GenAI4ELab/papercli-papers huggingface dataset:
FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models
Authors: Jiyoon Pyo; Yuankun Jiao; Dongwon Jung; Zekun Li; Leeje Jang; Sofia Kirsanova; Jina Kim; Yijun Lin; Qin Liu; Junyi Xie; Hadi Askari; Nan Xu; Muhao Chen; Yao-Yi Chiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on progress in chart and infographic understanding, recent large vision language model (LVLM) works on map visual question-answering (VQA) often simplify maps as a special case of charts. In contrast, map VQA demands comprehension of layered symbology (e.g., symbols, geometries, and text labels) as well as spatial relations tied to orientation and distance that often span multiple maps and are not captured by chart-style evaluations. To address this gap, we introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs. FRIEDA sources real map images from documents and reports in various domains (e.g., geology, urban planning, and environmental assessment) and geographical areas. Following classifications in Geographic Information System (GIS) literature, FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation). All questions require multi-step inference, and many require cross-map grounding and reasoning. We evaluate eleven state-of-the-art LVLMs under two settings: (1) the direct setting, where we provide the maps relevant to the question, and (2) the contextual setting, where the model may have to identify the maps relevant to the question before reasoning. Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20\% and 37.20\% accuracy, respectively, far below human performance of 84.87\%. These results reveal a persistent gap in multi-step cartographic reasoning, positioning FRIEDA as a rigorous benchmark to drive progress on spatial intelligence in LVLMs.
Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
Authors: Haodong Liang; Yanhao Jin; Krishna Balasubramanian; Lifeng Lai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study instrumental variable regression (IVaR) under differential privacy constraints. 
Classical IVaR methods (like two-stage least squares regression) rely on solving moment equations that directly use sensitive covariates and instruments, creating significant risks of privacy leakage and posing challenges in designing algorithms that are both statistically efficient and differentially private.
We propose a noisy two-stage gradient descent algorithm that ensures $\rho$-zero-concentrated
differential privacy by injecting carefully calibrated noise into the gradient updates. 
Our analysis establishes finite-sample convergence rates for the proposed method, showing that the algorithm achieves consistency while preserving privacy. 
In particular, we derive precise bounds quantifying the trade-off among optimization,
privacy, and sampling error. 
To the best of our knowledge, this is the first work to provide both privacy guarantees and provable convergence rates for instrumental variable regression in linear models. 
We further validate our theoretical findings with experiments on both synthetic and real datasets, demonstrating that our method offers practical accuracy-privacy trade-offs.
Towards Sampling Data Structures for Tensor Products in Turnstile Streams
Authors: Zhao Song; Shenghao Xie; Samson Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper studies the computational challenges of large-scale attention-based models in artificial intelligence by introducing innovative sampling methods in the streaming setting. Inspired by the classical definition of the $\ell_2$ sampler and the recent progress of the attention scheme in Large Language Models (LLMs), we propose the definition of the attention sampler. These attention samplers select the important coordinates in attention computation efficiently, bypassing the quadratic computational burden of computing the entire attention matrix. We demonstrate the effectiveness of the attention sampler from a theoretical perspective, including space and update time. Additionally, our framework exhibits scalability and broad applicability across various model architectures and domains.
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Authors: Ilia Azizi; Juraj Bodik; Jakob Heiss; Bin Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling. Existing methods typically address either aleatoric uncertainty due to measurement noise or epistemic uncertainty resulting from limited data, but not both in a balanced manner. We propose CLEAR, a calibration method with two distinct parameters, $\gamma_1$ and $\gamma_2$, to combine the two uncertainty components and improve the conditional coverage of predictive intervals for regression tasks. CLEAR is compatible with any pair of aleatoric and epistemic estimators; we show how it can be used with (i) quantile regression for aleatoric uncertainty and (ii) ensembles drawn from the Predictability–Computability–Stability (PCS) framework for epistemic uncertainty. Across 17 diverse real-world datasets, CLEAR achieves an average improvement of 28.3\% and 17.5\% in the interval width compared to the two individually calibrated baselines while maintaining nominal coverage. Similar improvements are observed when applying CLEAR to Deep Ensembles (epistemic) and Simultaneous Quantile Regression (aleatoric). The benefits are especially evident in scenarios dominated by high aleatoric or epistemic uncertainty. Project page: https://unco3892.github.io/clear/
Entropy-Based Block Pruning for Efficient Large Language Models
Authors: Liangwei Yang; Yuhui Xu; Juntao Tan; Doyen Sahoo; silvio savarese; Caiming Xiong; Huan Wang; Shelby Heinecke
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models continue to scale, their growing computational and storage demands pose significant challenges for real-world deployment. In this work, we investigate redundancy within Transformer-based models and propose an entropy-based pruning strategy to enhance efficiency while maintaining performance. Empirical analysis reveals that the entropy of hidden representations decreases in the early blocks but progressively increases across most subsequent blocks. This trend suggests that entropy serves as a more effective measure of information richness within computation blocks. Unlike cosine similarity, which primarily captures geometric relationships, entropy directly quantifies uncertainty and information content, making it a more reliable criterion for pruning. Extensive experiments demonstrate that our entropy-based pruning approach surpasses cosine similarity-based methods in reducing model size while preserving accuracy, offering a promising direction for efficient model deployment.
