GenAI4ELab/papercli-papers huggingface dataset:
Compositional amortized inference for large-scale hierarchical Bayesian models
Authors: Jonas Arruda; Vikas Pandey; Catherine Sherry; Margarida Barroso; Xavier Intes; Jan Hasenauer; Stefan T. Radev
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Amortized Bayesian inference (ABI) with neural networks has emerged as a powerful simulation-based approach for estimating complex mechanistic models. 
However, extending ABI to hierarchical models, a cornerstone of modern Bayesian analysis, has been a major hurdle due to the need to simulate and process massive datasets. 
Our study tackles these challenges by extending compositional score matching (CSM), a divide-and-conquer strategy for Bayesian updating using diffusion models. 
We develop a new error-damping estimator to address previous stability issues of CSM when aggregating large numbers of data points. 
We first verified the numerical stability with up to 100,000 data points on a controlled benchmark. 
We then evaluated our method on a hierarchical AR model, achieving competitive performance to direct ABI baselines on smaller problem sizes while using less than one full model simulation for larger problem sizes. 
Finally, we address a large-scale inverse problem in advanced microscopy with over 750,000 parameters, demonstrating its relevance to real scientific applications.
PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
Authors: Junseo Hwang; Wonguk Cho; Taesup Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning large foundation models is essential for building expert models tailored to specialized tasks and domains, but fully updating billions of parameters is computationally prohibitive. Reducing the number of trainable parameters using Parameter-Efficient Fine-Tuning (PEFT), such as Low-Rank Adaptation (LoRA), is therefore crucial not only to reduce training costs but also to mitigate storage, caching, and serving overheads during deployment. Prior works, such as Singular Vectors-guided Fine-Tuning (SVFT), have shown that exploiting the geometry of pre-trained weights based on Singular Value Decomposition (SVD) can significantly improve parameter-efficiency, but they lack a solid theoretical foundation. In this paper, we introduce Parameter-Efficient Fine-Tuning with Column Space Projection (PiCa), a novel theoretically grounded PEFT method. We prove that projecting gradients onto the principal column space of pre-trained weights provides an effective inductive bias for adaptation and further enhance parameter efficiency through a novel weight-sharing strategy. Across diverse NLP and vision tasks, PiCa consistently outperforms state-of-the-art baselines under comparable or smaller parameter budgets, demonstrating both theoretical rigor and practical effectiveness.
LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
Authors: Jinwoo Ahn; Ingyu Seong; Akhil Kedia; Junhan Kim; Hyemi Jang; Kangwook Lee; Yongkweon Jeon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer-based large language models (LLMs) rely on key–value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the cache size grows linearly with the input sequence length, quickly becoming a bottleneck for long‑context tasks. Existing solutions mitigate this problem by evicting prompt KV that are deemed unimportant, guided by estimated importance scores. Notably, a recent line of work proposes to improve eviction quality by “glimpsing into the future”, in which a draft generator produces a surrogate future response approximating the target model's true response, and this surrogate is subsequently used to estimate the importance of cached KV more accurately. However, these approaches rely on computationally expensive draft generation, which introduces substantial prefilling overhead and limits their practicality in real-world deployment. To address this challenge, we propose LookaheadKV, a lightweight eviction framework that leverages the strength of surrogate future response without requiring explicit draft generation. LookaheadKV augments transformer layers with parameter‑efficient modules trained to predict true importance scores with high accuracy. Our design ensures negligible runtime overhead comparable to existing inexpensive heuristics, while achieving accuracy superior to more costly approximation methods. Extensive experiments on long-context understanding benchmarks, across a wide range of models, demonstrate that our method not only outperforms recent competitive baselines in various long-context understanding tasks, but also reduces the eviction cost by up to $14.5$×, leading to significantly faster time-to-first-token. Our code is available at https://github.com/SamsungLabs/LookaheadKV.
