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m8sPQEd71W | 104 | m8sPQEd71W | Unified Multimodal Model as Auto-Encoder | The pursuit of unified multimodal models (UMMs) has long been hindered by a fundamental schism between multimodal understanding and generation. Current approaches typically disentangle the two and treat them as separate endeavors with disjoint objectives, missing the mutual benefits. We argue that true unification requ... | Exploring synergy between visual generation and perception by formulating the unified multimodal model as autoencoder. | ['Multimodal', 'Unified Multimodal Model', 'Generative Model'] | /pdf/61fc10b43f944f5731b7129602b602d5f0ec06d5.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission104/Authors'] |
74M7InKlVs | 103 | 74M7InKlVs | C$^3$-Bench: Evaluating and Achieving Controllable Code Completion in Code LLM | Code completion has become a central task, gaining significant attention with the rise of large language model (LLM)-based tools in software engineering. Although recent advances have greatly improved LLMs' code completion abilities, evaluation methods have not advanced equally. Most current benchmarks focus solely on ... | We created C³-Bench, a new benchmark for code LLMs that tests both code correctness and instruction following, revealing gaps in current models and developed a better-performing solution through automated training data generation. | ['Large Language Models', 'Code Language Models', 'Code Completion', 'Instruction Following'] | /pdf/2365463e7c923ffa6529d3000c4c06c547b44ea5.pdf | other topics in machine learning (i.e., none of the above) | null | ['ICLR.cc/2026/Conference/Submission103/Authors'] |
eAge74DIgk | 101 | eAge74DIgk | LitExplorer: Training-Free Diffusion Guidance with Adaptive Exploration-Filtering Framework | Diffusion models possess strong general generative capabilities, yet they remain insufficient when aligned with specific target objectives. Fine-tuning methods can enhance alignment but incur high training costs and face the risk of reward hacking. Consequently, training-free guidance mechanisms have emerged, which lev... | null | ['Diffusion Model;Traning-free'] | /pdf/1c6b6c00941091ec239340ef422fc7d9f01f4462.pdf | applications to computer vision, audio, language, and other modalities | /attachment/2b27b42c94f12c88f9c49a8e2c11c1adbec795e8.zip | ['ICLR.cc/2026/Conference/Submission101/Authors'] |
FGkknrhv09 | 100 | FGkknrhv09 | Curing "Miracle Steps'' in LLM Math Reasoning with Rubric Rewards | Large language models for mathematical reasoning are typically trained with outcome-based rewards, which credit only the final answer. In our experiments, we observe that this paradigm is highly susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. This is evidenced by... | This paper diagnoses how LLMs achieve correct math answers with flawed logic ("false positives") and introduces a "Rubric Reward Model" that rewards the entire problem-solving process to build more trustworthy and accurate reasoners. | ['faithful chain-of-thought', 'math reasoning', 'false positive', 'rubric'] | /pdf/365c5050a2ce26e04b0f1c843f16e9a72f9c704f.pdf | alignment, fairness, safety, privacy, and societal considerations | null | ['ICLR.cc/2026/Conference/Submission100/Authors'] |
I88toT6Leg | 99 | I88toT6Leg | The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies | Large Language Models (LLMs) are increasingly used for social simulation, where populations of agents are expected to reproduce human-like collective behavior. However, we find that many recent studies adopt experimental designs that systematically undermine the validity of their claims. From a survey of over 40 papers... | null | ['Large Language Model', 'Multi-Agent System', 'Social Simulation', 'Social Science'] | /pdf/69878b43abed6ff5ad1c4ca4539e64eb75e06895.pdf | alignment, fairness, safety, privacy, and societal considerations | /attachment/b67110f6633e800df1fd66d725185552fa32de05.zip | ['ICLR.cc/2026/Conference/Submission99/Authors'] |
5HHkCSVHaU | 98 | 5HHkCSVHaU | Teaching LLMs According to Their Aptitude: Adaptive Switching Between CoT and TIR for Mathematical Problem Solving | Existing supervised fine-tuning (SFT) approaches to enhance the mathematical reasoning of large language models (LLMs) rely either on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation. While efforts have been made to combine these methods, they primarily rely on post... | we propose TATA, an adaptive framework that enables LLMs to personalize their reasoning strategy for different problems spontaneously, aligning it with their intrinsic aptitude. | ['Large Language Models', 'math QA', 'chain-of-thought', 'tool-integrated reasoning', 'fine-tuning'] | /pdf/d166d32d51c34eb2be6da6ef8e733c286e3e78a7.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission98/Authors'] |
GymjF88oGQ | 97 | GymjF88oGQ | The Pensieve Paradigm: Stateful Language Models with Learned Memory Management | In the world of Harry Potter, when Dumbledore's mind is overburdened, he extracts memories into a Pensieve to be revisited later. In the world of AI, while we possess the Pensieve—mature databases and retrieval systems, our models inexplicably lack the "wand" to operate it. They remain like a Dumbledore without agency,... | null | ['LLM', 'memory management'] | /pdf/d411b45856f6dfaf3ae0c24c5b9aa995014326ba.pdf | foundation or frontier models, including LLMs | /attachment/bceafcddb1daa855bd0be813fc8c88bb16a1e0ff.zip | ['ICLR.cc/2026/Conference/Submission97/Authors'] |
