GenAI4ELab/papercli-papers huggingface dataset:
String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation
Authors: Kou Misaki; Takuya Akiba
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce _String Seed of Thought (SSoT)_, a novel prompting method for LLMs that improves _Probabilistic Instruction Following (PIF)_. We define PIF as a task requiring an LLM to select its answer from a predefined set of options, each associated with a specific probability, such that the empirical distribution of the generated answers aligns with the target distribution when prompted multiple times. While LLMs excel at tasks with single, deterministic answers, they often fail at PIF, exhibiting biases problematic for applications requiring non-deterministic behaviors, such as human-behavior simulation, content diversification, and multiplayer games.
It also harms the diversity of generated responses, a crucial factor in test-time scaling, by causing the outputs to collapse into a limited set of answers. To address this, we propose SSoT, a simple prompting method that instructs an LLM to first output a random string to generate sufficient entropy. SSoT also instructs the LLM to extract randomness by manipulating this string to derive a final answer, thereby preserving diversity while adhering to specific constraints. We demonstrate that SSoT significantly improves the PIF performance of LLMs, approaching the ideal performance of a pseudo-random number generator. Notably, our experiments on NoveltyBench show SSoT's benefits extend beyond closed-set tasks to open-ended tasks by enhancing response diversity.
Riemannian Variational Flow Matching for Material and Protein Design
Authors: Olga Zaghen; Floor Eijkelboom; Alison Pouplin; Cong Liu; Max Welling; Jan-Willem van de Meent; Erik J Bekkers
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the benefits of VFM, we derive a variational flow matching objective for manifolds with closed-form geodesics based on Riemannian Gaussian distributions. Crucially, in Euclidean space, predicting endpoints (VFM), velocities (FM), or noise (diffusion) is largely equivalent due to affine interpolations. However, on curved manifolds this equivalence breaks down. We formally analyze the relationship between our model and Riemannian Flow Matching (RFM), revealing that the RFM objective lacks a curvature-dependent penalty -- encoded via Jacobi fields -- that is naturally present in RG-VFM. Based on this relationship, we hypothesize that endpoint prediction provides a stronger learning signal by directly minimizing geodesic distances. Experiments on synthetic spherical and hyperbolic benchmarks, as well as real-world tasks in material and protein generation, demonstrate that RG-VFM more effectively captures manifold structure and improves downstream performance over Euclidean and velocity-based baselines.
WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving
Authors: Ziyue Zhu; Zhanqian Wu; Zhenxin Zhu; Lijun Zhou; Haiyang Sun; BING WANG; Kun Ma; Guang Chen; Hangjun Ye; Jin Xie; jian Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in driving-scene generation and reconstruction have demonstrated significant potential for enhancing autonomous driving systems by producing scalable and controllable training data. Existing generation methods primarily focus on synthesizing diverse and high-fidelity driving videos; however, due to limited 3D consistency and sparse viewpoint coverage, they struggle to support convenient and high-quality novel-view synthesis (NVS). Conversely, recent 3D/4D reconstruction approaches have significantly improved NVS for real-world driving scenes, yet inherently lack generative capabilities. To overcome this dilemma between scene generation and reconstruction, we propose \textbf{WorldSplat}, a novel feed-forward framework for 4D driving-scene generation. Our approach effectively generates consistent multi-track videos through two key steps: ((i)) We introduce a 4D-aware latent diffusion model integrating multi-modal information to produce pixel-aligned 4D Gaussians in a feed-forward manner. ((ii)) Subsequently, we refine the novel view videos rendered from these Gaussians using a enhanced video diffusion model. Extensive experiments conducted on benchmark datasets demonstrate that \textbf{WorldSplat} effectively generates high-fidelity, temporally and spatially consistent multi-track novel view driving videos.