Minimax Sample Complexity of Graph Neural Networks: Lower Bounds and Structural Effects
Authors: Ahmad Ghasemi; Hossein Pishro-Nik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph Neural Networks (GNNs) achieve strong empirical performance across domains, yet their fundamental statistical behavior remains poorly understood. This paper develops a minimax analysis of ReLU message-passing GNNs with explicit architectural assumptions, in  both inductive (graph-level) and transductive (node-level) settings. For arbitrary graphs without structural constraints, we show that the worst-case generalization error scales as $\sqrt{\log d / n}$ with sample size $n$ and input dimension $d$, matching the $1/\sqrt{n}$ behavior of feed-forward networks. Under a spectral--homophily condition combining strong label homophily and bounded spectral expansion, we prove a stronger minimax lower bound of $d/\log n$ for transductive node prediction. We complement these results with a systematic empirical study on three large-scale benchmarks (ogbn\_arxiv, ogbn\_products\_50k, Reddit\_50k) and two controlled synthetic datasets representing the worst-case and structured regimes of our theory. All benchmark graphs we study fall in the slow-mixing, bottlenecked regime captured by our spectral-homophily condition, and ratio-based scaling tests show error decay consistent with the $d/\log n$ rate in real and structured settings, while the worst-case synthetic dataset follows the $\sqrt{\log d / n}$ curve. Together, these results indicate that practical GNN tasks often operate in the spectral-homophily regime, where our lower bound $d/\log n$ is tight and effective sample complexity is driven by graph topology rather than universal $1/\sqrt{n}$ behavior.
Post-hoc Probabilistic Vision-Language Models
Authors: Anton Baumann; Rui Li; Marcus Klasson; Santeri Mentu; Shyamgopal Karthik; Zeynep Akata; Arno Solin; Martin Trapp
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map images and text descriptions to a joint latent space in which their similarity is assessed using the cosine similarity. However, a deterministic mapping of inputs fails to capture uncertainties over concepts arising from domain shifts when used in downstream tasks. In this work, we propose post-hoc uncertainty estimation in VLMs that does not require additional training. Our method leverages a Bayesian posterior approximation over the last layers in VLMs and analytically quantifies uncertainties over cosine similarities. We demonstrate its effectiveness for uncertainty quantification and support set selection in active learning. Compared to baselines, we obtain improved and well-calibrated predictive uncertainties, interpretable uncertainty estimates, and sample-efficient active learning. Our results show promise for safety-critical applications of large-scale models.
Alternating Diffusion for Proximal Sampling with Zeroth Order Queries
Authors: Hirohane Takagi; Atsushi Nitanda
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical analyses have revealed that proximal sampling corresponds to alternating forward and backward iterations of the heat flow. The backward step was originally implemented by rejection sampling, whereas we directly simulate the dynamics. Unlike diffusion-based sampling methods that estimate scores via learned models or by invoking auxiliary samplers, our method treats the intermediate particle distribution as a Gaussian mixture, thereby yielding a Monte Carlo score estimator from directly samplable distributions. Theoretically, when the score estimation error is sufficiently controlled, our method inherits the exponential convergence of proximal sampling under isoperimetric conditions on the target distribution. In practice, the algorithm avoids rejection sampling, permits flexible step sizes, and runs with a deterministic runtime budget. Numerical experiments demonstrate that our approach converges rapidly to the target distribution, driven by interactions among multiple particles and by exploiting parallel computation.
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
Authors: Kuan Li; Zhongwang Zhang; Huifeng Yin; Rui Ye; Yida Zhao; Liwen Zhang; Litu Ou; Ding-Chu Zhang; Xixi Wu; Xinmiao Yu; Jialong Wu; Xinyu Wang; Zile Qiao; Zhen Zhang; Yong Jiang; Pengjun Xie; Fei Huang; Zhi-Qin John Xu; Shuai Wang; Minhao Cheng; Jingren Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: To significantly advance the capabilities of open-source web agents, we present WebSailor-V2, a complete post-training pipeline encompassing data construction, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Our methodology features two key innovations: (1) On the data front, we developed SailorFog-QA-2, a novel dataset built from a densely interconnected knowledge graph that introduces a wide variety of uncertainties beyond simple obfuscation, fostering more sophisticated reasoning. (2) For training, we engineered a dual-environment RL framework, combining a high-fidelity simulator for rapid, low-cost algorithmic iteration with a robust, managed real-world environment for stable final policy training, all integrated within a symbiotic data-policy feedback loop. Trained on the Qwen3-30B-A3B model, WebSailor-V2 achieves state-of-the-art results, scoring 35.3 on BrowseComp-EN, 44.1 on BrowseComp-ZH, and 30.6 on Humanity's Last Exam (HLE). Notably, our 30B-A3B MOE agent significantly outperforms all existing open-source agents and surpasses even the 671B DeepSeek-V3.1, demonstrating performance competitive with leading proprietary systems.
Generalizable Coarse-to-Fine Robot Manipulation via Language-Aligned 3D Keypoints
Authors: Jianshu Hu; Lidi Wang; Shujia Li; Yunpeng Jiang; Xiao Li; Paul Weng; Yutong Ban
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hierarchical coarse-to-fine policy, where a coarse branch predicts a region of interest to guide a fine-grained action predictor, has demonstrated significant potential in robotic 3D manipulation tasks by especially enhancing sample efficiency and enabling more precise manipulation.
