GenAI4ELab/papercli-papers huggingface dataset:
Seeing Through Words: Controlling Visual Retrieval Quality with Language Models
Authors: Jianglin Lu; Simon Jenni; Kushal Kafle; Jing Shi; Handong Zhao; Yun Fu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios it is often challenged by short and underspecified user queries. Such queries are typically only one or two words long, rendering them semantically ambiguous, prone to collisions across diverse visual interpretations, and lacking explicit control over the quality of retrieved images. To address these issues, we propose a new paradigm of quality-controllable retrieval, which enriches short queries with contextual details while incorporating explicit notions of image quality. Our key idea is to leverage a generative language model as a query completion function, extending underspecified queries into descriptive forms that capture fine-grained visual attributes such as pose, scene, and aesthetics. We introduce a general framework that conditions query completion on discretized quality levels, derived from relevance and aesthetic scoring models, so that query enrichment is not only semantically meaningful but also quality-aware. The resulting system provides three key advantages: 1) flexibility, it is compatible with any pretrained vision-language model (VLMs) without modification; 2) transparency, enriched queries are explicitly interpretable by users; and 3) controllability, enabling retrieval results to be steered toward user-preferred quality levels. Extensive experiments demonstrate that our proposed approach significantly improves retrieval results and provides effective quality control, bridging the gap between the expressive capacity of modern VLMs and the underspecified nature of short user queries. Our code is available at https://github.com/Jianglin954/QCQC.
Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
Authors: Chengzhi Mao; Xudong Lin; Wen-Sheng Chu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision foundation models are typically trained as static feature extractors, forcing the burden of task adaptation onto large downstream models. We propose a different paradigm: instead of solely feeding visual features into language, we use language itself to dynamically guide the vision encoder. Our method, Language-Instructed Vision Embeddings (LIVE), leverages language as high-level guidance to produce task-centric embeddings at inference time—without requiring task-specific retraining. This enables the encoder to focus attention on contextually relevant aspects of the input, yielding more controllable and generalizable representations. Empirically, LIVE reduces visual hallucinations (+34 points on MMVP), outperforms vision–language models with orders of magnitude more parameters on visual question answering, and generalizes to unseen instructions and  tasks---offering a direct path toward adaptive, instruction-driven visual intelligence.
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
Authors: Jinbang Huang; Yixin Xiao; Zhanguang Zhang; Mark Coates; Jianye HAO; Yingxue Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP) addresses this by grounding symbolic plans in low-level execution, yet it relies heavily on manually engineered planning domains. To improve long-horizon planning reliability and reduce human intervention, we present Planning Domain Derivation with LLMs (PDDLLM), a framework that automatically induces symbolic predicates and actions directly from demonstration trajectories by combining LLM reasoning with physical simulation roll-outs. Unlike prior domain-inference methods that rely on partially predefined or language descriptions of planning domains, PDDLLM constructs domains with minimal manual domain initialization and automatically integrates them with motion planners to produce executable plans, enhancing long-horizon planning automation. Across 1,200 tasks in nine environments, PDDLLM outperforms six LLM-based planning baselines, achieving at least 20% higher success rates, reduced token costs, and successful deployment on multiple physical robot platforms.
AlphaAgentEvo: Evolution-Oriented Alpha Mining via Self-Evolving Agentic Reinforcement Learning
Authors: Ziyi Tang; Xuexiong Yin; Weixing Chen; Zechuan Chen; Yongsen Zheng; Wenxuan Ye; Keze Wang; Liang Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Alpha mining seeks to identify predictive alpha factors that generate excess returns relative to the market from a vast and noisy search space; however, existing evolution-based approaches struggle to facilitate the systematic evolution of alphas. Traditional methods, such as Genetic Programming (GP), cannot interpret natural language instructions and often fail to extract valuable insights from unsuccessful attempts, leading to low interpretability and inefficient exploration. Analogously, without mechanisms for systematic evolution, e.g., long-term planning and reflection, existing multi-agent approaches may easily fall into repetitive evolutionary routines, resulting in inefficient evolution. To overcome these limitations, we introduce AlphaAgentEvo, a self-evolving Agentic Reinforcement Learning (ARL) framework for alpha mining, which moves alpha mining beyond the brittle search-backtest-restart cycle toward a continuous trajectory of evolution. Guided by a hierarchical reward function, our agent engages in self-exploration of the search space, progressively learning basic requirements (e.g., valid tool calls) and then harder objectives (e.g., continuous performance improvements). Through this process, the agent acquires advanced behaviors such as long-horizon planning and reflective reasoning, which enable it to actively react to the underlying state (e.g., market regime shifts) and realize a self-evolving agent, marking a step toward more principled and scalable alpha mining. Extensive experiments demonstrate that AlphaAgentEvo achieves more efficient alpha evolution and generates diverse and transferable alphas, consistently surpassing a wide range of baselines. Notably, with only 4B parameters, it outperforms LLM-driven evolution methods configured with state-of-the-art closed-source reasoning models, highlighting the promise of ARL for next-generation alpha mining.
