GenAI4ELab/papercli-papers huggingface dataset:
Towards One-step Causal Video Generation via Adversarial Self-Distillation
Authors: Yongqi Yang; Huayang Huang; Xu Peng; Xiaobin Hu; Donghao Luo; Jiangning Zhang; Chengjie Wang; Yu Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads to error accumulation and long inference times. In this work, we propose a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extreme limited denoising steps. Our approach builds upon Distribution Matching Distillation (DMD) framework and proposes a novel form of Adversarial Self-Distillation (ASD) strategy, which aligns the outputs of the student model's $n$-step denoising process with its $(n+1)$-step version in the distribution level. This design provides smoother supervision by bridging small intra-student gaps and more informative guidance by combining teacher knowledge with locally consistent student behavior, substantially improving training stability and generation quality in extremely few-step scenarios. In addition, we present a First-Frame Enhancement (FFE) strategy, which allocates more denoising steps to the initial frames to mitigate error propagation while applying larger skipping steps to later frames. Extensive experiments on VBench demonstrate that our method surpasses state-of-the-art approaches in both one-step and two-step video generation. Notably, our framework produces a single distilled model that flexibly supports multiple inference-step settings, eliminating the need for repeated re-distillation and enabling efficient, high-quality video synthesis.
Urban Socio-Semantic Segmentation with Vision-Language Reasoning
Authors: Yu Wang; Yi Wang; Rui Dai; Yujie Wang; Kaikui Liu; Xiangxiang Chu; Yansheng Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As hubs of human activity, urban surfaces consist of a wealth of semantic entities. Segmenting these various entities from satellite imagery is crucial for a range of downstream applications. Current advanced segmentation models can reliably segment entities defined by physical attributes (e.g., buildings, water bodies) but still struggle with socially defined categories (e.g., schools, parks). In this work, we achieve socio-semantic segmentation by vision-language model reasoning. To facilitate this, we introduce the Urban Socio-Semantic Segmentation dataset named SocioSeg, a new resource comprising satellite imagery, digital maps, and pixel-level labels of social semantic entities organized in a hierarchical structure. Additionally, we propose a novel vision-language reasoning framework called SocioReasoner that simulates the human process of identifying and annotating social semantic entities via cross-modal recognition and multi-stage reasoning. We employ reinforcement learning to optimize this non-differentiable process and elicit the reasoning capabilities of the vision-language model. Experiments demonstrate our approach's gains over state-of-the-art models and strong zero-shot generalization. The dataset and code are open-sourced under the Apache License 2.0 at https://github.com/AMAP-ML/SocioReasoner.
Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
Authors: Arto Maranjyan; Peter Richtárik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Asynchronous stochastic gradient methods are central to scalable distributed optimization, particularly when devices differ in computational capabilities. Such settings arise naturally in federated learning, where training takes place on smartphones and other heterogeneous edge devices. In addition to varying computation speeds, these devices often hold data from different distributions. However, existing asynchronous SGD methods struggle in such heterogeneous settings and face two key limitations. First, many rely on unrealistic assumptions of similarity across workers' data distributions. Second, methods that relax this assumption still fail to achieve theoretically optimal performance under heterogeneous computation times. We introduce Ringleader ASGD, the first asynchronous SGD algorithm that attains the theoretical lower bounds for parallel first-order stochastic methods in the smooth nonconvex regime, thereby achieving optimal time complexity under data heterogeneity and without restrictive similarity assumptions. Our analysis further establishes that Ringleader ASGD remains optimal under arbitrary and even time-varying worker computation speeds, closing a fundamental gap in the theory of asynchronous optimization.
RAR: Reversing Visual Attention Re-Sinking for  Unlocking Potential in Multimodal Large Language Models
Authors: Zhehan Kan; Xin Li; Yanlin Liu; Xiaochen Yang; Xinghua Jiang; Yinsong Liu; Deqiang Jiang; Xing Sun; Qingmin Liao; Wenming Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet they frequently exhibit suboptimal output layers, where intermediate decoder layers outperform the final ones, signaling underutilized model capacity. In this work, we delve into the root causes and attribute this issue to the Visual Attention Re-sinking phenomenon, precipitated by attention gradient sparsity driven by textual supervision dominance. This degradation causes attention heads to evolve into sink heads that prioritize low-semantic backgrounds, thereby disrupting modality fusion, neglecting visual information, and biasing outputs toward textual priors, ultimately impairing model performance. To mitigate this, we introduce a parameter-free Sink Attention Dynamic Sparsification (SADS) framework that dynamically identifies and retains all vision heads(concentrating visual attention on semantically relevant regions) while sparsifying sink heads, preserving essential global context through a shared head. Integrated into diverse MLLMs, our framework yields substantial performance gains across 20 benchmarks spanning five task categories (visual grounding, general VQA, OCR-related VQA, vision-centric tasks, and visual hallucination mitigation) surpassing supervised fine-tuning while boosting inference speed by 10.3\%. This approach offers a novel avenue for maximizing MLLMs capabilities.
