GenAI4ELab/papercli-papers huggingface dataset:
Reinforcement Unlearning via Group Relative Policy Optimization
Authors: Efstratios Zaradoukas; Bardh Prenkaj; Gjergji Kasneci
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: During pretraining, LLMs inadvertently memorize sensitive or copyrighted data, posing significant compliance challenges under legal frameworks like the GDPR and the EU AI Act. Fulfilling these mandates demands techniques that can remove information from a deployed model without retraining from scratch. Existing unlearning approaches attempt to address this need, but often leak the very data they aim to erase, sacrifice fluency and robustness, or depend on costly external reward models. We introduce PURGE (Policy Unlearning through Relative Group Erasure), a novel method grounded in the Group Relative Policy Optimization framework that formulates unlearning as a verifiable problem. PURGE uses an intrinsic reward signal that penalizes any mention of forbidden concepts, allowing safe and consistent unlearning. Our approach achieves up to $\times$46 lower token usage per target than state-of-the-art methods, while improving fluency by +5.48\% and adversarial robustness by +12.02\% over the base model. Extensive evaluation on the Real World Knowledge Unlearning (RWKU) benchmark shows that PURGE reaches 11\% unlearning effectiveness while preserving 98\% of original utility. PURGE shows that framing LLM unlearning as a verifiable task, enables more reliable, efficient, and scalable forgetting, suggesting a promising new direction for unlearning research that combines theoretical guarantees, improved safety, and practical deployment efficiency.
Dual Randomized Smoothing: Beyond Global Noise Variance
Authors: Chenhao Sun; Yuhao Mao; Martin Vechev
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Randomized Smoothing (RS) is a prominent technique for certifying the robustness of neural networks against adversarial perturbations. With RS, achieving high accuracy at small radii requires a small noise variance, while achieving high accuracy at large radii requires a large noise variance. However, the global noise variance used in the standard RS formulation leads to a fundamental limitation: there exists no global noise variance that simultaneously achieves strong performance at both small and large radii. To break through the global variance limitation, we propose a dual RS framework which enables input-dependent noise variances. To achieve that, we first prove that RS remains valid with input-dependent noise variances, provided the variance is locally constant around each input. Building on this result, we introduce two components which form our dual RS framework: (i) a variance estimator first predicts an optimal noise variance for each input, (ii) this estimated variance is then used by a standard RS classifier. The variance estimator is independently smoothed via RS to ensure local constancy, enabling flexible design. We also introduce efficient training strategies to iteratively optimize the two components involved in the framework. Extensive experiments on the CIFAR-10 dataset demonstrate that our dual RS method provides strong performance for both small and large radii—unattainable with global noise variance—while incurring only a 60\% computational overhead at inference. Moreover, it consistently outperforms prior input-dependent noise approaches across most radii, with particularly large gains at radii 0.5, 0.75, and 1.0, achieving relative improvements of 15.6\%, 20.0\%, and 15.7\%, respectively. On ImageNet, dual RS remains effective across all radii, with 8.6\%, 17.1\% and 9.1\% performance advantages at radii 0.5, 1.0 and 1.5 respectively. Additionally, the proposed dual RS framework naturally provides a routing perspective for certified robustness, improving the accuracy-robustness trade-off with off-the-shelf expert RS models.  Our code is available at https://github.com/eth-sri/Dual-Randomized-Smoothing.