NSjAYTNB11 | 95 | NSjAYTNB11 | PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization | Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured data remains largely unevaluated and underdeveloped. To address this gap, we introduce \textbf{PlotCraft}, a new benchmark featuring 1k chall... | LLMs are bad at complex charts. We built a small, specialized model, PlotCraftor, that fixes this and is now state-of-the-art. | ['Large Language Model; Code Generation; Data Visualization'] | /pdf/ff4d59f420150b9719d3866dffd007b2331fcf54.pdf | datasets and benchmarks | null | ['ICLR.cc/2026/Conference/Submission95/Authors'] |
9aB3BWye1j | 92 | 9aB3BWye1j | PairedContrast: A Multimodal Benchmark for Medical Image Translation | Contrast medium play a pivotal role in radiological imaging, as it amplifies lesion conspicuity and improves detection in the diagnosis of tumor-related diseases. However, depending on the patient’s health condition or the medical resources available, the use of contrast medium is not always feasible. Recent work has t... | null | ['benchmark; pan-cancer; paired datasets; medical image translation; contrast media'] | /pdf/dea3b2acd9ac51578b6ec8fb77b1aa575911de9e.pdf | datasets and benchmarks | null | ['ICLR.cc/2026/Conference/Submission92/Authors'] |
8pi1rP71qv | 91 | 8pi1rP71qv | FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning | General continual learning (GCL) challenges intelligent systems to learn from single-pass, non-stationary data streams without clear task boundaries. While recent advances in continual parameter-efficient tuning (PET) of pretrained models show promise, they typically rely on multiple training epochs and explicit task c... | We propose a brain-inspired method FlyPrompt that uses random-expanded routing and temporal-ensemble experts to effectively tackle General Continual Learning problem, achieving significant gains on major benchmarks. | ['Continual Learning', 'Life-long Learning', 'Brain-inspired AI', 'Catastrophic Forgetting', 'Prompt Tuning'] | /pdf/9bde35abdb2f177c878cde658e6f42cb93590032.pdf | transfer learning, meta learning, and lifelong learning | /attachment/a502549ab359383dbaa373fb0cb2e6c40e6ff16f.zip | ['ICLR.cc/2026/Conference/Submission91/Authors'] |
XHzrBDzKaX | 88 | XHzrBDzKaX | Castle-in-the-Air: Evaluating MLLM Visual Abilities on Human Cognitive Benchmarks | Despite significant progress on popular multimodal benchmarks, state-of-the-art Multimodal Large Language Models (MLLMs) continue to struggle with basic visual reasoning tasks that are trivially solved by humans, such as recognizing abstract patterns or identifying spatial relationships.
Such deficiencies undermine the... | null | ['Multimodal Large Language Model', 'Vision Language Model', 'Cognition', 'Evaluation'] | /pdf/1c1f48dc0ef033ef1f5986cdd84c20217453d3fc.pdf | applications to computer vision, audio, language, and other modalities | /attachment/70605ccf308eee0a1323bf598602ed76ea43a554.zip | ['ICLR.cc/2026/Conference/Submission88/Authors'] |
EXFKk4Y3yc | 87 | EXFKk4Y3yc | Spilled Energy in Large Language Models | We reinterpret the final softmax classifier over the vocabulary of Large Language Models (LLM) as an Energy-based Model (EBM). This allows us to decompose the chain of probabilities used in sequence-to-sequence modeling as multiple EBMs that interact together at inference time. Our decomposition offers a principled app... | We recast the LLM softmax as an Energy-Based Model, introducing training-free energy measures to detect hallucinations. Our method pinpoints errors, generalizes across tasks, and shows robust results on nine benchmarks. | ['LLM', 'hallucination detection', 'EBM'] | /pdf/c7f4a295dde283e8da45345b35965fcf90a31fbf.pdf | alignment, fairness, safety, privacy, and societal considerations | null | ['ICLR.cc/2026/Conference/Submission87/Authors'] |
6XvqXQq0ae | 86 | 6XvqXQq0ae | NextLocMoE: Enhancing Next Location Prediction via Location-Semantics Mixture-of-Experts and Personalized Mixture-of-Experts | Next location prediction is a key task in human mobility modeling. Existing methods face two challenges: (1) they fail to capture the multi-faceted semantics of real-world locations; and (2) they struggle to model diverse behavioral patterns across user groups. To address these issues, we propose NextLocMoE, a large la... | We propose NextLocMoE, a Mixture-of-Experts LLM framework for next-location prediction, which jointly modelslocation semantics and behavioral preferences via dual expert modules and history-aware routing. | ['next location prediction', 'Mixture-of-Experts', 'Large Language Model', 'Location Function MoE', 'Persona MoE'] | /pdf/f5b63891a6c4d26f62a5d31b7d29da7969c92e8c.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission86/Authors'] |
i4BiQK5Ndw | 83 | i4BiQK5Ndw | TopoMHC: Sequence–Topology Fusion for MHC Binding | Accurate prediction of peptide immunogenicity, particularly the binding affinity to major histocompatibility complex (MHC) molecules, is critical for vaccine design and immunotherapy. Existing approaches are predominantly sequence-based and often overlook structural variability and topological organization, which restr... | null | ['immunogenicity prediction', 'major histocompatibility complex', 'peptide representation learning', 'statistical topology', 'persistent homology', 'protein language models', 'cross-modal learning', 'vaccine design'] | /pdf/73d717f9219d719e35f3d8e629d5634b1dee6df2.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | null | ['ICLR.cc/2026/Conference/Submission83/Authors'] |
Tp70ig4iKN | 80 | Tp70ig4iKN | Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection | Detecting AI-generated images with multimodal large language models (MLLMs) has gained increasing attention, due to their rich world knowledge, common-sense reasoning, and potential for explainability.
