GenAI4ELab/papercli-papers huggingface dataset:
From Assistant to Independent Developer — Are GPTs Ready for Software Development?
Authors: Dezhi Ran; Yuan Cao; Mengzhou Wu; Simin Chen; Yuzhe Guo; Jun Ren; Zihe Song; Hao Yu; Jialei Wei; Linyi Li; Wei Yang; Baishakhi Ray; Tao Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have demonstrated remarkable capability in function-level code generation tasks. 
Unlike isolated functions, real-world applications demand reasoning over the entire software system: developers must orchestrate how different components interact, maintain consistency across states over time, and ensure the application behaves correctly within the lifecycle and framework constraints. Yet, no existing benchmark adequately evaluates whether LLMs can bridge this gap and construct entire software systems from scratch.

To address this gap, we propose \tool, a benchmark consisting of 101 software development problems drawn from real-world Android apps. Given a natural language specification detailing the app functionality, a language model is tasked with \textbf{implementing the functionality into an Android app from scratch}. Developing an Android app from scratch requires understanding and coordinating app states, lifecycle management, and asynchronous operations, calling for LLMs to generate context-aware, robust, and maintainable code. To construct \tool, we design a multi-agent system to automatically summarize the main functionalities from app documents and navigate the app to synthesize test cases validating the functional correctness of app implementation. Following rigorous manual verification by Android development experts, \tool incorporates the test cases within an automated evaluation framework that enables reproducible assessment without human intervention, making it easily adoptable for future research. Our evaluation on 12 flagship LLMs show that all evaluated models achieve low effectiveness, with the best-performing model (GPT-5) developing only 18.8\% functionally correct applications, highlighting fundamental limitations in current models' ability to handle complex, multi-component software engineering challenges.
Neuron-Level Analysis of Cultural Understanding in Large Language Models
Authors: Taisei Yamamoto; Ryoma Kumon; Danushka Bollegala; Hitomi Yanaka
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important.
However, LLMs exhibit cultural bias and limited awareness of underrepresented cultures, while the mechanisms underlying their cultural understanding remain underexplored.
To fill this gap, we conduct a neuron-level analysis to identify neurons that drive cultural behavior, introducing a gradient-based scoring method with additional filtering for precise refinement.
We identify culture-general neurons contributing to cultural understanding regardless of cultures, and culture-specific neurons tied to an individual culture.
Culture-general and culture-specific neurons account for less than 1% of all neurons and are concentrated in shallow to middle MLP layers.
We validate their role by showing that suppressing them substantially degrades performance on cultural benchmarks (by up to 30%), while performance on general natural language understanding (NLU) benchmarks remains largely unaffected.
Moreover, we show that culture-specific neurons support knowledge of not only the target culture, but also related cultures.
Finally, we demonstrate that training on NLU benchmarks can diminish models' cultural understanding when we update modules containing many culture-general neurons.
These findings provide insights into the internal mechanisms of LLMs and offer practical guidance for model training and engineering.
Our code is available at https://github.com/ynklab/CULNIG
Object Fidelity Diffusion for Remote Sensing Image Generation
Authors: Ziqi Ye; Shuran Ma; Jie Yang; Xiaoyi Yang; Yi Yang; Ziyang Gong; Xue Yang; Haipeng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High-precision controllable remote sensing image generation is both meaningful and challenging. Existing diffusion models often produce low-fidelity objects due to their inability to adequately capture morphological details, which may affect the robustness and reliability of object detection models. To enhance the accuracy and fidelity of generated objects in remote sensing, this paper proposes Object Fidelity Diffusion (OF-Diff), which effectively improves the fidelity of generated objects. Specifically, we are the first to extract the prior shapes of objects based on the layout for diffusion models in remote sensing. Then, we introduce a self-distillation diffusion model with consistency distillation loss, which can generate high-fidelity remote sensing images without providing real images during the sampling phase. Furthermore, we introduce DDPO to fine-tune the diffusion process, making the generated remote sensing images more diverse and semantically consistent. Comprehensive experiments demonstrate that OF-Diff outperforms state-of-the-art methods in the remote sensing across key quality metrics. Notably, the performance of several polymorphic and small object classes shows significant improvement. For instance, the mAP increases by 8.3%, 7.7%, and 4.0% for airplanes, ships, and vehicles, respectively.
