GenAI4ELab/papercli-papers huggingface dataset:
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do.
Authors: Rasmus Torp; Shailen Smith; Adam Breuer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show that extensive exposure can persist without rote memorization and is instead caused by a tunable connection to adversarial robustness. We begin by presenting three surprising results: (1) recent defenses that inhibit reconstruction by Model Inversion Attacks (MIAs), which evaluate leakage under an idealized attacker, do not reduce standard measures of information dependency (HSIC); (2) models that maximally memorize their training datasets remain robust to MIA reconstruction; and (3) models trained without seeing 97% of the training pixels, where recent information-theoretic bounds give arbitrarily strong privacy guarantees under standard assumptions, can still be devastatingly reconstructed by MIA. 

To explain these findings, we provide causal evidence that privacy under MIA arises from what the adversarial examples literature calls "non-robust" features (generalizable but imperceptible and unstable features). We further show that recent MIA defenses obtain their privacy improvements by unintentionally shifting models toward such features. To establish this causal relationship, we introduce **A**n**t**i **A**dversarial **T**raining (**AT-AT**), a training regime that intentionally learns non-robust features to obtain both superior reconstruction defense and higher accuracy than state-of-the-art defenses. Our results revise the prevailing understanding of training data exposure and reveal a new privacy-robustness tradeoff.
Conjuring Semantic Similarity
Authors: Tian Yu Liu; Stefano Soatto
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The semantic similarity between sample expressions measures the distance between their latent `meaning'.These meanings are themselves typically represented by textual expressions.  We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the imagery they evoke. While this is not possible with humans, generative models allow us to easily visualize and compare generated images, or their distribution, evoked by a textual prompt. Therefore, we characterize the semantic similarity between two textual expressions simply as the distance between image distributions they induce, or 'conjure.' We show that by choosing the Jeffreys divergence between the reverse-time diffusion stochastic differential equations (SDEs) induced by each textual expression, this can be directly computed via Monte-Carlo sampling. Our method contributes a novel perspective on semantic similarity that not only aligns with human-annotated scores, but also opens up new avenues for the evaluation of text-conditioned generative models while offering better interpretability of their learnt representations.
AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation
Authors: Tongfei Chen; Shuo Yang; Yuguang Yang; Linlin Yang; Runtang Guo; Changbai Li; He Long; Chunyu Xie; Dawei Leng; Baochang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Referring Image Segmentation (RIS) aims to segment the object in an image uniquely referred to by a natural language expression. However, RIS training often contains hard-to-align and instance-specific visual signals; optimizing on such pixels injects misleading gradients and drives the model in the wrong direction. By explicitly estimating pixel-level vision–language alignment, the learner can suppress low-alignment regions, concentrate on reliable cues, and acquire more generalizable alignment features.
In this paper, we propose Alignment-Aware Masked Learning (AML), a simple yet effective training strategy that quantifies region–referent alignment (PMME) and filters out unreliable pixels during optimization (AFM). Specifically, each sample first computes a similarity map between visual and textual features, and then masks out pixels falling below an adaptive similarity threshold, thereby excluding poorly aligned regions from the training process. AML does not require architectural changes and incurs no inference overhead, directing attention to the areas aligned with the textual description. Experiments on the RefCOCO (vanilla/+/g) datasets show that AML achieves state-of-the-art results across all 8 splits, and beyond improving RIS performance, AML also enhances the model’s robustness to diverse descriptions and scenarios.
Boolean Satisfiability via Imitation Learning
Authors: Zewei Zhang; Huan Liu; YUANHAO YU; Jun Chen; Xiangyu Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose ImitSAT, a branching policy for conflict-driven clause learning (CDCL) solvers based on imitation learning for the Boolean satisfiability problem (SAT). Unlike previous methods that predict instance-level signals to improve CDCL branching indirectly, or rely on reinforcement learning and insufficient CDCL information to enhance branching, ImitSAT learns from expert KeyTrace that collapses a full run into the sequence of surviving decisions. Replaying a KeyTrace on the same instance is nearly conflict-free, providing dense decision- level supervision and directly reducing propagations—the dominant contributor to wall-clock time. This prefix-conditioned supervision enables ImitSAT to reproduce high-quality branches without exploration, yielding faster convergence, stable training, and seamless integration into CDCL. Extensive experiments demonstrate that ImitSAT reduces propagation counts and runtime, outperforming state-of-the-art learned approaches. We released the source code and trained model at https://github.com/zewei-Zhang/ImitSAT.