Online Decision Making with Generative Action Sets
Authors: Jianyu Xu; Vidhi Jain; Bryan Wilder; Aarti Singh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With advances in generative AI, decision-making agents can now dynamically create new actions during online learning, but action generation typically incurs costs that must be balanced against potential benefits. We study an online learning problem where an agent can generate new actions at any time step by paying a one-time cost, with these actions becoming permanently available for future use. The challenge lies in learning the optimal sequence of two-fold decisions: which action to take and when to generate new ones, further complicated by the triangular tradeoffs among exploitation, exploration and *creation*. To solve this problem, we propose a doubly-optimistic algorithm that employs Lower Confidence Bounds (LCB) for action selection and Upper Confidence Bounds (UCB) for action generation. Empirical evaluation on healthcare question-answering datasets demonstrates that our approach achieves favorable generation-quality trade-offs compared to baseline strategies. From theoretical perspectives, we prove that our algorithm achieves the optimal regret of $O(T^{\frac{d}{d+2}}d^{\frac{d}{d+2}} + d\sqrt{T\log T})$, providing the first sublinear regret bound for online learning with expanding action spaces.
Pretraining with hierarchical memories: separating long-tail and common knowledge
Authors: Hadi Pouransari; David Grangier; C Thomas; Michael Kirchhof; Oncel Tuzel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all world knowledge into parameters is unnecessary, as only a fraction is used per prompt, and impractical for edge devices with limited inference-time memory and compute. We address this shortcoming by a memory-augmented architecture and a pretraining strategy aligned with existing hardware paradigms. We introduce small language models that access large hierarchical parametric memory banks encoding world knowledge. Our pretraining learns to store long-tail world knowledge in the memory parameters, while the small language model acts as an anchor capturing common knowledge and general reasoning abilities. Through trillion-token-scale experiments, we show significant gains: a 160M-parameters model augmented with an 18M-parameters memory fetched from a 4.6B memory bank obtains comparable performance to a regular model with more than 2x the parameters. We study the optimal type and size of parametric memories in transformers, scaling them to over 21B parameters. We find that our proposed hierarchical feed-forward memories work robustly across transformer architectures, whether added during pretraining or post-hoc.
EquAct: An SE(3)-Equivariant Multi-Task Transformer for 3D Robotic Manipulation
Authors: Xupeng Zhu; Yu Qi; Yizhe Zhu; Robin Walters; Robert Platt
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-task manipulation policy often builds on transformer's ability to jointly process language instructions and 3D observations in a shared embedding space. However, real-world tasks frequently require robots to generalize to novel 3D object poses. Policies based on shared embedding break geometric consistency and struggle in 3D generation. To address this issue, we propose EquAct, which is theoretically guaranteed to generalize to novel 3D scene transformations by leveraging SE(3) equivariance shared across both language, observations, and action. EquAct makes two key contributions: (1) an efficient SE(3)-equivariant point cloud-based U-net with spherical Fourier features for policy reasoning, and (2) SE(3)-invariant Feature-wise Linear Modulation (iFiLM) layers for language conditioning. Finally, EquAct demonstrates strong spatial generalization ability and achieves state-of-the-art across $18$ RLBench tasks with both SE(3) and SE(2) scene perturbations, different amounts of training data, and on $4$ physical tasks.
Quantized Gradient Projection for Memory-Efficient Continual Learning
Authors: Dongjun Kim; Seohyeon Cha; Huancheng Chen; Chianing Wang; Haris Vikalo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world deployment of machine learning models requires the ability to continually learn from non-stationary data while preserving prior knowledge and user privacy. Therefore, storing knowledge acquired from past data in a resource- and privacy-friendly manner is a crucial consideration in determining their viability. We introduce Quantized Gradient Projection Memory (QGPM), a systematic framework for continual learning that compresses and preserves the previous gradient subspace. QGPM integrates three key components: (i) distribution-aware, basis-wise quantization to minimize storage overhead, (ii) a Quantization Error-Aware (QEA) gradient projection that selectively relaxes orthogonality to mitigate gradient drift caused by accumulated quantization noise, and (iii) an on-the-fly sparse sketching strategy that improves runtime memory and computational efficiency. Experiments across multiple benchmarks demonstrate that QGPM achieves state-of-the-art performance under fixed memory budgets, highlighting its effectiveness in scalable, privacy-preserving continual learning.