However, naively applying those MLLMs for detection often leads to suboptimal performance.
We argue that the root of t... | We propose a unified MLLM-based framework that simultaneously perceives low-level artifacts and reasons dialectically about high-level plausibility, without reliance on external detectors. | ['AI-Generated Image Detection', 'MLLM', 'Media Forensics'] | /pdf/0f0450b32e796e0cde2b002e3c20ad8a749d6c10.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission80/Authors'] |
NlMXI17iou | 77 | NlMXI17iou | Reordered SparseGPT: Optimizing the Pruning Order in Second-Order LLM Pruning | Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient inference. One classic and prominent path of one-shot LLM pruning is to leverage the second-order gradients (i.e., Hessian), represented by the pioneering works like Spa... | This paper presents a new SoTA Hessian-based one-shot LLM pruning algorithm, which can be applied to unstructured and semi-structured sparsities. | ['LLM', 'Network Pruning', 'Hessian-based Pruning'] | /pdf/af7361eb2c49fd861f47a41b43506dee223d3eb4.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission77/Authors'] |
oKyDZabG0I | 74 | oKyDZabG0I | More Than a Snapshot: Forcing Temporal Reasoning in Video Segmentation | Video Reasoning Segmentation (VRS) inherits the settings of reasoning based on world knowledge and spatial contents, lacking queries demanding temporal reasoning according to the unique temporal dynamics of videos.
To bridge the gap, we introduce TempVRS, a large-scale Temporal Video Reasoning Segmentation dataset con... | null | ['Video Reasoning Segmentation', 'Temporal Dynamics'] | /pdf/57772ac96c8fcc2c882888bf4e50ebcd74e67222.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission74/Authors'] |
RKYO6R8Jgb | 72 | RKYO6R8Jgb | Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners | Reinforcement Learning with Verifiable Reward (RLVR) effectively solves complex tasks but demands extremely long context lengths during training, leading to substantial computational costs. While multi-stage training can partially mitigate this, starting with overly short contexts often causes irreversible performance ... | We propose Thinking-Free Policy Initialization, a stage prior to RL that can accelerate RL convergence to a higher performance ceiling and naturally yield reasoning-efficient models | ['Large Language Models', 'Reasoning', 'Reinforcement Learning with Verifiable Rewards', 'Long Chain-of-Thought'] | /pdf/9485752602f24c1d423333799dadade407c91cf6.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission72/Authors'] |
WEg7e5pcso | 70 | WEg7e5pcso | ABConformer: Physics‑inspired Sliding Attention for Antibody-Antigen Interface Prediction | Accurate prediction of antibody-antigen (Ab-Ag) interfaces is critical for vaccine design, immunodiagnostics and therapeutic antibody development. However, achieving reliable predictions from sequences alone remains a challenge. In this paper, we present \textsc{ABConformer}, a model based on the Conformer backbone tha... | null | ['Antibody–antigen interface prediction', 'Protein sequence modeling', 'Conformer', 'Sliding attention mechanism', 'Epitope prediction', 'Paratope prediction', 'Structural bioinformatics'] | /pdf/38039f8f48fb41930fb9d9ea4cf56c01bf411aab.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/76811f6954e6a1df174951d8ce851b45a4a300af.zip | ['ICLR.cc/2026/Conference/Submission70/Authors'] |
84vy8ZomFn | 68 | 84vy8ZomFn | Breaking Scale Anchoring: Frequency Representation Learning for Accurate High-Resolution Inference from Low-Resolution Training | Zero-Shot Super-Resolution Spatiotemporal Forecasting requires a deep learning model to be trained on low-resolution data and deployed for inference on high-resolution. Existing studies consider **maintaining** similar error across different resolutions as indicative of successful multi-resolution generalization perfor... | null | ['Scale Anchoring', 'Zero-Shot Super-Resolution', 'Spatiotemporal Forecasting', 'Frequency Representation'] | /pdf/6dab9cd5dfc2a8ac07dbb4dda69abb99c96e651c.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/5c15803f970e08058eb5c6c9ec1fd16dadd86cb9.zip | ['ICLR.cc/2026/Conference/Submission68/Authors'] |