A Memory-Efficient Hierarchical Algorithm for Large-scale Optimal Transport Problems
Authors: Wenzhou Xia; Ya-Nan Zhu; Jingwei Liang; Xiaoqun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose HALO, a memory-efficient hierarchical algorithm for solving large-scale optimal transport (OT) problems with squared Euclidean cost, particularly effective in moderate-dimensional settings. 
The core of \ours lies in combining a hierarchical representation of the OT problem with parallel-friendly linear programming solvers, within which an active pruning technique is integrated to further reduce memory usage and computational cost.
Theoretically, we establish a scale-independent iteration-complexity upper bound for the refinement phase, which is consistent with our numerical observations. 
Numerically, experiments on the image dataset \dataset and the 3D point cloud dataset \datasetnongrid demonstrate that \ours effectively alleviates the memory and scalability bottlenecks of existing solvers.
Our method demonstrates significant advantages compared to state-of-the-art baselines: for images with $n=1024^2$ pixels, it achieves an $8.9\times$ speedup and $70.5$% reduction in memory usage under comparable accuracy; for 3D point clouds at scale $n=2^{18}$, it achieves a $1.84\times$ speedup and an $83.2$% reduction in memory usage with $24.9$% lower transport cost.
Conditional Advantage Estimation for Reinforcement Learning in Large Reasoning Models
Authors: Guanxu Chen; Yafu Li; Yuxian Jiang; Chen Qian; Qihan Ren; Yang JingYi; Yu Cheng; Dongrui Liu; Jing Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for large language models (LLMs) has achieved remarkable progress in enhancing LLMs’ reasoning capabilities on tasks with clear correctness criteria, such as mathematical reasoning tasks. Several training metrics, such as entropy or response length, have been observed to correlate with different reasoning behaviors in reinforcement learning. Prior approaches incorporate such priors through reward or advantage shaping, which often relies on hand-crafted penalties and preferences (e.g., higher-is-better or lower-is-better). However, without careful hyper-parameter tuning, these directional priors can be overly biased and may lead to failure. To this end, we introduce ***C****onditional adv****AN****tage estimati****ON*** (***CANON***), amplifying the impact of the target metric without presuming its direction. Specifically, *CANON* regroups the sampled responses into two groups based on the higher or lower value of a target metric, measures which metric trend contributes to better performance through inter-group comparison, and identifies the better response within the same group. In summary, *CANON* based on entropy consistently outperforms prior methods across three LLMs on both math reasoning and high-complexity logic tasks. When applied to response length, *CANON* further improves token efficiency, yielding a more favorable Pareto frontier in the performance–cost trade-off.
One2Scene: Geometric Consistent Explorable 3D Scene Generation from a Single Image
Authors: Pengfei Wang; Liyi Chen; Zhiyuan Ma; Yanjun Guo; Guowen Zhang; Lei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating explorable 3D scenes from a single image is a highly challenging problem in 3D vision. Existing methods struggle to support free exploration, often producing severe geometric distortions and noisy artifacts when the viewpoint moves far from the original perspective. We introduce One2Scene, an effective framework that decomposes this ill-posed problem into three tractable sub-tasks to enable immersive explorable scene generation.
We first use a panorama generator to produce anchor views from a single input image as initialization. Then, we lift these 2D anchors into an explicit 3D geometric scaffold via a generalizable, feed-forward Gaussian Splatting network. 
