GenAI4ELab/papercli-papers huggingface dataset:
Misaligned Roles, Misplaced Images: Structural Input Perturbations Expose Multimodal Alignment Blind Spots
Authors: Erfan Shayegani; G M Shahariar; Sara Abdali; Lei Yu; Nael Abu-Ghazaleh; Yue Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Language Models (MMLMs) typically undergo post-training alignment to prevent harmful content generation. However, these alignment stages focus primarily on the *assistant* role, leaving the *user* role unaligned, and sticking to a fixed input prompt structure of special tokens, making the model vulnerable when inputs deviate from these expectations. We introduce Role-Modality Attacks (RMA), a novel class of adversarial attacks that exploit role confusion between the *user* and *assistant* and alter the position of the *image* token to elicit harmful outputs. Unlike existing attacks that modify query content, RMAs manipulate the input structure without altering the query itself. We systematically evaluate these attacks across multiple Vision Language Models (VLMs) on eight distinct settings, showing that they can be composed to create stronger adversarial prompts, as also evidenced by their increased projection in the negative refusal direction in the residual stream, a property observed in prior successful attacks. Finally, for mitigation, we propose an adversarial training approach that makes the model robust against input prompt perturbations. By training the model on a range of harmful and benign prompts all perturbed with different RMA settings, the model loses its sensitivity to Role Confusion and Modality Manipulation attacks and is trained to only pay attention to the query content in the input prompt structure, effectively reducing Attack Success Rate (ASR) while preserving the model’s general utility.
Adaptive Logit Adjustment for Debiasing Multimodal Language Models
Authors: Hoin Jung; Junyi Chai; Xiaoqian Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) and Large Multimodal Models (LMMs) have significantly advanced image-to-text generation tasks such as image captioning and visual question answering (VQA). 
However, these models often exhibit biases, including attribute misalignment between the generated text and the input image, or the reinforcement of harmful stereotypes.
Existing debiasing techniques primarily focus on modifying representations at the encoder or decoder level, which can degrade model performance and may be susceptible to bias reintroduction from external sources. In this work, we propose **Adaptive Logit Adjustment (ALA) for Bias Alignment and Neutralization**, a post-hoc debiasing method that operates directly on logits during autoregressive text generation. Unlike prior approaches that modify internal representations, ALA selectively adjusts token probabilities to mitigate biases without distorting essential model outputs. Our approach leverages external classifiers to measure bias misalignment between image and text, applies gradient-based importance analysis to identify bias-inducing tokens, and dynamically refines token probabilities to reduce undesired biases. 
We evaluate ALA on image captioning and various VQA tasks, demonstrating its effectiveness in mitigating bias while maintaining contextual accuracy. Notably, our approach is applicable to various multimodal architectures in a model-agnostic manner, including VLMs and LMMs, across different tasks that involve autoregressive text generation. Our results show that logit-based debiasing offers a flexible and efficient alternative to existing encoder- and embedding-centric approaches, providing a more practical solution for building fairer multimodal AI systems.
Revela: Dense Retriever Learning via Language Modeling
Authors: Fengyu Cai; Tong Chen; Xinran Zhao; Sihao Chen; Hongming Zhang; Tongshuang Wu; Iryna Gurevych; Heinz Koeppl
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly to create and scarce in specialized domains (e.g., code) or in complex settings (e.g., requiring reasoning). These practical challenges have sparked growing interest in self-supervised retriever learning.
Since LMs are trained to capture token-level dependencies through a self-supervised learning objective (i.e., next token prediction), we can analogously cast retrieval as learning dependencies among chunks of tokens. This analogy naturally leads to the question: How can we adapt self‑supervised learning objectives in the spirit of language modeling to train retrievers?

To answer this question, we introduce Revela, a unified and scalable training framework for self-supervised retriever learning via language modeling. Revela models semantic dependencies among documents by conditioning next token prediction on local and cross-document context through an in-batch attention mechanism. This attention is weighted by retriever-computed similarity scores, enabling the retriever to be optimized as part of language modeling. We evaluate Revela on domain-specific (CoIR), reasoning-intensive (BRIGHT), and general-domain (BEIR) benchmarks across various retriever backbones. Without annotated or synthetic query-document pairs, Revela surpasses larger supervised models and proprietary APIs on both CoIR and BRIGHT. It achieves BEIR's unsupervised SoTA with ~1000x less training data and 10x less compute. Performance increases with batch size and model size, highlighting Revela's scalability and its promise for self‑supervised retriever learning.
