GenAI4ELab/papercli-papers huggingface dataset:
MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
Authors: Dingdong WANG; Junan Li; Jincenzi Wu; Dongchao Yang; Xueyuan Chen; Tianhua Zhang; Helen M. Meng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken communication, effective interpretation often requires integrating semantic meaning (e.g., content), paralinguistic features (e.g., emotions, speed, pitch) and phonological characteristics (e.g., prosody, intonation, rhythm), which are embedded in speech. While recent multimodal Speech Large Language Models (SpeechLLMs) have demonstrated remarkable capabilities in processing audio, their ability to perform fine-grained perception and complex reasoning in natural speech remains largely unexplored. To address this gap, we introduce MMSU, a comprehensive benchmark designed specifically for understanding and reasoning in speech. MMSU comprises 5,000 meticulously curated audio-question-answer triplets across 47 distinct tasks. Notably, linguistic theory forms the foundation of speech language understanding (SLU), yet existing benchmarks have paid insufficient attention to this fundamental aspect and fail to capture the broader linguistic picture. To ground our benchmark in linguistic principles, we systematically incorporate a wide range of linguistic phenomena, including phonetics, prosody, rhetoric, syntactics, semantics, and paralinguistics. Through a rigorous evaluation of 22 advanced SpeechLLMs, we identify substantial room for improvement in existing models. MMSU establishes a new standard for comprehensive assessment of SLLU, providing valuable insights for developing more sophisticated human-AI speech interaction systems. MMSU benchmark is available at https://huggingface.co/datasets/ddwang2000/MMSU.
Assembling the Mind's Mosaic: Towards EEG Semantic Intent Decoding
Authors: Jiahe Li; Junru Chen; Fanqi Shen; Jialan Yang; Jada Li; Zhizhang Yuan; Baowen Cheng; Meng Li; Yang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Enabling natural communication through brain–computer interfaces (BCIs) remains one of the most profound challenges in neuroscience and neurotechnology. While existing frameworks offer partial solutions, they are constrained by oversimplified semantic representations and a lack of interpretability. To overcome these limitations, we introduce **Semantic Intent Decoding(SID)**, a novel framework that translates neural activity into natural language by modeling meaning as a flexible set of compositional semantic units.
SID is built on three core principles: semantic compositionality, continuity and expandability of semantic space, and fidelity in reconstruction.
We present **BrainMosaic**, a deep learning architecture implementing SID. BrainMosaic decodes multiple semantic units from EEG/SEEG signals using set matching and then reconstructs coherent sentences through semantic-guided reconstruction. 
This approach moves beyond traditional pipelines that rely on fixed-class classification or unconstrained generation, enabling a more interpretable and expressive communication paradigm. Extensive experiments on multilingual EEG and clinical SEEG datasets demonstrate that SID and BrainMosaic offer substantial advantages over existing frameworks, paving the way for natural and effective BCI-mediated communication.
QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capture
Authors: Cuong Le; Pavlo Melnyk; Urs Waldmann; Mårten Wadenbäck; Bastian Wandt
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-based 3D human motion capture from videos remains a challenge in computer vision.
Traditional 3D pose estimation approaches often ignore the temporal consistency between frames, causing implausible and jittery motion.
The emerging field of kinematics-based 3D motion capture addresses these issues by estimating the temporal transitioning between poses instead.
A major drawback in current kinematics approaches is their reliance on Euler angles.
Despite their simplicity, Euler angles suffer from discontinuity that leads to unstable motion reconstructions, especially in online settings where trajectory refinement is unavailable.
Contrarily, quaternions have no discontinuity and can produce continuous transitions between poses.
In this paper, we propose QuaMo, a novel Quaternion Motions method using quaternion differential equations (QDE) for human kinematics capture.
We utilize the state-space model, an effective system for describing real-time kinematics estimations, with quaternion state and the QDE describing quaternion velocity.
The corresponding angular acceleration are computed from a meta-PD controller with a novel acceleration enhancement that adaptively regulates the control signals as the human quickly change to new pose.
Unlike previous work, our QDE is solved under the quaternion geometric constraints that results in more accurate estimations.
Experimental results show that our novel formulation of the QDE with acceleration enhancement accurately estimates 3D human kinematics with no discontinuity and minimal implausible artifact.
QuaMo outperforms comparable state-of-the-art methods on multiple datasets, namely Human3.6M, Fit3D, SportsPose and a subset of AIST.
