GenAI4ELab/papercli-papers huggingface dataset:
Unlocking the Power of Co-Occurrence in CLIP: A DualPrompt-Driven Method for Training-Free Zero-Shot Multi-Label Classification
Authors: Ming-Kun Xie; Zhiqiang Kou; Zhongnian Li; Gang Niu; Masashi Sugiyama
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Contrastive Language-Image Pretraining (CLIP) has exhibited powerful zero-shot capacity in various single-label image classification tasks. However, when applied to the multi-label scenarios, CLIP suffers from significant performance declines due to the lack of explicit exploitation of co-occurrence information. In pretraining, due to the contrastive property of its objective, the model focuses on the prominent object in an image, while overlooking other objects and their co-occurrence relationships; during inference, it uses a discriminative prompt containing only a target label name to make predictions, which does not introduce any co-occurrence information. Then, an important question is as follows: \textit{Do we need label co-occurrence in CLIP for achieving effective zero-shot multi-label learning?} In this paper, we propose to rewrite the original prompt into a correlative form consisting of both the target label and its co-occurring labels. An interesting finding is that such a simple modification can effectively introduce co-occurrence information into CLIP and it exhibits both good and bad effects. On the one hand, it can enhance the recognition capacity of CLIP by exploiting the correlative pattern activated by the correlative prompt; on the other hand, it leads to object hallucination in CLIP, where the model predicts objects that do not actually exist in the image, due to overfitting to co-occurrence. To address this problem, we propose to calibrate CLIP predictions by keeping the positive effect while removing the negative effect caused by suspicious co-occurrence. This can be achieved by using dual prompts consisting of the discriminative and correlative prompts, which introduce label co-occurrence while emphasizing the discriminative pattern of the target object. Experimental results verify that our method can achieve better performance than the state-of-the-art methods.
LANE: Label-Aware Noise Elimination for Fine-Grained Text Classification
Authors: Tiberiu Sosea; Cornelia Caragea
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we propose Label-Aware Noise Elimination (LANE), a new approach to learning with noisy labels. At its core, LANE introduces a new metric---label-aware margin---aimed at quantifying the degree of noise of each training example (or quality thereof). LANE leverages the semantic relations between classes and monitors the training dynamics of the model on each training example to dynamically lower the weight of training examples that are perceived to have noisy labels. We test the effectiveness of LANE on multiple text classification tasks and benchmark our approach on a wide variety of datasets with various numbers of classes and amounts of label noise. LANE considerably outperforms strong baselines on all datasets and settings, obtaining significant improvements ranging from an average improvement of 2.88% in F1 on manually annotated datasets to a considerable average improvement of 4.75% F1 on datasets with high level of injected label noise. We carry out a comprehensive analysis of LANE and identify the key components that lead to its success.
Pallatom-Ligand: an All-Atom Diffusion Model for Designing Ligand-Binding Proteins
Authors: Haochen Wang; Qianyi Wang; Rui Ma; Jiawei Guan; Weikun.Wu; Haobo Wang; Jiayi Dou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Small-molecule ligands extend protein functionality beyond natural amino acids, enabling sophisticated processes like catalysis, signal transduction, and light harvesting. However, designing proteins with high affinity and selectivity for arbitrary ligands remains a major challenge. We present Pallatom-Ligand, a diffusion model that performs end-to-end generation of ligand-binding proteins at atomic resolution. By directly learning the joint distribution of all atoms in the protein–ligand complexes, Pallatom-Ligand delivers state-of-the-art performance, achieving the highest *in silico* success rates in a comprehensive benchmark. In addition, Pallatom-Ligand's novel conditioning framework enables programmable control over global protein fold and atomic-level ligand solvent accessibility. With these capabilities, Pallatom-Ligand opens new opportunities for exploring the protein function space, advancing both generative modeling and computational protein engineering.
MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning
Authors: Kun Huang; Weikai Xu; Yuxuan Liu; Quandong Wang; Pengzhi Gao; Wei Liu; Jian Luan; Bin Wang; Bo An
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-based mobile agents in GUI tasks. However, the scarcity of diverse CoaT trajectories limits the expressiveness and generalization ability of such agents. While self-training is commonly employed to address data scarcity, existing approaches either overlook the correctness of intermediate reasoning steps or depend on expensive process-level annotations to construct process reward models (PRM). To address the above problems, we propose an Iterative Preference Learning (IPL) that constructs a CoaT-tree through interative sampling, scores leaf nodes using rule-based reward, and backpropagates feedback to derive Thinking-level Direct Preference Optimization (T-DPO) pairs. To prevent overfitting during warm-up supervised fine-tuning, we further introduce a three-stage instruction evolution, which leverages GPT-4o to generate diverse Q&A pairs based on real mobile UI screenshots, enhancing both generality and layout understanding. Experiments on three standard Mobile GUI-agent benchmarks demonstrate that our agent MobileIPL outperforms strong baselines, including continual pretraining models such as OS-ATLAS and UI-TARS. It achieves state-of-the-art performance across three standard Mobile GUI-Agents benchmarks and shows strong generalization to out-of-domain scenarios.
