GenAI4ELab/papercli-papers huggingface dataset:
Zeros can be Informative: Masked Binary U-Net for Image Segmentation on Tensor Cores
Authors: Chunshu Wu; Ruibing Song; Sushant Kondguli; Tong Geng; Ang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-time image segmentation is a key enabler for AR/VR, robotics, drones, and autonomous systems, where tight accuracy, latency, and energy budgets must be met on resource‑constrained edge devices. While U‑Net offers a favorable balance of accuracy and efficiency compared to large transformer‑based models, achieving real‑time performance on high‑resolution input remains challenging due to compute, memory, and power limits. Extreme quantization, particularly binary networks, is appealing for its hardware‑friendly operations. However, two obstacles limit practicality: (1) severe accuracy degradation, and (2) a lack of end‑to‑end implementations that deliver efficiency on general‑purpose GPUs.

We make two empirical observations. (1) An explicit zero state is essential: training with zero masking to binary U‑Net weights yields noticeable sparsity. (2) Quantization sensitivity is relatively uniform across layers. Motivated by these findings, we introduce Masked Binary U‑Net (MBU‑Net), obtained through a cost‑aware masking strategy that prioritizes masking where it yields the highest accuracy‑per‑cost, reconciling accuracy with near‑binary efficiency.

To realize these gains in practice, we develop a GPU execution framework that maps MBU‑Net to Tensor Cores via a subtractive bit‑encoding scheme, efficiently implementing masked binary weights with binary activations. This design leverages native binary Tensor Core BMMA instructions, enabling high throughput and energy savings on widely available GPUs. Across 3 segmentation benchmarks, MBU‑Net attains near full‑precision accuracy (3\% average drop) while delivering 2.04$\times$ speedup and 3.54$\times$ energy reductions over a 16-bit floating point U‑Net. The code is available at https://github.com/ChunshuWu/MBU-Net.
RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation
Authors: Yash Jangir; Yidi Zhang; Kashu Yamazaki; Chenyu Zhang; Kuan-Hsun Tu; Tsung-Wei Ke; Lei Ke; Yonatan Bisk; Katerina Fragkiadaki
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The pursuit of robot generalists, instructable agents capable of performing diverse tasks across diverse environments, demands rigorous and scalable evaluation. Yet real-world testing of robot policies remains fundamentally constrained: it is labor-intensive, slow, unsafe at scale, and difficult to reproduce. As policies expand in scope and complexity, these barriers only intensify, since defining ``success'' in robotics often hinges on nuanced human judgments of execution quality. We introduce RobotArenaInf, a new benchmarking framework that overcomes these challenges by shifting VLA evaluation into large-scale simulated environments augmented with online human feedback. Leveraging advances in vision-language models, 2D-to-3D generative modeling, and differentiable rendering, our approach automatically converts video demonstrations from widely used robot datasets into simulated counterparts. Within these digital twins, we assess VLA policies using both automated VLM-guided scoring and scalable human preference judgments collected from crowdworkers, transforming human involvement from tedious scene setup, resetting, and safety supervision into lightweight preference comparisons.
To measure robustness, we systematically perturb simulated environments along multiple axes, including textures and object placements, stress-testing policy generalization under controlled variation. The result is a continuously evolving, reproducible, and scalable benchmark for real-world-trained robot manipulation policies, addressing a critical missing capability in today’s robotics landscape. Benchmark website at \href{https://robotarenainf.github.io}{\texttt{robotarenainf.github.io}}.
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
Authors: Barna Pásztor; Thomas Kleine Buening; Andreas Krause
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two policies: a Leader, which commits to an action, and a Follower, which responds conditionally on the Leader's action. This approach decomposes preference optimization into a refinement problem for the Follower and an optimization problem against an adversary for the Leader. Unlike Reinforcement Learning from Human Feedback (RLHF), which assigns scalar rewards to actions, or Nash Learning from Human Feedback (NLHF), which seeks a simultaneous-move equilibrium, SLHF leverages the asymmetry of sequential play to capture richer preference structures. The sequential design of SLHF naturally enables inference-time refinement, as the Follower learns to improve the Leader’s actions, and these refinements can be leveraged through iterative sampling. We compare the solution concepts of SLHF, RLHF, and NLHF, and lay out key advantages in consistency, data sensitivity, and robustness to intransitive preferences. Experiments on large language models demonstrate that SLHF achieves strong alignment across diverse preference datasets, scales from 0.5B to 8B parameters, and yields inference-time refinements that transfer across model families without further fine-tuning.