The code is available at https://github.com/cuongle1206/QuaMo
Transformers Learn Latent Mixture Models In-Context via Mirror Descent
Authors: Francesco D'Angelo; Nicolas Flammarion
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sequence modelling requires determining which past tokens are causally relevant from the context and their importance: a process inherent to the attention layers in transformers, yet whose underlying learned mechanisms remain poorly understood. In this work, we formalize the task of estimating token importance as an in-context learning problem by introducing a framework based on Mixture of Transition Distributions, where a latent variable determines the influence of past tokens on the next. The distribution over this latent variable is parameterized by unobserved mixture weights that transformers must learn in-context. We demonstrate that transformers can implement Mirror Descent to learn these weights from the context. Specifically, we give an explicit construction of a three-layer transformer that exactly implements one step of Mirror Descent and prove that the resulting estimator is a first-order approximation of the Bayes-optimal predictor. Corroborating our construction and its learnability via gradient descent, we empirically show that transformers trained from scratch learn solutions consistent with our theory: their predictive distributions, attention patterns, and learned transition matrix closely match the construction, while deeper models achieve performance comparable to multi-step Mirror Descent.
From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation
Authors: Cheng Cheng; Lin Song; Di An; Yicheng Xiao; Xuchong Zhang; Hongbin Sun; Ying Shan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoregressive (AR) models have emerged as a powerful framework for image generation, yet they remain bound by a fundamental limitation: once a prediction is made, it cannot be revised. Each step marches forward in a strict left-to-right sequence, causing small errors to accumulate and compromise the final image. In this work, we reimagine this process with TensorAR, a decoder-only AR model that shifts from predicting discrete tokens to predicting overlapping tensor windows. This simple change transforms image synthesis into a process of next-tensor prediction, enabling the model to refine earlier outputs while preserving the causal structure that defines autoregression. To guard against information leakage during training, we introduce a discrete tensor noising mechanism inspired by discrete diffusion theory, which injects categorical noise into input tensors. TensorAR is designed to be plug-and-play: unlike masked AR methods, it requires no architectural modifications, and unlike autoregressive diffusion, it preserves the familiar AR training paradigm. We evaluate TensorAR across both class-to-image and text-to-image tasks, showing consistent gains in generation quality and instruction-following ability, while achieving a superior balance between quality and latency. In doing so, TensorAR offers a new path forward for autoregressive generation---one where predictions are not just produced, but continually refined.
Watermarking Diffusion Language Models
Authors: Thibaud Gloaguen; Robin Staab; Nikola Jovanović; Martin Vechev
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce the first watermark tailored for diffusion language models (DLMs), an emergent LLM paradigm able to generate tokens in arbitrary order, in contrast to standard autoregressive language models (ARLMs) which generate tokens sequentially. While there has been much work in ARLM watermarking, a key challenge when attempting to apply these schemes directly to the DLM setting is that they rely on previously generated tokens, which are not always available with DLM generation. In this work we address this challenge by: (i) applying the watermark in expectation over the context even when some context tokens are yet to be determined, and (ii) promoting tokens which increase the watermark strength when used as context for other tokens. This is accomplished while keeping the watermark detector unchanged. Our experimental evaluation demonstrates that the DLM watermark leads to a >99\% true positive rate with minimal quality impact and achieves similar robustness to existing ARLM watermarks, enabling for the first time reliable DLM watermarking.
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Authors: Emanuele Mule; Stefano Fiorini; Antonio Purificato; Federico Siciliano; Stefano Coniglio; Fabrizio Silvestri
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed hypergraphs, which can model oriented group interactions, remains largely under-explored despite its relevance for many applications. Recent approaches in this direction often exhibit an implicit bias toward homophily, which limits their effectiveness in heterophilic settings. Rooted in the algebraic topology notion of Cellular Sheaves, Sheaf Neural Networks (SNNs) were introduced as an effective solution to circumvent such a drawback. While a generalization to hypergraphs is known, it is only suitable for undirected hypergraphs, failing to tackle the directed case. In this work, we introduce Directional Sheaf Hypergraph Networks (DSHN), a framework integrating sheaf theory with a principled treatment of asymmetric relations within a hypergraph. From it, we construct the Directed Sheaf Hypergraph Laplacian, a complex-valued operator by which we unify and generalize many existing Laplacian matrices proposed in the graph-and hypergraph-learning literature. Across 7 real-world datasets and against 13 baselines, DSHN achieves relative accuracy gains from 2% up to 20%, showing how a principled treatment of directionality in hypergraphs, combined with the expressive power of sheaves, can substantially improve performance.