DirMoE: Dirichlet-Routed Mixture of Experts
Authors: Amirhossein Vahidi; Hesam Asadollahzadeh; Navid Akhavan Attar; Marie Moullet; Kevin Ly; Xingyi Yang; Mohammad Lotfollahi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture-of-Experts (MoE) models have demonstrated exceptional performance in large-scale language models. Existing routers typically rely on non-differentiable Top-$k$+Softmax, limiting their performance and scalability. We argue that two distinct decisions, which experts to activate and how to distribute expert contributions among them, are conflated in standard Top-$k$+Softmax. We introduce Dirichlet-Routed MoE (DirMoE), a novel end-to-end differentiable routing mechanism built on a Dirichlet variational autoencoder framework. This design fundamentally disentangles the core routing problems: expert selection, modeled by a Bernoulli component, and expert contribution among chosen experts, handled by a Dirichlet component. The entire forward pass remains fully differentiable through the use of Gumbel-Sigmoid relaxation for the expert selection and implicit reparameterization for the Dirichlet distribution. Our training objective, a variational ELBO, includes a direct sparsity penalty that precisely controls the number of active experts in expectation, alongside a schedule for key hyperparameters that guides the model from an exploratory to a definitive routing state. Moreover, our DirMoE router matches or exceeds other methods while improving expert specialization.
Inference-time scaling of diffusion models through classical search
Authors: XiangCheng Zhang; Haowei Lin; Haotian Ye; James Zou; Jianzhu Ma; Yitao Liang; Yilun Du
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Classical search algorithms have long underpinned modern artificial intelligence. In this work, we tackle the challenge of inference-time control in diffusion models—adapting generated outputs to meet diverse test-time objectives—using principles from classical search. We propose a general framework that orchestrates local and global search to efficiently navigate the generative space. It performs compute-efficient global exploration using breadth-first and depth-first tree search and employs a theoretically grounded, scalable local search via annealed Langevin MCMC. We evaluate our approach on a range of challenging domains, including planning, offline reinforcement learning, and image generation, and observe significant gains in both performance and efficiency over baseline methods. These results demonstrate that classical search offers a principled and practical foundation for inference-time scaling in diffusion models. By jointly scaling local and global search for the first time, our framework establishes a new Pareto frontier across challenging decision-making domains.
Towards True Speech-to-Speech Models Without Text Guidance
Authors: Xingjian Zhao; Zhe Xu; Luozhijie Jin; Yang Wang; Hanfu Chen; Yaozhou Jiang; Ke Chen; Ruixiao Li; Mingshu Chen; Ruiming Wang; Wenbo Zhang; Qinyuan Cheng; Zhaoye Fei; Shimin Li; Xipeng Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spoken dialogue systems often rely on cascaded pipelines that transcribe, process, and resynthesize speech. While effective, this design discards paralinguistic cues and limits expressivity. Recent end-to-end methods reduce latency and better preserve these cues, yet still rely on text intermediates, creating a fundamental bottleneck. We present a true speech-to-speech large language model that directly understands and generates speech without relying on text guidance. Our approach combines a modality-based layer-splitting architecture with a frozen pre-training strategy, preserving the reasoning and knowledge of pretrained text LLMs while adding native speech capabilities. Experiments show that our model achieves state-of-the-art results in spoken question answering and delivers comparable speech-to-speech performance relative to existing text-guided systems, while still maintaining competitive text performance. By narrowing the gap between text-guided and direct speech generation, our work establishes a new paradigm for expressive and efficient end-to-end speech interaction. We will release our code and models to support further research in true speech-to-speech foundation models.
DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving
Authors: Ying Yuan; Pengfei Zuo; Bo Wang; Zhangyu Chen; Zhipeng Tan; Zhou Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In large language model (LLM) serving, reusing the key-value (KV) cache of prompts across requests is a key technique for reducing time-to-first-token (TTFT) and lowering serving costs. Cache-affinity scheduling, which co-locates requests with the same prompt prefix to maximize KV cache reuse, often conflicts with load-balancing scheduling, which aims to distribute requests evenly across compute instances. Existing schedulers struggle to reconcile this trade-off, as they operate within a single mapping space, typically applying cache-affinity routing to a subset of requests and load-balanced routing to the rest, without a unified solution to achieve both goals. To overcome this limitation, we propose DualMap, a dual-mapping scheduling strategy for distributed LLM serving that simultaneously enables cache affinity and load balancing. The key idea of DualMap is to map each request to two candidate instances using two independent hash functions based on the request prompt, and then intelligently select the better candidate based on current system states. This design increases the likelihood that requests with shared prefixes are co-located, while evenly dispersing distinct prefixes across the cluster via ``the power of two choices''. To make DualMap robust under dynamic and skewed real-world workloads, we incorporate three techniques: 1) SLO-aware request routing,  which prioritizes cache affinity but switches to load-aware scheduling when TTFT exceeds the SLO, enhancing load balance without sacrificing cache reuse; 2) hotspot-aware rebalancing, which dynamically migrates requests from overloaded to underloaded instances, mitigating hotspots and rebalancing the system; 3) lightweight dual-hash-ring scaling, which leverages a dual-hash-ring mapping to support fast and low-overhead instance scaling without costly global remapping. Experiments on real-world workloads show that DualMap improves effective request capacity by up to 2.25$\times$ under the same TTFT SLO constraints, compared with the state-of-the-art work.