Conformal Prediction for Long-Tailed Classification
Authors: Tiffany Ding; Jean-Baptiste Fermanian; Joseph Salmon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) be a reasonable size, allowing users to easily verify candidate labels. Unfortunately, existing conformal prediction methods, when applied to the long-tailed setting, force practitioners to make a binary choice between small sets with poor class-conditional coverage or sets that have very good class-conditional coverage but are extremely large. We propose methods with marginal coverage guarantees that smoothly trade off set size and class-conditional coverage. First, we introduce a new conformal score function called prevalence-adjusted softmax that optimizes for macro-coverage, defined as the average class-conditional coverage across classes. Second, we propose a new procedure that interpolates between marginal and class-conditional conformal prediction by linearly interpolating their conformal score thresholds. We demonstrate our methods on Pl@ntNet-300K and iNaturalist-2018, two long-tailed image datasets with 1,081 and 8,142 classes, respectively.
Composer: A Search Framework for Hybrid Neural Architecture Design
Authors: Bilge Acun; Prasoon Sinha; Newsha Ardalani; Sangmin Bae; Alicia Golden; Meghana Madhyastha; Chien-Yu Lin; Fei Sun; Neeraja J. Yadwadkar; Carole-Jean Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hybrid model architectures that combine computational primitives (e.g., Attention, MLP) in different ratios have shown promising performance beyond Transformers. Some studies have shown that different interleavings of primitives can affect model quality as well. However, prior works explore the hybrid model architecture design space manually. Due to the large design space and training costs, discovering hybrid models that combine key computational primitives for pre-training is challenging. In this work, we take a principled approach in designing a modular hybrid model architecture search framework — Composer. Composer explores model architectures at a small scale and extrapolates the top-performing model architectures to a larger scale using our proposed scaling strategies. Using Composer, we discover new hybrid LLM architectures that outperform Llama 3.2. Compared to Llama 3.2 and previous state-of-the-art baselines, the new model architectures consistently reduce validation loss at parameter scales of 350M-8B and improve evaluation accuracy on the downstream tasks by up to 2-2.1% on average while improving both training and inference efficiency.
Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Authors: Zhe Li; Wei Zhao; Yige Li; Jun Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking outputs to their training data. We systematically evaluate our method for tasks that include tracking harmful content, detecting backdoor poisoning, and identifying knowledge contamination. The results demonstrate that our approach not only excels at sample-level attribution but also enables fine-grained token-level analysis, precisely identifying the specific samples and phrases that causally influence model behavior. This work provides a powerful diagnostic tool to understand, audit, and ultimately mitigate the risks associated with LLMs.
VERINA: Benchmarking Verifiable Code Generation
Authors: Zhe Ye; Zhengxu Yan; Jingxuan He; Timothe Kasriel; Kaiyu Yang; Dawn Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging and often requires costly manual review. Verifiable code generation---jointly generating code, specifications, and proofs of code-specification alignment---offers a promising path to address this limitation and further unleash LLMs' benefits in coding. Yet, there exists a significant gap in evaluation: current benchmarks often focus on only individual components rather than providing a holistic evaluation framework of all tasks. In this paper, we introduce VERINA (Verifiable Code Generation Arena), a high-quality benchmark enabling a comprehensive and modular evaluation of code, specification, and proof generation as well as their compositions. VERINA consists of 189 manually curated coding tasks in Lean, with detailed problem descriptions, reference implementations, formal specifications, and extensive test suites. Our extensive evaluation of state-of-the-art LLMs reveals significant challenges in verifiable code generation, especially in proof generation, underscoring the need for improving LLM-based theorem provers in verification domains.
The best model, OpenAI o3, achieves a 72.6% code correctness rate, 52.3% for specification soundness and completeness, and a mere 4.9% proof success rate (based on one trial per task). We hope VERINA will catalyze progress in verifiable code generation by providing a rigorous and comprehensive benchmark. We release out dataset on https://huggingface.co/datasets/sunblaze-ucb/verina and our evaluation code on https://github.com/sunblaze-ucb/verina.
