GenAI4ELab/papercli-papers huggingface dataset:
Closing the Modality Gap Aligns Group-Wise Semantics
Authors: Eleonora Grassucci; Giordano Cicchetti; Emanuele Frasca; Aurelio Uncini; Danilo Comminiello
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In multimodal learning, CLIP has been recognized as the \textit{de facto} method for learning a shared latent space across multiple modalities, placing similar representations close to each other and moving them away from dissimilar ones. Although CLIP-based losses effectively align modalities at the semantic level, the resulting latent spaces often remain only partially shared, revealing a structural mismatch known as the modality gap. While the necessity of addressing this phenomenon remains debated, particularly given its limited impact on instance-wise tasks (e.g., retrieval), we prove that its influence is more pronounced in group-level tasks (e.g., clustering). To support this claim, we introduce a novel method designed to consistently reduce this discrepancy in two-modal settings, with a straightforward extension to the general $n$-modal case. Through our extensive evaluation, we prove our novel insight: while reducing the gap provides only marginal or inconsistent improvements in traditional instance-wise tasks, it significantly enhances group-wise tasks. These findings may reshape our understanding of the modality gap, highlighting its key role in improving performance on tasks requiring semantic grouping.
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
Authors: Minjun Kim; Jaehyeon Choi; Hyunwoo Yang; Jongjin Kim; Jinho Song; U Kang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: What happens when multiple compression methods are combined—does the order in which they are applied matter?
Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization.
A central but underexplored factor in joint model compression is the compression order, or the sequence of different methods within the compression pipeline.
Most prior studies have either sidestepped the issue by assuming orthogonality between techniques, while a few have examined them only in highly constrained cases.
Consequently, the broader role of compression order in shaping model performance remains poorly understood.
In this paper, we address the overlooked problem of compression order and provide both theoretical and empirical analysis.
We formulate the problem of optimizing the compression order and introduce the Progressive Intensity Hypothesis, which states that weaker perturbations should precede stronger ones.
We provide theoretical guarantees showing that the relative benefit of one order increases with the underlying performance gap.
Extensive experiments on both language and vision models validate the hypothesis, and further show its generality to broader setups such as multi-stage compression and mixed-precision quantization.
Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
Authors: Wangkai Li; Zhaoyang Li; Yuwen Pan; Rui Sun; Yujia Chen; Tianzhu Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts.
Existing augmentation-based methods attempt to enhance robustness by simulating weather interference during training. 
However, they struggle to fully exploit the potential of augmentations due to the trade-off between minor and aggressive augmentations. 
To address this, we propose A3Point, an adaptive augmentation-aware latent learning framework that effectively utilizes a diverse range of augmentations while mitigating the semantic shift, which refers to the change in the semantic meaning caused by augmentations. 
A3Point consists of two key components: 
semantic confusion prior (SCP) latent learning, which captures the model's inherent semantic confusion information, and semantic shift region (SSR) localization, which
decouples semantic confusion and semantic shift, enabling adaptive optimization strategies for different disturbance levels.
Extensive experiments on multiple standard generalized LiDAR segmentation benchmarks under adverse weather demonstrate the effectiveness of our method, setting new state-of-the-art results.
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
Authors: Louis Van Langendonck; Guillermo Bernardez; Nina Miolane; Pere Barlet-Ros
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing approaches are confined to single-graph, transductive settings where models train and test on the same graph structure. Addressing this gap, we introduce GraphUniverse, a framework for generating entire families of graphs to enable the first systematic evaluation of inductive generalization at scale. Our core innovation is the generation of graphs with persistent semantic communities, ensuring conceptual consistency while allowing fine-grained control over structural properties like homophily and degree distributions. This enables crucial but underexplored robustness tests, such as performance under controlled distribution shifts. Benchmarking a wide range of architectures—from GNNs to graph transformers and topological architectures—reveals that strong transductive performance is a poor predictor of inductive generalization. Furthermore, we find that robustness to distribution shift is highly sensitive not only to model architecture choice but also to the initial graph regime (e.g., high vs. low homophily). Beyond benchmarking, GraphUniverse’s flexibility and scalability can facilitate the development of robust and truly generalizable architectures. An interactive demo is available at https://graphuniverse.streamlit.app.
