GenAI4ELab/papercli-papers huggingface dataset:
Random-projection ensemble dimension reduction
Authors: Wenxing Zhou; Timothy Ivor Cannings
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce a new, flexible, and theoretically justified framework for dimension reduction in high-dimensional regression, based on an ensemble of random projections. Specifically, we consider disjoint groups of independent random projections, retain the best projection in each group according to the empirical regression performance on the projected covariates, and then aggregate the selected projections via singular value decomposition. The singular values quantify the relative importance of corresponding projection directions and guide the dimension selection process. We investigate various aspects of our framework, including the choice of projection distribution and the number of projections used. Our theoretical results show that the expected estimation error decreases as the number of groups of projections increases. Finally, we demonstrate that our proposal consistently matches or outperforms state-of-the-art methods through extensive numerical studies on simulated and real data.
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning
Authors: Zhenghai Xue; Longtao Zheng; Qian Liu; Yingru Li; Xiaosen Zheng; Zejun MA; Bo An
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) can enhance their reasoning by interacting with external tools, a paradigm known as Tool-Integrated Reasoning (TIR). However, extending TIR to multi-turn settings using Reinforcement Learning (RL) often exhibits training instability and degraded performance. We attribute the instability to harmful negative samples resulting from distributional drift and compounding errors induced by using external tool outputs during multi-turn rollout. To address this issue, we introduce SimpleTIR, a simple method that stabilizes multi-turn TIR training via filtering out trajectories with "void turns", i.e., turns that yield neither a code block nor a final answer. Specifically, we remove those trajectories from the policy update to block harmful gradients, while retaining them in advantage estimation to keep the estimate unbiased. Extensive experiments show that SimpleTIR effectively mitigates gradient norm explosion and stabilizes multi-turn RL training from base models. It achieves state-of-the-art performance on challenging math reasoning benchmarks, including an AIME24 score of 50.5 starting from the Qwen2.5-7B base model. SimpleTIR also promotes more diverse reasoning behaviors such as self-correction and cross-validation, outperforming prior methods trained from stronger instruction-tuned models.
Predicting LLM Output Length via Entropy-Guided Representations
Authors: Huanyi Xie; Yubin Chen; Liangyu Wang; Lijie Hu; Di Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched inference. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling scenarios. We introduce a lightweight framework that reuses the main model's internal hidden states for efficient length prediction. Our framework features two core components: 1) Entropy-Guided Token Pooling (EGTP), which uses on-the-fly activations and token entropy for highly accurate static prediction with negligible cost, and 2) Progressive Length Prediction (PLP), which dynamically estimates the remaining length at each decoding step to handle stochastic generation. To validate our approach, we build and release ForeLen, a comprehensive benchmark with long-sequence, Chain-of-Thought, and RL data. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline. Integrating our methods with a length-aware scheduler yields significant end-to-end throughput gains. Our work provides a new technical and evaluation baseline for efficient LLM inference.
STAR: Strategy-driven Automatic Jailbreak Red-teaming For Large Language Model
Authors: Jianing Liu; Qingming Li; Jiahao Chen; Rui Zeng; Binbin Zhao; Shouling Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Jailbreaking refers to techniques that bypass the safety alignment of large language models (LLMs) to elicit harmful outputs, and automated red-teaming has become a key approach for detecting such vulnerabilities before deployment. However, most existing red-teaming methods operate directly in text space, where they tend to generate semantically similar prompts and thus fail to probe the broader spectrum of latent vulnerabilities within a model. To address this limitation, we shift the exploration of jailbreak strategies from conventional text space to the model’s latent activation space and propose STAR (**ST**rategy-driven **A**utomatic Jailbreak **R**ed-teaming), a black-box framework for systematically generating jailbreak prompts. STAR is composed of two modules: (i) strategy generation module, which extracts the principal components of existing strategies and recombines them to generate novel ones; and (ii) prompt generation module, which translates abstract strategies into concrete jailbreak prompts with high success rates. Experimental results show that STAR substantially outperforms state-of-the-art baselines in terms of both attack success rate and strategy diversity. These findings highlight critical vulnerabilities in current alignment techniques and establish STAR as a more powerful paradigm for comprehensive LLM security evaluation.
Imagine How To Change: Explicit Procedure Modeling for Change Captioning
Authors: Jiayang Sun; Zixin Guo; Min Cao; Guibo Zhu; Jorma Laaksonen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Change captioning generates descriptions that explicitly describe the differences between two visually similar images. Existing methods operate on static image pairs, thus ignoring the rich temporal dynamics of the change procedure, which is the key to understand not only what has changed but also how it occurs. We introduce ProCap, a novel framework that reformulates change modeling from static image comparison to dynamic procedure modeling.