SelvaBox: A high‑resolution dataset for tropical tree crown detection
Authors: Hugo Baudchon; Arthur Ouaknine; Martin Weiss; Mélisande Teng; Thomas R. Walla; Antoine Caron-Guay; Christopher Pal; Etienne Laliberté
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventions and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open‑access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than $83\,000$ manually labeled crowns -- an order of magnitude larger than all previous tropical forest datasets combined. Extensive benchmarks on SelvaBox reveal two key findings: 1) higher-resolution inputs consistently boost detection accuracy; and 2) models trained exclusively on SelvaBox achieve competitive zero-shot detection performance on unseen tropical tree crown datasets, matching or exceeding competing methods. Furthermore, jointly training on SelvaBox and three other datasets at resolutions from 3 to 10 cm per pixel within a unified multi-resolution pipeline yields a detector ranking first or second across all evaluated datasets. Our dataset, code, and pre-trained weights are made public.
Distributional value gradients for stochastic environments
Authors: Baptiste Debes; Tinne Tuytelaars
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Gradient-regularized value learning methods improve sample efficiency by leveraging learned models of transition dynamics and rewards to estimate return gradients. However, existing approaches, such as MAGE, struggle in stochastic or noisy environments,  limiting their applicability. In this work, we address these limitations by extending distributional reinforcement learning on continuous state-action spaces to model not only the distribution over scalar state-action value functions but also over their gradients. We refer to this approach as Distributional Sobolev Training. Inspired by Stochastic Value Gradients (SVG), our method utilizes a one-step world model of reward and transition distributions implemented via a conditional Variational Autoencoder (cVAE). The proposed framework is sample-based and employs Max-sliced Maximum Mean Discrepancy (MSMMD) to instantiate the distributional Bellman operator. We prove that the Sobolev-augmented Bellman operator is a contraction with a unique fixed point, and highlight a fundamental smoothness trade-off underlying contraction in gradient-aware RL. To validate our method, we first showcase its effectiveness on a simple stochastic reinforcement‐learning toy problem, then benchmark its performance on several MuJoCo environments.
AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents
Authors: Jingxu Xie; Dylan Xu; Xuandong Zhao; Dawn Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce AgentSynth, a scalable and cost-efficient pipeline for automatically synthesizing high-quality tasks and trajectory datasets for generalist computer-use agents. Leveraging information asymmetry, AgentSynth constructs subtasks that are simple during generation but significantly more challenging when composed into long-horizon tasks, enabling the creation of over 6,000 diverse and realistic tasks. A key strength of AgentSynth is its ability to precisely modulate task complexity by varying the number of subtasks. Empirical evaluations show that state-of-the-art LLM agents suffer a steep performance drop, from 18\% success at difficulty level 1 to just 4\% at level 6, highlighting the benchmark's difficulty and discriminative power. Moreover, our pipeline achieves a low average cost of \$0.60 per trajectory, orders of magnitude cheaper than human annotations. Our code and data are available at https://github.com/sunblaze-ucb/AgentSynth
Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution
Authors: Deming Sheng; Ricardo Henao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Survival analysis plays a critical role in modeling time-to-event outcomes across various domains. 
Although recent advances have focused on improving _predictive accuracy_ and _concordance_, fine-grained _calibration_ remains comparatively underexplored. 
In this paper, we propose a survival modeling framework based on the Individually Calibrated Asymmetric Laplace Distribution (ICALD), which unifies _parametric_ and _nonparametric_ approaches based on the ALD.
We begin by revisiting the probabilistic foundation of the widely used _pinball_ loss in _quantile regression_ and its reparameterization as the _asymmetry form_ of the ALD. 
This reparameterization enables a principled shift to _parametric_ modeling while preserving the flexibility of _nonparametric_ methods.
Furthermore, we show theoretically that ICALD, with the _quantile regression_ loss is probably approximately individually calibrated.
Then we design an extended ICALD framework that supports both _pre-calibration_ and _post-calibration_ strategies. 
Extensive experiments on 14 synthetic and 7 real-world datasets demonstrate that our method achieves competitive performance in terms of _predictive accuracy_, _concordance_, and _calibration_, while outperforming 12 existing baselines including recent _pre-calibration_  and _post-calibration_ methods.
