GenAI4ELab/papercli-papers huggingface dataset:
Web-CogReasoner: Towards Multimodal Knowledge-Induced Cognitive Reasoning for Web Agents
Authors: Yuhan Guo; Guocong; Aiwen Sun; Hongliang He; Xinyu Yang; Yue Lu; Yingji Zhang; Xuntao Guo; Dong Zhang; Jianzhuang Liu; Jiang Duan; Yijia Xiao; Liangjian Wen; Haiming Xu; Yong Dai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large-scale models have significantly advanced the development of web agents, enabling them to perceive and interact with the digital environment in a manner analogous to human cognition. In this paper, we argue that web agents must first acquire sufficient knowledge to engage in cognitive reasoning effectively. Therefore, we decompose a web agent's capabilities into two essential stages: knowledge content learning and cognitive processes. To formalize this, we propose Web-CogKnowledge Framework, which categorizes knowledge into Factual, Conceptual, and Procedural domains. In this framework, knowledge content learning corresponds to the agent's processes of Memorizing and Understanding, which rely on the former two types of knowledge, respectively, representing the "what" of learning. Conversely, cognitive processes correspond to Exploring, grounded in Procedural knowledge, defining the "how" of reasoning and action. To facilitate knowledge acquisition, we construct the Web-CogDataset, a structured resource curated from 14 real-world websites, designed to instill the core knowledge necessary for a web agent systematically. This dataset serves as the agent's conceptual grounding—the "nouns" upon which comprehension is built—as well as the basis for learning how to reason and act. Building on this foundation, we operationalize these processes through a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing and training our proposed multimodal web agent, the Web-CogReasoner. Extensive experimentation reveals its significant superiority over existing models, particularly in its capacity for generalization to unseen tasks where its structured knowledge proves decisive. To facilitate rigorous and systematic evaluation, we introduce the Web-CogBench, a comprehensive evaluation suite designed to assess and compare agent performance across the delineated knowledge domains and cognitive capabilities. Our code and data are open sourced at https://github.com/Gnonymous/Web-CogReasoner.
ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
Authors: Jia-Nan Li; Jian Guan; Wei Wu; Chongxuan Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from critical drawbacks: high computational overhead from precluding Key-Value (KV) caching, and incoherent generation arising from learning dependencies over an intractable space of token combinations. To address these limitations, we introduce ReFusion, a novel masked diffusion model that integrates sequence reorganization into the causal attention framework. By elevating parallel decoding from the token level to a higher slot level, ReFusion interleaves inter-slot diffusion-based selection with intra-slot autoregressive infilling, while reordering newly generated slots ahead of the remaining masks after each iteration. Consequently, this design simultaneously unlocks full KV cache reuse and reduces learning complexity from an intractable token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with a 34\% performance gain and an over 18$\times$ speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33$\times$ average speedup.
Temporal Slowness in Central Vision Drives Semantic Object Learning
Authors: Timothy Schaumlöffel; Arthur Aubret; Gemma Roig; Jochen Triesch
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear. Importantly, the visual system only processes the center of its field of view with high resolution and it learns similar representations for visual inputs occurring close in time. This emphasizes slowly changing information around gaze locations.
This study investigates the role of central vision and slowness learning in the formation of semantic object representations from human-like visual experience. We simulate five months of human-like visual experience using the Ego4D dataset and a state-of-the-art gaze prediction model. We extract image crops around predicted gaze locations to train a time-contrastive Self-Supervised Learning model.
Our results show that exploiting temporal slowness when learning from central visual field experience improves the encoding of different facets of object semantics. Specifically, focusing on central vision strengthens the extraction of foreground object features, while considering temporal slowness, especially in conjunction with eye movements, allows the model to encode broader semantic information about objects. These findings provide new insights into the mechanisms by which humans may develop semantic object representations from natural visual experience. Our code will be made public upon acceptance. Code is available at https://github.com/t9s9/central-vision-ssl.
Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models
Authors: Filippo Rinaldi; Aniello Panariello; Giacomo Salici; Fengyuan Liu; Marco Ciccone; Angelo Porrello; Simone Calderara
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When a new release of a foundation model is published, practitioners typically need to repeat fine-tuning, even if the same task was already tackled in the previous version. A promising alternative is to reuse the parameter changes (i.e., task vectors) that capture how a model adapts to a specific task. However, these vectors often fail to transfer across different pre-trained models because their parameter spaces are misaligned. In this work, we show that successful transfer depends strongly on the gradient-sign structure of the new model. Based on this insight, we propose GradFix, which approximates the ideal sign structure and leverages it to transfer knowledge using only a handful of labeled samples. Notably, this requires no additional fine-tuning: we only compute a few target-model gradients without parameter updates and mask the source task vector accordingly. This yields an update that is locally aligned with the target loss landscape, effectively rebasing the task vector onto the new pre-training. We provide a theoretical guarantee that our method ensures first-order descent. Empirically, we demonstrate significant performance gains on vision and language benchmarks, consistently outperforming naive task vector addition and few-shot fine-tuning. We further show that transporting task vectors improves multi-task and multi-source model merging. Code is available at https://github.com/fillo-rinaldi/GradFix.
