GenAI4ELab/papercli-papers huggingface dataset:
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
Authors: Chenda Duan; Yipeng Zhang; Sotaro Kanai; Yuanyi Ding; Atsuro Daida; Pengyue Yu; Tiancheng Zheng; Naoto Kuroda; Shaun A. Hussain; Eishi Asano; Hiroki Nariai; vwani Roychowdhury
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent in format and metadata, lack standardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. With extensive efforts to reconcile heterogeneous iEEG formats, metadata, and recordings across publicly available sources,
we present $\textbf{Omni-iEEG}$, a large-scale, pre-surgical iEEG resource comprising $\textbf{302 patients}$ and $\textbf{178 hours}$ of high-resolution recordings. The dataset includes harmonized clinical metadata such as seizure onset zones, resections, and surgical outcomes, all validated by board-certified epileptologists. In addition, Omni-iEEG provides over 36K expert-validated annotations of pathological events, enabling robust biomarker studies. Omni-iEEG serves as a bridge between machine learning and epilepsy research. It defines clinically meaningful tasks with unified evaluation metrics grounded in clinical priors, enabling systematic evaluation of models in clinically relevant settings. Beyond benchmarking, we demonstrate the potential of end-to-end modeling on long iEEG segments and highlight the transferability of representations pretrained on non-neurophysiological domains. Together, these contributions establish Omni-iEEG as a foundation for reproducible, generalizable, and clinically translatable epilepsy research. The project page with dataset and code links is available at $\url{https://omni-ieeg.github.io/omni-ieeg/}$.
Readout Representation: Redefining Neural Codes by Input Recovery
Authors: Shunsuke Onoo; Yoshihiro Nagano; Yukiyasu Kamitani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sensory representation is typically understood through a hierarchical-causal framework where progressively abstract features are extracted sequentially. However, this causal view fails to explain misrepresentation, a phenomenon better handled by an informational and teleological view based on decodable content and downstream functions. This creates a tension: how does a system that abstracts away details preserve the fine-grained information needed for downstream functions? We propose readout representation to resolve this, defining representation by the information recoverable from features, rather than their causal origin.
Empirically, we show that inputs can be accurately reconstructed even from heavily perturbed mid-level features, demonstrating that a single input corresponds to a broad, redundant region of feature space, challenging the causal mapping perspective.
To quantify this property, we introduce representation size, a metric linked to model robustness and representational redundancy. Our framework offers a new lens for analyzing how both biological and artificial neural systems learn complex features while maintaining robust, information-rich representations of the world.
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Authors: Haozhan Li; Yuxin Zuo; Jiale Yu; Yuhao Zhang; Yang Zhaohui; Kaiyan Zhang; Xuekai Zhu; Yuchen Zhang; Tianxing Chen; Ganqu Cui; Dehui Wang; Dingxiang Luo; Yuchen Fan; Youbang Sun; Jia Zeng; Jiangmiao Pang; Shanghang Zhang; Yu Wang; Yao Mu; Bowen Zhou; Ning Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and supervised fine-tuning (SFT), these models face two fundamental challenges:
(i) the scarcity and high cost of large-scale robotic trajectories required for SFT scaling,
and (ii) limited generalization to tasks under distribution shift.
To overcome these limitations, we explore reinforcement learning (RL) as a pathway to scaling VLA training beyond limited datasets.
Inspired by LLM breakthroughs where RL with outcome rewards enhances step-by-step reasoning, we ask: Can outcome-driven RL improve long-horizon step-by-step action planning of VLA?
In this work, we introduce SimpleVLA-RL, an efficient RL framework tailored for VLA models.
Building upon veRL, we introduce VLA-specific trajectory sampling, scalable parallelization, multi-environment rendering, and optimized loss computation.
Applied to OpenVLA-OFT, SimpleVLA-RL achieves 99\% of SoTA performance on LIBERO and 80\% relative
improvement on RoboTwin 1.0\&2.0, outperforming $\pi_0$ with our proposed exploration-enhancing strategies.
SimpleVLA-RL reduces dependence on large-scale data, enables robust generalization, and remarkably surpasses SFT in real-world tasks.
Moreover, we identify a novel phenomenon "pushcut'' during RL training, wherein the policy discovers unseen patterns beyond those seen in previous training process.