OrchestrationBench: LLM-Driven Agentic Planning and Tool Use in Multi-Domain Scenarios
Authors: Aelim Ahn; Sooyeon Lee; Hyosun Wang; Chiwan Park; Daeryong Kim; Jihyeon Roh; Kichang Yang; Wonjun Jang; Hwang Woosung; Min Seok Kim; Jihoon kang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent progress in Large Language Models (LLMs) has transformed them from text generators into agentic systems capable of multi-step reasoning, structured planning, and tool use. However, existing benchmarks inadequately capture their ability to orchestrate complex workflows across multiple domains under realistic constraints. To address this, we propose OrchestrationBench, a bilingual (English/Korean) benchmark that systematically evaluates (1) workflow-based planning and (2) constraint-aware tool execution. OrchestrationBench spans 17 representative domains with nearly 100 realistic virtual tools, covering scenarios that require sequential/parallel planning and compliance with business constraints. Unlike previous work, it explicitly disentangles planning evaluation from tool execution evaluation, which assesses tool selection, argument extraction, validation, and rejection handling. Constructed entirely through manual annotation with cultural adaptation, the benchmark ensures authenticity, diversity, and freedom from model-specific biases. Extensive experiments across state-of-the-art models show that function calling performance is relatively consistent, whereas planning capabilities exhibit substantial variation across models, emphasizing the need for structured planning evaluation. As a living benchmark, OrchestrationBench is designed to expand toward new domains, tools, and integration enabling rigorous, cross-cultural, and service-ready evaluation of LLM orchestration capabilities. The benchmark is publicly available.
SafeMPO: Constrained Reinforcement Learning with Probabilistic Incremental Improvement
Authors: Alexander Mattick; Dominik Seuß; Christopher Mutschler
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning (RL) has demonstrated significant success in optimizing complex control and planning problems. However, scaling RL to real-world applications with multiple, potentially conflicting requirements requires an effective handling of constraints. We propose a novel approach to constraint satisfaction in RL algorithms, focusing on incrementally improving policy safety rather than directly projecting the policy onto a feasible region. We accomplish this by first solving a nonparametric surrogate problem which is guaranteed to contract towards the feasible set, and then cloning that solution into a neural network policy. As a result, our approach improves stability, particularly during early training stages, when the policy lacks knowledge of constraint boundaries. We provide general theoretical results guaranteeing convergence to the safe set for this class of incremental systems. Notably, even the simplest algorithm produced by our theory produces comparable or superior performance when compared to highly tuned constrained RL baselines in challenging constrained environments.
ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
Authors: Yichao Liang; Thanh Dat Nguyen; Cambridge Yang; Tianyang Li; Joshua B. Tenenbaum; Carl Edward Rasmussen; Adrian Weller; Zenna Tavares; Tom Silver; Kevin Ellis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-horizon embodied planning is challenging because the world does not only change through an agent's actions: exogenous processes (e.g., water heating, dominoes cascading) unfold concurrently with the agent's actions. We propose a framework for abstract world models that jointly learns (i) symbolic state representations and (ii) causal processes for both endogenous actions and exogenous mechanisms. Each causal process models the time course of a stochastic cause-effect relation. We learn these world models from limited data via variational Bayesian inference combined with LLM proposals. Across five simulated tabletop robotics environments, the learned models enable fast planning that generalizes to held-out tasks with more objects and more complex goals, outperforming a range of baselines.
A Noise is Worth Diffusion Guidance
Authors: Donghoon Ahn; Jiwon Kang; Sanghyun Lee; Jaewon Min; Minjae Kim; Wooseok Jang; Hyoungwon Cho; Sayak Paul; SeonHwa Kim; Eunju Cha; Kyong Hwan Jin; Seungryong Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have demonstrated remarkable image generation capabilities, but their performance heavily relies on sampling guidance such as classifier-free guidance (CFG). While sampling guidance significantly enhances image quality, it requires two forward passes at every denoising step, leading to substantial computational overhead. Existing approaches mitigate this cost through distillation, training a student network to learn the guided predictions. In contrast, we take a distinct approach by refining the initial Gaussian noise, a critical yet under-explored factor in the diffusion-based generation pipelines. 