Instead of treating the panorama as a single image for reconstruction, we project it into multiple sparse anchor views and reformulate the reconstruction task as multi-view stereo matching, which allows us to leverage robust geometric priors learned from large-scale multi-view datasets.
A bidirectional feature fusion module is used to enforce cross-view consistency, yielding an efficient and geometrically reliable scaffold.
Finally, the scaffold serves as a strong prior for a novel view generator to produce photorealistic and geometrically accurate views at arbitrary cameras. By explicitly conditioning on a 3D-consistent scaffold to perform reconstruction, One2Scene works stably under large camera motions, supporting immersive scene exploration. Extensive experiments show that One2Scene substantially outperforms state-of-the-art methods in panorama depth estimation, feed-forward 360° reconstruction, and explorable 3D scene generation. Project page can be found at: https://one2scene5406.github.io
Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
Authors: Chao Wang; Tao Yang; Hongtao Tian; Yunsheng Shi; Qiyao Ma; XiaotaoLiu; Ting Yao; Wenbo Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Critic-free methods like GRPO reduce memory demands by estimating advantages from multiple rollouts but tend to converge slowly, as critical learning signals are diluted by an abundance of uninformative samples and tokens. To tackle this challenge, we propose the **Dynamic Dual-Level Down-Sampling (D$^3$S)** framework that prioritizes the most informative samples and tokens across groups to improve the efficiency of policy optimization. D$^3$S operates along two levels: (1) the sample-level, which selects a subset of rollouts to maximize advantage variance ($\text{Var}(A)$). We theoretically proved that this selection is positively correlated with the upper bound of the policy gradient norms, yielding higher policy gradients. (2) the token-level, which prioritizes tokens with a high product of advantage magnitude and policy entropy ($|A_{i,t}|\times H_{i,t}$), focusing updates on tokens where the policy is both uncertain and impactful. Moreover, to prevent overfitting to high-signal data, D$^3$S employs a dynamic down-sampling schedule inspired by curriculum learning. This schedule starts with aggressive down-sampling to accelerate early learning and gradually relaxes to promote robust generalization. Extensive experiments on Qwen2.5 and Llama3.1 demonstrate that integrating D$^3$S into advanced RL algorithms achieves state-of-the-art performance with generalization while requiring fewer samples and tokens across diverse reasoning benchmarks.
QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
Authors: Hyesung Jeon; Seojune Lee; Beomseok Kang; Yulhwa Kim; Jae-Joon Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The demand for efficient deployment of large language models (LLMs) has driven interest in quantization, which reduces inference cost, and parameter-efficient fine-tuning (PEFT), which lowers training overhead. This motivated the development of quantization-aware PEFT to produce accurate yet efficient quantized models.
In this setting, reducing quantization error prior to fine-tuning is crucial for achieving high model accuracy. However, existing methods that rely on low-rank adaptation suffer from limited representational capacity. Recent Fourier-related transform (FT)-based adapters offer greater representational power than low-rank adapters, but their direct integration into quantized models often results in ineffective error reduction and increased computational overhead.  
To overcome these limitations, we propose QWHA, a method that integrates FT-based adapters into quantized models by employing the Walsh-Hadamard Transform (WHT) as the transform kernel, together with a novel adapter initialization scheme incorporating adaptive parameter selection and value refinement. We demonstrate that QWHA effectively mitigates quantization errors while facilitating fine-tuning, and that its design substantially reduces computational cost. Experimental results show that QWHA consistently outperforms baselines in low-bit quantization accuracy and achieves significant training speedups over existing FT-based adapters.
HARP: Hallucination Detection via Reasoning Subspace Projection
Authors: Junjie Hu; Gang Tu; Cheng Shengyu; JinXin Li; Jinting Wang; Rui Chen; Zhilong Zhou; Dongbo Shan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hallucinations in Large Language Models (LLMs) pose a major barrier to their reliable use in critical decision-making. Although existing hallucination detection methods have improved accuracy, they still struggle with disentangling semantic and reasoning information and maintaining robustness. To address these challenges, we propose HARP (Hallucination detection via reasoning subspace projection), a novel hallucination detection framework. HARP establishes that the hidden state space of LLMs can be decomposed into a direct sum of a semantic subspace and a reasoning subspace, where the former encodes linguistic expression and the latter captures internal reasoning processes. Moreover, we demonstrate that the Unembedding layer can disentangle these subspaces, and by applying Singular Value Decomposition (SVD) to its parameters, the basis vectors spanning the semantic and reasoning subspaces are obtained.