Y9b5UuGi9O | 66 | Y9b5UuGi9O | CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models | Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, leading to object hallucination. To tackle this, recent works mostly depend on expensive manual annotations and training cost, or decoding st... | We propose Caption-sensitive Attention Intervention (CAI), a training-free method, that refines caption-sensitive attention heads outputs during inference to enhance the fine-grained visual perception capability and mitigate object hallucination. | ['Larger Vision-Language Model', 'Hallucination'] | /pdf/e1c8340e562f9d274c2e634e4f49374ce76b0d78.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission66/Authors'] |
CuzTXLB7Jz | 65 | CuzTXLB7Jz | OmniSAT: Compact Action Token, Faster Auto Regression | Existing Vision-Language-Action (VLA) models can be broadly categorized into diffusion-based and auto-regressive (AR) approaches: diffusion models capture continuous action distributions but rely on computationally heavy iterative denoising. In contrast, AR models enable efficient optimization and flexible sequence con... | null | ['Imitation Learning; Action Representation; Vision-Language-Action Learning'] | /pdf/ccc987a5b4b404f0a409b34c2eba4139a884ce88.pdf | applications to robotics, autonomy, planning | null | ['ICLR.cc/2026/Conference/Submission65/Authors'] |
jov79sMFHn | 64 | jov79sMFHn | NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks | 3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits p... | null | ['3D Computer Vision', '3D Editing', '3D Generation', 'Flow', 'Image Editiing'] | /pdf/cbf3e28722c3010620160fa33672819483eba27a.pdf | generative models | null | ['ICLR.cc/2026/Conference/Submission64/Authors'] |
9qOF3zgVfa | 63 | 9qOF3zgVfa | A Needle In A Haystack: Referring Hour-Level Video Object Segmentation | Long-term videos over minutes are ubiquitous in daily life while existing Referring Video Object Segmentation (RVOS) datasets are limited to short-term videos with a duration of only 5-60 seconds.
To unveil the dilemma of referring object segmentation towards hour-level videos, we construct the first Hour-level Referr... | null | ['Referring Video Object Segmentation', 'Hierarchical Memory'] | /pdf/3b643a86f53d4d2476c0f3ea238941b545fde51e.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission63/Authors'] |
tw1IWcVKTT | 62 | tw1IWcVKTT | Automated Optimization Modeling via a Localizable Error-Driven Perspective | Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivotal technique to enhance LLMs' capabilities in this domain, its effectiveness is severely constrained by the scarcity and underutilization of... | null | ['LLM post-training', 'automated optimization modeling'] | /pdf/23fb085ea34ec9c3758c3b82f1b0675987c4f205.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission62/Authors'] |
AaZVrbElhC | 61 | AaZVrbElhC | CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning | Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However, due to the discrete and non-differentiable nature of spikes, directly trained SNNs rely heavily on Batch Normalization (BN) to stabilize g... | null | ['Spiking Neural Networks', 'Batch Normalization', 'Reinforcement Learning'] | /pdf/c059da07546cb4a9c34c3abff3df59e0351f2515.pdf | applications to neuroscience & cognitive science | /attachment/d24c798f75e7488595b21d7268076fb8c487bb43.zip | ['ICLR.cc/2026/Conference/Submission61/Authors'] |
Fa3C0TkWYi | 60 | Fa3C0TkWYi | RectiWeather: Photo-Realistic Adverse Weather Removal via Zero-shot Soft Weather Perception and Rectified Flow | Despite significant progress in Adverse Weather Removal (AWR), challenges remain in applying existing methods to real-world scenarios and in generating photo-realistic and visually compelling outcomes. The limited generalization of current approaches can be attributed to their inability to accurately perceive complex d... | null | ['zero-shot', 'soft perception', 'rectified flow'] | /pdf/0ccbf8172fd9da11e5a1c3badd0efedef04b4355.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission60/Authors'] |
hQhqq6G3Be | 58 | hQhqq6G3Be | Adaptive Text and Feature Embedding for Consistent Story Generation | Recent advancements in text-to-image (T2I) generation have significantly improved image quality and text alignment. However, generating multiple coherent images that maintain consistent character identities across diverse textual descriptions remains challenging. Existing methods face trade-offs between identity consis... | null | ['consistent generation'] | /pdf/8d375ec00fc86c1fb6e13bf50e2685577220a456.pdf | generative models | null | ['ICLR.cc/2026/Conference/Submission58/Authors'] |
SGsxxbAjXH | 53 | SGsxxbAjXH | MVCustom: Multi-View Customized Diffusion via Geometric Latent Rendering and Completion | Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models.
