traces and averages their next-token logits at synchronization points to produce a single coherent output. THINKMERGE integrates seamlessly with vLLM/SGLang and remains compatible with standard decoding techniques such as Top-p/Top-k. Empirically, it matches or surpasses majority voting on AIME and GPQA, while delivering consistent gains on open-ended coding tasks: on LiveCodeBench (hard), pass@1 improves by +8.28% for DeepCoder-14B-Preview and +7.58% for Qwen3-8B. Beyond code, we further show that THINKMERGE improves web-based deep-research agents (e.g., WebSailor-7B/32B) across GAIA, BrowseComp-en/zh, and XbenchDeepSearch. These results demonstrate that parallel test-time scaling can benefit open-ended reasoning without relying on voting over complete outputs.
DiffPBR: Point-Based Rendering via Spatial-Aware Residual Diffusion
Authors: Yiping Xie; Yuchi Huo; Yunlong Ran; Zijian Huang; Lincheng Li; Yingfeng Chen; Jiming Chen; Qi Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural radiance fields and 3D Gaussian splatting (3DGS) have significantly advanced 3D reconstruction and novel view synthesis (NVS). Yet, achieving high-fidelity and view-consistent renderings directly from point clouds---without costly per-scene optimization---remains a core challenge. In this work, we present DiffPBR, a diffusion-based framework that synthesizes coherent, photorealistic renderings from diverse point cloud inputs. We demonstrate that diffusion models, when guided by viewpoint-projected noise explicitly constrained by scene geometry and visibility, naturally enforce geometric consistency across camera motion. To achieve this, we first introduce adaptive CoNo-Splatting, a technique for fast and faithful rasterization that ensures efficient and effective handling of point clouds. Secondly, we integrate residual learning into the neural re-rendering pipeline, which improves convergence, generalization, and visual quality across diverse rendering tasks. Extensive experiments show that our method outperforms existing baselines with an improvement of **3~5dB** in rendered image quality, a reduction from **41 to 8** in GPU hours for training, and an increase from **3.6fps to 10fps** (our one-step variant) in rendering speed frequency.
Aurelius: Relation Aware Text-to-Audio Generation At Scale
Authors: Yuhang He; He Liang; Yash Jain; Andrew Markham; Vibhav Vineet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present  Aurelius, a new framework that enables relation aware text-to-audio (TTA) generation research at scale. Given the lack of essential audio event and relation corpora, Aurelius contributes a large-scale audio event corpus AudioEventSet and another large-scale relation corpus AudioRelSet. Comprising 110 event categories, AudioEventSet maximally covers all commonly heard audio events and each event is unique, realistic and of high-quality. AudioRelSet consists of 100 relations, comprehensively covering the relations that present in the physical world or can be neatly described by text.  As the two corpora provide audio event and relation independently, they can be combined to create massive <text,audio> pairs with our pair generation strategy to support relation aware TTA investigation at scale. We comprehensively benchmark all existing TTA models from both general and relation aware evaluation perspective. We further provide an in-depth investigation into scaling existing TTA models' relation aware generation by either training from scratch or leveraging cross-domain general TTA knowledge. The introduced corpora and the findings from investigation potentially facilitate future research on relation aware TTA generation.
Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation
Authors: Chengbing Wang; Yang Zhang; Wenjie Wang; Xiaoyan Zhao; Fuli Feng; Xiangnan He; Tat-Seng Chua
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Preference alignment has enabled large language models (LLMs) to better reflect human expectations, but current methods mostly optimize for population-level preferences, overlooking individual users. Personalization is essential, yet early approaches—such as prompt customization or fine-tuning—struggle to reason over implicit preferences, limiting real-world effectiveness. Recent “think-then-generate” methods address this by reasoning before response generation. However, they face challenges in long-form generation: their static one-shot reasoning must capture all relevant information for the full response generation, making learning difficult and limiting adaptability to evolving content. To address this issue, we propose **FlyThinker**, an efficient “think-while-generating” framework for personalized long-form generation. FlyThinker employs a separate reasoning model that generates latent token-level reasoning in parallel, which is fused into the generation model to dynamically guide response generation. This design enables reasoning and generation to run concurrently, ensuring inference efficiency. In addition, the reasoning model is designed to depend only on previous responses rather than its own prior outputs, which preserves training parallelism across different positions—allowing all reasoning tokens for training data to be produced in a single forward pass like standard LLM training, ensuring training efficiency. Extensive experiments on real-world benchmarks demonstrate that FlyThinker achieves better personalized generation while keeping training and inference efficiency.
Long-range Modeling and Processing of Multimodal Event Sequences
Authors: Jichu Li; Yilun Zhong; Zhiting Li; Feng Zhou; Quyu Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Temporal point processes (TPPs) have emerged as powerful tools for modeling asynchronous event sequences. While recent advances have extended TPPs to handle textual information, existing approaches are limited in their ability to generate rich, multimodal content and reason about event dynamics. A key challenge is that incorporating multimodal data dramatically increases sequence length, hindering the ability of attention-based models to generate coherent, long-form textual descriptions that require long-range understanding. In this paper, we propose a novel framework that extends LLM-based TPPs to the visual modality, positioning text generation as a core capability alongside time and type prediction. Our approach addresses the long-context problem through an adaptive sequence compression mechanism based on temporal similarity, which reduces sequence length while preserving essential patterns. 
