GenAI4ELab/papercli-papers huggingface dataset:
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
Authors: Makoto Shing; Masanori Koyama; Takuya Akiba
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: End-to-end backpropagation requires storing activations throughout all layers, creating memory bottlenecks that limit model scalability. Existing block-wise training methods offer means to alleviate this problem, but they rely on ad-hoc local objectives and remain largely unexplored beyond classification tasks. We propose $\textit{DiffusionBlocks}$, a principled framework for transforming transformer-based networks into genuinely independent trainable blocks that maintain competitive performance with end-to-end training. Our key insight leverages the fact that residual connections naturally correspond to updates in a dynamical system. With minimal modifications to this system, we can convert the updates to those of a denoising process, where each block can be learned independently by leveraging the score matching objective. This independence enables training with gradients for only one block at a time, thereby reducing memory requirements in proportion to the number of blocks. Our experiments on a range of transformer architectures (vision, diffusion, autoregressive, recurrent-depth, and masked diffusion) demonstrate that DiffusionBlocks training matches the performance of end-to-end training while enabling scalable block-wise training on practical tasks beyond small-scale classification. DiffusionBlocks provides a theoretically grounded approach that successfully scales to modern generative tasks across diverse architectures.
Global and Local Topology-Aware Graph Generation via Dual Conditioning Diffusion
Authors: Yuhang Xie; Sinno Jialin Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph generation plays an important role in various domains such as molecular design, protein prediction, and drug discovery. However, generating graph-structured data poses challenges due to the complex dependencies inherent in graphs, spanning from intricate local substructures to broad global topologies.  Although recent advances in graph-generative models have made notable progress, traditional node-level generative paradigms may have difficulty simultaneously capturing the multiscale dependencies in graphs. To address these challenges, we propose a unified latent diffusion model that jointly learns local and global topological information, enabling effective and efficient graph generation. Besides, our approach introduces a dual conditioning mechanism designed to promote dynamic interaction between local and global information, equipping the generative model with global and local awareness to better capture the coupled dependencies within graphs. Our method can largely promote the joint modeling of global and local information and substantially improve the quality of the generated graphs. Extensive experiments consistently demonstrate the effectiveness of our method.
Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
Authors: Zhanghan Ni; Yanjing Li; Zeju Qiu; Bernhard Schölkopf; Hongyu Guo; Weiyang Liu; Shengchao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models have recently advanced $\textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non-rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively model the rich dynamic and conformational information of protein structures. To overcome these issues, we introduce $\textbf{RigidSSL}$ ($\textit{Rigidity-Aware Self-Supervised Learning}$), a geometric pretraining framework that front-loads geometry learning prior to generative finetuning. Phase I (RigidSSL-Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations. Phase II (RigidSSL-MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions. Underpinning both phases is a bi-directional, rigidity-aware flow matching objective that jointly optimizes translational and rotational dynamics to maximize mutual information between conformations. Empirically, RigidSSL variants improve designability by up to 43\% while enhancing novelty and diversity in unconditional generation. Furthermore, RigidSSL-Perturb improves the success rate by 5.8\% in zero-shot motif scaffolding and RigidSSL-MD captures more biophysically realistic conformational ensembles in G protein-coupled receptor modeling. The code is available at: https://github.com/ZhanghanNi/RigidSSL.git.
$PhyWorldBench$: A Comprehensive Evaluation of Physical Realism in Text-to-Video Models
Authors: Jing Gu; Xian Liu; Yu Zeng; Ashwin Nagarajan; Fangrui Zhu; Daniel Hong; Yue Fan; Qianqi Yan; Kaiwen Zhou; Ming-Yu Liu; Xin Eric Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains a critical and unresolved challenge. This paper presents $PhyWorldBench$
, a comprehensive benchmark designed to evaluate video generation models based on their adherence to the laws of physics. The benchmark covers multiple levels of physical phenomena, ranging from fundamental principles like object motion and energy conservation to more complex scenarios involving rigid body interactions and human or animal motion. Additionally, we introduce a novel "Anti-Physics" category, where prompts intentionally violate real-world physics, enabling the assessment of whether models can follow such instructions while maintaining logical consistency. Besides large-scale human evaluation, we also design a simple yet effective method that could utilize current MLLM to evaluate the physics realism in a zero-shot fashion. We evaluate 10 state-of-the-art text-to-video generation models, including five open-source and five proprietary models, with a detailed comparison and analysis. we identify pivotal challenges models face in adhering to real-world physics. Through systematic testing of their outputs across 1,050 curated prompts—spanning fundamental, composite, and anti-physics scenarios—we identify pivotal challenges these models face in adhering to real-world physics. We then rigorously examine their performance on diverse physical phenomena with varying prompt types, deriving targeted recommendations for crafting prompts that enhance fidelity to physical principles.