Hot PATE: Private Aggregation of Distributions  for Diverse Tasks
Authors: Edith Cohen; Benjamin Cohen-Wang; Xin Lyu; Jelani Nelson; Tamas Sarlos; Uri Stemmer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptations of PATE to tasks with inherent output diversity such as text generation, where the desired output is a sample from a distribution, face a core tension: 
as diversity increases, samples from different teachers are less likely to agree, but lower agreement results in reduced utility for the same privacy requirements.  Yet suppressing diversity to artificially increase agreement is undesirable, as it distorts the output of the underlying model, and thus reduces output quality.
 
We propose Hot PATE, a variant of PATE designed for diverse generative settings. 
We formalize the notion of a *diversity-preserving*  *ensemble sampler* and introduce an efficient sampler that provably transfers diversity without incurring additional privacy cost.
Hot PATE requires only API access to proprietary models and can be used as a drop-in replacement for existing *Cold* PATE samplers. 
Our empirical evaluations corroborate and quantify the benefits, showing significant improvements in the privacy–utility trade-off on evaluated in-context learning tasks, both in preserving diversity and in returning relevant responses.
gen2seg: Generative Models Enable Generalizable Instance Segmentation
Authors: Om Khangaonkar; Hamed Pirsiavash
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: By pretraining to synthesize coherent images from perturbed inputs, generative models inherently learn to understand object boundaries and scene compositions. How can we repurpose these generative representations for general-purpose perceptual organization?  We finetune Stable Diffusion and MAE (encoder+decoder) for category-agnostic instance segmentation using our instance coloring loss exclusively on a narrow set of object types (indoor furnishings and cars). Surprisingly, our models exhibit strong zero-shot generalization, accurately segmenting objects of types and styles unseen in finetuning. This holds even for MAE, which is pretrained on unlabeled ImageNet-1K only. When evaluated on unseen object types and styles, our best-performing models closely approach the heavily supervised SAM, and outperform it when segmenting fine structures and ambiguous boundaries. In contrast, existing promptable segmentation architectures or discriminatively pretrained models fail to generalize. This suggests that generative models learn an inherent grouping mechanism that transfers across categories and domains, even without internet-scale pretraining. Please see our website for additional qualitative figures, code, and a demo: https://reachomk.github.io/gen2seg/
FSPO: Few-Shot Optimization of Synthetic Preferences Effectively Personalizes to Real Users
Authors: Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context capabilities of LLMs, we propose few-shot preference optimization (FSPO), an algorithm for LLM personalization that reframes reward modeling as a meta-learning problem. Under FSPO, an LLM learns to quickly infer a personalized reward function for a user via a few labeled preferences. FSPO also utilizes user description rationalization (RAT) to encourage better reward modeling and instruction following, recovering performance with the oracle user description. Since real-world preference data is challenging to collect at scale, we propose careful design choices to construct synthetic preference datasets for personalization, generating over 1M synthetic personalized preferences using publicly available LLMs. To successfully transfer from synthetic data to real users, we find it crucial for the data to exhibit both high diversity and coherent, self-consistent structure. We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across three domains: movie reviews, education, and open-ended question answering. We also run a controlled human study. Overall, FSPO achieves an 87% Alpaca Eval win-rate in generating responses that are personalized to synthetic users and a 70% win-rate with real human users in open-ended question answering.
Estimating Dimensionality of Neural Representations from Finite Samples
Authors: Chanwoo Chun; Abdulkadir Canatar; SueYeon Chung; Daniel Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological neural networks. However, all existing measures of global dimensionality are sensitive to the number of samples, i.e., the number of rows and columns of the sample matrix. We show that, in particular, the participation ratio of eigenvalues, a popular measure of global dimensionality, is highly biased with small sample sizes, and propose a bias-corrected estimator that is more accurate with finite samples and with noise. On synthetic data examples, we demonstrate that our estimator can recover the true known dimensionality. We apply our estimator to neural brain recordings, including calcium imaging, electrophysiological recordings, and fMRI data, and to the neural activations in a large language model, and show that our estimator is invariant to the sample size. Finally, our estimators can additionally be used to measure the local dimensionalities of curved neural manifolds by weighting the finite samples appropriately.