ProCap features a two-stage design: The first stage trains a procedure encoder to learn the change procedure from a sparse set of keyframes. 
These keyframes are obtained by automatically generating intermediate frames to make the implicit procedural dynamics explicit and then sampling them to mitigate redundancy.
Then the encoder learns to capture the latent dynamics of these keyframes via a caption-conditioned, masked reconstruction task.
The second stage integrates this trained encoder within an encoder-decoder model for captioning.
Instead of relying on explicit frames from the previous stage---a process incurring computational overhead and sensitivity to visual noise---we introduce learnable procedure queries to prompt the encoder for inferring the latent procedure representation, which the decoder then translates into text. The entire model is then trained end-to-end with a captioning loss, ensuring the encoder's output is both temporally coherent and captioning-aligned. Experiments on three datasets demonstrate the effectiveness of ProCap. Code and pre-trained models are available at https://github.com/BlueberryOreo/ProCap.
BigMaQ: A Big Macaque Motion and Animation Dataset Bridging Image and 3D Pose Representations
Authors: Lucas Martini; Alexander Lappe; Anna Bognár; Rufin Vogels; Martin A. Giese
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The recognition of dynamic and social behavior in animals is fundamental for advancing several areas of the life sciences, including ethology, ecology, medicine and neuroscience. Recent progress in deep learning has enabled an automated recognition of such behavior from video data. However, an accurate reconstruction of the three-dimensional (3D) pose and shape has not been integrated into this process. Especially for non-human primates, the animals phylogenetically closest to humans, mesh-based tracking efforts lag behind those for other species, leaving pose descriptions restricted to sparse keypoints that are unable to fully capture the richness of action dynamics. To address this gap, we introduce the $\textbf{Big Ma}$ca$\textbf{Q}$ue 3D Motion and Animation Dataset ($\texttt{BigMaQ}$), a large-scale dataset comprising more than 750 scenes of interacting rhesus macaques with detailed 3D pose descriptions of skeletal joint rotations. Recordings were obtained from 16 calibrated cameras and paired with action labels derived from a curated ethogram. Extending previous surface-based animal tracking methods, we construct subject-specific textured avatars by adapting a high-quality macaque template mesh to individual monkeys. This allows us to provide pose descriptions that are more accurate than previous state-of-the-art surface-based animal tracking methods. From the original dataset, we derive BigMaQ500, an action recognition benchmark that links surface-based pose vectors to single frames across multiple individual monkeys. By pairing features extracted from established image and video encoders with and without our pose descriptors, we demonstrate substantial improvements in mean average precision (mAP) when pose information is included. With these contributions, $\texttt{BigMaQ}$ establishes the first dataset that both integrates dynamic 3D pose-shape representations into the learning task of animal action recognition and provides a rich resource to advance the study of visual appearance, posture, and social interaction in non-human primates. The code and data are publicly available at [https://martinivis.github.io/BigMaQ/](https://martinivis.github.io/BigMaQ/).
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Authors: Emil Javurek; Valentyn Melnychuk; Jonas Schweisthal; Konstantin Hess; Dennis Frauen; Stefan Feuerriegel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predicting individualized potential outcomes in sequential decision-making is central
 for optimizing therapeutic decisions in personalized medicine (e.g., which
dosing sequence to give to a cancer patient). However, predicting potential out-
comes over long horizons is notoriously difficult. Existing methods that break the
curse of the horizon typically lack strong theoretical guarantees such as orthogonality
 and quasi-oracle efficiency. In this paper, we revisit the problem of predicting
 individualized potential outcomes in sequential decision-making (i.e., estimating
 Q-functions in Markov decision processes with observational data) through a
causal inference lens. In particular, we develop a comprehensive theoretical foundation
 for meta-learners in this setting with a focus on beneficial theoretical properties.
 As a result, we yield a novel meta-learner called DRQ-learner and establish
that it is: (1) doubly robust (i.e., valid inference under model misspecification),
(2) Neyman-orthogonal (i.e., insensitive to first-order estimation errors in the nuisance
 functions), and (3) achieves quasi-oracle efficiency (i.e., behaves asymptotically
 as if the ground-truth nuisance functions were known). Our DRQ-learner is
applicable to settings with both discrete and continuous state spaces. Further, our
DRQ-learner is flexible and can be used together with arbitrary machine learning
 models (e.g., neural networks). We validate our theoretical results through
numerical experiments, thereby showing that our meta-learner outperforms state-of-the-art baselines.