CellAgent: LLM-Driven Multi-Agent Framework  for Natural Language-Based Single-Cell Analysis
Authors: Yihang Xiao; Jinyi Liu; YAN ZHENG; Shaoqing Jiao; Jianye HAO; Xiaohan Xie; Limingzhi; Ruitao Wang; Fei Ni; Yuxiao Li; Zhen Wang; Xuequn Shang; Zhijie Bao; Changxiao Yang; Jiajie Peng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data analysis are pivotal for advancing biological research, enabling precise characterization of cellular heterogeneity. However, existing analysis approaches require extensive manual programming and complex tool integration, posing significant challenges for researchers. To address this, we introduce CellAgent, an autonomous, LLM-driven approach that performs end-to-end scRNA-seq and spatial transcriptomics data analysis through natural language interactions. CellAgent employs a multi-agent hierarchical decision-making framework, simulating a “deep-thinking” workflow to ensure that analytical steps are logically coherent and aligned with the overarching research goal. To further enhance its capabilities, we develop sc-Omni, a high-performance, expert-curated toolkit that consolidates essential tools for scRNA-seq and spatial transcriptomics analysis. Additionally, we introduce a self-reflective optimization mechanism, enabling automated, iterative refinement of results through specialized evaluation methods, effectively replacing traditional manual assessments. Benchmarking against human experts demonstrates that CellAgent achieves significant improvement in efficiency across multiple downstream applications while maintaining excellent performance comparable to existing approaches and preserving natural language interactions. By translating high-level scientific questions into optimized computational workflows, CellAgent represents a step toward a new, more accessible paradigm in bioinformatics, allowing researchers to perform complex data analyses autonomously. In lowering technical barriers, CellAgent serves to advance the democratization of the scientific discovery process in genomics.
Routing, Cascades, and User Choice for LLMs
Authors: Rafid Mahmood
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: To mitigate the trade-offs between performance and costs, LLM providers route user tasks to different models based on task difficulty and latency. We study the effect of LLM routing with respect to user behavior. We propose a game between an LLM provider with two models (standard and reasoning) and a user who can re-prompt or abandon tasks if the routed model cannot solve them. The user's goal is to maximize their utility minus the delay from using the model, while the provider minimizes the cost of servicing the user. We solve this Stackelberg game by fully characterizing the user best response and simplifying the provider problem. We observe that in nearly all cases, the optimal routing policy involves a static policy with no cascading that depends on the expected utility of the models to the user.
Furthermore, we reveal a misalignment gap between the provider-optimal and user-preferred routes when the user's and provider's rankings of the models with respect to utility and cost differ. Finally, we demonstrate conditions for extreme misalignment where providers are incentivized to throttle the latency of the models to minimize their costs, consequently depressing user utility. The results yield simple threshold rules for single-provider, single-user interactions and clarify when routing, cascading, and throttling help or harm.
Out of the Memory Barrier: A Highly Memory-Efficient Training System for LLMs with Million-Token Contexts
Authors: Wenhao Li; Daohai Yu; Gen Luo; Yuxin Zhang; Yifan Wu; Jiaxin Liu; Ziyang Gong; Zimu Liao; Fei Chao; Rongrong Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training Large Language Models (LLMs) on long contexts is severely constrained by prohibitive GPU memory overhead, not training time. The primary culprits are the activations, whose memory footprints scale linearly with sequence length. We introduce OOMB, a highly memory-efficient training system that directly confronts this barrier. Our approach employs a chunk-recurrent training framework with on-the-fly activation recomputation, which maintains a constant activation memory footprint ($\mathcal{O}(1)$) and shifts the primary bottleneck to the growing KV cache. To manage the KV cache, OOMB integrates a suite of synergistic optimizations: a paged memory manager for both the KV cache and its gradients to eliminate fragmentation, asynchronous CPU offloading to hide data transfer latency, and page-level sparse attention to reduce both computational complexity and communication overhead. The synergy of these techniques yields exceptional efficiency. Our empirical results show that for every additional 10K tokens of context, the end-to-end training memory overhead increases by a mere 10MB for Qwen2.5-7B. This allows training Qwen2.5-7B with a 4M-token context on a single H200 GPU, a feat that would otherwise require a large cluster using context parallelism. This work represents a substantial advance in resource efficiency for long-context LLM training. The source code is available for review at https://anonymous.4open.science/r/oomb/README.md.
