GenAI4ELab/papercli-papers huggingface dataset:
IGC-Net for conditional average potential outcome estimation over time
Authors: Konstantin Hess; Dennis Frauen; Valentyn Melnychuk; Stefan Feuerriegel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent limitations (e.g., division by propensity scores that are often close to zero), which result in poor performance. As a remedy, we introduce the iterative G-computation network (IGC-Net). Our IGC-Net is a novel, neural end-to-end model which adjusts for time-varying confounding in order to estimate conditional average potential outcomes (CAPOs) over time. Specifically, our IGC-Net is the first neural model to perform fully regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our IGC-Net across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.
Task-free Adaptive Meta Black-box Optimization
Authors: Chao Wang; Licheng Jiao; Lingling Li; Jiaxuan Zhao; Guanchun Wang; Fang Liu; Shuyuan Yang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure optimizers for low-level BBO tasks, thereby eliminating heuristic dependencies. However, existing methods typically require extensive handcrafted training tasks to learn meta-strategies that generalize to target tasks, which poses a critical limitation for realistic applications with unknown task distributions. To overcome the issue, we propose the Adaptive meta Black-box Optimization Model (ABOM), which performs online parameter adaptation using solely optimization data from the target task, obviating the need for predefined task distributions. Unlike conventional metaBBO frameworks that decouple meta-training and optimization phases, ABOM introduces a closed-loop adaptive parameter learning mechanism, where parameterized evolutionary operators continuously self-update by leveraging generated populations during optimization. This paradigm shift enables zero-shot optimization: ABOM achieves competitive performance on synthetic BBO benchmarks and realistic unmanned aerial vehicle path planning problems without any handcrafted training tasks. Visualization studies reveal that parameterized evolutionary operators exhibit statistically significant search patterns, including natural selection and genetic recombination.
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Authors: Siyin Wang; Jinlan Fu; Feihong Liu; Xinzhe He; Huangxuan Wu; Junhao Shi; Kexin Huang; Zhaoye Fei; Jingjing Gong; Zuxuan Wu; Yu-Gang Jiang; See-Kiong Ng; Tat-Seng Chua; Xipeng Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision–Language–Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue instructions directly. Effective collaboration requires robots to infer user intentions proactively.
In this work, we introduce *cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands.* To address this new setting, we present **RoboOmni**, a *Perceiver-Thinker-Talker-Executor* framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution. RoboOmni fuses auditory and visual signals spatiotemporally for robust intention recognition, while supporting direct speech interaction. 
To address the absence of training data for proactive intention recognition in robotic manipulation, we build **OmniAction**, comprising 140k episodes, 5k+ speakers, 2.4k event sounds, 640 backgrounds, and six contextual instruction types. Experiments in simulation and real-world settings show that RoboOmni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance. All datasets, code, and real-world demonstration videos will be released publicly.
Rethinking the Gold Standard: Why Discrete Curvature Fails to Fully Capture Over-squashing in GNNs?
Authors: Jialong Chen; Bowen Deng; Zibin Zheng; Chuan Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As a topological invariant for discrete structures, discrete curvature has been widely adopted in the study of complex networks and graph neural networks. A prevailing viewpoint posits that edges with highly negative curvature will induce graph bottlenecks and the over-squashing phenomenon. In this paper, we critically re-examine this view and put forward our central claim: **high negative curvature is a sufficient but not a necessary condition for over-squashing**. We first construct a family of counterexamples demonstrating the failure of discrete curvature, where some edges are severely squashed, but the curvature still appears positive. Furthermore, extensive experiments demonstrate that the most commonly used discrete curvature measure --- Ollivier–Ricci curvature --- fails to detect as many as 30%~40% of over-squashed edges. To alleviate this limitation, we propose Weighted Augmented Forman-3 Curvature ($\mathsf{WAF3}$), which significantly improves the detection of over-squashed edges. Additionally, we develop a highly efficient approximation algorithm for $\mathsf{WAF3}$, enabling curvature computation on graphs with five million edges in only 23.6 seconds, which is 133.7 times faster than the existing algorithm with the lowest complexity for curvatures.