Beyond Linear Processing: Dendritic Bilinear Integration in Spiking Neural Networks
Authors: Jingyang Ma; Chongming Liu; Songting Li; Douglas Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As widely used neuron model in Spiking Neural Networks (SNNs), the Leaky Integrate-and-Fire (LIF) model assumes the linear summation of injected currents. However, recent studies have revealed that a biological neuron can integrate inputs nonlinearly and perform computations such as XOR while an LIF neuron cannot. To bridge this gap, we propose the Dendritic LIF (DLIF) model, which incorporates a bilinear dendritic integration rule derived from neurophysiological experiments. At the single-neuron level, we theoretically demonstrate that a DLIF neuron can capture input correlations, enabling it to perform nonlinear classification tasks. At the network level, we prove that DLIF neurons can preserve and propagate correlation structures from the input layer to the readout layer. These theoretical findings are further confirmed by our numerical experiments. Extensive experiments across diverse architectures—including ResNet, VGG, and Transformer—demonstrate that DLIF achieves state-of-the-art performance on static (CIFAR-10/100, ImageNet) and neuromorphic (DVS-Gesture, DVS-CIFAR10) benchmarks, surpassing LIF and other advanced alternatives while maintaining comparable computational cost. This work provides a biologically plausible and computationally powerful spiking neuron model, paving the way for next-generation brain-inspired computing.
Moving Beyond Medical Exams: A Clinician-Annotated Fairness Dataset of Real-World Tasks and Ambiguity in Mental Healthcare
Authors: Max Lamparth; Declan Grabb; Amy Franks; Scott Gershan; Kaitlyn N Kunstman; Aaron Lulla; Monika Drummond Roots; Manu Sharma; Aryan Shrivastava; Nina Vasan; Colleen Waickman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions. 
In psychiatry especially, these challenges are worsened by fairness and bias issues, since models can be swayed by patient demographics even when those factors should not influence clinical decisions. 
Thus, we present an expert-created and annotated dataset spanning five critical domains of decision-making in mental healthcare: treatment, diagnosis, documentation, monitoring, and triage. 
This U.S. centric dataset — created without any LM assistance — is designed to capture the nuanced clinical reasoning and daily ambiguities mental health practitioners encounter, reflecting the inherent complexities of care delivery that are missing from existing datasets. 
Almost all base questions with five answer options each have had the decision-irrelevant demographic patient information removed and replaced with variables, e.g., for age or ethnicity, and are available for male, female, or non-binary-coded patients. 
This design enables systematic evaluations of model performance and bias by studying how demographic factors affect decision-making. 
For question categories dealing with ambiguity and multiple valid answer options, we create a preference dataset with uncertainties from the expert annotations.
We outline a series of intended use cases and demonstrate the usability of our dataset by evaluating sixteen off-the-shelf
and six (mental) health fine-tuned LMs on category-specific task accuracy, on the fairness impact of patient demographic information on decision-making, and how consistently free-form responses deviate from human-annotated samples.
Scaling up Memory for Robotic Control via Experience Retrieval
Authors: Ajay Sridhar; Jennifer Pan; Satvik Sharma; Chelsea Finn
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans rely on memory to perform tasks; our goal is to endow robot policies with the same ability. Naively conditioning on long observation histories is computationally expensive and brittle under covariate shift, while indiscriminate subsampling of history leads to irrelevant or redundant information. We propose a hierarchical policy framework, where the high-level policy is trained to select and track previous task-relevant keyframes from its experience. The high-level policy uses selected keyframes and the most recent frames when generating text instructions for a low-level policy to execute. This design is compatible with existing vision-language-action (VLA) models and enables the system to efficiently reason over long-horizon dependencies. In our experiments, we fine-tune Qwen2.5-VL-7B-Instruct and $\pi_{0.5}$ as the high-level and low-level policies respectively, using demonstrations supplemented with minimal language annotations. Our approach, MemER, outperforms prior methods on three real-world long-horizon robotic manipulation tasks that require minutes of memory. Videos and code can be found at https://jen-pan.github.io/memer/.
Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees
Authors: Zhuoping Zhou; Davoud Ataee Tarzanagh; Sima Didari; Wenjun Hu; Baruch Gutow; Oxana Verkholyak; Masoud Faraki; Heng Hao; Hankyu Moon; Seungjai Min
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retrieval struggles with, enabling multi-hop reasoning. Yet most existing graph-based methods suffer from (i) heuristic designs lacking theoretical guarantees for subgraph quality or relevance and/or (ii) the use of static exploration strategies that ignore the query's holistic meaning, retrieving neighborhoods or communities regardless of intent. We propose \textit{Query-Aware Flow Diffusion RAG} (QAFD-RAG), a training-free framework that dynamically adapts graph traversal to each query's holistic semantics. The central innovation is \emph{query-aware traversal}: during graph exploration, edges are dynamically weighted by how well their endpoints align with the query's embedding, guiding flow along semantically relevant paths while avoiding structurally connected but irrelevant regions. These query-specific reasoning subgraphs enable the first statistical guarantees for query-aware graph retrieval, showing that QAFD-RAG recovers relevant subgraphs with high probability under mild signal-to-noise conditions. The algorithm converges exponentially fast, with complexity scaling with the retrieved subgraph size rather than the full graph. Experiments on question answering and text-to-SQL tasks demonstrate consistent improvements over state-of-the-art graph-based RAG methods.
EvolProver: Advancing Automated theorem proving by Evolving Formalized Problems via Symmetry and Difficulty
Authors: Yuchen Tian; Ruiyuan Huang; WANG XUANWU; Jing Ma; Zengfeng Huang; Ziyang Luo; Hongzhan Lin; Da Zheng; Lun Du
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of problem statements. To address this limitation, we introduce a novel data augmentation pipeline designed to enhance model robustness from two perspectives: symmetry and difficulty. From the symmetry perspective, we propose two complementary methods: **EvolAST**, an Abstract Syntax Tree (AST) based approach that targets syntactic symmetry to generate semantically equivalent problem variants, and **EvolDomain**, which leverages LLMs to address semantic symmetry by translating theorems across mathematical domains. From the difficulty perspective, we propose **EvolDifficulty**, which uses carefully designed evolutionary instructions to guide LLMs in generating new theorems with a wider range of difficulty. We then use the evolved data to train **EvolProver**, a 7B-parameter non-reasoning theorem prover. EvolProver establishes a new state-of-the-art (SOTA) on FormalMATH-Lite with a 53.8\% pass@32 rate, surpassing all models of comparable size, including reasoning-based models. It also sets new SOTA records for non-reasoning models on MiniF2F-Test (69.8\% pass@32), Ineq-Comp-Seed (52.2\% pass@32), and Ineq-Comp-Transformed (34.0\% pass@32). Ablation studies further confirm our data augmentation pipeline's effectiveness across multiple benchmarks.
Long Chain-of-Thought Reasoning Across Languages
Authors: Josh Barua; Seun Eisape; Kayo Yin; Alane Suhr
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While large reasoning models have shown remarkable ability to generate long chains-of-thought (CoTs) in English, we still lack understanding of how these long-form reasoning abilities transfer to the vast majority of the world’s languages. In this work, we systematically investigate four key stages of model development–scaling, pretraining, post-training, and inference–to understand how long CoT capabilities extend beyond English. We compare two reasoning settings across nine non-English target languages: En-CoT, where models process target-language inputs, but reason in English; and Target-CoT, where models both process inputs and generate long CoTs in the target language. We find that scaling reasoning model size improves multilingual task performance in En-CoT, but Target-CoT performance lags behind. This gap widens for tasks requiring long, multi-step CoTs such as mathematical reasoning. Shifting to pretraining, we find that adding a specialized reasoning stage enhances En-CoT performance but degrades Target-CoT, whereas broad multilingual pretraining improves both modes simultaneously. Given the scarcity of high-quality reasoning traces in languages other than English, we explore synthetic data curation approaches for post-training. We demonstrate that fine-tuning on reasoning traces
automatically translated from gold English traces outperforms fine-tuning on target-language traces distilled from large reasoning models. Finally, we report disparities in inference efficiency between languages and uncover language-specific failure modes
in CoTs. We release models, datasets, and code to foster further research.
Extreme Weather Nowcasting via Local Precipitation Pattern Prediction
Authors: Chang hoon Song; Teng Yuan Chang; Youngjoon Hong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate forecasting of extreme weather events such as heavy rainfall or storms is critical for risk management and disaster mitigation. Although high-resolution radar observations have spurred extensive research on nowcasting models, precipitation nowcasting remains particularly challenging due to pronounced spatial locality, intricate fine-scale rainfall structures, and variability in forecasting horizons. 
While recent diffusion-based generative ensembles show promising results, they are computationally expensive and unsuitable for real-time applications. In contrast, deterministic models are computationally efficient but remain biased toward normal rainfall. Furthermore, the benchmark datasets commonly used in prior studies are themselves skewed--either dominated by ordinary rainfall events or restricted to extreme rainfall episodes--thereby hindering general applicability in real-world settings.