VoMP: Predicting Volumetric Mechanical Property Fields
Authors: Rishit Dagli; Donglai Xiang; Vismay Modi; Charles Loop; Clement Fuji Tsang; Anka He Chen; Anita Hu; Gavriel State; David Levin I.W.; Maria Shugrina
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Physical simulation relies on spatially-varying mechanical properties, typically laboriously hand-crafted. We present the first feed-forward model to predict fine-grained mechanical properties, Young’s modulus ($E$), Poisson’s ratio ($\nu$), and density ($\rho$), throughout *the volume* of 3D objects. Our model supports any 3D representation that can be rendered and voxelized, including Signed Distance Fields (SDFs), Gaussian Splats and Neural Radiance Fields (NeRFs). To achieve this, we aggregate per-voxel multi-view features for any input, which are passed to our trained Geometry Transformer to predict per-voxel material latent codes. These latents reside on the trained manifold of physically plausible materials, which we train on a real-world dataset, guaranteeing the validity of decoded per-voxel materials. To obtain object-level training data, we propose an annotation pipeline combining knowledge from segmented 3D datasets, material databases, and a vision-language model. Experiments show that VoMP estimates accurate volumetric properties and can convert 3D objects into simulation-ready assets, resulting in realistic deformable simulations and far outperforming prior art.
Pretraining with Re-parametrized Self-Attention: Unlocking Generalizationin  SNN-Based Neural Decoding Across Time, Brains, and Tasks
Authors: Yuqi Yang; Tengjun Liu; Haiyan Zhang; Wang Ruixue; Xuchao Chen; Mingkang Li; Yansong Chua; Nenggan Zheng; Shaomin Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The emergence of large-scale neural activity datasets provides new opportunities to enhance the generalization of neural decoding models. However, it remains a practical challenge to design neural decoders for fully implantable brain-machine interfaces (iBMIs) that achieve high accuracy, strong generalization, and low computational cost, which are essential for reliable, long-term deployment under strict power and hardware constraints.
To address this, we propose the Re-parametrized self-Attention Spiking Neural Network (RAT SNN) with a cross-condition pretraining framework to integrate neural variability and adapt to stringent computational constraints. 
Specifically, our approach introduces multi-timescale dynamic spiking neurons to capture the complex temporal variability of neural activity.
We refine spike-driven attention within a lightweight, re-parameterized architecture that enables accumulate-only operations between spiking neurons without sacrificing decoding accuracy.
Furthermore, we develop a stepwise training pipeline to systematically integrate neural variability across conditions, including neural temporal drift, subjects and tasks.
Building on these advances, we construct a pretrained model capable of rapid generalization to unseen conditions with high performance. 
We demonstrate that RAT SNN consistently outperforms leading SNN baselines and matches the accuracy of state-of-the-art artificial neural network (ANN) models with much lower computational cost under both seen and unseen conditions across various datasets. 
Collectively, pretrained-RAT SNN represents a high-performance, highly generalizable, and energy-efficient prototype of an SNN foundation model for fully iBMI. 
Code is available at [RAT SNN GitHub](https://github.com/YangYangYangYuqi/RAT-SNN).
ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation
Authors: Jiahao Chang; Chongjie Ye; Yushuang Wu; Yuantao Chen; Yidan Zhang; Zhongjin Luo; Chenghong Li; Yihao Zhi; Xiaoguang Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements in diffusion-based 3D generative techniques offer the potential to address these limitations by leveraging learned generative priors to ‘‘hallucinate" invisible parts of objects, thereby generating plausible 3D structures. However, the stochastic nature of the inference process limits the accuracy and reliability of generation results, preventing existing reconstruction frameworks from integrating such 3D generative priors. In this work, we comprehensively analyze the reasons why diffusion-based 3D generative methods fail to achieve high consistency, including (a) the insufficiency in constructing and leveraging cross-view connections when extracting multi-view image features as conditions, 
and (b) the poor controllability of iterative denoising during local detail generation, which easily leads to plausible but inconsistent fine geometric and texture details with inputs. Accordingly, we propose ReconViaGen to innovatively integrate reconstruction priors into the generative framework and devise several strategies that effectively address these issues. Extensive experiments demonstrate that our ReconViaGen can reconstruct complete and accurate 3D models consistent with input views in both global structure and local details.