Bridging the performance-gap between target-free and target-based reinforcement learning
Authors: Théo Vincent; Yogesh Tripathi; Tim Faust; Abdullah Akgül; Yaniv Oren; Melih Kandemir; Jan Peters; Carlo D'Eramo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The use of target networks in deep reinforcement learning is a widely popular solution to mitigate the brittleness of semi-gradient approaches and stabilize learning. However, target networks notoriously require additional memory and delay the propagation of Bellman updates compared to an ideal target-free approach. In this work, we step out of the binary choice between target-free and target-based algorithms. We introduce a new method that uses a copy of the last linear layer of the online network as a target network, while sharing the remaining parameters with the up-to-date online network. This simple modification enables us to keep the target-free's low-memory footprint while leveraging the target-based literature. We find that combining our approach with the concept of iterated $Q$-learning, which consists of learning consecutive Bellman updates in parallel, helps improve the sample-efficiency of target-free approaches. Our proposed method, iterated Shared $Q$-Learning (iS-QL), bridges the performance gap between target-free and target-based approaches across various problems while using a single $Q$-network, thus stepping towards resource-efficient reinforcement learning algorithms.
NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Authors: Tianshi Zheng; Kelvin Kiu Wai Tam; Newt Nguyen Kim Hue Nam; Baixuan Xu; Zhaowei Wang; Cheng Jiayang; Hong Ting Tsang; Weiqi Wang; Jiaxin Bai; Tianqing Fang; Yangqiu Song; Ginny Wong; Simon See
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science.
However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive exploration of complex model systems. To address these critical gaps, we introduce **NewtonBench**, a benchmark comprising 324 scientific law discovery tasks across 12 physics domains. Our design mitigates the evaluation trilemma by using counterfactual law shifts - systematic alterations of canonical laws - to generate a vast suite of problems that are scalable, scientifically relevant, and memorization-resistant.
Moreover, we elevate the evaluation from static function fitting to interactive model discovery, requiring agents to experimentally probe simulated complex systems to uncover hidden principles. Our extensive evaluation of 11 state-of-the-art LLMs reveals a clear but fragile capability for discovery in frontier models: this ability degrades precipitously with increasing system complexity and exhibits extreme sensitivity to observational noise. Notably, we uncover a paradoxical effect of tool assistance: providing a code interpreter can hinder more capable models by inducing a premature shift from exploration to exploitation, causing them to satisfice on suboptimal solutions. These results demonstrate that robust, generalizable discovery in complex, interactive environments remains the core challenge for the future of automated science. By providing a scalable, robust, and scientifically authentic testbed, NewtonBench offers a crucial tool for measuring true progress and guiding the development of next-generation AI agents capable of genuine scientific discovery.
PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints
Authors: Jiahao Huo; Shuliang Liu; Bin Wang; Junyan Zhang; Yibo Yan; Aiwei Liu; Xuming Hu; Mingxun Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing attacks by treating the sentence as the fundamental unit. However, existing methods still lack strong theoretical guarantees of robustness, and reject-sampling–based generation often introduces significant distribution distortions compared with unwatermarked outputs. In this work, we introduce a new theoretical framework on SWM through the concept of proxy functions (PFs) -- functions that map sentences to scalar values. Building on this framework, we propose **PMark**, a simple yet powerful SWM method that estimates the PF median for the next sentence dynamically through sampling while enforcing multiple PF constraints (which we call channels) to strengthen watermark evidence. Equipped with solid theoretical guarantees, **PMark** achieves the desired distortion-free property and improves the robustness against paraphrasing-style attacks. We also provide an empirically optimized version that further removes the requirement for dynamical median estimation for better sampling efficiency. Experimental results show that **PMark** consistently outperforms existing SWM baselines in both text quality and robustness, offering a more effective paradigm for detecting machine-generated text. The source code is available at https://anonymous.4open.science/r/PMark.
A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond
Authors: Nikos Tsikouras; Yorgos Pantis; Ioannis Mitliagkas; Christos Tzamos
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding the dynamics of feature learning in neural networks (NNs) remains a significant challenge.
  The work of (Mousavi-Hosseini et al., 2023) analyzes a multiple index teacher-student setting and shows that a two-layer student attains a low-rank structure in its first-layer weights when trained with stochastic gradient descent (SGD) and a strong regularizer.
  This structural property is known to reduce sample complexity of generalization.
  Indeed, in a second step, the same authors establish algorithm-specific learning guarantees under additional assumptions.
  In this paper, we focus exclusively on the structure discovery aspect and study it under weaker assumptions, more specifically: we allow (a) NNs of arbitrary size and depth, (b) with all parameters trainable, (c) under any smooth loss function, (d) tiny regularization, and (e) trained by any method that attains a second-order stationary point (SOSP), e.g. perturbed gradient descent (PGD). At the core of our approach is a key $\textit{derandomization}$ lemma, which states that optimizing the function $E_{x} \left[g_{\theta}(Wx + b)\right]$ converges to a point where $W = 0$, under mild conditions. The fundamental nature of this lemma directly explains structure discovery and has immediate applications in other domains including an end-to-end approximation for MAXCUT, and computing Johnson-Lindenstrauss embeddings.
Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
Authors: Dexia Chen; Qianjie Zhu; Weibing Li; Yue Yu; Tong Zhang; Ruixuan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pretrained vision-language models (VLMs), such as CLIP, have shown remarkable potential in few-shot image classification and led to numerous effective transfer learning strategies. These methods leverage the pretrained knowledge of VLMs to enable effective domain adaptation while mitigating overfitting through parameter-efficient tuning or instance-based consistency constraints. However, such regularizations often neglect the geometric structure of data distribution, which may lead to distortion of the overall semantic representation. To overcome this limitation, we propose a novel fine-tuning method, Manifold-Preserving and Sculpting Tuning (MPS-Tuning). Regarding the data distribution in feature space as a semantic manifold, MPS-Tuning explicitly constrains the intrinsic geometry of this manifold while further sculpting it to enhance class separability. Specifically, MPS-Tuning preserves both macroscopic and microscopic topological structures of the original manifold by aligning Gram matrices of features before and after fine-tuning. Theoretically, this constraint is shown to approximate an upper bound of the Gromov-Wasserstein distance. Furthermore, features from the image and text modalities are paired, and pairwise similarities are optimized to enhance the manifold’s class discriminability. Extensive experiments demonstrate that MPS-Tuning significantly improves model performance while effectively preserving the structure of the semantic manifold.
PRISM: Festina Lente Proactivity—Risk-Sensitive, Uncertainty-Aware Deliberation for Proactive Agents
Authors: Yuxuan Fu; Xiaoyu Tan; Teqi Hao; Chen Zhan; Xihe Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Proactive agents must decide not only what to say but also whether and when to intervene. Many current systems rely on brittle heuristics or indiscriminate long reasoning, which offers little control over the benefit-burden tradeoff. We formulate the problem as cost-sensitive selective intervention and present PRISM, a novel framework that couples a decision-theoretic gate with a dual-process reasoning architecture. At inference time, the agent intervenes only when a calibrated probability of user acceptance exceeds a threshold derived from asymmetric costs of missed help and false alarms. Inspired by festina lente (Latin: "make haste slowly"), we gate by an acceptance-calibrated, cost-derived threshold and invoke a resource-intensive Slow mode with counterfactual checks only near the decision boundary, concentrating computation on ambiguous and high-stakes cases. Training uses gate-aligned, schema-locked distillation: a teacher running the full PRISM pipeline provides dense, executable supervision on unlabeled interaction traces, while the student learns a response policy that is explicitly decoupled from the intervention gate to enable tunable and auditable control. On ProactiveBench, PRISM reduces false alarms by 22.78% and improves F1 by 20.14% over strong baselines. These results show that principled decision-theoretic gating, paired with selective slow reasoning and aligned distillation, yields proactive agents that are precise, computationally efficient, and controllable. To facilitate reproducibility, we release our code, models, and resources at https://prism-festinalente.github.io/; all experiments use the open-source ProactiveBench benchmark.
D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Authors: Meixi Song; Xin Lin; Dizhe Zhang; Haodong Li; Xiangtai Li; Bo Du; Lu Qi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations. However, performance degradation and instability remain significant under sparse-view conditions. In this work, we identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage. To address these challenges, we propose a unified framework \modelname{}, comprising two key components: a Depth-and-Density Guided Dropout strategy that suppresses overfitting by adaptively masking redundant Gaussians based on density and depth, and a Distance-Aware Fidelity Enhancement module that improves reconstruction quality in under-fitted far-field areas through targeted supervision. Moreover, we introduce a new evaluation metric to quantify the stability of learned Gaussian distributions, providing insights into the robustness of the sparse-view 3DGS. Extensive experiments on multiple datasets demonstrate that our method significantly improves both visual quality and robustness under sparse view conditions. The source code and trained models will be made publicly available.
CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving
Authors: Shuhang Chen; Yunqiu Xu; Junjie Xie; Aojun Lu; Tao Feng; ZEYING HUANG; ZHANG NING; Yi Sun; Yi Yang; Hangjie Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite significant progress, multimodal large language models continue to struggle with visual mathematical problem solving.
Some recent works recognize that visual perception is a bottleneck in visual mathematical reasoning, but their solutions are limited to improving the extraction and interpretation of visual inputs.