Spatially Guided Training for Vision-Language-Action Model
Authors: Jinhui Ye; Fangjing Wang; Ning Gao; Junqiu Yu; Zhu Yangkun; Bin Wang; Jinyu Zhang; Weiyang Jin; Yanwei Fu; Feng Zheng; Yilun Chen; Jiangmiao Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large vision–language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks, where instructions must be transformed into low-level motor actions. We introduce SP-VLA, a dual-system **V**ision–**L**anguage–**A**ction framework that leverages **S**patial **P**riors as a bridge between linguistic instructions and embodiment-specific control. 
introduce SP-VLA aligns action learning with spatial priors through two stages: (i) spatial grounding pre-training, which equips the VLM with transferable priors via scalable point, box, and trajectory prediction from both web-scale and robot-specific data, and (ii) spatially guided action post-training, which encourages the model to produce richer spatial priors to guide action generation via spatial prompting.
This design preserves spatial grounding during policy learning and promotes consistent optimization across spatial and action objectives. Empirically, introduce SP-VLA achieves substantial improvements over vanilla VLA, with performance increasing from $66.1{\rightarrow}84.6$ on Google Robot and from $54.7{\rightarrow}73.2$ on WidowX, establishing new state-of-the-art results on SimplerEnv. It also demonstrates stronger generalization to unseen objects and paraphrased instructions, as well as robustness to long-horizon perturbations in real-world settings. These results highlight scalable spatially guided training as a promising direction for robust, generalizable robot learning. We will release code, data, and model checkpoints to support future research.
See more visualization results at the anonymous page: https://sp-vla-anonymous.vercel.app
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Authors: Huanlin Gao; Ping Chen; Fuyuan Shi; Ruijia Wu; Li YanTao; Qiang Hui; Youyuren; Ting Lu; Chao Tan; Shaoan Zhao; Zhaoxiang Liu; Fang Zhao; Kai Wang; Shiguo Lian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g., feature caching), which often leads to severe trajectory deviations and error accumulation under high acceleration ratios. MeanCache introduces an average-velocity perspective: by leveraging cached Jacobian--vector products (JVP) to construct interval average velocities from instantaneous velocities, it effectively mitigates local error accumulation. To further improve cache timing and JVP reuse stability, we develop a trajectory-stability scheduling strategy as a practical tool, employing a Peak-Suppressed Shortest Path under budget constraints to determine the schedule. Experiments on FLUX.1, Qwen-Image, and HunyuanVideo demonstrate that MeanCache achieves $4.12\times$, $4.56\times$, and $3.59\times$ acceleration, respectively, while consistently outperforming state-of-the-art caching baselines in generation quality. We believe this simple yet effective approach provides a new perspective for Flow Matching inference and will inspire further exploration of stability-driven acceleration in commercial-scale generative models.
From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
Authors: Zhengshen Zhang; Hao Li; Yalun Dai; Zhengbang Zhu; Lei Zhou; Chenchen Liu; Dong Wang; Francis E. H. Tay; Sijin Chen; Ziwei Liu; Yuxiao Liu; Xinghang Li; Pan Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce **FALCON (From Spatial to Action)**, a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an *Embodied Spatial Model* that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a *Spatial-Enhanced Action Head* rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height. Project page: https://falcon-vla.github.io/
The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM
Authors: Kwanhee Lee; Hyeondo Jang; Dongyeop Lee; Dan Alistarh; Namhoon Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural network pruning is a promising technique to mitigate the excessive computational and memory requirements of large language models (LLMs).
Despite its promise, however, progress in this area has diminished, as conventional methods are seemingly unable to surpass moderate sparsity levels (50-60\%) without severely degrading model accuracy.
This work breaks through the current impasse, presenting a principled and effective method called $ \text{Elsa}$, which achieves extreme sparsity levels of up to 90\% while retaining high model fidelity.
This is done by identifying several limitations in current practice, all of which can be traced back to their reliance on a surrogate objective formulation.
$ \text{Elsa}$ tackles this issue directly and effectively via standard and well-established constrained optimization techniques based on ADMM.