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
Authors: Fangyu Ding; Ding Ding; Sijin Chen; Kaibo Wang; Peng Xu; Zijin Feng; Haoli Bai; Kai Han; Youliang Yan; Binhang Yuan; Jiacheng Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Masked Diffusion Language Models (MDLMs) relying on token masking and unmasking have shown promise in language modeling, their computational efficiency and generation flexibility remain constrained by the masking paradigm. In this paper, we propose Deletion-Insertion Diffusion language models (DID) that rigorously formulate token deletion and insertion as discrete diffusion processes, replacing the masking and unmasking processes in current MDLMs. DID improves training and inference efficiency by eliminating two major sources of computational overhead in MDLMs: the computations on non-informative 1) $\texttt{\<MASK\>}$ tokens inherent to its paradigm, and 2) $\texttt{\<PAD\>}$ tokens introduced in variable-length settings. Furthermore, DID offers greater flexibility by: 1) natively supporting variable-length sequences without requiring fixed-length padding, and 2) an intrinsic self-correction mechanism during generation due to insertion that dynamically adjusts token positions. To train DID, we design a score-based approach that assigns scores to token insertion operations and derive appropriate training objectives. The objectives involve subsequence counting problems, which we efficiently solve via a parallelized dynamic programming algorithm. Our experiments across fixed and variable-length settings demonstrate the advantage of DID over baselines of MDLMs and existing insertion-based LMs, in terms of modeling performance, sampling quality, and training/inference speed, without any hyperparameter tuning. 

Code: https://github.com/FMD-NEXT/DID
Towards Multimodal Data-Driven Scientific Discovery Powered by LLM Agents
Authors: Fan Liu; Xiaozhao Zeng; Hao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) have enabled agents that automate scientific discovery by interpreting data, generating analysis pipelines, and executing them with computational tools. However, existing benchmarks remain largely limited to unimodal datasets and slice-level tasks, overlooking the fact that real discovery requires multimodal integration, modeling, and hypothesis-driven reasoning. To address this gap, we introduce MoSciBench, the first benchmark for multimodal scientific discovery, constructed from peer-reviewed studies through a principled four-stage pipeline. MoSciBench spans six scientific domains, seven data modalities, and five categories of discovery questions, yielding 88 individual, end-to-end, data-driven tasks. Each task is designed as a cross-modal hypothesis verification workflow, requiring agents to align and integrate heterogeneous datasets before modeling and reasoning. We further evaluate four representative agent frameworks across multiple LLM families. Results show that multimodal discovery is substantially harder than unimodal tasks: even the strongest agents achieve only 48.94\% accuracy, with over 60\% of failures due to cross-modal alignment. Lightweight workflow scaffolding consistently improves performance, reducing alignment errors by 5–10\% and raising accuracy by 5.7\% on average. Our benchmark and evaluation framework thus establish a rigorous testbed for advancing LLM agents toward realistic, multimodal scientific discovery.
Nonparametric Contextual Online Bilateral Trade
Authors: Emanuele Coccia; Martino Bernasconi; Andrea Celli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the problem of contextual online bilateral trade. At each round, the learner faces a seller-buyer pair and must propose a trade price without observing their private valuations for the item being sold. The goal of the learner is to post prices to facilitate trades between the two parties. Before posting a price, the learner observes a $d$-dimensional context vector that influences the agent's valuations. Prior work in the contextual setting has focused on linear valuation models, where valuations are linear functions of the context. We provide the first characterisation of a general nonparametric setting in which the buyer’s and seller’s valuations behave according to arbitrary Lipschitz functions of the context. We design an algorithm that leverages contextual information through a hierarchical tree construction and guarantees regret $\widetilde{O}(T^{(d-1)/d})$. Remarkably, our algorithm operates under two stringent features of the setting: (1) one-bit feedback, where the learner only observes whether a trade occurred or not, and (2) strong budget balance, where the learner cannot subsidize or profit from the market participants. We further provide a matching lower bound in the full-feedback setting, demonstrating the tightness of our regret bound.