AlignSep: Temporally-Aligned Video-Queried Sound Separation with Flow Matching
Authors: Xize Cheng; Chenyuhao Wen; Slytherin Wang; Yongqi Wang; Zehan Wang; Rongjie Huang; Tao Jin; Zhou Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video Query Sound Separation (VQSS) aims to isolate target sounds conditioned on visual queries while suppressing off-screen interference—a task central to audiovisual understanding. However, existing methods often fail under conditions of homogeneous interference and overlapping soundtracks, due to limited temporal modeling and weak audiovisual alignment.
We propose \textbf{AlignSep}, the first generative VQSS model based on flow matching, designed to address common issues such as spectral holes and incomplete separation. To better capture cross-modal correspondence, we introduce a series of temporal consistency mechanisms that guide the vector field estimator toward learning robust audiovisual alignment, enabling accurate and resilient separation in complex scenes.
As a \textit{multi-conditioned generation} task, VQSS presents unique challenges that differ fundamentally from traditional flow matching setups. We provide an in-depth analysis of these differences and their implications for generative modeling. To systematically evaluate performance under realistic and difficult conditions, we further construct \textbf{VGGSound-Hard}, a challenging benchmark composed entirely of separation cases with homogeneous interference and strong reliance on temporal visual cues.
Extensive experiments across multiple benchmarks demonstrate that AlignSep achieves state-of-the-art performance both quantitatively and perceptually, validating its practical value for real-world applications. More results and audio examples are available at: \url{https://AlignSep.github.io}.
A Resolution-Agnostic Geometric Transformer for Chromosome Modeling Using Inertial Frame
Authors: Yize Zhou; Haorui Li; Shengchao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Chromosomes are the carriers of genetic information. Further understanding their 3D structure can help reveal gene-regulatory mechanisms and cellular functions. However, high-resolution 3D structures are often missing due to the high cost and inherent noise of experimental screening. A standard pipeline for reconstructing the chromosome 3D structure first applies the single-cell Hi-C high-throughput screening method to measure pairwise interactions between DNA fragments at different resolutions; then it adopts computational methods to reconstruct the 3D structures from these contacts. These include traditional numerical methods and deep learning models, which struggle with limited model expressiveness and poor generalization across resolutions. To handle this issue, we propose InertialGenome, a novel transformer-based framework for robust and resolution-agnostic chromosome reconstruction. InertialGenome first adopts the inertial frame for the pose canonicalization. Then, based on such an invariant pose, it proposes a Transformer with geometry-aware positional encoding, leveraging Nyström estimation. To verify the effectiveness of InertialGenome, we conduct experiments on two single-cell 3D reconstruction datasets with four resolutions, reaching superior performance over all four computational baselines. Additionally, we observe that the 3D structure reconstructed by InertialGenome is more in line with the results of real experimental results on two functional verification tasks. Finally, we leverage InertialGenome for cross-resolution transfer learning, yielding up to a 5\% improvement from low to high resolution. The source code is available at https://github.com/yize1203/InertialGenome.
PepBenchmark: A Standardized Benchmark for Peptide Machine Learning
Authors: Jiahui Zhang; Rouyi Wang; Kuangqi Zhou; Tianshu Xiao; Lingyan Zhu; Yaosen Min; Yang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Peptide therapeutics are widely regarded as the “third generation” of drugs, yet progress in peptide Machine Learning (ML) are hindered by the absence of standardized benchmarks. Here we present \textbf{PepBenchmark}, which unifies datasets, preprocessing, and evaluation protocols for peptide drug discovery. PepBenchmark comprises three components: (1) \textbf{PepBenchData}, a well-curated collection comprising 29 canonical-peptide and 6 non-canonical-peptide datasets across 7 groups, systematically covering key aspects of peptide drug development—representing, to the best of our knowledge, the most comprehensive AI-ready dataset resource to date; (2) \textbf{PepBenchPipeline}, a standardized preprocessing pipeline that ensures consistent dataset cleaning, construction, splitting, and feature transformation, mitigating quality issues common in ad hoc pipelines; and (3) \textbf{PepBenchLeaderboard}, a unified evaluation protocol and leaderboard with strong baselines across 4 major methodological families: Fingerprint-based, GNN-based, PLM-based, and SMILES-based models. Together, PepBenchmark provides the first standardized and comparable foundation for peptide drug discovery, facilitating methodological advances and translation into real-world applications. The data and code are publicly available at \href{https://github.com/ZGCI-AI4S-Pep/PepBenchmark/}{\texttt{https://github.com/ZGCI-AI4S-Pep/PepBenchmark/}}.