Reducing Semantic Mismatch in Brain-to-Text Decoding Through Personalized Multimodal Masking
Authors: Jiaxuan Chen; Yu Qi; Yueming Wang; Gang Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid progress of large vision-language models (VLMs), such as CLIP, has spurred the development of a wide range of neural decoding frameworks. Nevertheless, most existing approaches still suffer from semantic mismatches during representational alignment. This challenge may stem from the fact that the human brain does not distribute attention uniformly across a visual scene, but rather selectively encodes salient or relevant regions. Moreover, such selectivity is closely related to individual interests and varies from person to person. To address this challenge, we propose Yo'Mind, a novel optimal transport (OT)-driven personalized multimodal semantic masking framework designed to bridge the semantic gap between brain and machines in interpreting visual scenes. Technically, Yo'Mind introduces a dynamic semantic pruning and allocation mechanism that adaptively masks redundant visual semantic components in stimulus images based on individual neural responses—without requiring extra human supervision or hyperparameter tuning. This strategy can be used to enhance semantic consensus between brain and machine representations during decoding. Furthermore, the inherent flexibility of OT theory enables Yo'Mind to perform brain-visual-linguistic alignment and cross-subject decoding within a unified end-to-end architecture. Extensive experiments demonstrate that our Yo'Mind offers several advantages, including state-of-the-art brain-to-text reconstruction performance and improved interpretability of the decoding process.
DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning
Authors: Yifan Wang; Bolian Li; Junlin Wu; Zhaoxuan Tan; Zheli Liu; Ruqi Zhang; Ananth Grama; Qingkai Zeng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world large language model deployments (e.g., conversational AI systems, code generation assistants) naturally generate abundant implicit user dissatisfaction (DSAT) signals, as users iterate toward better answers through refinements, corrections, and expressed preferences, while explicit satisfaction (SAT) feedback is scarce. Existing preference learning approaches are poorly aligned with this data profile, as they rely on costly human annotations or assume plentiful positive responses. In this paper, we introduce \textbf{DRIFT} (\textbf{D}issatisfaction-\textbf{R}efined \textbf{I}terative pre\textbf{F}erence \textbf{T}raining), which anchors training on real-world DSAT signals and samples positives dynamically from the evolving policy. Empirically, DRIFT models trained on real-world \textit{WildFeedback} datasets and synthetic \textit{UltraFeedback} datasets achieve up to +6.23\% (7B) / +7.61\% (14B) on WildBench Task Score and up to +8.95\% (7B) / +12.29\% (14B) on AlpacaEval2 win rate over base models, outperforming strong baseline methods such as iterative DPO and SPIN. At larger scales, the improvements are particularly pronounced: 14B models trained with DRIFT surpass GPT-4o-mini on WildBench. Further analysis shows that DRIFT also preserves exploratory capacity, yielding more diverse high-reward solutions rather than collapsing to narrow subsets. Theoretically, we demonstrate that this design preserves preference margins and avoids the gradient degeneration. These results show that DRIFT is an effective and scalable recipe for real-world post-training that leverages the most abundant and informative signal.
GhostEI-Bench: Do Mobile Agent Resilience to Environmental Injection in Dynamic On-Device Environments?
Authors: Chiyu Chen; Xinhao Song; Yunkai Chai; Yang Yao; Haodong Zhao; Lijun Li; Jie Li; Yan Teng; Gongshen Liu; Yingchun Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile Graphical User Interfaces (GUIs).
However, their operation within dynamic on-device ecosystems, which include notifications, pop-ups, and inter-app interactions, exposes them to a unique and underexplored threat vector: environmental injection. 
Unlike traditional prompt-based attacks that manipulate textual instructions, environmental injection contaminates the agent's visual perception by inserting adversarial UI elements, such as deceptive overlays or spoofed notifications, directly into the GUI. 
This bypasses textual safeguards and can derail agent execution, leading to privacy leakage, financial loss, or irreversible device compromise. 

To systematically evaluate this threat, we introduce GhostEI-Bench, the first benchmark dedicated to assessing mobile agents under environmental injection attacks within dynamic, executable environments. 
Moving beyond static image-based assessments, our benchmark injects adversarial events into realistic application workflows inside fully operational Android emulators, assessing agent performance across a range of critical risk scenarios. 