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
Authors: Konstantin Hess; Dennis Frauen; Mihaela van der Schaar; Stefan Feuerriegel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases exponentially with longer prediction horizons. Thus, the observed data contain little support for many plausible treatment sequences, which creates severe overlap problems. Existing meta-learners for the time-varying setting typically assume adequate treatment overlap, and thus suffer from exploding estimation variance when the overlap is low. To address this problem, we introduce a novel overlap-weighted orthogonal WO meta-learner for estimating HTEs that targets regions in the observed data with high probability of receiving the interventional treatment sequences. This offers a fully data-driven approach through which our WO-learner can counteract instabilities as in existing meta-learners and thus obtain more reliable HTE estimates. Methodologically, we develop a novel Neyman-orthogonal population risk function that minimizes the overlap-weighted oracle risk. We show that our WO-learner has the favorable property of Neyman-orthogonality, meaning that it is robust against misspecification in the nuisance functions. Further, our WO-learner is fully model-agnostic and can be applied to any machine learning model. Through extensive experiments with both transformer and LSTM backbones, we demonstrate the benefits of our novel WO-learner.
AnyUp: Universal Feature Upsampling
Authors: Thomas Wimmer; Prune Truong; Marie-Julie Rakotosaona; Michael Oechsle; Federico Tombari; Bernt Schiele; Jan Eric Lenssen
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propose an *inference-time* feature-agnostic upsampling architecture to alleviate this limitation and improve upsampling quality. In our experiments, AnyUp sets a new state of the art for upsampled features, generalizes to different feature types, and  preserves feature semantics while being efficient and easy to apply to a wide range of downstream tasks.
From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Authors: Saket Tiwari; Tejas Kotwal; George Konidaris
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control.
Building on previous work, we introduce a viable model of actor–critic algorithm that incorporates both exploration and stochastic transitions.
For single-hidden-layer neural networks, we show that the state of the environment can be formulated as a two time scale process: the environment time and the gradient time.
Within this formulation, we characterize how the time-dependent random variables that represent the environment's state and estimate of the cumulative discounted return evolve over gradient steps in the infinite width limit of two-layer networks.
Using the theory of stochastic differential equations, we derive, for the first time in continuous RL, an equation describing the infinitesimal change in the state distribution at each gradient step, under a vanishingly small learning rate.
Overall, our work provides a novel nonparametric formulation for studying overparametrized neural actor-critic algorithms.
We empirically corroborate our theoretical result using a toy continuous control task.
A2D: Any-Order, Any-Step Safety Alignment for Diffusion Language Models
Authors: Wonje Jeung; Sangyeon Yoon; Yoonjun Cho; Dongjae Jeon; Sangwoo Shin; Hyesoo Hong; Albert No
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion large language models (dLLMs) enable any-order generation, but this flexibility enlarges the attack surface: harmful spans may appear at arbitrary positions, and template-based prefilling attacks such as DIJA bypass response-level refusals.
We introduce A2D (Any-Order, Any-Step Defense), a token-level alignment method that aligns dLLMs to emit an [EOS] refusal signal whenever harmful content arises. By aligning safety directly at the token-level under randomized masking, A2D achieves robustness to both any-decoding-order and any-step prefilling attacks under various conditions. It also enables real-time monitoring: dLLMs may begin a response but automatically terminate if unsafe continuation emerges. On safety benchmarks, A2D consistently prevents the generation of harmful outputs, slashing DIJA success rates from over 80\% to near-zero (1.3\% on LLaDA-8B-Instruct, 0.0\% on Dream-v0-Instruct-7B), and thresholded [EOS] probabilities allow early rejection, yielding up to 19.3× faster safe termination.
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
Authors: Shangkun Sun; Ruyang Liu; Haoran Tang; Yixiao Ge; Haibo Lu; Jiankun Yang; Chen Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The past year has witnessed the significant advancement of video-based large language models. However, the challenge of developing a unified model for both short and long video understanding remains unresolved. Most existing video LLMs cannot handle hour-long videos, while methods custom for long videos tend to be ineffective for shorter videos and images. In this paper, we identify the key issue as the redundant content in videos. To address this, we propose a novel pooling strategy that simultaneously achieves token compression and instruction-aware visual feature aggregation. Our model is termed Prompt-guided Pooling LLaVA, or PPLLaVA for short. Specifically, PPLLaVA consists of three core components: the CLIP-based visual-prompt alignment that extracts visual information relevant to the user's instructions, the prompt-guided pooling that compresses the visual sequence to arbitrary scales using convolution-style pooling, and the clip context extension designed for lengthy prompt common in visual dialogue. Extensive experiments have validated the performance of our model. With superior throughput, PPLLaVA achieves better results on image benchmarks as a video LLM, while achieving state-of-the-art performance across various video benchmarks, excelling in tasks ranging from caption generation to multiple-choice questions, and handling video lengths from seconds to hours.