However, even augmented with pre-trained models, these hierarchical policies still suffer from generalization issues.
To enhance generalization to novel instructions and environment variations, we propose Coarse-to-fine Language-Aligned manipulation Policy (CLAP), a framework that integrates three key components: 1) task decomposition, 2) VLM fine-tuning for 3D keypoint prediction, and 3) 3D-aware representation.
Through comprehensive experiments in simulation and on a real robot, we demonstrate its superior generalization capability.
Specifically, on GemBench, a benchmark designed for evaluating generalization, our approach achieves a 12\% higher average success rate than the SOTA method while using only 1/5 of the training trajectories.
In real-world experiments, our policy, trained on only 10 demonstrations, successfully generalizes to novel instructions and environments.
MoAlign: Motion-Centric Representation Alignment for Video Diffusion Models
Authors: Aritra Bhowmik; Denis Korzhenkov; Cees G. M. Snoek; Amir Habibian; Mohsen Ghafoorian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text-to-video diffusion models have enabled high-quality video synthesis, yet often fail to generate temporally coherent and physically plausible motion. A key reason is the models' insufficient understanding of complex motions that natural videos often entail. Recent works tackle this problem by aligning diffusion model features with those from pretrained video encoders. However, these encoders mix video appearance and dynamics into entangled features, limiting the benefit  of such alignment. In this paper, we propose a motion-centric alignment framework that learns a disentangled motion subspace from a pretrained video encoder. This subspace is optimized to predict ground-truth optical flow, ensuring it captures true motion dynamics. We then align the latent features of a text-to-video diffusion model to this new subspace, enabling the generative model to internalize motion knowledge and generate more plausible videos. Our method improves the physical commonsense in a state-of-the-art video diffusion model, while preserving adherence to textual prompts, as evidenced by empirical evaluations on VideoPhy, VideoPhy2, VBench, and VBench-2.0, along with a user study.
Branch and Bound Search for Exact MAP Inference in Credal Networks
Authors: Radu Marinescu; Fabio Cozman; Denis Mauá; Debarun Bhattacharjya; Junkyu Lee; Alexander G. Gray
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Credal networks extend Bayesian networks by incorporating imprecise probabilities through convex sets of probability distributions known as credal sets. MAP inference in credal networks, which seeks the most probable variable assignment given evidence, becomes inherently more difficult than in Bayesian networks because it involves computations over a complex joint credal set. In this paper, we introduce two tasks called \emph{maximax} and \emph{maximin} MAP, and develop depth-first branch-and-bound search algorithms for solving them \emph{exactly}. The algorithms exploit problem decomposition by exploring an AND/OR search space and use a partitioning-based heuristic function enhanced with a cost-shifting scheme to effectively guide the search. Our experimental results obtained on both random and realistic credal networks clearly demonstrate the effectiveness of the proposed algorithms as they scale to large and complex problem instances.
Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models
Authors: Dung Anh Hoang; Cuong Pham; Trung Le; Jianfei Cai; Thanh-Toan Do
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of noise to a real image. Unfortunately, their practical deployment is limited by slow inference speed, high memory usage, and the computational demands of the noise estimation process. Post-training quantization (PTQ) emerges as a promising solution to accelerate sampling and reduce the memory overhead of diffusion models.  Existing PTQ methods for diffusion models typically apply uniform weights to calibration samples across timesteps, which is sub-optimal since data at different timesteps may contribute differently to the diffusion process. Additionally, due to varying activation distributions and gradients across timesteps, a uniform quantization approach is sub-optimal. Each timestep requires a different gradient direction for optimal quantization, and treating them equally can lead to conflicting gradients that degrade performance. In this paper, we propose a novel PTQ method that addresses these challenges by assigning appropriate weights to calibration samples. 
    Specifically, our approach learns to assign optimal weights to calibration samples to align the quantized model’s gradients across timesteps, facilitating the quantization process.  Extensive experiments on CIFAR-10, LSUN-Bedrooms, and ImageNet datasets demonstrate the superiority of our method compared to other PTQ methods for diffusion models.