We employ a two-stage paradigm of pre-training on compressed sequences followed by supervised fine-tuning for downstream tasks. Extensive experiments, including on the challenging DanmakuTPP-QA benchmark, demonstrate that our method outperforms state-of-the-art baselines in both predictive accuracy and the quality of its generated textual analyses.
PIRN: Prototypical-based Intra-modal Reconstruction with Normality Communication for Multi-modal Anomaly Detection.
Authors: YITING LI; Xulei Yang; Jing Zhang; Sichao Tian; Jingyi Liao; Fayao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unsupervised multimodal anomaly detection (MAD) aims to detect anomalies by using both RGB and 3D modalities. However, existing methods struggle in few-shot scenarios where the number of normal training samples is limited. Specifically, cross-modal alignment approaches fail to learn reliable correspondences from scarce normal data, whereas memory-based methods often misclassify unseen normal variations as anomalies. To address these issues, we propose \mtd, a prototype-driven reconstruction framework equipped with explicit cross-modal knowledge transfer. Instead of relying on dense feature alignment or heavy memory banks, \mtd uses a compact set of learnable prototypes to capture diverse normal patterns and constrain feature reconstruction. Specifically, our framework incorporates three core innovations. We introduce Balanced Prototype Assignment (BPA), which employs balanced optimal transport to ensure uniform prototype utilization and prevent codebook collapse. Next, we propose Adaptive Prototype Refinement (APR), which uses gated prototype updates to dynamically expand the model's knowledge of unseen normal variations during inference. To enable each modality to assist the other in reconstructing, we further develop a Multimodal Normality Communication (MNC) module that exchanges high-level normal cues between modalities via gated cross-attention.  Extensive experiments on the MVTec 3D-AD, Eyecandies, and Real-IAD benchmarks validate the effectiveness of \mtd, where it consistently achieves superior performance compared to existing baselines under challenging few-shot settings.
DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity
Authors: Haowei Zhu; Ji Liu; Ziqiong Liu; Dong Li; Jun-Hai Yong; Bin Wang; Emad Barsoum
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models demonstrate outstanding performance in image generation, but their multi-step inference mechanism requires immense computational cost. Previous works accelerate inference by leveraging layer or token cache techniques to reduce computational cost. However, these methods fail to achieve superior acceleration performance in few-step diffusion transformer models due to inefficient feature caching strategies, manually designed sparsity allocation, and the practice of retaining complete forward computations in several steps in these token cache methods.
To tackle these challenges, we propose a differentiable layer-wise sparsity optimization framework for diffusion transformer models, leveraging token caching to reduce token computation costs and enhance acceleration. Our method optimizes layer-wise sparsity allocation in an end-to-end manner through a learnable network combined with a dynamic programming solver. Additionally, our proposed two-stage training strategy eliminates the need for full-step processing in existing methods, further improving efficiency.
We conducted extensive experiments on a range of diffusion-transformer models, including DiT-XL/2, PixArt-$\alpha$, FLUX, and Wan2.1. Across these architectures, our method consistently improves efficiency without degrading sample quality. For example, on PixArt-$\alpha$ with 20 sampling steps, we reduce computational cost by 54% while achieving generation metrics that surpass those of the original model, substantially outperforming prior approaches. These results demonstrate that our method delivers large efficiency gains while often improving generation quality.
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CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting
Authors: Jiyuan Xu; Wenyu Zhang; Xin Jing; Jiahao Nie; Shuai Chen; Shuai Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current methods for multivariate time series forecasting can be classified into channel-dependent and channel-independent models. Channel-dependent models learn cross-channel features but often overfit the channel ordering, which hampers adaptation when channels are added or reordered. Channel-independent models treat each channel in isolation to increase flexibility, yet this neglects inter-channel dependencies and limits performance. To address these limitations, we propose CPiRi, a channel permutation invariant (CPI) framework that infers cross-channel structure from data rather than memorizing a fixed ordering, enabling deployment in settings with structural and distributional co-drift without retraining. CPiRi couples spatio-temporal decoupling architecture with permutation-invariant regularization training strategy: a frozen pretrained temporal encoder extracts high-quality temporal features, a lightweight spatial module learns content-driven inter-channel relations, while a channel shuffling strategy enforces CPI during training. We further ground CPiRi in theory by analyzing permutation equivariance in multivariate time series forecasting. Experiments on multiple benchmarks show state-of-the-art results. CPiRi remains stable when channel orders are shuffled and exhibits strong inductive generalization to unseen channels even when trained on only half of the channels, while maintaining practical efficiency on large-scale datasets. The source code is released at https://github.com/JasonStraka/CPiRi.
FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates
Authors: Jiaqi Li; Yao Qian; Yuxuan Hu; leying zhang; Xiaofei Wang; Heng Lu; Manthan Thakker; Jinyu Li; sheng zhao; Zhizheng Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural audio codecs are foundational to speech language models. It is expected to have a low frame rate and decoupled semantic and acoustic information. A lower frame rate codec can reduce the computational cost of speech language models by shortening the sequence length. Recent studies have developed 12.5Hz low-frame-rate audio codecs, but even lower frame rate codecs remain underexplored. We find that pushing existing audio codecs to very low frame rates loses much semantic information. We suggest that low-frame-rate codecs' limitations are in both insufficient semantic decoupling and insufficient time resolution at capturing transient phonetic details. This paper introduces **FlexiCodec** to address this limitation. FlexiCodec improves semantic preservation with a **dynamic frame rate** approach and introduces a novel architecture featuring an **ASR feature-assisted dual stream** encoding and Transformer bottlenecks.
With dynamic frame rates, it uses less frames at information-sparse regions through adaptively merging semantically similar frames. 
A dynamic frame rate also allows FlexiCodec to support inference-time **controllable frame rates** between 3Hz and 12.5Hz.
Experiments on **6.25Hz, 8.3Hz and 12.5Hz** average frame rates confirm that FlexiCodec excels over baseline systems in semantic information preservation and delivers a high audio reconstruction quality. We also validate the effectiveness of FlexiCodec in language model-based TTS. Demos are available at: https://flexicodec.github.io. Code is available at: https://github.com/amphionteam/flexicodec.
Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation
Authors: Xingyu Zhu; Kesen Zhao; Liang Yi; Shuo Wang; Zhicai Wang; Beier Zhu; Hanwang Zhang; Xiangnan He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision–language reasoning, yet they remain vulnerable to hallucination, where generated content deviates from the visual evidence. Existing mitigation strategies either demand costly supervision during training or introduce additional latency at inference. Recent vision-enhancement methods attempt to address this by reinforcing visual tokens during decoding, but they typically inject all tokens indiscriminately, leading to interference from background regions and distracting the model from critical cues.  To overcome this challenge, we propose an **A**daptive v**I**sual **R**einforcement framework for MLLMs, dubbed as **AIR**. AIR consists of two main components: prototype-based token reduction, which condenses the large pool of visual tokens into a compact subset to suppress redundancy, and OT-guided patch reinforcement, which quantifies the alignment between hidden state and patch embeddings to selectively integrate the most consistent patches into the feed-forward layers.  As a result, AIR enhances the model’s reliance on salient visual information and effectively mitigates hallucination. Extensive experiments across representative MLLMs demonstrate that AIR substantially reduces hallucination while preserving general capabilities, establishing it as an effective and independent solution for building reliable MLLMs.
Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization
Authors: Tong Shao; Yusen Fu; Guoying Sun; Jingde Kong; Zhuotao Tian; Jingyong Su
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Although Diffusion Transformer (DiT) has emerged as a predominant architecture for image and video generation, its iterative denoising process results in slow inference, which hinders broader applicability and development. Caching-based methods achieve training-free acceleration, while suffering from considerable computational error. Existing methods typically incorporate error correction strategies such as pruning or prediction to mitigate it. However, their fixed caching strategy fails to adapt to the complex error variations during denoising, which limits the full potential of error correction. To tackle this challenge, we propose a novel fidelity-optimization plugin for existing error correction methods via cumulative error minimization, named CEM. CEM predefines the error to characterize the sensitivity of model to acceleration jointly influenced by timesteps and cache intervals. Guided by this prior, we formulate a dynamic programming algorithm with cumulative error approximation for strategy optimization, which achieves the caching error minimization, resulting in a substantial improvement in generation fidelity. CEM is model-agnostic and exhibits strong generalization, which is adaptable to arbitrary acceleration budgets. It can be seamlessly integrated into existing error correction frameworks and quantized models without introducing any additional computational overhead. Extensive experiments conducted on nine generation models and quantized methods across three tasks demonstrate that CEM significantly improves generation fidelity of existing acceleration models, and outperforms the original generation performance on FLUX.1-dev, PixArt-$\alpha$, StableDiffusion1.5 and Hunyuan. Our code is released publicly at https://github.com/leaves162/CEM.
GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
Authors: Xingyu Zhu; Beier Zhu; Junfeng Fang; Shuo Wang; Yin Zhang; Xiang Wang; Xiangnan He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large vision-language models (LVLMs) have achieved remarkable progress in vision–language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side defenses detect unsafe images with CLIP and prepend safety prefixes to prompts, but they still suffer from inaccurate detection in complex scenes and unstable safety signals during decoding. To address these issues, we propose **GuardAlign**, a training-free defense framework that integrates two strategies. First, OT-enhanced safety detection leverages optimal transport to measure distribution distances between image patches and unsafe semantics, enabling accurate identification of malicious regions without additional computational cost. Second, cross-modal attentive calibration strengthens the influence of safety prefixes by adaptively reallocating attention across layers, ensuring that safety signals remain consistently activated throughout generation. Extensive evaluations on six representative MLLMs demonstrate that GuardAlign reduces unsafe response rates by up to 39\% on SPA-VL, while preserving utility, achieving an improvement on VQAv2 from 78.51\% to 79.21\%.