Next-ToBE: Probabilistic Next Token-Bag Exploitation for Activating Anticipatory Capacity in LLMs
Authors: Yihe Liu; Huibin Wang; Xianming Hu; Pinyi Zhang; Jiahao Xiong; Chenglin Wang; Nuoyi Chen; Hongbo Zhao; Jie Zhang; Kai Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Auto-regressive large language models (LLMs) exhibit a non-trivial capacity to "anticipate'' long-range future tokens despite being trained to predict only one token at a time. Nevertheless, how to systematically profile, enhance and leverage such capacity to practically improve LLM reasoning performance remains unclear. In this paper, we propose **Next Token-Bag Exploitation (Next-ToBE)** to tackle this challenge. Next-ToBE quantifies LLM’s anticipatory capacity by measuring how well tokens in the future window are pre-captured by the model’s current softmax probabilities. This capacity is  strongly correlated with LLM generative quality but often suppressed by the rigid one-hot objective in next-token prediction. To address this, we replace the {one-hot target vector} in next-token prediction  with a soft target distribution 
spanning additional future tokens. Specifically, the immediate next token retains the highest importance, while more distant ``look-ahead tokens'' are also included to enrich supervision, with their importance  dynamically determined by temporal and semantic relevance patterns to   inject forward-looking pressure. 
Besides, the fitting process emphasizes the model’s intrinsic anticipatory tendency, thus preserving the confidence and fidelity of the pre-trained model to improve training stability. 
Overall, Next-ToBE not only effectively activates LLM  anticipatory capacity through fine-tuning, yielding notable gains in  reasoning performance with higher memory and computational efficiency against the MTP baselines, but also shows great potential in pretraining setting by successfully cultivating  this capacity from scratch. These  highlight its value as an effective strategy to extend the prediction horizon of LLMs, enabling them to see further, and reason better.
Confident Block Diagonal Structure-Aware Invariable Graph Completion for Incomplete Multi-view Clustering
Authors: Shuping Zhao; Yulong Chen; Jie Wen; Lunke Fei; Jinrong Cui; Tingting Chai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-view clustering (MVC) adopts complementary information from multiple views to reveal the underlying structure of the data. However, the conventional MVC-based methods remain a crucial challenge on the incomplete multi-view clustering (IMVC) tasks,
when some views of the multi-view data are missing. Particularly, current IMVC methods suffer from two main limitations: 1) they focused on recovering the missing data, yet often overlooked the potential inaccuracies in imputed values caused by the absence of true label
information; 2) the recovered features were learned from the complete data, neglecting the distributional discrepancy between the complete and incomplete instances. In order to tackle these issues, in this paper, a confident block diagonal structure-aware invariable
graph completion-based incomplete multi-view clustering method (CBDS_IMVC) is proposed. Specifically, we first design a confident-aware missing-view inferring strategy, where the confident block diagonal structures (CBDS) are learned to guarantee that recovered
instances of all views have the same strict invariable local structure with the constraint of CBDS. Subsequently, we proposed an invariable graph completion strategy to learn the intrinsic structure across all views. Each parts are jointly trained, complementing and promoting
each other to achieve the optimum together. Compared to other state-of-the-art methods, the proposed CBDS_IMVC demonstrates superior performance across multiple benchmark datasets.
InfoNCE Induces Gaussian Distribution
Authors: Roy Betser; Eyal Gofer; Meir Yossef Levi; Guy Gilboa
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive
unlabeled data for both task-specific and general (foundation) models.
A prototypical loss in contrastive training is InfoNCE and its variants. 
In this work, we show that the InfoNCE objective induces Gaussian structure in representations that emerge from contrastive training. 
We establish this result in two complementary regimes. 
First, we show that under certain  alignment and concentration assumptions, projections of the high-dimensional representation asymptotically approach a multivariate Gaussian distribution.
Next, under less strict assumptions, we show that adding a small asymptotically vanishing regularization term that promotes low feature norm and high feature entropy leads to similar asymptotic results.
We support our analysis with experiments on synthetic and CIFAR-10 datasets across multiple encoder architectures and sizes, demonstrating consistent Gaussian behavior.
This perspective provides a principled explanation for commonly observed Gaussianity in contrastive representations. 
The resulting Gaussian model enables principled analytical treatment of learned representations and is expected to support a wide range of applications in contrastive learning.