We also introduce a novel evaluation protocol where a judge LLM performs fine-grained failure analysis by reviewing the agent's action trajectory alongside the corresponding sequence of screenshots.
This protocol identifies the precise point of failure, whether in perception, recognition, or reasoning.

Our comprehensive evaluation of state-of-the-art agents reveals their profound vulnerability to deceptive environmental cues. The results demonstrate that current models systematically fail to perceive and reason about manipulated UIs. 
GhostEI-Bench provides an essential framework for quantifying and mitigating this emerging threat, paving the way for the development of more robust and secure embodied agents.
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Authors: Linhao Luo; Zicheng Zhao; Junnan Liu; Zhangchi Qiu; Junnan Dong; Serge Panev; Chen Gong; Thuy-Trang Vu; Gholamreza Haffari; Dinh Phung; Alan Wee-Chung Liew; Shirui Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by incorporating external knowledge, yet existing RAGs struggle with knowledge-intensive tasks due to fragmented information and weak modeling of knowledge structure. Graphs offer a natural way to model relationships within knowledge, but LLMs are inherently unstructured and cannot effectively reason over graph-structured data. Recent graph-enhanced RAG (GraphRAG) attempts to bridge this gap by constructing tailored graphs and enabling LLMs to reason on them. However, these methods often depend on ad-hoc graph designs, heuristic search, or costly agent pipelines, which hinder scalability and generalization. To address these challenges, we present G-reasoner, a unified framework that integrates graph and language foundation models for scalable reasoning over diverse graph-structured knowledge. Central to our approach is QuadGraph, a standardized four-layer abstraction that unifies heterogeneous knowledge sources into a common graph representation. Building on this, we introduce a 34M-parameter graph foundation model (GFM) that jointly captures graph topology and textual semantics, and is integrated with LLMs to enhance reasoning in downstream applications. To ensure scalability and efficiency, mixed-precision training and distributed message-passing are implemented to scale GFM with more GPUs. Extensive experiments on six benchmarks show that G-reasoner consistently outperforms state-of-the-art baselines, significantly enhances LLM reasoning, and achieves strong efficiency and cross-graph generalization.
BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
Authors: Xin Xu; Xunzhi He; Churan Zhi; Ruizhe Chen; Julian McAuley; Zexue He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing studies on bias mitigation methods for large language models (LLMs) use diverse baselines and metrics to evaluate debiasing performance, leading to inconsistent comparisons among them. Moreover, their evaluations are mostly based on the comparison between LLMs' probabilities of biased and unbiased contexts, which ignores the gap between such evaluations and real-world use cases where users interact with LLMs by reading model responses and expect fair and safe outputs rather than LLMs' probabilities. To enable consistent evaluation across debiasing methods and bridge this gap, we introduce **BiasFreeBench**, an empirical benchmark that comprehensively compares eight mainstream bias mitigation techniques (covering four prompting-based and four training-based methods) on two test scenarios (multi-choice QA and open-ended multi-turn QA) by reorganizing existing datasets into a unified query-response setting. We further introduce a response-level metric, **Bias-Free Score**, to measure the extent to which LLM responses are fair, safe, and anti-stereotypical. Debiasing performances are systematically compared and analyzed across key dimensions: the prompting vs. training paradigm, model size, and generalization of different training strategies to unseen bias types. We release our benchmark, aiming to establish a unified testbed for bias mitigation research [https://github.com/xxupiano/BiasFreeBench](https://github.com/xxupiano/BiasFreeBench).
Relational Feature Caching for Accelerating Diffusion Transformers
Authors: Byunggwan Son; Jeimin Jeon; Jeongwoo Choi; Bumsub Ham
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Feature caching approaches accelerate diffusion transformers (DiTs) by storing the output features of computationally expensive modules at certain timesteps, and exploiting them for subsequent steps to reduce redundant computations. Recent forecasting-based caching approaches employ temporal extrapolation techniques to approximate the output features with cached ones. Although effective, relying exclusively on temporal extrapolation still suffers from significant prediction errors, leading to performance degradation. Through a detailed analysis, we find that 1) these errors stem from the irregular magnitude of changes in the output features, and 2) an input feature of a module is strongly correlated with the corresponding output. Based on this, we propose relational feature caching (RFC), a novel framework that leverages the input-output relationship to enhance the accuracy of the feature prediction. Specifically, we introduce relational feature estimation (RFE) to estimate the magnitude of changes in the output features from the inputs, enabling more accurate feature predictions. We also present relational cache scheduling (RCS), which estimates the prediction errors using the input features and performs full computations only when the errors are expected to be substantial. Extensive experiments across various DiT models demonstrate that RFC consistently outperforms prior approaches significantly. Project page is available at https://cvlab.yonsei.ac.kr/projects/RFC
Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
Authors: Ziru Niu; Hai Dong; A. K. Qin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated Learning (FL) is a privacy-preserving machine learning framework facilitating collaborative training across distributed clients. However, its performance is often compromised by data heterogeneity among participants, which can result in local models with limited generalization capability. Traditional model-homogeneous approaches address this issue primarily by regularizing local training procedures or dynamically adjusting client weights during aggregation. Nevertheless, these methods become unsuitable in scenarios involving clients with heterogeneous model architectures. In this paper, we propose a model-heterogeneous FL framework that enhances clients’ generalization performance on unseen data without relying on parameter aggregation. Instead of model parameters, clients share feature distribution statistics (mean and covariance) with the server. Then each client trains a variational transposed convolutional neural network using Gaussian latent variables sampled from these distributions, and use it to generate synthetic data. By fine-tuning local models with the synthetic data, clients achieve significant improvement of generalization ability. Experimental results demonstrate that our approach not only attains higher generalization accuracy compared to existing model-heterogeneous FL frameworks, but also reduces communication costs and memory consumption.
TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models
Authors: Peiran Li; Jiawei Wang; Haoran Zhang; Xiaodan Shi; Noboru Koshizuka; Chihiro Shimizu; Renhe Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The importance of mobile phone GPS trajectory data is widely recognized across many fields, yet the use of real data is often hindered by privacy concerns, limited accessibility, and high acquisition costs. As a result, generating pseudo–GPS trajectory data has become an active area of research. Recent diffusion-based approaches have achieved strong fidelity but remain limited in spatial scale (small urban areas), transportation-mode diversity, and efficiency (requiring numerous sampling steps). To address these challenges, we introduce TrajFlow, which to the best of our knowledge is the first flow-matching-based generative model for GPS trajectory generation. TrajFlow leverages the flow-matching paradigm to improve robustness and efficiency across multiple geospatial scales, and incorporates a trajectory harmonization \& reconstruction strategy to jointly address scalability, diversity, and efficiency. Using a nationwide mobile phone GPS dataset with millions of trajectories across Japan, we show that TrajFlow or its variants consistently outperform diffusion-based and deep generative baselines at urban, metropolitan, and nationwide levels. As the first nationwide, multi-scale GPS trajectory generation model, TrajFlow demonstrates strong potential to support inter-region urban planning, traffic management, and disaster response, thereby advancing the resilience and intelligence of future mobility systems.
Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
Authors: Zhengyan Wan; Yidong Ouyang; Liyan Xie; Fang Fang; Hongyuan Zha; Guang Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When modeling discrete data, existing approaches mostly focus on guidance with the first-order approximation to improve the sampling efficiency. However, such an approximation is inappropriate in discrete state spaces since the approximation error could be large. A novel guidance framework for discrete data is proposed to address this problem: we derive the exact transition rate for the desired distribution given a learned discrete flow matching model, leading to guidance that only requires a single forward pass in each sampling step, significantly improving efficiency. This unified novel framework is general enough, encompassing existing guidance methods as special cases, and it can also be seamlessly applied to the masked diffusion model. We demonstrate the effectiveness of our proposed guidance on energy-guided simulations and preference alignment on text-to-image generation and multimodal understanding tasks.
VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Authors: Yixiao Wang; Mingxiao Huo; Zhixuan Liang; Yushi Du; Lingfeng Sun; Haotian Lin; Jinghuan Shang; Chensheng Peng; Mohit Bansal; Mingyu Ding; Masayoshi Tomizuka
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generality across tasks. Distilling multiple VFMs into a unified representation can mitigate this limitation but often yields inflexible task-specific feature selection and requires costly full retraining to incorporate robot-domain knowledge.
We propose VER, a Vision Expert transformer for Robot learning. During pretraining, VER distills multiple VFMs into a vision expert library. We then fine-tune only a lightweight routing network (fewer than 0.4% of parameters) to dynamically select task-relevant experts from the pretrained library for downstream robot tasks. We further introduce Patchwise Expert Routing with Curriculum Top-K Annealing to improve both flexibility and precision of dynamic expert selection. Moreover, VER supports parameter-efficient finetuning for scalable expert utilization and robot-domain knowledge integration. Across 17 diverse robotic tasks and multiple policy heads, VER achieves state-of-the-art performance. We find that VER reduces large-norm outliers in task-irrelevant regions (e.g., background) and concentrates on task-critical regions. More visualizations and codes are available in https://yixiaowang7.github.io/ver_page/.