RM-R1: Reward Modeling as Reasoning
Authors: Xiusi Chen; Gaotang Li; Ziqi Wang; Bowen Jin; Cheng Qian; Yu Wang; Hongru WANG; Yu Zhang; Denghui Zhang; Tong Zhang; Hanghang Tong; Heng Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) should stimulate deep thinking and conduct interpretable reasoning before assigning a score or a judgment. Inspired by recent advances of long chain-of-thought on reasoning-intensive tasks, we hypothesize and validate that integrating reasoning into reward modeling significantly enhances RM's interpretability and performance. We introduce a new class of generative reward models, Reasoning Reward Models (ReasRMs), which formulate reward modeling as a reasoning task. We propose a reasoning-oriented training pipeline and train a family of ReasRMs, RM-R1. RM-R1 features a chain-of-rubrics (CoR) mechanism -- self-generating sample-level chat rubrics or math/code solutions, and evaluating candidate responses against them. The training of RM-R1 consists of two key stages: (1) distillation of high-quality reasoning chains and (2) reinforcement learning with verifiable rewards. Empirically, our models achieve superior performance across three reward model benchmarks on average, outperforming much larger open-weight models (e.g., INF-ORM-Llama3.1-70B) and proprietary ones (e.g., GPT-4o) by up to 4.9%. Beyond final performance, we perform thorough analyses to understand the key ingredients of successful ReasRM training.
GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation
Authors: Jiyong Rao; Yu Wang; Shengjie Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Category-agnostic pose estimation (CAPE) aims to localize keypoints on query images from arbitrary categories, using only a few annotated support examples for guidance. Recent approaches either treat keypoints as isolated entities or rely on manually defined skeleton priors, which are costly to annotate and inherently inflexible across diverse categories. Such oversimplification limits the model’s capacity to capture instance-wise structural cues critical for accurate pixel-level localization. To overcome these limitations, we propose \textbf{GenCape}, a \textbf{Gen}erative-based framework for \textbf{CAPE} that infers keypoint relationships solely from image-based support inputs, without additional textual descriptions or predefined skeletons. Our framework consists of two principal components: an iterative Structure-aware Variational Autoencoder (i-SVAE) and a Compositional Graph Transfer (CGT) module. The former infers soft, instance-specific adjacency matrices from support features through variational inference, embedded layer-wise into the Graph Transformer Decoder for progressive structural priors refinement. The latter adaptively aggregates multiple latent graphs into a query-aware structure via Bayesian fusion and attention-based reweighting, enhancing resilience to visual uncertainty and support-induced bias. This structure-aware design facilitates effective message propagation among keypoints and promotes semantic alignment across object categories with diverse keypoint topologies. Experimental results on the MP-100 dataset show that our method achieves substantial gains over graph-support baselines under both 1- and 5-shot settings, while maintaining competitive performance against text-support counterparts.
Probing Rotary Position Embeddings through Frequency Entropy
Authors: Yui Oka; Kentaro Hanafusa; Taku Hasegawa; Kyosuke Nishida; Kuniko Saito
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Rotary Position Embeddings (RoPE) are widely used in Transformers to encode positional information in token representations, yet the internal frequency structure of RoPE remains poorly understood. Previous studies have reported conflicting findings on the roles of high- and low-frequency dimensions, offering empirical observations but no unifying explanation. In this paper, we present a systematic framework that bridges these disparate results. We introduce Frequency Entropy (FE), a metric that quantifies the effective utilization of each RoPE frequency dimension, and we provide an analysis of how RoPE’s sinusoidal components contribute to model representations on a per-dimension basis. Based on an analysis of the Llama-4 model, which incorporates both RoPE and NoPE layers, we find that the periodicity captured by FE appears in RoPE layers but not in NoPE layers. Furthermore, FE identifies dimensions in which energy concentrates under RoPE. These characteristics are observed across the spectrum rather than being confined to specific dimensions. Moreover, attenuating extreme-entropy dimensions at inference yields downstream accuracy that is statistically indistinguishable from the baseline, with modest perplexity improvements on average, suggesting that such dimensions are often redundant. Overall, FE provides a simple, general diagnostic for RoPE with implications for analysis and design.
PoseX: AI Defeats Physics-based Methods on Protein Ligand Cross-Docking
Authors: Yize Jiang; Xinze Li; Yuanyuan Zhang; Jin Han; Youjun Xu; Ayush Pandit; ZAIXI ZHANG; Mengdi Wang; Mengyang Wang; Chong Liu; Guang Yang; Yejin Choi; Yingzhou Lu; Wu-Jun Li; Tianfan Fu; Fang Wu; Junhong Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, significant progress has been made in protein-ligand docking, especially in deep learning methods, and some benchmarks were proposed, such as PoseBench and PLINDER. However, these studies typically focus on the self-docking scenario, which is less practical in real-world applications. Moreover, some studies employ complex frameworks that require extensive training, posing challenges for convenient and efficient assessment of docking methods. To address these gaps, we introduce PoseX, an open-source benchmark for evaluating both self-docking and cross-docking, enabling a practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self-docking and 1,312 entries for cross-docking; secondly, we incorporated 23 docking methods in three methodological categories, including physics-based methods (e.g., Schrödinger Glide), AI docking methods (e.g., DiffDock) and AI co-folding methods (e.g., AlphaFold3); thirdly, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourthly, we established a public leaderboard to rank submitted models in real-time. We derived some key insights and conclusions through extensive experiments: (1) AI-based approaches consistently outperform physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI-based approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics-based post-processing could achieve excellent performance. (3) AI co-folding methods exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting that stereochemical modeling greatly improves the structural plausibility of the predicted protein-ligand complexes. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for AI co-folding methods, in future modeling efforts.