VisCoder2: Building Multi-Language Visualization Coding Agents
Authors: Yuansheng Ni; Songcheng Cai; Xiangchao Chen; Jiarong Liang; Zhiheng Lyu; Jiaqi Deng; Kai Zou; Ping Nie; Fei Yuan; Xiang Yue; Wenhu Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical workflows due to limited language coverage, unreliable execution, and lack of iterative correction mechanisms. Progress has been constrained by narrow datasets and benchmarks that emphasize single-round generation and single-language tasks. To address these challenges, we introduce three complementary resources for advancing visualization coding agents. **VisCode-Multi-679K** is a large-scale, supervised dataset containing 679K validated and executable visualization samples with multi-turn correction dialogues across 12 programming languages. **VisPlotBench** is a benchmark for systematic evaluation, featuring executable tasks, rendered outputs, and protocols for both initial generation and multi-round self-debug. Finally, we present **VisCoder2**, a family of multi-language visualization models trained on VisCode-Multi-679K. Experiments show that VisCoder2 significantly outperforms strong open-source baselines and approaches the performance of proprietary models like GPT-4.1, with further gains from iterative self-debug, reaching **82.4%** overall execution pass rate at the 32B scale, particularly in symbolic or compiler-dependent languages.
PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecasting
Authors: Yiming Niu; Jinliang Deng; Yongxin Tong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Periodicity is a fundamental characteristic of time series data and has long played a central role in forecasting. Recent deep learning methods strengthen the exploitation of periodicity by treating patches as basic tokens, thereby improving predictive effectiveness. However, their efficiency remains a bottleneck due to large parameter counts and heavy computational costs. This paper provides, for the first time, a clear explanation of why patch-level processing is inherently inefficient, supported by strong evidence from real-world data. To address these limitations, we introduce a phase perspective for modeling periodicity and present an efficient yet effective solution, PhaseFormer. 
PhaseFormer features phase-wise prediction through compact phase embeddings and efficient cross-phase interaction enabled by a lightweight routing mechanism. Extensive experiments demonstrate that PhaseFormer achieves state-of-the-art performance on the evaluated benchmarks with around 1k parameters, consistently across benchmark datasets. Notably, it excels on large-scale and complex datasets, where models with comparable efficiency often struggle. This work marks a significant step toward truly efficient and effective time series forecasting.
Code is available at this repository: https://github.com/neumyor/PhaseFormer_TSL
R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
Authors: Naoki Morihira; Amal Nahar; Kartik Bharadwaj; Yasuhiro Kato; Akinobu Hayashi; Tatsuya Harada
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A central challenge in image-based Model-Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction-based methods often waste capacity on large task-irrelevant regions. Decoder-free methods instead learn robust representations by leveraging Data Augmentation (DA), but reliance on such external regularizers limits versatility. We propose R2-Dreamer, a decoder-free MBRL framework with a self-supervised objective that serves as an internal regularizer, preventing representation collapse without resorting to DA. The core of our method is a \emph{redundancy-reduction} objective inspired by Barlow Twins, which can be easily integrated into existing frameworks. On DeepMind Control Suite and Meta-World, R2-Dreamer is competitive with strong baselines such as DreamerV3 and TD-MPC2 while training 1.59$\times$ faster than DreamerV3, and yields substantial gains on DMC-Subtle with tiny task-relevant objects. These results suggest that an effective internal regularizer can enable versatile, high-performance decoder-free MBRL. Code is available at https://github.com/NM512/r2dreamer.
FATE: A Formal Benchmark Series for Frontier Algebra of Multiple Difficulty Levels
Authors: Jiedong Jiang; Wanyi He; Wang Yuefeng; Guoxiong Gao; Yongle Hu; Jingting Wang; Nailin Guan; Peihao Wu; Bryan Dai; Liang Xiao; Bin Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, particularly on contest-based mathematical benchmarks like the IMO. However, these contests do not reflect the depth, breadth, and abstraction of modern mathematical research. To bridge this gap, we introduce **FATE**, a new benchmark series in formal algebra designed to chart a course toward advanced mathematical reasoning. We present two new components, FATE-H and FATE-X, each with 100 problems in abstract and commutative algebra. The FATE series spans a difficulty spectrum from undergraduate exercises to problems exceeding PhD qualifying exams. Notably, FATE-X is the first formal benchmark to surpass both PhD-level exam difficulty and the coverage of the Mathlib library. Our evaluations of state-of-the-art LLM provers on this new benchmark reveal a stark performance gap compared to contest math: the best model achieves only 3\% (pass@64) accuracy on FATE-H and 0\% on FATE-X. Our two-stage evaluation reveals that models' natural-language reasoning is notably more accurate than their ability to formalize this reasoning. We systematically classify the common errors that arise during this formalization process. Furthermore, a comparative study shows that a specialized prover can exhibit less effective reflection than general-purpose models, reducing its accuracy at the natural-language stage. We believe FATE provides a robust and challenging benchmark that establishes essential checkpoints on the path toward research-level formal mathematical reasoning.