A tale of two tails: Preferred and anti-preferred natural stimuli in visual cortex
Authors: Rabia Gondur; Patricia L. Stan; Matthew A. Smith; Benjamin R. Cowley
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: An ongoing quest in neuroscience is to find the preferred stimulus of a sensory neuron. This search lays the foundation for understanding how selectivity emerges in the primate visual stream---from simple edge-detecting neurons to highly-selective face neurons---as well as for the architectures and activation functions of deep neural networks. The prevailing notion is that a visual neuron primarily responds to a single preferred visual feature, like an oriented edge or the shape of an object, resulting in a 'one-tailed' distribution of responses to natural images. However, surprisingly, we instead find 'two-tailed' response distributions of primate visual cortical neurons, suggesting that these neurons have both preferred and anti-preferred stimuli. We experimentally validated anti-preferred stimuli by recording responses from macaque V4 to model-optimized stimuli. We find that these anti-preferred stimuli are important for describing a neuron's tuning, as both preferred and anti-preferred images are needed to predict a neuron's responses to natural images. Moreover, in a psychophysics task, humans rely on anti-preferred images to interpret and predict V4 stimulus tuning; this was not the case for internal units from a deep neural network. Interestingly, we find no discernible differences in image statistics between preferred and anti-preferred images. This suggests that by encoding anti-preferred features, a V4 population seemingly doubles its capacity for feature selectivity, allowing for a more flexible downstream readout. Overall, we establish anti-preferred stimuli as an important encoding property of V4 neurons. Our work embarks on a new quest in neuroscience to search for anti-preferred stimuli along the visual stream and offers a new perspective on how feature selectivity arises in the visual cortex and deep neural networks.
ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks
Authors: Samin Mahdizadeh Sani; Max Ku; Nima Jamali; Matina Mahdizadeh Sani; Paria Khoshtab; Wei-Chieh Sun; Parnian Fazel; Zhi Rui Tam; Thomas Chong; Edisy Kin Wai Chan; Donald Wai Tong Tsang; Chiao-Wei Hsu; Lam Ting Wai; Ho Yin Sam Ng; Chiafeng Chu; Chak-Wing Mak; Keming Wu; Hiu Tung Wong; Yik Chun Ho; Chi Ruan; Zhuofeng Li; I-Sheng Fang; Shih-Ying Yeh; Ho Kei Cheng; Ping Nie; Wenhu Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advances in diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet, existing benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores without explaining failure modes. We introduce \textbf{ImagenWorld}, a benchmark of 3.6K condition sets spanning six core tasks (generation and editing, with single or multiple references) and six topical domains (artworks, photorealistic images, information graphics, textual graphics, computer graphics, and screenshots). The benchmark is supported by 20K fine-grained human annotations and an explainable evaluation schema that tags localized object-level and segment-level errors, complementing automated VLM-based metrics. Our large-scale evaluation of 14 models yields several insights: (1) models typically struggle more in editing tasks than in generation tasks, especially in local edits. (2) models excel in artistic and photorealistic settings but struggle with symbolic and text-heavy domains such as screenshots and information graphics. (3) closed-source systems lead overall, while targeted data curation (e.g., Qwen-Image) narrows the gap in text-heavy cases. (4) modern VLM-based metrics achieve Kendall accuracies up to 0.79, approximating human ranking, but fall short of fine-grained, explainable error attribution. ImagenWorld provides both a rigorous benchmark and a diagnostic tool to advance robust image generation.
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Authors: Andrea Zerio; Maya Bechler-Speicher; Maor Huri; Marie Vibeke Vestergaard; Tine Jess; Ran Gilad-Bachrach; Samir Bhatt; Aleksejs Sazonovs
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests can be measured at different times and frequencies, resulting in fragmented and unevenly scattered temporal data. Similar issues of irregular sampling occur in other domains, such as the monitoring of large systems using event log files. Effectively learning from such data requires handling sets of temporal sparse and heterogeneous signals. In this work, we propose Super Mixing Additive Networks (SuperMAN), a novel and interpretable-by-design framework for learning directly from such heterogeneous signals, by modeling them as sets of implicit graphs. SuperMAN provides diverse interpretability capabilities, including node-level, graph-level, and subset-level importance, and enables practitioners to trade finer-grained interpretability for greater expressivity when domain priors are available. SuperMAN achieves state-of-the-art performance in real-world high-stakes tasks, including predicting Crohn’s disease onset and hospital length of stay from routine blood test measurements and detecting fake news. Furthermore, we demonstrate how SuperMAN’s interpretability properties assist in revealing disease development phase transitions and provide crucial insights in the healthcare domain.