TS-DDAE: A Novel Temporal-Spectral Denoising Diffusion AutoEncoder for Wireless Signal Recognition Model Pre-training
Authors: Yaoqi Liu; Jin Wang; Hui Wang; Chuan Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Wireless Signal Recognition (WSR) aims to identify the property of received signals using Artificial Intelligence (AI) without any prior knowledge, which has been widely used in civil and military radios. The current AI trend of pre-training and fine-tuning has shown great performance, and the existing pre-trained WSR models also achieve impressive results. However, they either apply the ``mask-reconstruction" pre-training strategy, which may disrupt intricate local dependencies of signals, or overlook latent spectral characteristics. Therefore, in this paper, we follow the diffusion models and propose a pre-training framework for WSR, named the Temporal-Spectral Denoising Diffusion AutoEncoder (TS-DDAE), which learns signal representations by corrupting signals with temporal and spectral noise, and then reconstructing the original data with a learned neural network. Moreover, we design a novel neural architecture, named TS-Net, which couples self-attention for temporal encoder with channel attention for spectral encoder. Extensive experiments on several datasets and WSR tasks show that TS-DDAE achieves superior performance compared to state-of-the-art (SOTA) baselines, which demonstrate the potential to be a foundation model for WSR. Code is available at https://github.com/BUPT-GAMMA/FoundWSR.
Model-Guided Microstimulation Steers Primate Visual Behavior
Authors: Johannes Mehrer; Ben Lonnqvist; Anna Mitola; Paolo Papale; Martin Schrimpf
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Brain stimulation is a powerful tool for understanding cortical function and holds the promise of therapeutic interventions to treat neuropsychiatric disorders such as impaired vision. Prototypical approaches to visual prosthetics apply patterns of electric microstimulation to the early visual cortex and can evoke percepts of simple symbols such as letters. However, these approaches are limited by the number of electrodes that can be implanted in early visual regions. Instead, higher-level visual regions are known to underlie the representations of complex visual objects such as faces and scenes and thus constitute a promising target for stimulating the cortex to elicit more complex visual experience. We developed a computational framework composed of two main components to address the challenge of stimulating cortex in high-dimensional object space spanned by higher-level visual cortex: 1. a causally predictive model that predicts primate behavior from image and stimulation input via topographic models and perturbation modules. 2. a mapping procedure that translates optimal model stimulation sites to monkey cortex. Testing our approach in two macaque monkeys that perform a visual recognition task, our results suggest that model-guided microstimulation is a promising approach to steer complex visual behavior. This proof-of-principle establishes a foundation for next-generation visual prosthetics that could restore complex visual experiences by stimulating higher-level visual cortex.
Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
Authors: Yukuan Wei; Xudong Li; Lin F. Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model. However, much less is known about the constrained average-reward MDP (CAMDP), where policies must satisfy long-run average constraints. In this work, we address this gap by studying the sample complexity of learning an $\epsilon$-optimal policy in CAMDPs under a generative model. We propose a model-based algorithm that operates under two settings: (i) relaxed feasibility, which allows small constraint violations, and (ii) strict feasibility, where the output policy satisfies the constraint. 
We show that our algorithm achieves sample complexities of $\tilde{O}\left(\frac{S A (B+H)}{ \epsilon^2}\right)$ and $\tilde{O} \left(\frac{S A (B+H)}{\epsilon^2 \zeta^2} \right)$ under the relaxed and strict feasibility settings, respectively. Here, $\zeta$ is the Slater constant indicating the size of the feasible region, $H$ is the span bound of the bias function, and $B$ is the transient time bound. Moreover, a matching lower bound of $\tilde{\Omega}\left(\frac{S A (B+H)}{ \epsilon^2\zeta^2}\right)$ for the strict feasibility case is established, thus providing the first minimax-optimal bounds for CAMDPs. Our results close the theoretical gap in understanding the complexity of constrained average-reward MDPs.