SSVPO: Effective Step-Level Credit Assignment for RL Training of Language Models
Authors: Yugu Li; Zehong Cao; Jianglin Qiao; Siyi Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Language models have shown strong performance on mathematical reasoning tasks. Post-training with outcome-based reinforcement learning (RL) can further enhance reasoning but is inefficient because it relies solely on final rewards. Recent credit assignment–based RL methods provide intermediate feedback, yet they often struggle to fairly evaluate each step’s importance, especially in partially correct reasoning chains. We propose Sequential Shapley Value Policy Optimization (SSVPO), a step-level credit assignment framework inspired by multi-agent RL. SSVPO introduces an insertion MDP and Sequential Shapley Values (SSV), which measure each step’s marginal contribution by reordering reasoning steps into alternative chains, ensuring fair credit assignment to all possible steps. By identifying steps with zero credit, SSVPO can shorten reasoning chains to improve training efficiency. We further provide a theoretical proof that SSV fairness to allocate credits and demonstrate that SSV as the new advantage baseline is consistent with Proximal Policy Optimization (PPO). Across 7 benchmarks, SSVPO outperforms state-of-the-art RL methods, both outcome-based (RLOO, GRPO, DAPO) and credit assignment–based (VinePPO, SPO), achieving up to an 11.6\% gain in accuracy, an 18.1\% reduction in token usage, and a 1.6× improvement in reasoning efficiency over vanilla methods. Our findings highlight that SSVPO provides effective step-level credit assignment, advancing post-training LLM reasoning performance while reducing token budgets.
SAQ: Stabilizer-Aware Quantum Error Correction Decoder
Authors: David Zenati; Eliya Nachmani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Quantum Error Correction (QEC) decoding faces a fundamental accuracy-efficiency tradeoff. Classical methods like Minimum Weight Perfect Matching (MWPM) exhibit variable performance across noise models and suffer from polynomial complexity, while tensor network decoders achieve high accuracy but at prohibitively high computational cost. Recent neural decoders reduce complexity but lack the accuracy needed to compete with computationally expensive classical methods. We introduce SAQ-Decoder, a unified framework combining transformer-based learning with constraint aware post-processing that achieves both near Maximum Likelihood (ML) accuracy and linear computational scalability with respect to the syndrome size. Our approach combines a dual-stream transformer architecture that processes syndromes and logical information with asymmetric attention patterns, and a novel differentiable logical loss that directly optimizes Logical Error Rates (LER) through smooth approximations over finite fields. 
SAQ-Decoder achieves high accuracy decoding, with error thresholds of 10.99\% (independent noise) and 18.6\% (depolarizing noise) on toric codes that closely approach the theoretical ML bounds of 11.0\% and 18.9\% while outperforming existing neural and classical baselines in accuracy, complexity, and parameter efficiency. Our findings establish that learned decoders can simultaneously achieve competitive decoding accuracy and computational efficiency, addressing key requirements for practical fault-tolerant quantum computing systems.
BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models
Authors: Ziheng Zhang; Xinyue Ma; Arpita Chowdhury; Elizabeth G Campolongo; Matthew J Thompson; Net Zhang; Samuel Stevens; Hilmar Lapp; Tanya Berger-Wolf; Yu Su; Wei-Lun Chao; Jianyang Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This work investigates descriptive captions as an additional source of supervision for biological multimodal foundation models. Images and captions can be viewed as complementary samples from the latent morphospace of a species, each capturing certain biological traits. Incorporating captions during training encourages alignment with this shared latent structure, emphasizing potentially diagnostic characters while suppressing spurious correlations. The main challenge, however, lies in obtaining faithful, instance-specific captions at scale. This requirement has limited the utilization of natural language supervision in organismal biology compared with many other scientific domains. We complement this gap by generating synthetic captions with multimodal large language models (MLLMs), guided by Wikipedia-derived visual information and taxon-tailored format examples. These domain-specific contexts help reduce hallucination and yield accurate, instance-based descriptive captions. Using these captions, we train BioCAP (i.e., BioCLIP with Captions), a biological foundation model that captures rich semantics and achieves strong performance in species classification and text-image retrieval. These results demonstrate the value of descriptive captions beyond labels in bridging biological images with multimodal foundation models
SAES-SVD: Self-Adaptive Suppression of Accumulated and Local Errors for SVD-based LLM Compression
Authors: Xing Hu; Dawei Yang; Yuan Cheng; Zhixuan Chen; Zukang Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid growth in the parameter scale of large language models (LLMs) has created a high demand for efficient compression techniques. 