Our extensive experiments across a wide range of models and scales show that $ \text{Elsa}$ achieves substantial improvements over existing methods;
e.g., it achieves 7.8$ \times$ less perplexity than the best existing method on LLaMA-2-7B at 90\% sparsity.
Moreover, we show that $ \text{Elsa}$ remains stable even at extreme sparsity (e.g., 95\%), yielding up to $\times$3.98 inference speedup and $\times$7.80 memory compression over its dense counterpart.
We also present $ \text{Elsa}_ {-L}$, a quantized variant that scales to extremely large models (27B), and establish its theoretical convergence guarantees.
These results highlight meaningful progress in advancing the frontier of LLM sparsity, while promising that significant opportunities for further advancement may remain in directions that have so far attracted limited exploration.
Joint Distribution–Informed Shapley Values for Sparse Counterfactual Explanations
Authors: Lei You; Yijun Bian; Lele Cao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Counterfactual explanations (CE) aim to reveal how small input changes flip a model’s prediction, yet many methods modify more features than necessary, reducing clarity and actionability. We introduce COLA, a model- and generator-agnostic post-hoc framework that refines any given CE by computing a coupling via optimal transport (OT) between factual and counterfactual sets and using it to drive a Shapley-based attribution p-SHAP that selects a minimal set of edits while preserving the target effect. Theoretically, OT minimizes an upper bound on the $W_1$ divergence between factual and counterfactual outcomes and that, under mild conditions, refined counterfactuals are guaranteed not to move farther from the factuals than the originals. Empirically, across four datasets, twelve models, and five CE generators, COLA achieves the same target effects with only 26–45% of the original feature edits. On a small-scale benchmark, COLA shows near-optimality.
DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics
Authors: Yuanhang Lei; Boming Zhao; Zesong Yang; Xingxuan Li; Tao Cheng; Haocheng Peng; Ru Zhang; Yang Yang; Siyuan Huang; Yujun Shen; Ruizhen Hu; Hujun Bao; Zhaopeng Cui
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling wind-driven object dynamics from video observations is highly challenging due to the invisibility and spatio–temporal variability of wind, as well as the complex deformations of objects. We present DiffWind, a physics-informed differentiable framework that unifies wind–object interaction modeling, video-based reconstruction, and forward simulation. Specifically, we represent wind as a grid-based physical field and objects as particle systems derived from 3D Gaussian Splatting, with their interaction modeled by the Material Point Method (MPM). To recover wind-driven object dynamics, we introduce a reconstruction framework that jointly optimizes the spatio–temporal wind force field and object motion through differentiable rendering and simulation. To ensure physical validity, we incorporate the Lattice Boltzmann Method (LBM) as a physics-informed constraint, enforcing compliance with fluid dynamics laws. Beyond reconstruction, our method naturally supports forward simulation under novel wind conditions and enable new applications such as wind retargeting. We further introduce WD-Objects, a dataset of synthetic and real-world wind-driven scenes. Extensive experiments demonstrate that our method significantly outperforms prior dynamic scene modeling approaches in both reconstruction accuracy and simulation fidelity, opening a new avenue for video-based wind–object interaction modeling. The project page is available at: [https://zju3dv.github.io/DiffWind/](https://zju3dv.github.io/DiffWind/).
Mitigating Noise Shift in Denoising Generative Models with Noise Awareness Guidance
Authors: Jincheng Zhong; Boyuan Jiang; Xin Tao; Pengfei Wan; Kun Gai; Mingsheng Long
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing denoising generative models rely on solving discretized reverse-time SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue in this family of models: a misalignment between the pre-defined noise level and the actual noise level encoded in intermediate states during sampling. We refer to this misalignment as noise shift. Through empirical analysis, we demonstrate that noise shift is widespread in modern diffusion models and exhibits a systematic bias, leading to sub-optimal generation due to both out-of-distribution generalization and inaccurate denoising updates. To address this problem, we propose Noise Awareness Guidance (NAG), a simple yet effective correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule. We further introduce a classifier-free variant of NAG, which jointly trains a noise-conditional and a noise-unconditional model via noise-condition dropout, thereby eliminating the need for external classifiers. Extensive experiments, including ImageNet generation and various supervised fine-tuning tasks, show that NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models. Code is publicly available at https://github.com/KlingAIResearch/noise-awareness-guidance.