Latent Wasserstein Adversarial Imitation Learning
Authors: Siqi Yang; Kai Yan; Alex Schwing; Yu-Xiong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality demonstrations as well as actions of expert demonstrations, both of which are often unavailable. To reduce this need, we propose Latent Wasserstein Adversarial Imitation Learning (LWAIL), a novel adversarial imitation learning framework that focuses on state-only distribution matching. It benefits from the Wasserstein distance computed in a dynamics-aware latent space. This dynamics-aware latent space differs from prior work and is obtained via a pre-training stage, where we train the Intention Conditioned Value Function (ICVF) to capture a dynamics-aware structure of the state space using a small set of randomly generated state-only data. We show that this enhances the policy's understanding of state transitions, enabling the learning process to use only one or a few state-only expert episodes to achieve expert-level performance. Through experiments on multiple MuJoCo environments, we demonstrate that our method outperforms prior Wasserstein-based IL methods and prior adversarial IL methods, achieving better results across various tasks.
Homeostatic Adaptation of Optimal Population Codes under Metabolic Stress
Authors: Yi-Chun Hung; Gregory W Schwartz; Emily A. Cooper; Emma Alexander
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Information processing in neural populations is inherently constrained by metabolic resource limits and noise properties, with dynamics that are not accurately described by existing mathematical models. Recent data, for example, shows that neurons in mouse visual cortex go into a "low power mode" in which they maintain firing rate homeostasis while expending less energy. This adaptation leads to increased neuronal noise and tuning curve flattening in response to metabolic stress. We have developed a theoretical population coding framework that captures this behavior using two novel, surprisingly simple constraints: an approximation of firing rate homeostasis and an energy limit tied to noise levels via biophysical simulation. A key feature of our contribution is an energy budget model directly connecting adenosine triphosphate (ATP) use in cells to a fully explainable mathematical framework that generalizes existing optimal population codes. Specifically, our simulation provides an energy-dependent dispersed Poisson noise model, based on the assumption that the cell will follow an optimal decay path to produce the least-noisy spike rate that is possible at a given cellular energy budget. Each state along this optimal path is associated with properties (resting potential and leak conductance) which can be measured in electrophysiology experiments and have been shown to change under prolonged caloric deprivation. We analytically derive the optimal coding strategy for neurons under varying energy budgets and coding goals, and show how our method uniquely captures how populations of tuning curves adapt while maintaining homeostasis, as has been observed empirically.
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Authors: Mike A Merrill; Alexander Glenn Shaw; Nicholas Carlini; Boxuan Li; Harsh Raj; Ivan Bercovich; Lin Shi; Jeong Yeon Shin; Thomas Walshe; E. Kelly Buchanan; Junhong Shen; Guanghao Ye; Haowei Lin; Jason Poulos; Maoyu Wang; Marianna Nezhurina; Di Lu; Orfeas Menis Mastromichalakis; Zhiwei Xu; Zizhao Chen; Yue Liu; Robert Zhang; Leon Liangyu Chen; Anurag Kashyap; Jan-Lucas Uslu; Jeffrey Li; Jianbo Wu; Minghao Yan; Song Bian; Vedang Sharma; Ke Sun; Steven Dillmann; Akshay Anand; Andrew Lanpouthakoun; Bardia Koopah; Changran Hu; Etash Kumar Guha; Gabriel H. S. Dreiman; Jiacheng Zhu; Karl Krauth; Li Zhong; Niklas Muennighoff; Robert Kwesi Amanfu; Shangyin Tan; Shreyas Pimpalgaonkar; Tushar Aggarwal; Xiangning Lin; Xin Lan; Xuandong Zhao; Yiqing Liang; Yuanli Wang; Zilong Wang; Changzhi Zhou; David Heineman; Hange Liu; Harsh Trivedi; John Yang; Junhong Lin; Manish Shetty; Michael Yang; Nabil Omi; Negin Raoof; Shanda Li; Terry Yue Zhuo; Wuwei Lin; Yiwei Dai; Yuxin Wang; Wenhao Chai; Shang Zhou; Dariush Wahdany; Ziyu She; Jiaming Hu; Zhikang Dong; Yuxuan Zhu; Sasha Cui; Ahson Saiyed; Arinbjörn Kolbeinsson; Christopher Michael Rytting; Ryan Marten; Yixin Wang; Jenia Jitsev; Alex Dimakis; Andy Konwinski; Ludwig Schmidt
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at tbench.ai.