Jailbreaking on Text-to-Video Models via Scene Splitting Strategy
Authors: Wonjun Lee; Haon Park; Doehyeon Lee; Bumsub Ham; Suhyun Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent studies have explored vulnerabilities in models like LLMs, VLMs, and Text-to-Image (T2I) models through jailbreak attacks, T2V models remain largely unexplored, leaving a significant safety gap. To address this gap, we introduce SceneSplit, a novel black-box jailbreak method that works by fragmenting a harmful narrative into multiple scenes, each individually benign. This approach manipulates the generative output space, the abstract set of all potential video outputs for a given prompt, using the combination of scenes as a powerful constraint to guide the final outcome. While each scene individually corresponds to a wide and safe space where most outcomes are benign, their sequential combination collectively restricts this space, narrowing it to an unsafe region and significantly increasing the likelihood of generating a harmful video. This core mechanism is further enhanced through iterative scene manipulation, which bypasses the safety filter within this constrained unsafe region. Additionally, a strategy library that reuses successful attack patterns further improves the attack's overall effectiveness and robustness. To validate our method, we evaluate SceneSplit across 11 safety categories from T2VSafetyBench on T2V models. Our results show that it achieves a high average Attack Success Rate (ASR) of 77.2% on Luma Ray2, 84.1% on Hailuo, 78.2% on Veo2, 78.6% on Kling V1.0, and 68.6% on Sora2, significantly outperforming the existing baselines. Through this work, we demonstrate that current T2V safety mechanisms are vulnerable to attacks that exploit narrative structure, providing new insights for understanding and improving the safety of T2V models.
A Comprehensive Information-Decomposition Analysis of Large Vision-Language Models
Authors: Lixin Xiu; Xufang Luo; Hideki Nakayama
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large vision-language models (LVLMs) achieve impressive performance, yet their internal decision-making processes remain opaque, making it difficult to determine if the success stems from true multimodal fusion or from reliance on unimodal priors. To address this attribution gap, we introduce a novel framework using partial information decomposition (PID) to quantitatively measure the ``information spectrum'' of LVLMs---decomposing a model's decision-relevant information into redundant, unique, and synergistic components. By adapting a scalable estimator to modern LVLM outputs, our model-agnostic pipeline profiles 26 LVLMs on four datasets across three dimensions---\emph{breadth} (cross-model \& cross-task), \emph{depth} (layer-wise information dynamics), and \emph{time} (learning dynamics across training). Our analysis reveals two key results: (i) two task regimes (synergy-driven vs.\ knowledge-driven) and (ii) two stable, contrasting family-level strategies (fusion-centric vs.\ language-centric). We also uncover a consistent three-phase pattern in layer-wise processing and identify visual instruction tuning as the key stage where fusion is learned. Together, these contributions provide a quantitative lens beyond accuracy-only evaluation and offer insights for analyzing and designing the next generation of LVLMs. Code and data are available at \url{https://github.com/RiiShin/pid-lvlm-analysis}.
High-dimensional Analysis of Synthetic Data Selection
Authors: Parham Rezaei; Filip Kovačević; Francesco Locatello; Marco Mondelli
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Despite the progress in the development of generative models, their usefulness in creating synthetic data that improve prediction performance of classifiers has been put into question. Besides heuristic principles such as ''synthetic data should be close to the real data distribution'', it is actually not clear which specific properties affect the generalization error. Our paper addresses this question through the lens of high-dimensional regression. Theoretically, we show that, for linear models, the *covariance shift* between the target distribution and the distribution of the synthetic data affects the generalization error but, surprisingly, the mean shift does not. Furthermore, in some regimes, we prove that matching the covariance of the target distribution is optimal. Remarkably, the theoretical insights for linear models carry over to deep neural networks and generative models. We empirically demonstrate that the *covariance matching* procedure (matching the covariance of the synthetic data with that of the data coming from the target distribution) performs well against several recent approaches for synthetic data selection, across various training paradigms, datasets and generative models used for augmentation.