Finally, HARP projects hidden states onto the basis vectors of the reasoning subspace, and the resulting projections are then used as input features for hallucination detection in LLMs. By using these projections, HARP reduces the dimension of the feature to approximately 5% of the original, filters out most noise, and achieves enhanced robustness. Experiments across multiple datasets show that HARP achieves state-of-the-art hallucination detection performance; in particular, it achieves an AUROC of 92.8% on TriviaQA, outperforming the previous best method by 7.5%.
OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
Authors: Yang Zhou; Yifan Wang; Jianjun Zhou; Wenzheng Chang; Haoyu Guo; Zizun Li; Kaijing Ma; Xinyue Li; Yating Wang; Haoyi Zhu; Mingyu Liu; Dingning Liu; Jiange Yang; Zhoujie Fu; Junyi Chen; Chunhua Shen; Jiangmiao Pang; Kaipeng Zhang; Tong He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The field of 4D world modeling—aiming to jointly capture spatial geometry and temporal dynamics—has witnessed  remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning.  However, the development of truly general 4D world models remains fundamentally constrained by the availability  of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity,  and spatial-temporal annotations required to support key tasks such as 4D geometric reconstruction, future  prediction, and camera-controlled video generation. To address this gap, we introduce OmniWorld, a large-scale,  multi-domain, multi-modal dataset specifically designed for 4D world modeling. OmniWorld consists of a newly  collected OmniWorld-Game dataset and several curated public datasets spanning diverse domains. Compared with  existing synthetic datasets, OmniWorld-Game provides richer modality coverage, larger scale, and more realistic  dynamic interactions. Based on this dataset, we establish a challenging benchmark that exposes the limitations of  current state-of-the-art (SOTA) approaches in modeling complex 4D environments. Moreover, fine-tuning existing  SOTA methods on OmniWorld leads to significant performance gains across 4D reconstruction and video generation  tasks, strongly validating OmniWorld as a powerful resource for training and evaluation. We envision OmniWorld  as a catalyst for accelerating the development of general-purpose 4D world models, ultimately advancing machines’  holistic understanding of the physical world.
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
Authors: Xinhao Li; Yi Wang; Jiashuo Yu; Xiangyu Zeng; Yuhan Zhu; Haian Huang; Jianfei Gao; Kunchang Li; Yinan He; Chenting Wang; Yu Qiao; Yali Wang; Limin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-context video modeling is critical for multimodal large language models
(MLLMs), enabling them to process movies, online video streams, and so on.
Despite its advances, handling long videos remains challenging due to the difficulty
in efficiently understanding the extremely long video context. This paper aims to
address this issue from aspects of the model architecture, training data, training
strategy, and evaluation benchmark. First, we propose a novel Hierarchical video
token Compression (HiCo) method, which leverages visual redundancy in long
videos to compress long video context from Clip-level to Video-level, reducing the
computation significantly while preserving essential details, achieving an extreme
compression ratio of approximately 1/50 with almost no performance loss. Second,
we introduce a multi-stage short-to-long learning scheme, a large-scale dataset of
real-world long videos named LongVid, and a challenging “Multi-Hop Needle-In-
A-Video-Haystack” benchmark. Finally, we build a powerful video MLLM named
VideoChat-Flash, which shows a leading performance on both mainstream long
and short video benchmarks at the 2B and 7B model scales. It first gets 99.1%
accuracy over 10,000 frames in NIAH among open-source models.