Optimal transport unlocks end-to-end learning for single-molecule localization
Authors: Romain Seailles; Jean Baptiste Masson; Jean Ponce; Julien Mairal
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Single‑molecule localization microscopy (SMLM) allows reconstructing cellular organelles and biology-relevant structures far beyond the limited spatial resolution imposed by optics constrains, using tagged biomolecule positions. Currently, efficient SMLM requires non‑overlapping emitting fluorophores, to ensure proper image deconvolution leading to long acquisition times that hinders live‑cell imaging. Recent deep‑learning approaches can handle denser emissions, but they rely on variants of non‑maximum suppression (NMS) layers, which are unfortunately non‑differentiable and may discard true positives with their local fusion strategy. 
In this presentation, we reformulate the SMLM training objective as a set‑matching problem, deriving an optimal‑transport loss that eliminates the need for NMS during inference and enables end‑to‑end training. 
Additionally, we propose an iterative neural network that integrates knowledge of the microscope’s optical system inside our model. Experiments on synthetic benchmarks and real biological data show that both our new loss function and architecture surpass the state of the art at moderate and high emitter densities. Code is available at https://github.com/RSLLES/SHOT.
ClarifyVC: Clarifying Ambiguous Commands in Vehicle Control with a Hybrid Data Augmentation Pipeline
Authors: Hange Zhou; Zhonglin Jiang; yingjie cui; Mingzhe Zhang; Xiaotang Wang; Hengwei Dai; Qiyao Yu; Yong Chen; Yongqi Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Natural language interfaces for vehicle control must contend with vague commands, evolving dialogue context, and strict protocol constraints. 
We introduce ClarifyVC, a unified framework that integrates a hybrid data-augmentation pipeline (ClarifyVC-Data), reference models trained on the data (ClarifyVC-Models)
and a evaluation protocol (ClarifyVC-Eval). 
The agent-orchestrated pipeline generates diverse, ambiguity-rich dialogues from real-world seeded queries under schema and safety constraints, while the evaluation protocol systematically probes single-turn parsing, conservative clarification under extreme fuzziness, and multi-turn grounding. 
Fine-tuning on ClarifyVC-Data yields consistent gains—up to 15\% higher parsing accuracy, 20\% stronger ambiguity resolution, and 98\% protocol compliance—across realistic in-cabin scenarios, with human-in-the-loop assessments confirming high realism, coherence, and applicability. 
ClarifyVC thus advances beyond simulation-only datasets by tightly coupling real-world grounding with scalable generation and standardized evaluation, and provides a generalizable pipeline for broader interactive control domains.
Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in LLMs
Authors: Soyeon Kim; Jindong Wang; Xing Xie; Steven Euijong Whang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Facts change over time, making it essential for Large Language Models (LLMs) to handle time-sensitive factual knowledge accurately and reliably. Although factual Time-Sensitive Question-Answering (TSQA) tasks have been widely developed, existing benchmarks often face manual bottlenecks that limit scalable and comprehensive TSQA evaluation. To address this issue, we propose TDBench, a new benchmark that systematically constructs TSQA pairs by harnessing temporal databases and database techniques, such as temporal functional dependencies, temporal SQL, and temporal joins. We also introduce a new evaluation metric called time accuracy, which assesses the validity of time references in model explanations alongside traditional answer accuracy for a more fine-grained TSQA evaluation. Extensive experiments on contemporary LLMs show how TDBench enables scalable and comprehensive TSQA evaluation while reducing the reliance on human labor, complementing current TSQA evaluation approaches that largely center on Wikipedia/Wikidata by enabling LLM evaluation on application-specific data.
LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
Authors: Junhong Wu; Jinliang Lu; Zixuan Ren; Gangqiang Hu; Zhi Wu; Dai Dai; Hua Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Human cognition naturally engages with abstract and fluid concepts, whereas existing reasoning models often rely on generating discrete tokens, potentially constraining their expressive capabilities. Recent advancements aim to address this limitation by enabling large language models (LLMs) to generate soft, abstract tokens, thus facilitating reasoning within a continuous concept space. In this paper, we investigate the $\textit{Soft Thinking}$ capabilities of various LLMs through a systematic analysis of their internal behavior using a suite of probing techniques. Contrary to the prevailing belief that Soft Thinking supports parallel exploration of diverse reasoning paths, our findings reveal that $\textbf{LLMs behave as single-threaded reasoners}$—they predominantly rely on the token with the highest probability in the soft input to predict the next step. This behavior induces a greedy feedback loop that suppresses alternative reasoning paths and undermines the benefits of transmitting richer information via Soft Tokens. To address this $\textit{Greedy Pitfall}$, we propose $\textbf{Stochastic Soft Thinking}$, which introduces stochasticity to break free from the greedy tendency. Our experiments demonstrate that incorporating $\textit{randomness}$—particularly with the $\textbf{Gumbel-Softmax trick}$—can alleviate the limitations of vanilla approaches and unleash the potential of Soft Thinking, resulting in superior performance across eight reasoning benchmarks.