In this paper, we propose exPreCast, an efficient deterministic framework for generating finely detailed radar forecasts, and introduce a newly constructed balanced radar dataset from the Korea Meteorological Administration (KMA), which encompasses both ordinary precipitation and extreme events. Our model integrates local spatiotemporal attention, a texture-preserving cubic dual upsampling decoder, and a temporal extractor to flexibly adjust forecasting horizons. Experiments on established benchmarks (SEVIR and MeteoNet) as well as on the balanced KMA dataset demonstrate that our approach achieves state-of-the-art performance, delivering accurate and reliable nowcasts across both normal and extreme rainfall regimes.
PixelVLA: Advancing Pixel-level Understanding in Vision-Language-Action Model
Authors: Wenqi Liang; Gan Sun; Yao He; Jiahua Dong; Suyan Dai; Ivan Laptev; Salman Khan; Yang Cong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language-Action models (VLAs) are emerging as powerful tools for learning generalizable visuomotor control policies. However, current VLAs are mostly trained on large-scale image–text–action data and remain limited in two key ways: (i) they struggle with pixel-level scene understanding, and (ii) they rely heavily on textual prompts, which reduces their flexibility in real-world settings. 
To address these challenges, we introduce PixelVLA, the first VLA model designed to support both pixel-level reasoning and multimodal prompting with text and visual inputs. Our approach is built on a new visuomotor instruction tuning framework that integrates a multiscale pixel-aware encoder with a visual prompting encoder. To train PixelVLA effectively, we further propose a two-stage automated annotation pipeline that generates Pixel-160K, a large-scale dataset with pixel-level annotations derived from existing robot data.
Experiments on three standard VLA benchmarks and two VLA model variants show that PixelVLA improves manipulation success rates by $10.1\%\sim28.7\%$ over OpenVLA, while requiring only $1.5\%$ of its pretraining cost.  These results demonstrate that PixelVLA can be integrated into existing VLAs to enable more accurate, efficient, and versatile robot control in complex environments. The dataset and code will be released as open source.
Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
Authors: Behrooz Tahmasebi; Melanie Weber
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Enforcing exact symmetry in machine learning models often yields significant gains in scientific applications, serving as a powerful inductive bias. However, recent work suggests that relying on approximate symmetry can offer greater flexibility and robustness. Despite promising empirical evidence, there has been little theoretical understanding, and in particular, a direct comparison between exact and approximate symmetry is missing from the literature. In this paper, we initiate this study by asking:
What is the cost of enforcing exact versus approximate symmetry?
To address this question, we introduce averaging complexity, a framework
for quantifying the cost of enforcing symmetry via averaging. Our main result is an exponential separation: under standard conditions, exact symmetry requires linear averaging complexity, whereas approximate symmetry can be attained with only logarithmic complexity in the group size.
To the best of our knowledge, this provides the first theoretical separation of these two cases, formally justifying why approximate symmetry may be preferable in practice. Beyond this, our tools and techniques may be of independent interest for the broader study of symmetries in machine learning.
SMAN-Bench: A Cross-System Benchmark for Mobile Agents under Single- and Multi-path, Ambiguous, and Noisy Tasks
Authors: Weikai Xu; Zhizheng Jiang; Yuxuan Liu; Pengzhi Gao; Wei Liu; Jian Luan; Yunxin Liu; Yuanchun Li; Bin Wang; Bo An
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: VLM-based mobile agents are increasingly popular due to their capabilities to interact with smartphone GUIs and XML-structured texts and to complete daily tasks. However, existing online benchmarks fail to obtain stable critical reward signals under dynamic environmental changes, and neglect the influence of noise components and interactive instructions. Offline benchmarks evaluate the agents through single-path trajectories, which stand in contrast to the inherently multi-solution characteristics of GUI tasks. To address these limitations, we introduce SMAN-Bench, a benchmark designed to evaluate agents under Single-path, Multi-path, Ambiguous, and Noisy task settings. We employ a slot-based instruction generation method to match templates with GUI trajectories from an existing, graph-structured, unlabeled mobile corpus. SMAN-Bench includes a common task split, with offline multi-path evaluation to assess the agent’s ability to obtain step rewards during task execution. It contains a noisy split based on pop-ups and ad apps, and a contaminated split named AITZ-Noise to simulate a realistic noisy environment. Furthermore, an ambiguous instruction split with preset Q&A interactions is released to evaluate the agent’s proactive interaction capabilities. Our evaluation covers mobile agent frameworks like AppAgent-v1, Mobile-Agent-v2, and Mobile-Agent-E, and includes both open-source and closed-source mobile fundamental models, as well as several multimodal reasoning models.