MolLangBench: A Comprehensive Benchmark for Language-Prompted Molecular Structure Recognition, Editing, and Generation
Authors: Feiyang Cai; Jiahui Bai; Tao Tang; Guijuan He; Joshua Luo; Tianyu Zhu; Srikanth Pilla; Gang Li; Ling Liu; Feng Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Precise recognition, editing, and generation of molecules are essential prerequisites for both chemists and AI systems tackling various chemical tasks. We present MolLangBench, a comprehensive benchmark designed to evaluate fundamental molecule-language interface tasks: language-prompted molecular structure recognition, editing, and generation. To ensure high-quality, unambiguous, and deterministic outputs, we construct the recognition tasks using automated cheminformatics tools, and curate editing and generation tasks through rigorous expert annotation and validation. MolLangBench supports the evaluation of models that interface language with different molecular representations, including linear strings, molecular images, and molecular graphs. Evaluations of state-of-the-art models reveal significant limitations: the strongest model (GPT-5) achieves $86.2$\% and $85.5$\% accuracy on recognition and editing tasks, which are intuitively simple for humans, and performs even worse on the generation task, reaching only $43.0$\% accuracy. These results highlight the shortcomings of current AI systems in handling even preliminary molecular recognition and manipulation tasks. We hope MolLangBench will catalyze further research toward more effective and reliable AI systems for chemical applications. The dataset and code can be accessed at https://huggingface.co/datasets/ChemFM/MolLangBench and https://github.com/TheLuoFengLab/MolLangBench, respectively.
From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning
Authors: Seungdong Yoa; Sanghyu Yoon; Suhee Yoon; Dongmin Kim; Ye Seul Sim; Junhyun Lee; Woohyung Lim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an agent-centric benchmarking paradigm that moves beyond static datasets by introducing a dynamic protocol in which autonomous agents iteratively generate, validate, and solve problems. Within this protocol, a teacher agent generates candidate problems, an orchestrator agent rigorously verifies their validity and guards against adversarial attacks, and a student agent attempts to solve the validated problems. An invalid problem is revised by the teacher agent until it passes validation. If the student correctly solves the problem, the orchestrator prompts the teacher to generate more challenging variants. Consequently, the benchmark scales in difficulty automatically as more capable agents are substituted into any role, enabling progressive evaluation of large language models without manually curated datasets. Adopting text anomaly detection as our primary evaluation format, which demands cross-sentence logical inference and resists pattern-matching shortcuts, we demonstrate that this protocol systematically exposes corner-case reasoning errors that conventional benchmarks fail to reveal. We further advocate evaluating systems along several complementary axes including cross-model pairwise performance and progress between the initial and orchestrator-finalized problems. By shifting the focus from fixed datasets to dynamic protocols, our approach offers a sustainable direction for evaluating ever-evolving language models and introduces a research agenda centered on the co-evolution of agent-centric benchmarks.
LLMs Get Lost In Multi-Turn Conversation
Authors: Philippe Laban; Hiroaki Hayashi; Yingbo Zhou; Jennifer Neville
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Large Language Models (LLMs) are conversational interfaces. As such, LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also to help them define, explore, and refine what they need through multi-turn conversational exchange. Although analysis of LLM conversation logs has confirmed that underspecification occurs frequently in user instructions, LLM evaluation has predominantly focused on the single-turn, fully-specified instruction setting. In this work, we perform large-scale simulation experiments to compare LLM performance in single- and multi-turn settings. Our experiments confirm that all the top open- and closed-weight LLMs we test exhibit significantly lower performance in multi-turn conversations than single-turn, with an average drop of 39% across six generation tasks. Analysis of 200,000+ simulated conversations decomposes the performance degradation into two components: a minor loss in aptitude and a significant increase in unreliability. We find that LLMs often make assumptions in early turns and prematurely attempt to generate final solutions, on which they overly rely. In simpler terms, we discover that when LLMs take a wrong turn in a conversation, they get lost and do not recover.