GeoFAR: Geography-Informed Frequency-Aware Super-Resolution for Climate Data
Authors: Chang Xu; Gencer Sumbul; Li Mi; Robin Zbinden; Devis Tuia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Super-resolving climate data is crucial for fine-grained decision-making in various domains, ranging from agriculture to environmental conservation. However, existing super-resolution approaches struggle to generate the high-frequency spatial information present in climate data, especially over regions showing complex terrain variability. A key obstacle lies in a frequency bias existing in both deep neural networks (DNNs) and climate data: DNNs exhibit such bias by overfitting to low-frequency information, which is further exacerbated by the prevalence of low-frequency components in climate data (e.g., plains, oceans). As a consequence, geography-dependent high-frequency details are hard to reconstruct from coarse climate inputs with DNNs. To improve the fidelity of climate super-resolution (SR), we introduce GeoFAR: by explicitly encoding climatic patterns at different frequencies, while learning implicit geographical neural representations (i.e., related to location and elevation), our approach provides frequency-aware and geography-informed representations for climate SR, thereby reconstructing fine-grained climate information at high resolution. Experiments show that GeoFAR is a model-agnostic approach that can mitigate high-frequency prediction errors in both deterministic and generative SR models, demonstrating state-of-the-art performance across various spatial resolutions, atmospheric variables, and downscaling ratios. Datasets and code are available at https://eceo-epfl.github.io/GeoFAR/.
Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
Authors: Yotam Norman; Ron Meir
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a novel unsupervised framework for Invariant Risk Minimization (IRM), extending the concept of invariance to settings where labels are unavailable. Traditional IRM methods rely on labeled data to learn representations that are robust to distributional shifts across environments. In contrast, our approach redefines invariance through feature distribution alignment, enabling robust representation learning from unlabeled data. We introduce two methods within this framework: Principal Invariant Component Analysis (PICA), a linear method that extracts invariant directions under Gaussian assumptions, and Variational Invariant Autoencoder (VIAE), a deep generative model that separates environment-invariant and environment-dependent latent factors. Our approach is based on a novel ``unsupervised'' structural causal model and supports environment-conditioned sample-generation and intervention. Empirical evaluations on synthetic dataset, modified versions of MNIST, and CelebA demonstrate the effectiveness of our methods in capturing invariant structure, preserving relevant information, and generalizing across environments without access to labels.
OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
Authors: Jingdi Lei; Varun Gumma; Rishabh Bhardwaj; Seok Min Lim; Chuan Li; Amir Zadeh; Soujanya Poria
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such as models assisting users in harming themselves or others, enterprises face a more fundamental concern: whether LLM-based agents are safe for their intended use case. To address this, we introduce operational safety, defined as an LLM’s ability to appropriately accept or refuse user queries when tasked with a specific purpose. We further propose OffTopicEval, an evaluation suite and benchmark for measuring operational safety both in general and within specific agentic use cases. Our evaluations on six model families comprising 20 open-weight LLMs reveal that while performance varies across models, all of them remain highly operationally unsafe. Even the strongest models—Qwen-3 (235B) with 77.77% and Mistral (24B) with 79.96%—fall far short of reliable operational safety, while GPT models plateau in the 62–73% range, Phi achieves only mid-level scores (48–70%), and Gemma and Llama-3 collapse to 39.53% and 23.84%, respectively. While operation safety is core model's alignment issue, to suppress these failures, we propose prompt-based steering methods, query grounding (Q-ground), and system-prompt grounding (P-ground), which substantially improve OOD refusal. Q-ground provides consistent gains of up to 23%, while P-ground delivers even larger boosts, raising Llama-3.3 (70B) by 41% and Qwen-3 (30B) by 27%. These results highlight both the urgent need for operational safety interventions and the promise of prompt-based steering as a first step toward more reliable LLM-based agents. Our code and data are released at \url{https://github.com/declare-lab/OffTopicEval}.