Statistical and structural identifiability in representation learning
Authors: Walter Nelson; Marco Fumero; Theofanis Karaletsos; Francesco Locatello
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Representation learning models exhibit a surprising stability in their internal representations. Whereas most prior work treats this stability as a single property, we formalize it as two distinct concepts: **statistical identifiability** (consistency of representations across runs) and 
**structural identifiability** (alignment of representations with some unobserved ground truth). Recognizing that perfect pointwise identifiability is generally unrealistic for modern representation learning models, we propose new model-agnostic definitions of statistical and structural near-identifiability of representations up to some error tolerance $\epsilon$. Leveraging these definitions, we prove a statistical $\epsilon$-**near-identifiability** result for the representations of models with
nonlinear decoders, generalizing existing identifiability theory beyond last-layer representations in e.g. generative pre-trained transformers (GPTs) to near-identifiability of the intermediate representations of a broad class of models including (masked) autoencoders (MAEs) and supervised learners. 
Although these weaker assumptions confer weaker identifiability, we show that independent components 
analysis (ICA) can resolve much of the remaining linear ambiguity for this class of models, and validate and measure our near-identifiability claims empirically. With additional assumptions on the 
data-generating process, statistical identifiability extends to structural identifiability, yielding a simple and 
practical recipe for disentanglement: ICA post-processing of latent representations. On synthetic 
benchmarks, this approach achieves state-of-the-art disentanglement using a vanilla autoencoder. 
With a foundation model-scale MAE for cell microscopy, it disentangles biological variation from technical batch 
effects, substantially improving downstream generalization.
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization
Authors: Shizhan Liu; Hao ZHENG; Hang Yu; Jianguo Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image personalization enables customizing Text-to-Image models with a few reference images but is plagued by "concept coupling"—the model creating spurious associations between a subject and its context. Existing methods tackle this indirectly, forcing a trade-off between personalization fidelity and text control. This paper is the first to formalize concept coupling as a statistical dependency problem, identifying two root causes: a Denoising Dependence Discrepancy that arises during the generative process, and a Prior Dependence Discrepancy within the learned concept itself. To address this, we introduce ACCORD, a framework with two targeted, plug-and-play regularization losses. The Denoising Decouple Loss minimizes dependency changes across denoising steps, while the Prior Decouple Loss aligns the concept’s relational priors with those of its superclass. Extensive experiments across subject, style, and face personalization demonstrate that ACCORD achieves a superior balance between fidelity and text control, consistently improving upon existing methods.
A Faster Parameter-Free Regret Matching Algorithm
Authors: Linjian Meng; Youzhi Zhang; Shangdong Yang; Wenbin Li; Tianyu Ding; Yang Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Regret Matching (RM) and its variants are widely employed to learn a Nash equilibrium (NE) in large-scale games. However, most existing research only establishes a theoretical convergence rate of $O(1/\sqrt{T})$ for these algorithms in learning an NE. Recent studies have shown that smooth RM$^+$ variants, the advanced variants of RM, can achieve an improved convergence rate of $O(1/T)$. Despite this improvement, smooth RM$^+$ variants lose the parameter-free property, i.e., no parameters that need to be tuned, a highly desirable feature in practical applications. In this paper, we propose a novel smooth RM$^+$ variant called Monotone Increasing Smooth Predictive Regret Matching$^+$ (MI-SPRM$^+$), which retains the parameter-free property while still achieving a theoretical convergence rate of $O(1/T)$. To achieve these properties, MI-SPRM$^+$ employs a technology called Adaptive Regret Domain (ARD), which ensures that the lower bound for the 1-norm of accumulated regrets increases monotonically by adjusting the decision space at each iteration. This design is motivated by the observation that the range of step-sizes supporting the $O(1/T)$ convergence rate in existing smooth RM$^+$ variants is contingent on the lower bound for the 1-norm of accumulated regrets. Experimental results confirm that MI-SPRM$^+$ empirically attains an $O(1/T)$ convergence rate.