However, existing multi-view generation models do not support customization with geometric consistency, whereas customization models lack explicit viewpoint control, ... | null | ['Multi-view generation', 'Customizaton', 'Personalization'] | /pdf/7402b82185602eb505889e6c56ce19060b583db8.pdf | generative models | /attachment/64288fe47f2bb519516b57e495715432940c8b78.zip | ['ICLR.cc/2026/Conference/Submission53/Authors'] |
eGI1HQeCmn | 51 | eGI1HQeCmn | ImmunoTrace: A Meta-Agent for Immune History Tracking | The adaptive immune system encodes an individual's exposure history in the T-cell receptor (TCR) repertoire. We present ImmunoTrace, an AI agent for immune history tracking that estimates past pathogen exposure from a single time-point repertoire by linking TCRs and HLA alleles to proteome-scale peptide libraries. A sh... | ImmunoTrace is an AI agent that links a single-time-point TCR repertoire (with optional HLA) to proteome-scale peptide libraries. | ['AI Agent', 'Retrieval-Augmented Modeling', 'Contrastive Learning', 'Probabilistic Evidence Fusion', 'Immune Exposure'] | /pdf/d1ffbbfed5979176e21ac50a4ef3cc142581e5b4.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/0400c6e99d42ec68b2906e04d70169648f6a2e03.zip | ['ICLR.cc/2026/Conference/Submission51/Authors'] |
8IjxLiNXL1 | 49 | 8IjxLiNXL1 | Memory Forgetting Adapter Sculpting for Selective Multimodal Large Language Model Unlearning | Multimodal Large Language Models (MLLMs) achieve remarkable capabilities but can inadvertently memorize privacy-sensitive information. Existing unlearning methods can remove such knowledge, yet they often degrade the model’s general image understanding. To address this, we propose the Sculpted Memory Forgetting Adapter... | null | ['MLLMs', 'Machine Unlearning', 'MLLM Unlearning', 'Privacy Protection'] | /pdf/5b82a24c81db1a9f2c82edacb3914001b9b28546.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission49/Authors'] |
PaYo96rjij | 44 | PaYo96rjij | Lifelong Embodied Navigation Learning | Embodied navigation agents powered by large language models have shown strong performance on individual tasks but struggle to continually acquire new navigation skills, which suffer from catastrophic forgetting. We formalize this challenge as lifelong embodied navigation learning (LENL), where an agent is required to a... | We propose Uni-Walker, a lifelong embodied navigation framework that decouples navigation knowledge into task-shared and task-specific components with Decoder Extension LoRA (DE-LoRA). | ['Embodied Navigation', 'Lifelong Learning', 'Robotics Learning'] | /pdf/a2c3cf69753a38670628cc736ba09431d8cd98fc.pdf | applications to robotics, autonomy, planning | /attachment/27ecb2511cb145533bcdfaf495bc8e661f073efd.zip | ['ICLR.cc/2026/Conference/Submission44/Authors'] |
QYH7JGzEzM | 43 | QYH7JGzEzM | GrapHist: Large-Scale Graph Self-Supervised Learning for Histopathology | Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not designed to inherently account for fundamental biological elements of histopathology images, namely cells and their complex interactions. In this work, we hypothesize that a... | null | ['graph representation learning', 'digital pathology'] | /pdf/2052ad1273f1ab95b7b4c3bccd593425b3377553.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/a9efe21b02e5834a998f7c3922d90bfef6a411fa.zip | ['ICLR.cc/2026/Conference/Submission43/Authors'] |
cnrhmiw1VG | 39 | cnrhmiw1VG | GLEAM: Learning to Match and Explain in Cross-View Geo-Localization | Cross-View Geo-Localization (CVGL) focuses on identifying correspondences between images captured from distinct perspectives of the same geographical location. However, existing CVGL approaches are typically restricted to a single view or modality, and their direct visual matching strategy lacks interpretability: they ... | This work presents GLEAM-C and GLEAM-X, a unified pipeline that advances cross-view geo-localization by integrating multi-view alignment with interpretable, explainable reasoning. | ['Remote Sensing', 'Cross-View Geo-Localization', 'Multimodal Large Language Model'] | /pdf/7a130beba23634a98a969092af6d39b7b1dbd331.pdf | foundation or frontier models, including LLMs | /attachment/3649d00816711f2efb443d6c95c2566a816df980.zip | ['ICLR.cc/2026/Conference/Submission39/Authors'] |
15HYjY5ol7 | 37 | 15HYjY5ol7 | An AI Agent for Immune Receptor Fingerprint‑Based Diagnosis of Infection of Unknown Origin | When routine tests fail to find a pathogen, diagnosing infections of unknown origin stalls. We instead read the patient's immune response for AI-readable clues. We formalize a new machine learning task: inferring plausible epitopes directly from immune-receptor repertoires and localizing their pathogen sources. To addr... | Generative allele-aware epitope inference plus proteome retrieval turns TCR “fingerprints” into ranked pathogen hypotheses with calibrated confidence for IUO diagnosis. | ['AI Agent', 'multi-task representation learning', 'Conditional sequence generation', 'Immune repertoire modeling', 'Epitope inference', 'Clinical diagnostics'] | /pdf/c22586b13406a373b84019d98c4949f7c95ef57b.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/3ccac7098c85ede22a372fedd3bed2c138d4049a.zip | ['ICLR.cc/2026/Conference/Submission37/Authors'] |