As a hardware-agnostic and highly compatible technique, low-rank compression has been widely adopted. However, existing methods typically compress each layer independently by minimizing per-layer reconstruction error, overlooking a critical limitation: the reconstruction error propagates and accumulates through the network, which leads to amplified global deviations from the full-precision baseline.
To address this, we propose **Self-Adaptive Error Suppression SVD (SAES-SVD)**, a LLMs compression framework that jointly optimizes intra-layer reconstruction and inter-layer error compensation.
SAES-SVD is composed of two novel components:
**Cumulative Error-Aware Layer Compression (CEALC),**  which formulates the compression objective as a combination of local reconstruction and weighted cumulative error compensation. Based on it, we derive a closed-form low-rank solution relied on second-order activation statistics, which explicitly aligns each layer's output with its full-precision counterpart to compensate for accumulated errors.
\ding{183} Adaptive Collaborative Error Suppression (ACES), which automatically adjusts the weighting coefficient to enhance the low-rank structure of the compression objective in CELAC. Specifically, the coefficient is optimized to maximize the ratio between the Frobenius norm of the compressed layer's output and that of the compression objective under a fixed rank, thus ensuring that the rank budget is utilized effectively.
Extensive experiments across multiple LLM architectures and tasks show that, without fine-tuning or additional tricks, SAES-SVD consistently improves post-compression performance. For example, at a 0.2 compression ratio on LLaMA-7B, existing methods exhibit an average accuracy drop exceeding 0.05, whereas SAES-SVD restricts the drop to only 0.02. These improvements underscore the potential of SAES-SVD to effectively narrow the gap between compressed models and their full-precision counterparts, paving the way for more reliable compression of LLMs.
ARTDECO: Toward High-Fidelity On-the-Fly Reconstruction with Hierarchical Gaussian Structure and Feed-Forward Guidance
Authors: Guanghao Li; Kerui Ren; Linning Xu; Zhewen Zheng; Changjian Jiang; Xin Gao; Bo Dai; Jian Pu; Mulin Yu; Jiangmiao Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: On-the-fly 3D reconstruction from monocular image sequences is a long-standing challenge in computer vision, critical for applications such as real-to-sim, AR/VR, and robotics. Existing methods face a major tradeoff: per-scene optimization yields high fidelity but is computationally expensive, whereas feed-forward foundation models enable real-time inference but struggle with accuracy and robustness. In this work, we propose ARTDECO, a unified framework that combines the efficiency of feed-forward models with the reliability of SLAM-based pipelines. ARTDECO uses 3D foundation models for pose estimation and point prediction, coupled with a Gaussian decoder that transforms multi-scale features into structured 3D Gaussians. To sustain both fidelity and efficiency at scale, we design a hierarchical Gaussian representation with a LoD-aware rendering strategy, which improves rendering fidelity while reducing redundancy. Experiments on eight diverse indoor and outdoor benchmarks show that ARTDECO delivers interactive performance comparable to SLAM, robustness similar to feed-forward systems, and reconstruction quality close to per-scene optimization, providing a practical path toward on-the-fly digitization of real-world environments with both accurate geometry and high visual fidelity. Project page: https://city-super.github.io/artdeco/
Beyond Scattered Acceptance: Fast and Coherent Inference for DLMs via Longest Stable Prefixes
Authors: Pengxiang Li; Jiayin Cai; Hongwei Xue; Kunyu Shi; Shilin Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Language Models (DLMs) promise highly parallel text generation, yet their practical inference speed is often bottlenecked by suboptimal decoding schedulers. Standard approaches rely on ``scattered acceptance''---committing high-confidence tokens at disjoint positions throughout the sequence. This approach inadvertently fractures the Key-Value (KV) cache, destroys memory locality, and forces the model into costly, repeated repairs across unstable token boundaries. To resolve this, we present the \textbf{Longest Stable Prefix (LSP)} scheduler, a training-free and model-agnostic inference paradigm based on \textit{monolithic prefix absorption}. In each denoising step, LSP evaluates token stability via a single forward pass, dynamically identifies a contiguous left-aligned block of stable predictions, and snaps its boundary to natural linguistic or structural delimiters before an atomic commitment. This prefix-first topology yields dual benefits: systemically, it converts fragmented KV cache updates into efficient, contiguous appends; algorithmically, it preserves bidirectional lookahead over a geometrically shrinking active suffix, drastically reducing token flip rates and denoiser calls. Extensive evaluations on LLaDA-8B and Dream-7B demonstrate that LSP accelerates inference by up to 3.4$\times$ across rigorous benchmarks---including mathematical reasoning, code generation, multilingual (CJK) tasks, and creative writing---while matching or slightly improving output quality. By fundamentally restructuring the commitment topology, LSP bridges the gap between the theoretical parallelism of DLMs and practical hardware efficiency.
Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance
Authors: Yujie Wei; Shiwei Zhang; Hangjie Yuan; Yujin Han; Zhekai Chen; Jiayu Wang; Difan Zou; Xihui Liu; Yingya Zhang; Yu Liu; Hongming Shan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model capacity while preserving computational efficiency. Despite its notable success in large language models (LLMs), existing attempts to apply MoE to Diffusion Transformers (DiTs) have yielded limited gains. We attribute this gap to fundamental differences between language and visual tokens. Language tokens are semantically dense with pronounced inter-token variation, while visual tokens exhibit spatial redundancy and functional heterogeneity, hindering expert specialization in vision MoE. To this end, we present $\textbf{ProMoE}$, an MoE framework featuring a two-step router with explicit routing guidance that promotes expert specialization. Specifically, this guidance encourages the router to $\textit{first}$ partition image tokens into conditional and unconditional sets via conditional routing according to their functional roles, and $\textit{second}$ refine the assignments of conditional image tokens through prototypical routing with learnable prototypes based on semantic content. Moreover, the similarity-based expert allocation in latent space enabled by prototypical routing offers a natural mechanism for incorporating explicit semantic guidance, and we validate that such guidance is crucial for vision MoE. Building on this, we propose a routing contrastive loss that explicitly enhances the prototypical routing process, promoting intra-expert coherence and inter-expert diversity. Extensive experiments on ImageNet benchmark demonstrate that ProMoE surpasses state-of-the-art methods under both Rectified Flow and DDPM training objectives. Code and models will be made publicly available.
IVC-Prune: Revealing the Implicit Visual Coordinates in LVLMs for Vision Token Pruning
Authors: Zhichao Sun; Yidong Ma; Gang Liu; Nemo Chen; Xu Tang; Yao Hu; Yongchao Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Vision-Language Models (LVLMs) achieve impressive performance across multiple tasks. A significant challenge, however, is their prohibitive inference cost when processing high-resolution visual inputs. While visual token pruning has emerged as a promising solution, existing methods that primarily focus on semantic relevance often discard tokens that are crucial for spatial reasoning. We address this gap through a novel insight into how LVLMs process spatial reasoning. Specifically, we reveal that LVLMs implicitly establish visual coordinate systems through Rotary Position Embeddings (RoPE), where specific token positions serve as implicit visual coordinates (IVC tokens) that are essential for spatial reasoning. Based on this insight, we propose IVC-Prune, a training-free, prompt-aware pruning strategy that retains both IVC tokens and semantically relevant foreground tokens. IVC tokens are identified by theoretically analyzing the mathematical properties of RoPE, targeting positions at which its rotation matrices approximate identity matrix or the $90^\circ$ rotation matrix. Foreground tokens are identified through a robust two-stage process: semantic seed discovery followed by contextual refinement via value-vector similarity.  Extensive evaluations across four representative LVLMs and twenty diverse benchmarks show that IVC-Prune reduces visual tokens by approximately 50\% while maintaining $\geq$ 99\% of the original performance and even achieving improvements on several benchmarks.
Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Authors: Hao Tan; jun lan; Zichang Tan; Senyuan Shi; Ajian Liu; Chuanbiao Song; Huijia Zhu; Weiqiang Wang; Jun Wan; Zhen Lei
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Deepfake detection remains a formidable challenge due to the evolving nature of fake content in real-world scenarios. However, existing benchmarks suffer from severe discrepancies from industrial practice, typically featuring homogeneous training sources and low-quality testing images, which hinder the practical usage of current detectors. To mitigate this gap, we introduce **HydraFake**, a dataset that contains diversified deepfake techniques and in-the-wild forgeries, along with rigorous training and evaluation protocol, covering unseen model architectures, emerging forgery techniques and novel data domains. Building on this resource, we propose **Veritas**, a multi-modal large language model (MLLM) based deepfake detector. Different from vanilla chain-of-thought (CoT), we introduce *pattern-aware reasoning* that involves critical patterns such as "planning" and "self-reflection" to emulate human forensic process. We further propose a two-stage training pipeline to seamlessly internalize such deepfake reasoning capacities into current MLLMs. Experiments on HydraFake dataset reveal that although previous detectors show great generalization on cross-model scenarios, they fall short on unseen forgeries and data domains. Our Veritas achieves significant gains across different out-of-domain (OOD) scenarios, and is capable of delivering transparent and faithful detection outputs.