The Value of Information in Human-AI Decision-making
Authors: Ziyang Guo; Yifan Wu; Jason Hartline; Jessica Hullman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multiple agents are increasingly combined to make decisions with the expectation of achieving *complementary performance*, where the decisions they make together outperform those made individually. 
   However, knowing how to improve the performance of collaborating agents requires knowing what information and strategies each agent employs. 
   With a focus on human-AI pairings, we contribute a decision-theoretic framework for characterizing the value of information.
   By defining complementary information, our approach identifies opportunities for agents to better exploit available information in AI-assisted decision workflows.
   We present a novel explanation technique (ILIV-SHAP) that adapts SHAP explanations to highlight human-complementing information.
   We validate the effectiveness of our framework and ILIV-SHAP through a study of human-AI decision-making, and demonstrate the framework on examples from chest X-ray diagnosis and deepfake detection.
   We find that presenting ILIV-SHAP with AI predictions leads to reliably greater reductions in error over non-AI assisted decisions more 
   than vanilla SHAP.
A universal compression theory for lottery ticket hypothesis and neural scaling laws
Authors: Hong-Yi Wang; Di Luo; Tomaso Poggio; Isaac L. Chuang; Liu Ziyin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When training large-scale models, the performance typically scales with the number of parameters and the dataset size according to a slow power law. A fundamental theoretical and practical question is whether comparable performance can be achieved with significantly smaller models and substantially less data. In this work, we provide a positive and constructive answer. We prove that a generic permutation-invariant function of $d$ objects can be asymptotically compressed into a function of $\operatorname{polylog} d$ objects with vanishing error, which is proved to be the optimal compression rate. This theorem yields two key implications: (Ia) a large neural network can be compressed to polylogarithmic width while preserving its learning dynamics; (Ib) a large dataset can be compressed to polylogarithmic size while leaving the loss landscape of the corresponding model unchanged. Implication (Ia) directly establishes a proof of the dynamical lottery ticket hypothesis, which states that any ordinary network can be strongly compressed such that the learning dynamics and result remain unchanged. (Ib) shows that a neural scaling law of the form $L\sim d^{-\alpha}$ can be boosted to an arbitrarily fast power law decay, and ultimately to $\exp(-\alpha' \sqrt[m]{d})$.
QuoKA: Query-Oriented KV Selection for Efficient LLM Prefill
Authors: Dalton Jones; Junyoung Park; Matthew J Morse; Mingu Lee; Matthew Harper Langston; Christopher Lott
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present QuoKA: Query-oriented KV selection for efficient attention, a training-free and hardware agnostic sparse attention algorithm for accelerating transformer inference under chunked prefill. While many queries focus on a smaller group of keys in the attention operator, we observe that queries with low cosine similarity with respect to the mean query interact more strongly with more keys and have the greatest contribution to final attention logits. By prioritizing these low cosine similarity queries, the behavior of full attention during the prefill stage can be closely approximated. QuoKA leverages this observation, accelerating attention by (1) first retaining a small set of representative queries and (2) then subselecting the keys most aligned with those queries. Through experiments on Needle-In-A-Haystack, LongBench, RULER, and Math500, we show that, while realizing a 3× reduction in time-to-first-token, 5× speedup in attention on Nvidia GPUs and up to nearly a 7× speedup on Intel Xeon CPUs, QuoKA achieves near-baseline accuracy, utilizing 88% fewer key-value pairs per attention evaluation.
lmgame-Bench: How Good are LLMs at Playing Games?
Authors: Lanxiang Hu; Mingjia Huo; Yuxuan Zhang; Haoyang Yu; Eric P. Xing; Ion Stoica; Tajana Rosing; Haojian Jin; Hao Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Playing video games requires perception, reasoning, memory, and long-horizon planning—exactly the faculties expected of modern large language and vision–language models (LLMs/VLMs). We introduce LMGame-Bench, a benchmark built on six popular games spanning platformer, puzzle, and narrative games through a unified Gym‑style API. Unlike prior game benchmarks that entangle multiple skills, LMGame-Bench employs a modular harness—including perception, memory, and reasoning modules—that can be toggled to selectively probe distinct capabilities. The benchmark further improves robustness through prompt standardization and contamination mitigation. Evaluation of 13 state-of-the-art models demonstrates that LMGame-Bench remains challenging yet effectively discriminates among models. Correlation analysis reveals that individual games align with core LLM capabilities, providing a quantitative framework for interpreting performance. Finally, LMGame-Bench exposes models’ limitations in visual state extraction, reflection, spatiotemporal reasoning, and long-context reasoning, pointing to concrete directions for model improvement.