DiSRouter: Distributed Self-Routing for LLM Selections
Authors: Hang Zheng; Hongshen Xu; Yongkai.lin; Shuai Fan; Lu Chen; Kai Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The proliferation of Large Language Models (LLMs) has created a diverse ecosystem of models with highly varying performance and costs, necessitating effective query routing to balance performance and expense. Current routing systems often rely on a centralized external router trained on a fixed set of LLMs, making them inflexible and prone to poor performance since the small router can not fully understand the knowledge boundaries of different LLMs. We introduce DiSRouter (Distributed Self-Router), a novel paradigm that shifts from centralized control to distributed routing. In DiSRouter, a query traverses a network of LLM agents, each independently deciding whether to answer or route to other agents based on its own self-awareness—its ability to judge its competence. This distributed design offers superior flexibility, scalability, and generalizability. To enable this, we propose a two-stage Self-Awareness Training pipeline that enhances each LLM's self-awareness. Extensive experiments demonstrate that DiSRouter significantly outperforms existing routing methods in utility across various scenarios, effectively distinguishes between easy and hard queries, and shows strong generalization to out-of-domain tasks. Our work validates that leveraging an LLM's intrinsic self-awareness is more effective than external assessment, paving the way for more modular and efficient multi-agent systems.
Quotient-Space Diffusion Models
Authors: Yixian Xu; Yusong Wang; Shengjie Luo; Kaiyuan Gao; Tianyu He; Di He; Chang Liu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Diffusion-based generative models have reformed generative AI, and also enabled new capabilities in the science domain, _e.g._, fast generation of 3D structures of molecules. In such tasks, there is often a _symmetry_ in the system, identifying elements that can be converted by certain transformations as equivalent. Equivariant diffusion models guarantee a symmetric distribution, but miss the opportunity to make learning easier, while alignment-based simplification attempts fail to preserve the target distribution.
In this work, we develop _quotient-space diffusion models_, a principled generative framework to fully handle and leverage symmetry.
By viewing the intrinsic generation process on the quotient space, the exact construction that removes symmetry redundancy, the framework simplifies learning by allowing model output to have an arbitrary intra-equivalence-class movement, while generating the correct symmetric target distribution with guarantee.
We instantiate the framework for molecular structure generation which follows $\\mathrm{SE}(3)$ (rigid-body movement) symmetry.
It improves the performance over equivariant diffusion models and outperforms alignment-based methods universally for small molecules and proteins, representing a new framework that surpasses previous symmetry treatments in generative models.
How to Lose Inherent Counterfactuality in Reinforcement Learning
Authors: Ezgi Korkmaz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning in high-dimensional MDPs with complex state dynamics became possible with the progress achieved in reinforcement learning research. 
At the same time, deep neural policies have been observed to be highly unstable with respect to the minor variations in their state space, causing volatile and unpredictable behaviour. 
To alleviate these volatilities, a line of work suggested techniques to cope with this problem via explicitly regularizing the temporal difference loss to ensure local $\epsilon$-invariance in the state space.
In this paper,  we provide theoretical foundations on the impact of 
robust, i.e. adversarial,
training on reinforcement learning.
Our comprehensive theoretical and experimental analysis reveals that standard reinforcement learning inherently learns counterfactual values while recent training techniques that focus on explicitly enforcing $\epsilon$-local invariance cause policies to lose counterfactuality, and further result in learning misaligned and inconsistent values. 
In connection to this analysis, we further highlight that this line of training methods breaks the core intuition and the true biological inspiration of reinforcement learning, sacrifices essential inherent skills that enable reasoning and generalization, and introduces an intrinsic gap between how natural intelligence understands and interacts with an environment in contrast to AI agents trained via $\epsilon$-local invariance methods.
The misalignment, inaccuracy and the loss of counterfactuality revealed in our paper further demonstrate the need to rethink the approach in establishing truly reliable and generalizable reinforcement learning policies.
EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark
Authors: Deheng Zhang; Yuqian Fu; Runyi Yang; Yang Miao; Tianwen Qian; Xu Zheng; Guolei Sun; Ajad Chhatkuli; Xuanjing Huang; Yu-Gang Jiang; Luc Van Gool; Danda Pani Paudel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Most existing benchmarks for egocentric vision understanding focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. A key feature of EgoNight is the introduction of day–night aligned videos, which enhance night annotation quality using the daytime data and reveal clear performance gaps between lighting conditions. To achieve this, we collect both synthetic videos rendered by Blender and real-world recordings, ensuring that scenes and actions are visually and temporally aligned. Leveraging these paired videos, we construct EgoNight-VQA, supported by a novel day-augmented night auto-labeling engine and refinement through extensive human verification. Each QA pair is double-checked by annotators for reliability. In total, EgoNight-VQA contains 3658 QA pairs across 90 videos, spanning 12 diverse QA types, with more than 300 hours of human work. Evaluations of the state-of-the-art multimodal large language models (MLLMs) reveal substantial performance drops when transferring from day to night, underscoring the challenges of reasoning under low-light conditions. Beyond VQA, EgoNight also introduces two auxiliary tasks, day–night correspondence retrieval and egocentric depth estimation at night, that further explore the boundaries of existing models. We believe EgoNight-VQA provides a strong foundation for advancing application-driven egocentric vision research and for developing models that generalize across illumination domains.  The code and data can be found in https://github.com/dehezhang2/EgoNight
Learning to Generate Stylized Handwritten Text via a Unified Representation of Style, Content, and Noise
Authors: Honglie Wang; Yan-Ming Zhang; Wangzi Yao; Fei Yin; Cheng-Lin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion-based approaches have advanced generation fidelity, they typically rely on auxiliary style or content encoders with handcrafted objectives, leading to complex training pipelines and limited interaction across factors. In this work, we present InkSpire, a diffusion transformer based model that unifies style, content, and noise within a shared latent space. By eliminating explicit encoders, InkSpire streamlines optimization while enabling richer feature interaction and stronger in-context generation. To further enhance flexibility, we introduce a multi-line masked infilling strategy that allows training directly on raw text-line images, together with a revised positional encoding that supports arbitrary-length multi-line synthesis and fine-grained character editing. Moreover, InkSpire is trained on a bilingual Chinese–English corpus, enabling a single model to handle both Chinese and English handwriting generation with high fidelity and stylistic diversity, thereby overcoming the need for language-specific systems. Extensive experiments on IAM and ICDAR2013 demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared to prior state-of-the-art methods.
Dynamic Multi-sample Mixup with Gradient Exploration for Open-set Graph Anomaly Detection
Authors: Caiyang Yu; Wei Ju; Haixin Wang; Yifan Wang; Ziyue Qiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper studies the problem of open-set graph anomaly detection, which aims to generalize a graph neural network (GNN) trained with a small number of both normal and abnormal nodes to detect unseen anomalies different from training anomalies during inference. This problem is highly challenging due to both the data scarcity of unseen anomalies and the label scarcity for training nodes. Towards this end, we propose a novel approach named Dynamic Multi-sample Mixup with Gradient Exploration (DEMO) for open-set graph anomaly detection. The core of our proposed DEMO is to leverage a dynamic framework to adapt the optimization procedure with high generalizability. In particular, our DEMO first adaptively fuses multiple seen nodes to simulate the unseen anomalies, which expands the decision boundary for the detection model with enhanced generalizability. Moreover, we dynamically adjust sample weights based on their energy gradients to prioritize uncertain and informative nodes, ensuring a robust optimization procedure. To further address both label scarcity and severe class imbalance, we maintain a memory bank of historical records to guide the pseudo-labeling process of unlabeled nodes. Extensive experiments on various benchmark datasets validate the superiority of the proposed DEMO in comparison to various baselines.
ConvT3: Structured State Kernels for Convolutional State Space Models
Authors: Jaeyoung Hong; Yun Young Choi; Joohwan Ko; Minseon Gwak
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling long spatiotemporal sequences requires capturing both complex spatial correlations and temporal dependencies.