We introduce a noise refinement framework, NoiseRefine, where a refining network is trained to minimize the difference between images generated by unguided sampling from the refined noise and those produced by guided sampling from the input Gaussian noise.
This simple approach demonstrates that images from the refined noise alleviate artifacts and mitigate structural collapse, achieving significantly higher quality than those generated from pure Gaussian noise without modifying the diffusion model, thereby preserving its prior knowledge and compatibility with finetuned or timestep distilled variants. 
Beyond its practical benefits, we provide an in-depth analysis of refined noise, offering insights into its role in the denoising process and its interaction with guidance. Our findings suggest that structured noise initialization is key to efficient and high-fidelity image synthesis. 
Project page: https://cvlab-kaist.github.io/NoiseRefine/
SPECS: Decoupling Multimodal Learning via  Self-distilled Preference-based Cold Start
Authors: Kun Chen; Peng Shi; Haibo Qiu; Zhixiong Zeng; Siqi Yang; Wenji Mao; Lin Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently catalyzed a wave of "MLLM-r1" approaches that bring RL to vision language models. Most representative paradigms begin with a cold start, typically employing supervised fine-tuning (SFT), to initialize the policy before RL. However, SFT-based cold start adopts the reasoning paradigm intertwined with task solution and output format, which may induce instruction-style overfitting, weakens out-of-distribution generalization, and ultimately affects downstream RL. We revisit the cold start along two views, its training method and data construction, and introduce the Generalization Factor (GF) coefficient to quantify the generalization capability under different methods. Our empirical study finds that preference–based training methods (e.g. DPO) generalizes better than SFT-based methods in cold start. Motivated by this, we propose $\textbf{SPECS}$—a $\textbf{S}$elf-distilled, $\textbf{P}$r$\textbf{e}$ference-based $\textbf{C}$old $\textbf{S}$tart framework that decouples multimodal learning: (1) generates introspective preference data pairs via self-distillation, avoiding reliance on larger teachers or manual annotation; (2) performs preference–based training to learn, focusing on shallow, transferable surface-form criteria (format, structure, style) rather than memorizing content; and (3) hands off to RLVR for deep reasoning results. Experimental results across multiple multimodal benchmarks show that our decoupling learning framework yields consistent performance gains over strong baselines, improving MEGA-Bench by 4.1\% and MathVista by 12.2\%. Additional experiments indicate that SPECS contributes to reducing in-distribution "stuckness," improving exploration, stabilizing training, and raising the performance ceiling. 

Project Page: https://kwen-chen.github.io/SPECS-VL/
Video Unlearning via Low-Rank Refusal Vector
Authors: Simone Facchiano; Stefano Saravalle; Matteo Migliarini; Edoardo De Matteis; Alessio Sampieri; Andrea Pilzer; Emanuele Rodolà; Indro Spinelli; Luca Franco; Fabio Galasso
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video generative models achieve high-quality synthesis from natural-language prompts by leveraging large-scale web data. However, this training paradigm inherently exposes them to unsafe biases and harmful concepts, introducing the risk of generating undesirable or illicit content. To mitigate unsafe generations, existing machine unlearning approaches either rely on filtering, and can therefore be bypassed, or they update model weights, but with costly fine-tuning or training-free closed-form edits. We propose the first training-free weight update framework for concept removal in video diffusion models.
From five paired safe/unsafe prompts, our method estimates a refusal vector and integrates it into the model weights as a closed-form update. A contrastive low-rank factorization further disentangles the target concept from unrelated semantics, it ensures a selective concept suppression and it does not harm generation quality. Our approach reduces unsafe generations on the Open-Sora and ZeroScopeT2V models across the T2VSafetyBench and SafeSora benchmarks, with average reductions of 36.3% and 58.2% respectively, while preserving prompt alignment and video quality. This establishes an efficient and scalable solution for safe video generation without retraining nor any inference overhead.