BRIDGE: Bi-level Reinforcement Learning for Dynamic Group Structure in Coalition Formation Games
Authors: Shuqing Shi; Nam Phuong Tran; Hao Liang; Debmalya Mandal; Long Tran-Thanh; Yali Du
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The challenge of coalition formation games lies in efficiently navigating the exponentially large space of possible coalitions to identify the optimal partition. While existing approaches to solve coalition formation games either provide optimal solutions with limited scalability or approximate solutions without quality guarantees, we propose a novel scalable and sample-efficient approximation method based on deep reinforcement learning. Specifically, we model the coalition formation problem as a finite Markov decision process and use deep neural network to approximate optimal coalition structures within the full and abstracted coalition space. Moreover, our method is applicable to bi-level optimization problems in which coalition values are determined by the policies of individual agents at a lower decision-making level. This way, our approach facilitates dynamic, adaptive adjustments to coalition value assessments as they evolve over time. Empirical results demonstrate our algorithm's effectiveness in approximating optimal coalition structures in both normal-form and sequential mixed-motive games.
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
Authors: Ido Levy; Ben wiesel; Sami Marreed; Alon Oved; Avi Yaeli; Segev Shlomov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust. To integrate these agents into critical workflows, safety and trustworthiness (ST) are prerequisite conditions for adoption. We introduce ST-WebAgentBench, a configurable and extensible framework designed as a first step toward enterprise-grade evaluation. Each of its 375 tasks carries one or more ST policies (3,057 in total), concise rules encoding constraints, and is scored along six orthogonal dimensions (e.g., user consent, robustness). Tasks span three difficulty tiers for fine-grained capability profiling, and a “Modality Challenge” disentangles vision-only from DOM-only information retrieval, isolating the contribution of each perceptual modality to agent failures. Beyond raw task success, we propose the Completion Under Policy (CuP) metric, which credits only completions that respect all applicable policies, and the Risk Ratio, which quantifies ST breaches across dimensions. Evaluating three open state-of-the-art agents shows their average CuP is less than two-thirds of their nominal completion rate, revealing substantial safety gaps. To support growth and adaptation to new domains, ST-WebAgentBench provides modular code and extensible templates that enable new workflows to be incorporated with minimal effort, offering a practical foundation for advancing trustworthy web agents at scale.
Are we measuring oversmoothing in graph neural networks correctly?
Authors: Kaicheng Zhang; Piero Deidda; Desmond Higham; Francesco Tudisco
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as the Dirichlet energy. We argue that these metrics have critical limitations and fail to reliably capture oversmoothing in realistic scenarios. For instance, they provide meaningful insights only for very deep networks, while typical GNNs show a performance drop already with as few as 10 layers.  As an alternative, we propose measuring oversmoothing by examining the numerical or effective rank of the feature representations. We provide extensive numerical evaluation across diverse graph architectures and datasets to show that rank-based metrics consistently capture oversmoothing, whereas energy-based metrics often fail. Notably, we reveal that drops in the rank align closely with performance degradation, even in scenarios where energy metrics remain unchanged. Along with the experimental evaluation, we provide theoretical support for this approach, clarifying why Dirichlet-like measures may fail to capture performance drop and proving that the numerical rank of feature representations collapses to one for a broad family of GNN architectures.
DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning
Authors: Yaxuan Wang; Chris Yuhao Liu; Quan Liu; Jinlong Pang; Wei Wei; Yujia Bao; Yang Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unlearning in Large Language Models (LLMs) is crucial for protecting private data and removing harmful knowledge. Most existing approaches rely on fine-tuning to balance unlearning efficiency with general language capabilities. However, these methods typically require training or access to retain data, which is often unavailable in real world scenarios. Although these methods can perform well when both forget and retain data are available, few works have demonstrated equivalent capability in more practical, data-limited scenarios. To overcome these limitations, we propose Detect-Reasoning Augmented GeneratiON (DRAGON),  a systematic, reasoning-based framework that utilizes in-context chain-of-thought (CoT) instructions to guard deployed LLMs before inference. Instead of modifying the base model, DRAGON leverages the inherent instruction-following ability of LLMs and introduces a lightweight detection module to identify forget-worthy prompts without any retain data. These are then routed through a dedicated CoT guard model to enforce safe and accurate in-context intervention. To robustly evaluate unlearning performance, we introduce novel metrics for unlearning performance and the continual unlearning setting. Extensive experiments across three representative unlearning tasks validate the effectiveness of DRAGON, demonstrating its strong unlearning capability, scalability, and applicability in practical scenarios. The code is available at https://github.com/supergirl-os/DRAGON.