GenAI4ELab/papercli-papers huggingface dataset:
Subspace Kernel Learning on Tensor Sequences
Authors: Lei Wang; Xi Ding; Yongsheng Gao; Piotr Koniusz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that compares mode-wise subspaces derived from tensor unfoldings, enabling expressive and robust similarity measures. To handle large-scale tensor data, we propose a scalable Nystr\"{o}m kernel linearization with dynamically learned pivot tensors obtained via soft $k$-means clustering. A key innovation of UKTL is its uncertainty-aware subspace weighting, which adaptively down-weights unreliable mode components based on estimated confidence, improving robustness and interpretability in comparisons between input and pivot tensors. Our framework is fully end-to-end trainable and naturally incorporates both multi-way and multi-mode interactions through structured kernel compositions. Extensive evaluations on action recognition benchmarks (NTU-60, NTU-120, Kinetics-Skeleton) show that UKTL achieves state-of-the-art performance, superior generalization, and meaningful mode-wise insights. This work establishes a principled, scalable, and interpretable kernel learning paradigm for structured multi-way and multi-modal tensor sequences.
Neural Compression of 3D Meshes using Sparse Implicit Representation
Authors: Jianqiang Wang; Siyu Ren; Junhui Hou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The growing demand for high-quality 3D mesh models has fueled the need for efficient 3D mesh compression techniques. However, existing methods often exhibit suboptimal compression performance due to the inefficient representation of mesh data. To address this issue, we propose a novel neural mesh compression method based on Sparse Implicit Representation (SIR). Specifically, SIR records signed distance field (SDF) values only on regular grids near the surface, enabling high-resolution structured representation of arbitrary geometric data with a significantly lower memory cost, while still supporting precise surface recovery. Building on this representation, we construct a lightweight Sparse Neural Compression (SNC) network to extract compact embedded features from the SIR and encode them into a bitstream. Extensive experiments and ablation studies demonstrate that our method outperforms state-of-the-art mesh and point cloud compression approaches in both compression performance and computational efficiency across a variety of mesh models. The source code is available at https://github.com/yydlmzyz1/SIR-SNC.
Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs
Authors: Kyle O'Brien; Stephen Casper; Quentin Gregory Anthony; Tomek Korbak; Robert Kirk; Xander Davies; Ishan Mishra; Geoffrey Irving; Yarin Gal; Stella Biderman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Open-weight AI systems offer unique benefits, including enhanced transparency, open research, and decentralized access. However, they are vulnerable to tampering attacks which can efficiently elicit harmful behaviors by modifying weights or activations. Currently, there is not yet a robust science of open-weight model risk management. Existing safety fine-tuning methods and other post-training techniques have struggled to make LLMs resistant to more than a few dozen steps of adversarial fine-tuning. In this paper, we investigate whether filtering text about dual-use topics from training data can prevent unwanted capabilities and serve as a more tamper-resistant safeguard. We introduce a multi-stage pipeline for scalable data filtering and show that it offers a tractable and effective method for minimizing biothreat proxy knowledge in LLMs. We pretrain multiple 6.9B-parameter models from scratch and find that they exhibit substantial resistance to adversarial fine-tuning attacks on up to 10,000 steps and 300M tokens of biothreat-related text — outperforming existing post-training baselines by over an order of magnitude -- with no observed degradation to unrelated capabilities. However, while filtered models lack internalized dangerous knowledge, we find that they can still leverage such information when it is provided in context (e.g., via search tool augmentation), demonstrating a need for a defense-in-depth approach. Overall, these findings help to establish pretraining data curation as a promising layer of defense for open-weight AI systems
UniVideo: Unified Understanding, Generation, and Editing for Videos
Authors: Cong Wei; Quande Liu; Zixuan Ye; Qiulin Wang; Xintao Wang; Pengfei Wan; Kun Gai; Wenhu Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified multimodal models have shown promising results in multimodal content generation and editing, but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends unified modelling to the video domain. UniVideo adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design preserves the MLLM's original text generation capabilities, enables accurate interpretation of complex multimodal instructions, and maintains visual consistency in the generated content. Built on this architecture, UniVideo unifies diverse video generation and editing tasks under a single multimodal instruction paradigm and is jointly trained across them. Extensive experiments demonstrate that UniVideo matches or surpasses state-of-the-art task-specific baselines in text/image-to-video generation, in-context video generation and in-context video editing. Notably, the unified design of UniVideo enables two forms of generalization. First, UniVideo supports task composition, such as combining editing with style transfer, by integrating multiple capabilities within a single instruction. Second, even without explicit training on free-form video editing, UniVideo transfers its editing capability from large-scale image editing data to this setting, handling unseen instructions such as changing the environment or altering materials within a video. Beyond these core capabilities, UniVideo also supports generation with thinking, where the MLLM interprets complex prompts
and guides the MMDiT during synthesis. To foster future research, we released our model and code.