Test-Time Optimization of 3D Point Cloud LLM via Manifold-Aware In-Context Guidance and Refinement
Authors: Tiankai Chen; Nanqing Liu; Li Yang; Xulei Yang; Tianrui Li; Xun Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in textual and 2D visual reasoning, yet their ability to understand and reason over 3D data remains limited. The issues become more challenging for understanding standalone 3D point cloud due to the high interclass confusion. In this work, we propose Point-Graph LLM (PGLLM), a framework that enables more effective 3D point cloud understanding by integrating in-context prompting and score refinement at test-time, respecting supporting data manifold. Our method first employs a pre-trained point cloud encoder which are used to construct a graph where edges encode visual similarity. Each support point cloud sample is converted to a textual caption via pre-trained PointLLM. For a test query, the graph is used to retrieve relevant neighbors whose captions serve as contextual demonstrations for a second stage LLM for final reasoning, a process we term in-context guidance. Furthermore, we introduce a confidence score refinement mechanism based on label propagation to enhance the reliability of LLM predictions for classification and out-of-distribution (OOD) detection tasks. All the above optimizations are carried out fully at test-time. Extensive experiments across diverse 3D datasets and tasks demonstrate that PGLLM consistently improves accuracy and robustness over prior baselines with very almost no additional computation cost, showcasing a promising direction toward native 3D reasoning with MLLMs.
SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual Dyadic Interactive Human Generation
Authors: Youliang Zhang; Zhaoyang Li; Duomin Wang; jiahe zhang; Deyu Zhou; Zixin Yin; Xili Dai; Gang YU; Xiu Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid development of large-scale models has catalyzed significant breakthroughs in the digital human domain. These advanced methodologies offer high-fidelity solutions for avatar driving and rendering, leading academia to focus on the next major challenge: audio-visual dyadic interactive virtual human. To facilitate research in this emerging area, we present SpeakerVid-5M dataset, the first large-scale, high-quality dataset designed for audio-visual dyadic interactive virtual human generation. Totaling over $8,743$ hours, SpeakerVid-5M contains more than $5.2$ million video clips of human portraits. It covers diverse scales and interaction types, including monadic talking, listening, and dyadic conversations. Crucially, the dataset is structured along two key dimensions: interaction type and data quality. First, it is categorized into four types (dialogue branch, single branch, listening branch and multi-turn branch) based on the interaction scenario. Second, it is stratified into a large-scale pre-training subset and a curated, high-quality subset for Supervised Fine-Tuning (SFT). This dual structure accommodates a wide array of 2D virtual human tasks. In addition, we provide an autoregressive (AR)-based video chat baseline trained on this data, accompanied by a dedicated set of metrics and test data to serve as a benchmark (VidChatBench) for future work. Both the dataset and the corresponding data processing code will be publicly released.
PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting
Authors: Jiaming Ma; Qihe Huang; Haofeng Ma; Guanjun Wang; Sheng Huang; Zhengyang Zhou; Pengkun Wang; Xu Wang; Binwu Wang; Yang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While existing multivariate time series forecasting models have advanced significantly in modeling periodicity, they largely neglect the periodic heterogeneity common in real-world data, where variables exhibit distinct and dynamically changing periods. To effectively capture this periodic heterogeneity, we propose PHAT (Period Heterogeneity-Aware Transformer). Specifically, PHAT arranges multivariate inputs into a three-dimensional "periodic bucket" tensor, where the dimensions correspond to variable group characteristics with similar periodicity, time steps aligned by phase, and offsets within the period. By restricting interactions within buckets and masking cross-bucket connections, PHAT effectively avoids interference from inconsistent periods. We also propose a positive-negative attention mechanism, which captures periodic dependencies from two perspectives: periodic alignment and periodic deviation. Additionally, the periodic alignment attention scores are decomposed into positive and negative components, with a modulation term encoding periodic priors. This modulation constrains the attention mechanism to more faithfully reflect the underlying periodic trends. A mathematical explanation is provided to support this property. We evaluate PHAT comprehensively on 14 real-world datasets against 18 baselines, and the results show that it significantly outperforms existing methods, achieving highly competitive forecasting performance. Our sources is available at GitHub.