GjsE9C1grt | 36 | GjsE9C1grt | Nonlinear Steering for Token-Efficient Reasoning in LLMs via Flow Matching | Large Reasoning Models (LRMs) excel at complex reasoning tasks, but their efficiency is often hampered by overly verbose outputs. Prior steering methods attempt to address this issue by applying a single, global vector to hidden representations—a rigid approach grounded in the restrictive *linear representation hypothe... | This paper introduces a nonlinear steering method using Flow Matching to transform verbose reasoning paths into concise ones, achieving superior accuracy and token efficiency in LLMs. | ['representation steering; large reasoning models; LRMs; large language models; LLMs; efficient reasoning; flow matching'] | /pdf/3421f23aa0576a1a0ef1db91cfc97936c8c749b3.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission36/Authors'] |
sE8DCSJTzd | 35 | sE8DCSJTzd | Exploration v.s. Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward | This paper examines the exploration–exploitation trade-off in reinforcement learning with verifiable rewards (RLVR), a framework for improving the reasoning of Large Language Models (LLMs). Recent studies suggest that RLVR can elicit strong mathematical reasoning in LLMs through two seemingly paradoxical mechanisms: \t... | null | ['Reinforcement Learning with Verifiable Rewards', 'Group Relative Policy Optimization', 'LLM Reasoning'] | /pdf/cb6d1e97c04de37d8f35dd44516f78647f047f46.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission35/Authors'] |
6eSNG1VNkl | 33 | 6eSNG1VNkl | SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks | Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing approaches break under exploration complexity and intent drift. We propose SEMA, a simple yet effective framework that trains a multi-turn attacker without relying on any ex... | null | ['jailbreak', 'attack', 'multi-turn', 'reinforcement learning', 'large language model'] | /pdf/689aa1dbf5ca139920b52f3c93fd1376cf21b832.pdf | alignment, fairness, safety, privacy, and societal considerations | null | ['ICLR.cc/2026/Conference/Submission33/Authors'] |
KoLYNHJRBY | 32 | KoLYNHJRBY | CL-DPS: A Contrastive Learning Approach to Blind Nonlinear Inverse Problem Solving via Diffusion Posterior Sampling | Diffusion models (DMs) have recently become powerful priors for solving inverse problems. However, most work focuses on non-blind settings with known measurement operators, and existing DM-based blind solvers largely assume linear measurements, which limits practical applicability where operators are frequently nonline... | null | ['Diffusion Models', 'Blind Inverse Problems', 'Contrastive Learning'] | /pdf/60ec680452c3952a435815e5ec6fb69f635a1ee0.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission32/Authors'] |
AZ6lqcvHLX | 30 | AZ6lqcvHLX | Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer | The probabilistic diffusion model (DM), generating content by inferencing through a recursive chain structure, has emerged as a powerful framework for visual generation. After pre-training on enormous data, the model needs to be properly aligned to meet requirements for downstream applications. How to efficiently align... | null | ['perturbation-based gradient estimation', 'diffusion model', 'post-training'] | /pdf/1c4cb7e5e1ed617120bf74e26bf181ee341f737f.pdf | optimization | null | ['ICLR.cc/2026/Conference/Submission30/Authors'] |
lWc3QZkC9e | 27 | lWc3QZkC9e | WWW.Serve: A Decentralized Framework for Collaborative LLM Serving | Recent Large language model (LLM) services remain mostly centralized, restricting both scalability and privacy. Decentralization could address these limitations, but impose challenges of trustless coordination, fair scheduling, and efficiency. To this end, we propose WWW.Serve, a decentralized framework for interconnec... | We propose WWW.Serve, a fully decentralized framework for trustless and collaborative LLM serving, which improves efficiency, latency, and scalability while preserving privacy. | ['Large Language Model Serving', 'Efficienct Serving Systems', 'Decentralized LLM Serving', 'Distributed LLMs'] | /pdf/9ee240e1cc36c7066864a2f959d22211f84eb1dd.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission27/Authors'] |
FRXNMF0to7 | 26 | FRXNMF0to7 | The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs | Personality traits have long been studied as predictors of human behavior.
Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems, with advanced LLMs displaying consistent behavioral tendencies resembling human traits like agreeableness and self-regulation.