EdiVal-Agent: An Object-Centric Framework for  Automated,    Fine-Grained Evaluation of Multi-Turn  Editing
Authors: Tianyu Chen; Yasi Zhang; Zhi Zhang; Peiyu Yu; Shu Wang; Zhendong Wang; Kevin Lin; Xiaofei Wang; Zhengyuan Yang; Linjie Li; Chung-Ching Lin; Jianwen Xie; Oscar Leong; Lijuan Wang; Ying Nian Wu; Mingyuan Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images—resulting in limited coverage and inheriting biases from prior generative models—or (ii) rely *solely* on zero-shot vision–language models (VLMs), whose prompt-based assessments of instruction following, content consistency, and visual quality are often imprecise.

To address this, we introduce **EdiVal-Agent** , an automated and fine-grained evaluation framework grounded in an object-centric perspective, designed to assess not only standard single-turn but also multi-turn instruction-based editing with precision. Given an input image, **EdiVal-Agent** first decomposes it into semantically meaningful objects, then synthesizes diverse, context-aware editing instructions while dynamically updating object pools across turns. These two stages enable two novel object-centric metrics tailored for multi-turn evaluation and one global metric of visual quality: 1) EdiVal-IF, which measures instruction following by combining open-vocabulary object detectors for symbolic checks with VLMs for semantic verification on detector-guided crops; 2) EdiVal-CC, which evaluates content consistency by calculating semantic similarity of unchanged objects and background using the evolving object pools; and 3) EdiVal-VQ, which quantifies changes in overall visual quality with human preference models.
 
 



Instantiating this pipeline, we build **EdiVal-Bench**, a multi-turn editing benchmark  covering 9 instruction types and    16 state-of-the-art editing models spanning in-context, flow-matching, and diffusion paradigms.  Our results show that *Seedream 4.0* achieves the best overall performance, offering the strongest balance of instruction following, content consistency, and latency. *GPT-Image-1.5* clearly improves over *GPT-Image-1*, especially in content consistency across turns, while *Nano Banana 2* consistently outperforms *Nano Banana* in instruction following and overall score,. Among flow-matching models, *FLUX.2-max* is the strongest baseline, whereas *Qwen-Image-Edit* performs well on the first turn but degrades sharply in later turns, indicating strong exposure bias in multi-turn editing. We demonstrate that **EdiVal-Agent** can be used to identify existing failure modes, thereby informing the development of the next generation of editing models. Our code is available at https://github.com/TianyuCodings/EdiVal.
Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint
Authors: Runmin Zhang; Jialiang Wang; Si-Yuan Cao; Zhu Yu; Junchen Yu; Guangyi Zhang; Hui-liang Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This work presents DCFlow, a novel self-supervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous unsupervised approaches that implicitly learn flow estimation solely from appearance similarity, we introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly. This is achieved by collaboratively training a modality transfer network and a flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly optimize both networks, significantly improving flow prediction accuracy. For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-art performance among unsupervised approaches.
TopoFormer: Topology Meets Attention for Graph Learning
Authors: Md Joshem Uddin; Astrit Tola; Cuneyt Gurcan Akcora; Baris Coskunuzer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce *TopoFormer*, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is *Topo-Scan*, a novel module that decomposes a graph into a short, ordered sequence of topological tokens by slicing over node or edge filtrations. These sequences capture multi-scale structural patterns, from local motifs to global organization, and are processed by a Transformer to produce expressive graph-level embeddings. Unlike traditional persistent homology pipelines, *Topo-Scan* is parallelizable, avoids costly diagram computations, and integrates seamlessly with standard deep learning architectures. We provide theoretical guarantees on the stability of our topological encodings and demonstrate state-of-the-art performance across graph classification and molecular property prediction benchmarks. Our results show that *TopoFormer* matches or exceeds strong GNN and topology-based baselines while offering predictable and efficient compute. This work opens a new path for parallelizable and unifying approaches to graph representation learning that integrate topological inductive biases into attention frameworks.