Convolutional State Space Models (ConvSSMs) have been proposed to incorporate spatial modeling in State Space Models (SSMs) using the convolution of tensor-valued states and kernels. 
Yet, existing implementations remain limited to $1\times 1$ state kernels for computational feasibility, which limits the modeling capacity of ConvSSMs.
We introduce a novel spatiotemporal model, ConvT3 (ConvSSM using Tridiagonal Toeplitz Tensors), designed to equivalently realize ConvSSMs with extended $3\times 3$ state kernels.
ConvT3 structures a state kernel for its corresponding tensor to be composed as a structured SSM matrix on hidden state dimensions and a constrained tridiagonal Toeplitz tensor on spatial dimensions.
We show that the structured tensor can be diagonalized, which enables efficient parallel training while leveraging $3\times 3$ state convolutions.
We demonstrate that ConvT3 effectively embeds rich spatial and temporal information into the dynamics of tensor-valued states, achieving state-of-the-art performance on most metrics in long-range video generation and physical system modeling.
Cat-PO: Cross-modal Adaptive Token-rewards for Preference Optimization in Truthful Multimodal LLMs
Authors: Zhixiao Zheng; Zheren Fu; Zhiyuan Yao; Dongming Zhang; Zhendong Mao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-modal Large Language Models (MLLMs) have shown remarkable generative capabilities across multi-modal tasks, yet remain plagued by hallucinations where generated textual contents are semantically inconsistent with the input images. This work reveals that existing multi-modal preference optimization methods exhibit shortcomings at the preference data decoding stage. Specifically, different response tokens exhibit varying degrees of association with visual content, and consequently, their contributions to reducing hallucinations and generating high-quality responses differ. Nevertheless, most existing methods do not distinguish in their treatment, often handling them uniformly. To address this challenge, we propose a novel preference alignment method: Cross-modal Adaptive Token-rewarded Preference Optimization (Cat-PO). Building upon direct preference optimization, Cat-PO calculates hierarchical visual relevance rewards for each response token at global, local, and semantic levels. It then organically integrates these three rewards to construct a smooth reward mechanism and designs an innovative KL-based customized loss for rewarded tokens, thereby enabling fine-grained correction of hallucinatory outputs. Extensive experiments on various base models and evaluation benchmarks demonstrate that our Cat-PO can significantly reduce hallucinations and align with human preferences to enhance the truthfulness of MLLMs.
C-Evolve: Consensus-based Evolution for Prompt Groups
Authors: Tiancheng Li; Yuhang Wang; Zhiyang Chen; Zijun Wang; Liyuan Ma; Guo-Jun Qi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prompt evolution algorithms offer a powerful paradigm for enhancing AI systems based on closed-source models, while few work explores whether aggregating results from multiple prompts to reach a  consensus can further advance the system capability boundary. In this paper, we introduce Consensus-Evolve (C-Evolve), an evolutionary algorithm that discovers a group of prompts whose aggregated outputs after majority voting achieve optimal performance. More specifically, C-Evolve employs an island-based evolutionary algorithm to maintain population diversity, and prompts from distinct islands are selected to form groups to aggregate their outputs. The key difference from single individual evolution is a voting score, which evaluates each individual prompt's contribution within groups. We take this as the fitness score for evolution instead of individual performance. Consequently, C-Evolve is more likely to produce and maintain prompts with higher potential to form a high-performing group and eliminate low-performing ones, gradually improving the group performance after reaching consensus. Our method achieves state-of-the-art performance across a wide range of tasks, including both open-ended tasks like HotpotQA and closed-ended tasks like MATH. On Qwen3-8B, C-Evolve achieves 70.67\% on HotpotQA and 43.88\% on IFBench, which are 4.95\% and 2.73\% higher than GEPA, respectively. For GPT-4.1-mini, the accuracy on IFBench is further improved to 47.96\% and reaches 95.33\% in the MATH benchmark. These results demonstrate the C-Evolve's competitive performance.