Understandi... | LLMs develop stable self-reported trait profiles through instructional alignment, yet these traits fail to manifest in real-world behavior. | ['LLMs', 'personality traits', 'behavioral alignment', 'self-regulation', 'persona', 'trait manifestation', 'personality illusion', 'psychology of AI'] | /pdf/dd4504df8949b129861273747acae5ac0c9aa6ca.pdf | alignment, fairness, safety, privacy, and societal considerations | null | ['ICLR.cc/2026/Conference/Submission26/Authors'] |
oKHPJ0GTLG | 25 | oKHPJ0GTLG | De-hallucinating CLIP Embeddings to Improve Brain-Vision Mapping | Recent advances in vision-language models, such as CLIP, have enabled their widespread use in brain encoding and decoding, where global image embeddings serve as anchors linking visual stimuli to voxel-level brain responses. However, we observe that CLIP's global visual embeddings often exhibit hallucinatory semantics:... | null | ['Brain-vision mapping', 'neuro decoding', 'semantic selectivity'] | /pdf/023eb00fa2c555ec3dde2f9e72adb17b07ad5be3.pdf | applications to neuroscience & cognitive science | null | ['ICLR.cc/2026/Conference/Submission25/Authors'] |
cf0yp18EeD | 24 | cf0yp18EeD | Inductive Visual Logic for Few-Shot Out-of-Distribution Adaptation in VLMs | Few-shot visual reasoning requires models not only to learn from limited supervision while also adapting across domains, including those that are far from pretraining distributions. Modern vision-language models (VLMs) such as Qwen and LLaVA excel in zero-shot tasks while collapsing in these distant out-of-distribution... | Instead of fine-tuning VLMs on novel concepts they can't represent, IVL extracts and reasons over human-interpretable visual traits from few examples.Retry | ['VLM', 'LLM', 'FSDA', 'OOD'] | /pdf/81ac309434737e538d77f147b50938ac1de8dae4.pdf | transfer learning, meta learning, and lifelong learning | null | ['ICLR.cc/2026/Conference/Submission24/Authors'] |
G5YWhGslEr | 20 | G5YWhGslEr | History-Aware Transformation of ReID Features for Multiple Object Tracking | In Multiple Object Tracking (MOT), Re-identification (ReID) features are widely employed as a powerful cue for object association.
However, they are often wielded as a one-size-fits-all hammer, applied uniformly across all videos through simple similarity metrics. We argue that this overlooks a fundamental truth: MOT ... | null | ['tracking', 'multiple object tracking', 're-identification'] | /pdf/16835c94aa3e20c6a4b74bb0c5f020a23318f8c9.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission20/Authors'] |
KjHB7rebQO | 19 | KjHB7rebQO | RiskPO: Risk-based Policy Optimization with Verifiable Reward for LLM Post-Training | Reinforcement learning with verifiable reward has recently emerged as a central paradigm for post-training large language models (LLMs); however, prevailing mean-based methods, such as Group Relative Policy Optimization (GRPO), suffer from entropy collapse and limited reasoning gains. We argue that these issues stem fr... | null | ['Reinforcement Learning with Verifiable Reward', 'Risk-Sensitive RL'] | /pdf/2bfcde92ee156da77da0b811626948b78d757aaf.pdf | reinforcement learning | null | ['ICLR.cc/2026/Conference/Submission19/Authors'] |
6a2CJrizrh | 15 | 6a2CJrizrh | BALROG: Contextual Bandits meets Active Learning for Online Generative Model Selection | The rapid proliferation of open-platform text-to-image generative models has made prompt-wise model selection essential for producing high-quality and semantically accurate images, yet it remains a challenging problem. Existing approaches, including contextual bandit algorithms, often converge slowly and fail to exploi... | We propose a new method for online generative model selection based on Nearest Neighbors bandits and active learning. | ['Generative models', 'Online model selection', 'Contextual bandits'] | /pdf/54c87e6d7725a7415b0cb0d69f045032dce69826.pdf | reinforcement learning | /attachment/98f2aa81b8d04ee560ab457d2b6b09b7fd7dc1b0.zip | ['ICLR.cc/2026/Conference/Submission15/Authors'] |
CYmjrbQRyM | 13 | CYmjrbQRyM | ASMIL: Attention-Stabilized Multiple Instance Learning for Whole-Slide Imaging | Attention-based multiple instance learning (MIL) has emerged as a powerful framework for whole slide image (WSI) diagnosis, leveraging attention to aggregate instance-level features into bag-level predictions. Despite this success, we find that such methods exhibit a new failure mode: unstable attention dynamics. Acr... | null | ['Whole slide image', 'Multiple instance learning'] | /pdf/418d8e4d45ea48edbf688f51ac04e4883f5b9b31.pdf | applications to computer vision, audio, language, and other modalities | null | ['ICLR.cc/2026/Conference/Submission13/Authors'] |
0QPXvKE4SV | 12 | 0QPXvKE4SV | TCR-EML: Explainable Model Layers for TCR-pMHC Prediction | T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are bla... | We propose an approach to TCR-pMHC binding prediction, TCR-EML, that utilizes concept and prototype layers to provide accurate, detailed insights into the mechanisms of T cell response. | ['T Cell', 'TCR', 'Transformers', 'XAI', 'Interpretability'] | /pdf/375f047df05621d5eab2d0aeaca75d228a14f6fe.pdf | applications to physical sciences (physics, chemistry, biology, etc.) | /attachment/6d620dc959977bf2b0739218312766c3b2f70f47.zip | ['ICLR.cc/2026/Conference/Submission12/Authors'] |
jxyEci13Dd | 11 | jxyEci13Dd | Long-Text-to-Image Generation via Compositional Prompt Decomposition | While modern text-to-image models excel at generating images from intricate prompts, they struggle to capture the key details when the prompts are expanded into descriptive paragraphs. This limitation stems from the prevalence of short captions in their training data. Existing methods attempt to address this by either ... | We decompose long-prompts to allow pre-trained Text-to-Image models to handle long-prompts input, demonstrating superior generalization as prompt length increases. | ['Compositionality; Text-to-Image Generation; Generative Model Generalization'] | /pdf/627d989858c3b9c53434578fa91d6b150461ba83.pdf | generative models | /attachment/8d0b75d6bfa9ccefd81852db1fc8ec579a826281.zip | ['ICLR.cc/2026/Conference/Submission11/Authors'] |
Q5mkmW0cUD | 9 | Q5mkmW0cUD | Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning | Large Language Models (LLMs) have achieved strong performance in domains like mathematics, factual question answering, and code generation, yet their ability to reason on these tasks in different languages remains underdeveloped.