Deforming Videos to Masks: Flow Matching for Referring Video Segmentation
Authors: Zanyi Wang; Dengyang Jiang; Liuzhuozheng Li; Sizhe Dang; Chengzu Li; Harry Yang; Guang Dai; Mengmeng Wang; Jingdong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstract linguistic concepts onto a specific set of pixels and continuously segment them through the complex dynamics of a video. Faced with this difficulty, prior work has often decomposed the task into a pragmatic `locate-then-segment' pipeline. However, this cascaded design creates an information bottleneck by simplifying semantics into coarse geometric prompts (e.g, point), and struggles to maintain temporal consistency as the segmenting process is often decoupled from the initial language grounding. To overcome these fundamental limitations, we propose FlowRVS, a novel framework that reconceptualizes RVOS as a conditional continuous flow problem. This allows us to harness the inherent strengths of pretrained T2V models, fine-grained pixel control, text-video semantic alignment, and temporal coherence. Instead of conventional generating from noise to mask or directly predicting mask, we reformulate the task by learning a direct, language-guided deformation from a video's holistic representation to its target mask. Our one-stage, generative approach achieves new state-of-the-art results across all major RVOS benchmarks. Specifically, achieving a J&F of 51.1 in MeViS (+1.6 over prior SOTA) and 73.3 in the zero shot Ref-DAVIS17 (+2.7), demonstrating the significant potential of modeling video understanding tasks as continuous deformation processes.
SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learning
Authors: Momin Ahmad Khan; Yasra Chandio; Fatima M. Anwar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated Prompt Learning has emerged as a communication-efficient and privacy-preserving paradigm for adapting large vision-language models like CLIP across decentralized clients. However, the security implications of this setup remain underexplored. In this work, we present the first study of backdoor attacks in Federated Prompt Learning. We show that when malicious clients inject visually imperceptible, learnable noise triggers into input images, the global prompt learner becomes vulnerable to targeted misclassification while still maintaining high accuracy on clean inputs. Motivated by this vulnerability, we propose SABRE-FL, a lightweight, modular defense that filters poisoned prompt updates using an embedding-space anomaly detector trained offline on out-of-distribution data. SABRE-FL requires no access to raw client data or labels and generalizes across diverse datasets. We show, both theoretically and empirically, that malicious clients can be reliably identified and filtered using an embedding-based detector. Across five diverse datasets and four baseline defenses, SABRE-FL outperforms all baselines by significantly reducing backdoor accuracy while preserving clean accuracy, demonstrating strong empirical performance and underscoring the need for robust prompt learning in future federated systems.
Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
Authors: Sawan Kumar; Souvik Chakraborty
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling non-stationary processes, where statistical properties vary across the input domain, is a critical challenge in machine learning; yet most scalable methods rely on a simplifying assumption of stationarity. This forces a difficult trade-off: use expressive but computationally demanding models like Deep Gaussian Processes, or scalable but limited methods like Random Fourier Features (RFF). We close this gap by introducing Random Wavelet Features (RWF), a framework that constructs scalable, non-stationary kernel approximations by sampling from wavelet families. By harnessing the inherent localization and multi-resolution structure of wavelets, RWF generates an explicit feature map that captures complex, input-dependent patterns. Our framework provides a principled way to generalize RFF to the non-stationary setting and comes with a comprehensive theoretical analysis, including positive definiteness, unbiasedness, and uniform convergence guarantees. We demonstrate empirically on a range of challenging synthetic and real-world datasets that RWF outperforms stationary random features and offers a compelling accuracy-efficiency trade-off against more complex models, unlocking scalable and expressive kernel methods for a broad class of real-world non-stationary problems.
Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
Authors: Ammar Ahmed; Azal Ahmad Khan; Ayaan Ahmad; Sheng Di; Zirui Liu; Ali Anwar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models improve accuracy by producing long reasoning traces, but this inflates latency and cost, motivating inference-time efficiency. We propose Retrieval-of-Thought (RoT), which reuses prior reasoning as composable ``thought" steps to guide new problems. RoT organizes steps into a thought graph with sequential and semantic edges to enable fast retrieval and flexible recombination. At inference, RoT retrieves query-relevant nodes and applies reward-guided traversal to assemble a problem-specific template that guides generation. This dynamic template reuse reduces redundant exploration and, therefore, reduces output tokens while preserving accuracy. We evaluate RoT on reasoning benchmarks with multiple models, measuring accuracy, token usage, latency, and memory overhead. Findings show small prompt growth but substantial efficiency gains, with RoT reducing output tokens by up to 40%, inference latency by 82%, and cost by 59% while maintaining accuracy. RoT establishes a scalable paradigm for efficient LRM reasoning via dynamic template construction through retrieval.
Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer
Authors: Roman Beliy; Amit Zalcher; Jonathan Kogman; navve wasserman; michal Irani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present ``Brain-IT'', a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer (BIT), allowing effective interactions between clusters of functionally-similar brain-voxels. These functional-clusters are shared by all subjects, serving as building blocks for integrating information both within and across brains. All model components are shared by all clusters \& subjects, allowing efficient training with limited amount of data. To guide the image reconstruction, BIT predicts two complementary localized patch-level image features: (i) high-level semantic features which steer the diffusion model toward the correct semantic content of the image; and (ii) low-level  structural features which help to initialize the diffusion process with the correct coarse layout of the image. BIT's design enables direct flow of information from brain-voxel clusters to localized image features. Through these principles, our method achieves image reconstructions from fMRI that faithfully reconstruct the seen images, and surpass current SotA approaches both visually and by standard objective metrics.  Moreover, with only 1-hour of fMRI data from a new subject, we achieve results comparable to current methods trained on full 40-hour recordings.
Scalable Offline Model-Based RL with Action Chunks
Authors: Kwanyoung Park; Seohong Park; Youngwoon Lee; Sergey Levine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we study whether model-based reinforcement learning (RL), in particular model-based value expansion,
can provide a scalable recipe for tackling complex, long-horizon tasks in offline RL. 
Model-based value expansion fits an on-policy value function using length-$n$ imaginary rollouts generated by the current policy and a learned dynamics model.
While larger $n$ reduces bias in value bootstrapping, it amplifies accumulated model errors over long horizons, degrading future predictions.
We address this trade-off with 
an *action-chunk* model that predicts a future state from a sequence of actions (an "action chunk")
instead of a single action, which reduces compounding errors.
In addition, instead of directly training a policy to maximize rewards,
we employ rejection sampling from an expressive behavioral action-chunk policy,
which prevents model exploitation from out-of-distribution actions.
We call this recipe **Model-Based RL with Action Chunks (MAC)**.
Through experiments on highly challenging tasks with large-scale datasets of up to $100$M transitions,
we show that MAC achieves the best performance among offline model-based RL algorithms,
especially on challenging long-horizon tasks.
Geometry-aware 4D Video Generation for Robot Manipulation
Authors: Zeyi Liu; Shuang Li; Eric Cousineau; Siyuan Feng; Benjamin Burchfiel; Shuran Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environments. While recent video generation models have shown strong potential in modeling dynamic scenes, generating videos that are both temporally coherent and geometrically consistent across camera views remains a significant challenge. To address this, we propose a 4D video generation model that enforces multi-view 3D consistency of generated videos by supervising the model with cross-view pointmap alignment during training. Through this geometric supervision, the model learns a shared 3D scene representation, enabling it to generate spatio-temporally aligned future video sequences from novel viewpoints given a single RGB-D image per view, and without relying on camera poses as input. Compared to existing baselines, our method produces more visually stable and spatially aligned predictions across multiple simulated and real-world robotic datasets. We further show that the predicted 4D videos can be used to recover robot end-effector trajectories using an off-the-shelf 6DoF pose tracker, yielding robot manipulation policies that generalize well to novel camera viewpoints.