Difficult Examples Hurt Unsupervised Contrastive Learning: A Theoretical Perspective
Authors: Yi-Ge Zhang; Jingyi Cui; Qiran Li; Yisen Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However, its learning mechanism is fundamentally different from supervised learning. Previous works have shown that difficult examples (well-recognized in supervised learning as examples around the decision boundary),  which are essential in supervised learning, contribute minimally in unsupervised settings. In this paper, perhaps surprisingly, we find that the direct removal of difficult examples, although reduces the sample size, can boost the downstream classification performance of contrastive learning. To uncover the reasons behind this, we develop a theoretical framework modeling the similarity between different pairs of samples. Guided by this framework, we conduct a thorough theoretical analysis revealing that the presence of difficult examples negatively affects the generalization of contrastive learning. Furthermore, we demonstrate that the removal of these examples, and techniques such as margin tuning and temperature scaling can enhance its generalization bounds, thereby improving performance.
Empirically, we propose a simple and efficient mechanism for selecting difficult examples and validate the effectiveness of the aforementioned methods, which substantiates the reliability of our proposed theoretical framework.
Pixel-Perfect Puppetry: Precision-Guided Enhancement for Face Image and Video Editing
Authors: Yan Li; Zhenyi Wang; Guanghao Li; Wei Xue; Wenhan Luo; Yike Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Preserving identity while precisely manipulating attributes is a central challenge
in face editing for both images and videos. Existing methods often introduce visual artifacts or fail to maintain temporal consistency. We present **FlowGuide**,
a unified framework that achieves fine-grained control over face editing in diffusion models. Our approach is founded on the local linearity of the UNet bottleneck’s latent space, which allows us to treat semantic attributes as corresponding
to specific linear subspaces, providing a mathematically sound basis for disentanglement. FlowGuide first identifies a set of orthogonal basis vectors that span
these semantic subspaces for both the original content and the target edit, a representation that efficiently captures the most salient features of each. We then
introduce a novel guidance mechanism that quantifies the geometric alignment
between these bases to dynamically steer the denoising trajectory at each step.
This approach offers superior control by ensuring edits are confined to the desired
attribute’s semantic axis while preserving orthogonal components related to identity. Extensive experiments demonstrate that FlowGuide achieves state-of-the-art
performance, producing high-quality edits with superior identity preservation and
temporal coherence. Our code is available at: https://github.com/yl4467/flow_edit.
PQGAN: Product-Quantised Image Representation for High-Quality Image Synthesis
Authors: Denis Zavadski; Nikita Philip Tatsch; Carsten Rother
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Product quantisation (PQ) is a classical method for scalable vector encoding, yet it has seen limited usage for latent representations in high-fidelity image generation.
In this work, we introduce \textit{PQGAN}, a quantised image autoencoder that integrates PQ into the well-known vector quantisation (VQ) framework of VQGAN and adapts it to the regime of large-scale latent generative models.
PQGAN achieves a noticeable improvement over state-of-the-art methods in terms of reconstruction performance, including both quantisation methods and their continuous counterparts. We achieve a PSNR score of 37dB, where prior work achieves 27dB, 
and are able to reduce the FID, LPIPS, and CMMD score by up to 96\%. 
Our key to success is a thorough analysis of the interaction between codebook size, embedding dimensionality, and subspace factorisation, with vector and scalar quantisation as special cases. We obtain novel findings, such that the performance of VQ and PQ behaves in opposite ways when scaling the embedding dimension. Furthermore, our analysis shows performance trends for PQ that help guide optimal hyperparameter selection.
Finally, we demonstrate that PQGAN can be seamlessly integrated into pre-trained diffusion models. This enables either a significantly faster and more compute-efficient generation, or a doubling of the output resolution at no additional cost, positioning PQ as a strong extension for discrete latent representations in image synthesis.
AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models
Authors: Zihao Zhu; Xinyu Wu; Gehan Hu; Siwei Lyu; Ke Xu; Baoyuan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex problem-solving through Chain-of-Thought (CoT) reasoning. However, the multi-step nature of CoT introduces new safety challenges that extend beyond conventional language model alignment. We identify a failure mode in current safety CoT tuning methods: the snowball effect, where minor reasoning deviations progressively amplify throughout the thought process, leading to either harmful compliance or excessive refusal. This effect stems from models being trained to imitate perfect reasoning scripts without learning to self-correct. To address this limitation, we propose AdvChain, an alignment paradigm that teaches models dynamic self-correction through adversarial CoT tuning. Our method involves constructing a dataset containing Temptation-Correction and Hesitation-Correction samples, where models learn to recover from harmful reasoning drifts and unnecessary cautions. Extensive experiments show that AdvChain significantly enhances robustness against jailbreak attacks and CoT hijacking while substantially reducing over-refusal on benign prompts, achieving a superior safety-utility balance without compromising reasoning capabilities. Our work establishes a new direction for building more robust and reliable reasoning models.