STARK: Strategic Team of Agents for Refining Kernels
Authors: Juncheng Dong; Yang Yang; Tao Liu; Yang Wang; Feng Qi; Vahid Tarokh; Kaushik Rangadurai; Shuang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The efficiency of GPU kernels is central to the progress of modern AI, yet optimizing them remains a difficult and labor-intensive task due to complex interactions between memory hierarchies, thread scheduling, and hardware-specific characteristics. While recent advances in large language models (LLMs) provide new opportunities for automated code generation, existing approaches largely treat LLMs as single-shot generators or naive refinement tools, limiting their effectiveness in navigating the irregular kernel optimization landscape. We introduce an LLM agentic framework for GPU kernel optimization that systematically explores the design space through multi-agent collaboration, grounded instruction, dynamic context management, and strategic search. This framework mimics the workflow of expert engineers, enabling LLMs to reason about hardware trade-offs, incorporate profiling feedback, and refine kernels iteratively. We evaluate our approach on KernelBench, a benchmark for LLM-based kernel optimization, and demonstrate substantial improvements over baseline agents: our system produces correct solutions where baselines often fail, and achieves kernels with up to 16$\times$ faster runtime performance. These results highlight the potential of agentic LLM frameworks to advance fully automated, scalable GPU kernel optimization.
DispViT: Direct Stereo Disparity Regression with a Single-Stream Vision Transformer
Authors: Tongfan Guan; Jiaxin Guo; Tianyu Huang; Jinhu Dong; Chen Wang; Yun-Hui Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep stereo disparity estimation has long been dominated by a \textbf{matching-centric paradigm}, built on constructing cost volumes and iteratively refining local correspondences.
Despite its success, this paradigm exhibits an intrinsic vulnerability: visual ambiguities from occlusion or non-Lambertian surfaces invevitably induce errorneous matches that refinement cannot recover.
This paper introduces \textbf{DispViT}, a new architecture that establishes a \textbf{regression-centric paradigm}. 
Instead of explicit matching, DispViT directly regresses disparity from tokenized binocular representations using a single-stream Vision Transformer.
This is enabled by a set of lightweight yet critical designs, such as a probability-based disparity parameterization for stable training and an asymmetrically initialized stereo tokenizer for effective view distinction.
To better align the two views during stereo tokenization, we introduce a novel shift-embedding mechanism that encodes different disparity shifts into channel groups, preserving geometric cues even under large view displacements.
A lightweight refinement module then sharpens the regressed disparity map for fine-grained accuracy.
By prioritizing holistic regression over explicit matching, DispViT streamlines the stereo pipeline while improving robustness and efficiency.
Experiments on standard benchmarks show that our approach achieves state-of-the-art accuracy, with strong resilience to matching ambiguities and wide disparity ranges.
Code will be released.
Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios
Authors: Kihun Hong; Sejun Park; Ganguk Hwang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated learning (FL) has attracted significant attention for enabling collaborative learning without exposing private data. 
  Among the primary variants of FL, vertical federated learning (VFL) addresses feature-partitioned data held by multiple institutions, each holding complementary information for the same set of users. 
  However, existing VFL methods often impose restrictive assumptions such as a small number of participating parties, fully aligned data, or only using labeled data. 
  In this work, we reinterpret alignment gaps in VFL as missing data problems and propose a unified framework that accommodates both training and inference under arbitrary alignment and labeling scenarios, while supporting diverse missingness mechanisms. 
  In the experiments on 168 configurations spanning four benchmark datasets, six training-time missingness patterns, and seven testing-time missingness patterns, our method outperforms all baselines in 160 cases with an average gap of 9.6 percentage points over the next-best competitors.
  To the best of our knowledge, this is the first VFL framework to jointly handle arbitrary data alignment, unlabeled data, and multi-party collaboration all at once.
Cost-of-Pass: An Economic Framework for Evaluating Language Models
Authors: Mehmet Hamza Erol; Batu El; Mirac Suzgun; Mert Yuksekgonul; James Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The widespread adoption of AI systems in the economy hinges on their ability to generate economic value that outweighs their inference costs. Evaluating this tradeoff requires metrics that account for both performance and costs. Building on Farrell's theory of productive efficiency, we develop an economically grounded framework for evaluating language models' productivity by combining accuracy and inference cost. We formalize cost-of-pass, the expected monetary cost of generating a correct solution. We then define the frontier cost-of-pass as the minimum cost-of-pass achievable across available models or the human-expert(s), using the approximate cost of hiring an expert. Our analysis reveals distinct economic insights. First, lightweight models are most cost-effective for basic quantitative tasks, large models for knowledge-intensive ones, and reasoning models for complex quantitative problems, despite higher per-token costs. Second, tracking this frontier cost-of-pass over the past year reveals significant progress, particularly for complex quantitative tasks where the cost has roughly halved every few months. Third, to trace key innovations driving this progress, we examine counterfactual frontiers—estimates of cost-efficiency without specific model classes. We find that innovations in lightweight, large, and reasoning models have been essential for pushing the frontier in basic quantitative, knowledge-intensive, and complex quantitative tasks, respectively. Finally, we assess the cost-reductions from common inference-time techniques (majority voting and self-refinement), and a budget-aware technique (TALE-EP). We find that performance-oriented methods with marginal performance gains rarely justify the costs, while TALE-EP shows some promise. Overall, our findings underscore that complementary model-level innovations are the primary drivers of cost-efficiency, and our economic framework provides a principled tool for measuring this progress and guiding deployment.
Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading
Authors: Zifan Song; Kaitao Song; Guosheng Hu; Ding Qi; Junyao Gao; Xiaohua Wang; Dongsheng Li; Cairong Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
Authors: Haiquan Qiu; Quanming Yao
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The pursuit of computational efficiency has driven the adoption of low-precision formats for training transformer models. However, this progress is often hindered by notorious training instabilities. This paper provides the first mechanistic explanation for a long-standing and unresolved failure case where training with flash attention in low-precision settings leads to catastrophic loss explosion. Our in-depth analysis reveals that the failure is not a random artifact but caused by two intertwined phenomena: the emergence of similar low-rank representations within the attention mechanism and the compounding effect of biased rounding errors inherent in low-precision arithmetic. We demonstrate how these factors create a vicious cycle of error accumulation that corrupts weight updates, ultimately derailing the training dynamics. To validate our findings, we introduce a minimal modification to the flash attention that mitigates the bias in rounding errors. This simple change stabilizes the training process, confirming our analysis and offering a practical solution to this persistent problem. Code is available at https://github.com/ucker/why-low-precision-training-fails.
KnowProxy: Adapting Large Language Models by Knowledge-guided Proxy
Authors: Gukhyeon Lee; Yeachan Kim; SangKeun Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Adapting large language models (LLMs) using smaller proxy models has been shown to improve training efficiency, where the LLMs remain frozen while the proxies are tuned on top. However, this approach typically requires access to the output probability distributions of LLMs, which are often inaccessible or unstable. To address this limitation, we propose KnowProxy, a knowledge-guided proxy framework in which the proxy is trained with textual knowledge rather than probability distributions. Specifically, we first elicit textual knowledge and reasoning from frozen LLMs through prompting, and then the proxy model learns to adapt this reasoning to target task distributions. We evaluate KnowProxy on diverse reasoning benchmarks with different fine-tuning scenarios. Comprehensive results show that KnowProxy achieves competitive or even better performance without direct access to probability distributions, thereby providing a scalable and versatile alternative to traditional fine-tuning.
To View Transform or Not to View Transform: NeRF-based Pre-training Perspective
Authors: Hyeonjun Jeong; Juyeb Shin; Dongsuk Kum
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural radiance fields (NeRFs) have emerged as a prominent pre-training paradigm for vision-centric autonomous driving, which enhances 3D geometry and appearance understanding in a fully self-supervised manner. To apply NeRF-based pre-training to 3D perception models, recent approaches have simply applied NeRFs to volumetric features obtained from view transformation. However, coupling NeRFs with view transformation inherits conflicting priors; view transformation imposes discrete and rigid representations, whereas radiance fields assume continuous and adaptive functions. When these opposing assumptions are forced into a single pipeline, the misalignment surfaces as blurry and ambiguous 3D representations that ultimately limit 3D scene understanding. Moreover, the NeRF network for pre-training is discarded during downstream tasks, resulting in inefficient utilization of enhanced 3D representations through NeRF. In this paper, we propose a novel NeRF-Resembled Point-based 3D detector that can learn continuous 3D representation and thus avoid the misaligned priors from view transformation. NeRP3D preserves the pre-trained NeRF network regardless of the tasks, inheriting the principle of continuous 3D representation learning and leading to greater potentials for both scene reconstruction and detection tasks. Experiments on nuScenes dataset demonstrate that our proposed approach significantly improves previous state-of-the-art methods, outperforming not only pretext scene reconstruction tasks but also downstream detection tasks.