Topology Matters in RTL Circuit Representation Learning
Authors: Mingyu Zhao; Xun He; Jiawei Liu; Jianwang Zhai; Chuan Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Representation learning for register transfer level (RTL) circuits is fundamental to enabling accurate performance, power, and area (PPA) prediction, efficient circuit generation, and retrieval in automated chip design. Unlike general programming languages, RTL is inherently a structured dataflow graph where semantics are intrinsically bound to the topology from a hardware view. However, existing language-model-based approaches ignore the nature of RTL circuits and fail to capture topology-sensitive properties, leading to incomplete representation and limited performance for diverse downstream tasks. To address this, we introduce TopoRTL, a novel framework that explicitly learns topological differences across RTL circuits and preserves the behavior information. First, we decompose RTL designs into register cones and construct dual modalities initialized with behavior-aware tokenizers. Second, we design three topology-aware positional encodings and leverage attention mechanisms to enable the model to distinguish topological variations among register cones and RTL designs. Finally, we introduce a topology-guided cross-modal alignment strategy, employing contrastive learning over interleaved modality pairs under topological constraints to enforce semantic consistency and achieve superior modality alignment. 
Experiments demonstrate that explicit topological modeling is critical to improving RTL representation quality, and TopoRTL significantly outperforms existing methods across multiple downstream tasks.
CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition
Authors: Bruno Viti; Elias Karabelas; Martin Holler
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Most machine learning-based image segmentation models produce pixel-wise confidence scores that represent the model’s predicted probability for each class label at every pixel. While this information can be particularly valuable in high-stakes domains such as medical imaging, these scores are heuristic in nature and do not constitute rigorous quantitative uncertainty estimates.
Conformal prediction (CP) provides a principled framework for transforming heuristic confidence scores into statistically valid uncertainty estimates. However, applying CP directly to image segmentation ignores the spatial correlations between pixels, a fundamental characteristic of image data. This can result in overly conservative and less interpretable uncertainty estimates.
To address this, we propose CONSIGN (*Conformal Segmentation Informed by Spatial Groupings via Decomposition*), a CP-based method that incorporates spatial correlations to improve uncertainty quantification in image segmentation.
Our method generates meaningful prediction sets that come with user-specified, high-probability error guarantees.
It is compatible with any pre-trained segmentation model capable of generating multiple sample outputs.
We evaluate CONSIGN against two CP baselines across three medical imaging datasets and two COCO dataset subsets, using three different pre-trained segmentation models. Results demonstrate that accounting for spatial structure significantly improves performance across multiple metrics and enhances the quality of uncertainty estimates.
On the trade-off between expressivity and privacy in graph representation learning
Authors: Patrick Indri; Tamara Drucks; Thomas Gärtner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We investigate the trade-off between expressive power and privacy guarantees in graph representation learning. Privacy-preserving machine learning faces growing regulatory demands that pose a fundamental challenge: safeguarding sensitive data while maintaining expressive power. To address this challenge, we leverage homomorphism density vectors to obtain graph embeddings that are private and expressive.
Homomorphism densities are provably highly discriminative and offer a powerful tool for distinguishing non-isomorphic graphs. By adding noise calibrated to each density’s sensitivity, we ensure that the resulting embeddings satisfy formal differential privacy guarantees. Our theoretical construction preserves expressivity in expectation, as each private embedding remains unbiased with respect to the true homomorphism densities. We demonstrate the usefulness of our embeddings through experiments on molecular and social network datasets.
LCA: Local Classifier Alignment for Continual Learning
Authors: Tung Tran; Danilo Vasconcellos Vargas; Khoat Than
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A fundamental requirement for intelligent systems is the ability to learn continuously under changing environments. However, models trained in this regime often suffer from catastrophic forgetting. Leveraging pre-trained models has recently emerged as a promising solution, since their generalized feature extractors enable faster and more robust adaptation. While some earlier works mitigate forgetting by fine-tuning only on the first task, this approach quickly deteriorates as the number of tasks grows and the data distributions diverge. More recent research instead seeks to consolidate task knowledge into a unified backbone, or adapting the backbone as new tasks arrive. However, such approaches may create a (potential) \textit{mismatch} between task-specific classifiers and the adapted backbone. To address this issue, we propose a novel \textit{Local Classifier Alignment} (LCA) loss to better align the classifier with backbone. Theoretically, we show that this LCA loss can enable the classifier to not only generalize well for all observed tasks, but also improve robustness. Furthermore, we develop a complete solution for continual learning, following the model merging approach and using LCA. Extensive experiments on several standard benchmarks demonstrate that our method often achieves leading performance, sometimes surpasses the state-of-the-art methods with a large margin.