D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI
Authors: Suhwan Choi; Jaeyoon Jung; Haebin Seong; Minchan Kim; Minyeong Kim; Yongjun Cho; Yoonshik Kim; Park Yu Been; Youngjae Yu; Yunsung Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection.
Desktop environments---particularly gaming---offer a compelling alternative: they provide rich sensorimotor interactions at scale while maintaining the structured observation-action coupling essential for embodied learning.
We present D2E (Desktop to Embodied AI), a framework that demonstrates desktop interactions can serve as an effective pretraining substrate for robotics embodied AI tasks.
Unlike prior work that remained domain-specific (e.g., VPT for Minecraft) or kept data proprietary (e.g., SIMA), D2E establishes a complete pipeline from scalable desktop data collection to verified transfer in embodied domains.
Our framework comprises three components: (1) the OWA Toolkit that unifies diverse desktop interactions into a standardized format with 152× compression, (2) the Generalist-IDM that achieves strong zero-shot generalization across unseen games through timestamp-based event prediction, enabling internet-scale pseudo-labeling, and (3) VAPT that transfers desktop-pretrained representations to physical manipulation and navigation.
Using 1.3K+ hours of data (259 hours of human demonstrations and 1K+ hours of pseudo-labeled gameplay), our 1B-parameter model achieves 96.6\% success on LIBERO manipulation and 83.3\% on CANVAS navigation, matching or surpassing models up to 7$\times$ larger, such as $\pi_0$ (3.3B) and OpenVLA (7B).
These results demonstrate that sensorimotor primitives learned from digital interactions transfer effectively to real-world physical tasks, establishing desktop pretraining as a practical paradigm for embodied AI.
All resources are publicly available at https://worv-ai.github.io/d2e.
Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Authors: Bogdan Raonic; Siddhartha Mishra; Samuel Lanthaler
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across numerous scientific datasets, including PDE datasets, satellite imagery and brain tumor segmentation, we show that this likelihood strongly correlates with prediction error. Our work provides a foundational step towards building a verifiable 'certificate of trust', thereby offering a practical tool for assessing the trustworthiness of AI-based scientific predictions.
ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding
Authors: Yiran Guan; Sifan Tu; Dingkang Liang; Linghao Zhu; Jianzhong Ju; Zhenbo Luo; Jian Luan; Yuliang Liu; Xiang Bai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Omni-modal reasoning is essential for intelligent systems to understand and draw inferences from diverse data sources. While existing omni-modal large language models (OLLM) excel at perceiving diverse modalities, they lack the complex reasoning abilities of recent large reasoning models (LRM). However, enhancing the reasoning ability of OLLMs through additional training presents significant challenges, including the need for high-quality data, task-specific adaptation, and substantial computational costs. To address these limitations, we propose ThinkOmni, a training-free and data-free framework that lifts textual reasoning to omni-modal scenarios. ThinkOmni introduces two key components: 1) LRM-as-a-Guide, which leverages off-the-shelf LRMs to guide the OLLM decoding process; 2) Stepwise Contrastive Scaling, which adaptively balances perception and reasoning signals without manual hyperparameter tuning. Experiments on six multi-modal reasoning benchmarks demonstrate that ThinkOmni consistently delivers performance improvements, with main results achieving 70.2 on MathVista and 75.5 on MMAU. Overall, ThinkOmni offers a flexible and generalizable solution for omni-modal reasoning and provides new insights into the generalization and application of reasoning capabilities.