Notably, they all ignore the key issue of whether the extracted visual cues are faithfully integrated and properly utilized in subsequent reasoning.
Motivated by this, we present CogFlow, a novel cognitive-inspired three-stage framework that incorporates a knowledge internalization stage, explicitly simulating the hierarchical flow of human reasoning: perception$\Rightarrow$internalization$\Rightarrow$reasoning.
In line with this hierarchical flow, we holistically enhance all its stages.
We devise Synergistic Visual Rewards to boost perception capabilities in parametric and semantic spaces, jointly improving visual information extraction from symbols and diagrams.
To guarantee faithful integration of extracted visual cues into subsequent reasoning, we introduce a Knowledge Internalization Reward model in the internalization stage, bridging perception and reasoning.
Moreover, we design a Visual-Gated Policy Optimization algorithm to further enforce the reasoning is grounded with the visual knowledge, preventing models seeking shortcuts that appear coherent but are visually ungrounded reasoning chains.
Moreover, we contribute a new dataset MathCog for model training, which contains samples with over 120K high-quality perception-reasoning aligned annotations.
Comprehensive experiments and analysis on commonly used visual mathematical reasoning benchmarks validate the superiority of the proposed CogFlow.
Project page: https://shchen233.github.io/cogflow.
EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling
Authors: Xin Luo; Jiahao Wang; Chenyuan Wu; Shitao Xiao; Xiyan Jiang; Defu Lian; Jiajun Zhang; Dong Liu; Zheng Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Instruction-guided image editing has achieved remarkable progress, yet current models still face challenges with complex instructions and often require multiple samples to produce a desired result. Reinforcement Learning (RL) offers a promising solution, but its adoption in image editing has been severely hindered by the lack of a high-fidelity, efficient reward signal. In this work, we present a comprehensive methodology to overcome this barrier, centered on the development of a state-of-the-art, specialized reward model. We first introduce $\textbf{EditReward-Bench}$, a comprehensive benchmark to systematically evaluate reward models on editing quality. Guided by this benchmark, we develop $\textbf{EditScore}$, an efficient model to evaluate the quality of instruction-guided editing. Through meticulous data curation and filtering, EditScore effectively matches the performance of learning proprietary VLMs. Furthermore, coupled with an effective self-ensemble strategy tailored for the generative nature of EditScore, our largest variant even surpasses GPT-5 in the benchmark. We then demonstrate that a high-fidelity reward model is the key to unlocking online RL for image editing. Our experiments show that, while even the largest open-source VLMs fail to provide an effective learning signal, EditScore enables efficient and robust policy optimization. Applying our framework to a strong base model, OmniGen2, results in a final model that shows a substantial and consistent performance uplift. Overall, this work provides the first systematic path from benchmarking to reward modeling to RL training in image editing, showing that a high-fidelity, domain-specialized reward model is the key to unlocking the full potential of RL in this domain. Our code, models, and benchmark will be released publicly.
ROGA: Scaling Generalist Agents for Office Productivity Tasks via Tool Generation
Authors: Mugeng Liu; Xiaojun Ma; Yuhang Xie; Qin Chen; Xuanzhe Liu; Yun Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Automatic tool generation (ATG) has emerged as a key approach to enable the automatic adaptation across diverse tasks within a single generalist agent.
Despite their potential, we argue that current ATG agents, often built on reactive paradigms, fail to effectively adapt to realistic environments requiring long-term reasoning and stateful interaction, particularly in office ecosystems. We empirically show that current ATG agents underperform by up to 27.43%.
This performance degradation stems from three fundamental limitations of prevailing agent paradigms: (1) a failure to build a coherent world model from long, partially observable contexts; (2) a memory-less execution model where stateless actions fail to track state evolution during iterative tasks; and (3) a static capability generation model focusing on one-shot tool generation for immediate needs, thereby forcing redundant regeneration for similar steps.

To address these fundamental limitations, we propose ROGA, which instantiates a new agent paradigm for long-horizon, stateful environments. ROGA moves beyond simple reactive loops by introducing four foundational algorithmic innovations: (1) Active World Modeling, an iterative process where the agent actively probes the environment to construct its own world model; (2) a Persistent Symbolic Memory that explicitly tracks the state evolution for temporal reasoning; and (3) a Dynamic Capability Evolution model for long-term adaptation and meta-learning on the agent's own capabilities.
Comprehensive experiments on widely used benchmarks show that ROGA consistently outperforms existing ATG agents by up to 13.64%.
These results underscore ROGA's potential to advance the ATG paradigm, delivering a practical pathway toward building sustainable generalist agents in realistic environments.