Especially for low-resource languages such as Swahili or Thai, LLMs can often misinterpret... | null | ['LLM', 'multilingual reasoning', 'alignment', 'multilingualism', 'cross-lingual transfer', 'multilingual benchmarks', 'multilingual evaluation'] | /pdf/6e284e24dbdd3f0ebf98ecdf056906cc636a3291.pdf | foundation or frontier models, including LLMs | /attachment/480d0cda0e04bbe6a70db744b1241d6cf81398c1.zip | ['ICLR.cc/2026/Conference/Submission9/Authors'] |
6wA4qpyyU9 | 8 | 6wA4qpyyU9 | Directional Textual Inversion for Personalized Text-to-Image Generation | Textual Inversion (TI) is an efficient approach to text‑to‑image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned tokens drift to out‑of‑distribution magnitudes, degrading prompt conditioning in pre‑norm Transformers. Empirically, we show semantics are pri... | We propose Directional Textual Inversion that improves text fidelity for personalized text-to-image generation. | ['personalized generation', 'text-to-image models', 'textual inversion'] | /pdf/b17f9b520fbbc0e9058eadefe8a86be0c78c13fb.pdf | generative models | /attachment/af2f3b748c05b1e82967c55d394d9b19b47ee32b.zip | ['ICLR.cc/2026/Conference/Submission8/Authors'] |
SOxO7e6ySB | 5 | SOxO7e6ySB | Language Models Do Not Have Human-Like Working Memory | While Large Language Models (LLMs) exhibit remarkable reasoning abilities, we demonstrate that they fundamentally lack a core aspect of human cognition: working memory. Human working memory is an active cognitive system that enables not only the temporary storage of information but also its processing and utilization. ... | null | ['Large Language Model', 'Working Memory'] | /pdf/084d84afc131ae518ba31ce2e59c46fc31f7880a.pdf | alignment, fairness, safety, privacy, and societal considerations | /attachment/ee19770b3a6c3e66c892d5fda95d59c64d0dc169.zip | ['ICLR.cc/2026/Conference/Submission5/Authors'] |
iQsKotob31 | 4 | iQsKotob31 | HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models | Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emphasize general knowledge and vertical step-by-step reasoning typical of STEM disciplines, while overlooking the distinct needs and potential... | null | ['MLLMs', 'Benchmark', 'Dataset', 'Humanities and Social Sciences'] | /pdf/ff3c4ae9941ee045727fd87e2601e013ed3c6f69.pdf | datasets and benchmarks | /attachment/c208fbf6969a7a84b43b0fc88841c399c07c508e.zip | ['ICLR.cc/2026/Conference/Submission4/Authors'] |
WffiETiSeU | 3 | WffiETiSeU | Part-X-MLLM: Part-aware 3D Multimodal Large Language Model | We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level boun... | null | ['3D Computer Vision', '3D Vision-language Modeling', 'Part-aware 3D understanding', 'Multimodal Large Language Model'] | /pdf/b2fd606362abe100ac17ca69fffcf57890a3260b.pdf | foundation or frontier models, including LLMs | null | ['ICLR.cc/2026/Conference/Submission3/Authors'] |
7QjQ1mpNMX | 2 | 7QjQ1mpNMX | Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning | Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the model while allowing it to gain new skills. A valuable goal for all such models is robustness: the ability to perform well on out-of-distribut... | We demonstrate that models pretrained on larger datasets can exhibit poorer robustness after fine-tuning compared to models pretrained on smaller datasets when the fine-tuning dataset is small. We analyze this phenomenon using the proposed benchmark. | ['robust fine-tuning', 'catastrophic forgetting', 'transfer learning', 'representation learning', 'continual learning'] | /pdf/f6812f4dc804deda91be0eb90507f714bc417515.pdf | transfer learning, meta learning, and lifelong learning | /attachment/a63d1dfbf32d9198e061dcfdde77c5b8112095b4.zip | ['ICLR.cc/2026/Conference/Submission2/Authors'] |
h7qdCvhMdb | 1 | h7qdCvhMdb | Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise? | Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promising recent proposal, microcanonical Langevin Monte Carlo, has shown state-of-the-art performance across a wide range of problems. However, its reliance on full-dataset gra... | null | ['Microcanonical Langevin', 'Sampling', 'Bayesian Deep Learning'] | /pdf/39a21aa61c118533fef10b61bcc5eee5b5244840.pdf | probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.) | null | ['ICLR.cc/2026/Conference/Submission1/Authors'] |
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