On Smoothness Bounds for Non-Clairvoyant Scheduling with Predictions
Authors: Tianming Zhao; Albert Zomaya
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Algorithms with predictions leverage predictions for unknown inputs in online decision-making. These algorithms are analyzed by consistency, i.e., competitive ratio under perfect predictions, and robustness, i.e., competitive ratio under worst-case predictions. Smooth degrading performance with an increased prediction error is also desirable. This paper refines the notion of smoothness, a function of prediction error, defined as the competitive ratio over the problem instances where predictions are guaranteed to provide additional information. 

With our refined smoothness metric, we establish smoothness bounds for a few scheduling problems, including online total completion time minimization and makespan minimization. For a single machine to minimize the total completion time, we show a lower bound of $\eta$ and a $\eta^2$-smooth algorithm, where $\eta$ is the prediction error ($\eta \geq 1$); the bound holds for small errors. For parallel identical machines to minimize the makespan, we show a lower bound of $2 - O(\eta^{-2})$ and present an $O(\eta^2)$-smooth algorithm for small errors. Both bounds are tighter than the existing ones. For uniformly-related machines to minimize the makespan, we show a tight lower bound of $\lceil \log \eta \rceil$, matched by an $O(\log \eta)$-smooth algorithm.
A$^2$FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid Reasoning
Authors: Qianben Chen; Jingyi Cao; Jiayu Zhang; Tianrui Qin; LiXiaowan; King Zhu; Dingfeng Shi; He Zhu; Minghao Liu; Xiaobo Liang; Ge Zhang; Jian Yang; Yuchen Eleanor Jiang; Wangchunshu Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models split into two families: reasoning-centric LLMs, which strengthen internal chain-of-thought reasoning but cannot invoke external tools, and agentic LLMs, which learn to interact with environments and leverage tools but often lag in deep reasoning. This divide arises from fundamentally different training objectives, leading to mismatched strengths and inefficiency on simple queries, where both families tend to overthink or over-call tools. In this work, we present Adaptive Agent Foundation Model (A$^2$FM), a unified framework that follows a route-then-align principle: the model first learns task-aware routing and then aligns mode-specific trajectories under a shared backbone. To address the inefficiency gap, we introduce a third instant mode that handles simple queries directly, preventing unnecessary reasoning or tool calls while complementing the agentic and reasoning modes. To jointly enhance accuracy and efficiency, we propose Adaptive Policy Optimization (APO), which enforces adaptive sampling across modes and applies a cost-regularized reward. On the 32B scale, A$^2$FM achieves 13.4\% on BrowseComp, 70.4\% on AIME25, and 16.7\% on HLE, setting new SOTA among comparable models and performing competitively with frontier LLMs across agentic, reasoning, and general benchmarks. Notably, the adaptive execution achieves a cost of pass of only \$0.00487 per correct answer—cutting cost by 45.2\% relative to reasoning and 33.5\% relative to agentic, thus delivering substantially higher cost efficiency while maintaining comparable accuracy.
UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models
Authors: Guangxin He; Shen Nie; Fengqi Zhu; Yuankang Zhao; Tianyi Bai; Ran Yan; Jie Fu; Chongxuan Li; Binhang Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion LLMs have attracted growing interest, with plenty of recent work emphasizing their great potential in various downstream tasks; yet the long‑context behavior of diffusion LLMs remains largely uncharted. We present a case study of post‑training techniques for extending the context window of diffusion LLMs (i.e., LLaDA) without retraining from scratch. We show that a simple modification to the standard Rotary Positional Embeddings (RoPE) extension effectively accommodates the probabilistic modeling inherent in the diffusion process, enabling stable scaling to longer context ranges. We further compare masking strategies used during post‑training and analyze their impact on optimization stability and long‑range recall. Instantiating these insights, we introduce UltraLLaDA, a diffusion LLM with a 128K‑token context window that, in our empirical evaluation on long‑context tasks, significantly outperforms training‑free baselines. Our experimental results highlight the special positional extension as a key lever for scaling diffusion LLMs to extended contexts and offer practical guidance for practitioners seeking 128K‑scale context via efficient post‑training.