Solving the 2-norm k-hyperplane clustering problem via multi-norm formulations
Authors: Stefano Coniglio
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a method to solve $k$-HC$_2$—the $k$-Hyperplane Clustering problem that asks to find $k$ hyperplanes that minimize the sum of squared $2$-norm (Euclidean) distances between each point and its closest hyperplane—to global optimality via spatial branch-and-bound (SBB) techniques. Our method strengthens a mixed-integer quadratically constrained quadratic programming formulation for $k$-HC$_2$ with constraints that arise when formulating the problem in $p$-norms with $p \ge 2$.

In particular, we show that, for every (suitably scaled) $p \in \mathbb{N} \cup \{\infty\}$, one obtains a variant of $k$-HC$_2$ whose optimal solutions yield lower bounds within a multiplicative approximation factor. We focus on the case of polyhedral norms where $p = 1, \infty$ (which are disjunctive-programming representable), and prove that strengthening the original formulation by including, on top of its $2$-norm constraints, the constraints of one of the polyhedral norms leads to an SBB method where nonzero lower bounds are obtained in a number of nodes that is linear in $n$ and $k$ (rather than exponential).

Experimentally, our method leads to very large speedups, reducing median solve times by up to $41\times$ while increasing the total number of solved instances by up to $63\%$, drastically improving the problem's solvability to global optimality.
SciNav: A General Agent Framework for Scientific Coding Tasks
Authors: TIANSHU ZHANG; Huan Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autonomous science agents, built on large language models (LLMs), are increasingly being investigated to generate hypotheses, design experiments, and produce reports. Prior science agents primarily focus on open-ended scientific problems, where such outputs—hypotheses, experiments, or analyses are inherently subjective and thus difficult to evaluate rigorously. In contrast, existing scientific coding benchmarks provide tasks with clearly defined, executable outputs that enable objective assessment. However, current agent-based approaches to these benchmarks remain engineering-driven pipelines, lacking principled framework design. This mismatch exposes a gap: the absence of end-to-end, principled science agent frameworks for scientific coding tasks. We address this gap by focusing on scientific coding tasks, where evaluation can be made rigorously, and introducing an agent framework SciNav (Scientific Navigator) that enables more effective solution exploration. Our framework is designed to operate efficiently under constrained search budgets, moving beyond reliance on pre-defined success metrics and prolonged search cycles. Inspired by findings that comparative judgments often reveal finer-grained quality differences and therefore provide greater discriminative power than absolute scoring, our framework leverages pairwise relative judgments within a tree search process to select top-K promising solution branches, prune low-potential ones, and progressively narrow down the solution candidates on the selected branches guided by relative comparisons. We demonstrate our agent's effectiveness across different types of tasks on two benchmarks. Experiments show that SciNav significantly outperforms direct prompting and prior agents like OpenHands and Self-Debug across different base models, task types, and difficulty levels, and exceeds different frontier comparators such as random selection and LLM absolute scoring. These results confirm the strength of our agent design and highlight the effectiveness of relative judgment–guided top-K search for high-quality scientific coding, marking a step toward more practical science agents.
A Guardrail for Safety Preservation: When Safety-Sensitive Subspace Meets Harmful-Resistant Null-Space
Authors: Bingjie Zhang; Yibo Yang; Renzhe; Dandan Guo; Jindong Gu; Philip Torr; Bernard Ghanem
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have achieved remarkable success in diverse tasks, yet their safety alignment remains fragile during adaptation. 
Even when fine-tuning on benign data or with low-rank adaptation, pre-trained safety behaviors are easily degraded, leading to harmful responses in the fine-tuned models. 
To address this challenge, we propose GuardSpace, a guardrail framework for preserving safety alignment throughout fine-tuning, composed of two key components: a safety-sensitive subspace and a harmful-resistant null space. 
First, we explicitly decompose pre-trained weights into safety-relevant and safety-irrelevant components using covariance-preconditioned singular value decomposition, and initialize low-rank adapters from the safety-irrelevant ones, while freezing safety-relevant components to preserve their associated safety mechanism. 
Second, we construct a null space projector that restricts adapter updates from altering safe outputs on harmful prompts, thereby maintaining the original refusal behavior. 
Experiments with various pre-trained models on multiple downstream tasks demonstrate that GuardSpace achieves superior performance over existing methods.
Notably, for Llama-2-7B-Chat fine-tuned on GSM8K, GuardSpace outperforms the state-of-the-art method AsFT, reducing the average harmful score from 14.4\% to 3.6\%, while improving the accuracy from from 26.0\% to 28.0\%.