GenAI4ELab/papercli-papers huggingface dataset:
Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning
Authors: Yifei Chen; Guanting Dong; Zhicheng Dou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to enhance their internal reasoning ability by integrating external tools. However, models with TIR often exhibit suboptimal behaviors, including insufficient tool calls, excessive tool calls, and overthinking after receiving tool call results. How to empower LLMs to perform TIR efficiently and accurately, while stabilizing the reasoning process, remains an open challenge.
In this paper, we first analyze the impact of tool calls on model reasoning from the perspective of information entropy. We find that when tool call results are provided, the information entropy of subsequent reasoning content will show a clear trend of change, and the overall information entropy of the reasoning chain will vary depending on the number of tool calls. Based on these observations, we propose Tool-Light, a framework designed to encourage LLMs to perform TIR efficiently and accurately. Our framework consists of dataset construction and multi-stage fine-tuning. For dataset construction, we use the trained model for continuous self-evolved sampling, integrating two methods: vanilla sampling and entropy-guided sampling. At the same time, during the sampling process, we design strict criteria for selecting positive-negative pairs. For the training process, we introduce a two-stage method, which includes a Supervised Fine-Tuning (SFT), and Self-Evolved Direct Preference Optimization (DPO).
Test results on 10 datasets reveal the effectiveness of Tool-Light, significantly improving the efficiency and accuracy of the model in completing TIR tasks.
DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
Authors: Mohammad Hassan Vali; Tom Bäckström; Arno Solin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treats quantization as adding an error vector that mimics the quantization distortion, keeping the forward pass hard while letting gradients flow. We also present a space-filling variant (SF-DiVeQ) that assigns input to a curve constructed by the lines connecting codewords, resulting in less quantization error and full codebook usage. Both methods train end-to-end without requiring auxiliary losses or temperature schedules. In VQ-VAE image compression, VQGAN image generation, and DAC speech coding tasks across various data sets, our proposed methods improve reconstruction and sample quality over alternative quantization approaches.
Towards Lossless Memory-efficient Training of Spiking Neural Networks via Gradient Checkpointing and Spike Compression
Authors: Yifan Huang; Wei Fang; Zecheng Hao; Zhengyu Ma; Yonghong Tian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep spiking neural networks (SNNs) hold immense promise for low-power event-driven computing, but their direct training via backpropagation through time (BPTT) incurs prohibitive memory cost, which limits their scalability. Existing memory-saving approaches, such as online learning, BPTT-to-BP, and reversible networks, compromise accuracy, training speed, or applicability. In this work, we propose a novel and broadly applicable pipeline for memory-efficient SNN training that preserves BPTT's accuracy. Our pipeline integrates layer-wise gradient checkpointing with lossless spike compression to eliminate internal state storage and reduce the memory cost of per-layer input spikes. We also introduce a multi-stage checkpoint adjustment strategy that adaptively refines checkpoint placement based on profiling results to further optimize memory usage and improve training speed. Wrapped in an optimization pass, the pipeline automatically restructures the computation flow before training with minimal user effort. Extensive experiments on diverse architectures and tasks demonstrate up to $8\times$ memory efficiency gains with $\le 20\%$ speed reduction and no accuracy loss. Our method provides a practical solution for efficient and scalable SNN training. Code is available at https://github.com/AllenYolk/snn-gradient-checkpointing.
SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
Authors: Tengxue Zhang; Biao Ouyang; Yang Shu; Xinyang Chen; Chenjuan Guo; Bin Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pre-trained models exhibit strong generalization to various downstream tasks. However, given the numerous models available in the model hub, identifying the most suitable one by individually fine-tuning is time-consuming. In this paper, we propose \textbf{SwiftTS}, a swift selection framework for time series pre-trained models. To avoid expensive forward propagation through all candidates, SwiftTS adopts a learning-guided approach that leverages historical dataset-model performance pairs across diverse horizons to predict model performance on unseen datasets. It employs a lightweight dual-encoder architecture that embeds time series and candidate models with rich characteristics, computing patchwise compatibility scores between data and model embeddings for efficient selection. To further enhance the generalization across datasets and horizons, we introduce a horizon-adaptive expert composition module that dynamically adjusts expert weights, and the transferable cross-task learning with cross-dataset and cross-horizon task sampling to enhance out-of-distribution (OOD) robustness. Extensive experiments on 14 downstream datasets and 8 pre-trained models demonstrate that SwiftTS achieves state-of-the-art performance in time series pre-trained model selection. The code and datasets are available at \href{}{https://github.com/decisionintelligence/SwiftTS}.
CPQS-Tuning: A Model Self-Perception-Based Data Filtering Algorithm for Efficient Instruction Fine-Tuning
Authors: Yi Ren; Yanhui Li; Tianyi Zhang; Diandong Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Instruction fine-tuning is a key technique for enhancing the performance of large language models (LLMs), but low-quality and redundant data often hinder its effectiveness. Recent studies suggest that filtering a small amount of high-quality data for instruction fine-tuning can achieve faster and more efficient training performance. However, existing data filtering approaches predominantly depend on predefined evaluation models or manually designed metrics, without leveraging information from the target LLM itself. This limitation may result in a mismatch between the filtering criteria and the actual requirements of the LLM being fine-tuned, thereby reducing the effectiveness of the fine-tuning process.  To address these issues, we propose a novel perspective: the hidden states of LLMs implicitly reflect the quality of the training data. Based on this insight, we propose a novel data filtering method that extracts the hidden states that reflect the target LLM’s perception of the data as representative features, and builds a data classification model upon them, which outputs the Contrastive Perception Quality Score (CPQS) for dataset filtering. Our experiments are conducted in both general and downstream domains.
(1) In the general domain, our experiments show that training on under 10\% of the data from both the Alpaca\_GPT4 and DeepSeek-R1 synthesized reasoning datasets enables our method to outperform models trained on the complete datasets. Moreover, it surpasses the performance of current state-of-the-art data-selection techniques.
(2) In downstream tasks, our approach delivers an average performance gain exceeding 3.6\% over leading data-selection algorithms across multiple benchmarks, including GSM8K, HumanEval, and HumanEval-Plus.
OVID: Open-Vocabulary Intrusion Detection
Authors: Fujun Han; Jingqi Ye; Chenglong Zhang; Peng Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Various vision intrusion detection models have achieved great success in many scenarios, e.g., autonomous driving, intelligent monitoring, and security, etc. However, their reliance on pre-defined classes limits their applicability in open-world intrusion detection scenarios. To remedy these, we introduce the Open-Vocabulary Intrusion Detection (OVID) project for the first time. Specifically, we first develop a novel dataset, Cityintrusion-OpenV for OVID,  with more diverse intrusion categories and corresponding text prompts. Then, we design a multi-modal, multi-task, and end-to-end open-vocabulary intrusion detection framework named OVIDNet. It achieves open-world intrusion detection via aligning visual features with language embeddings. Further, two simple yet effective strategies are proposed to improve the generalization and performance of this specific task: (1) A Multi-Distributed Noise Mixing strategy is introduced to enhance the location information of unknown and unseen categories. (2) A Dynamic Memory-Gated module is designed to capture the contextual information under complex scenarios. Finally, comprehensive experiments and comparisons are conducted on multiple dominant datasets, e.g., COCO, Cityscape, Foggy-Cityscape, and Cityintrusion-OpenV. Besides, we also evaluate the universal applicability of our model in real scenarios. The results show that our method can outperform other classic and promising methods, and reach strong performance even under task-specific transfer and zero-shot settings, demonstrating its high practicality.
Speech World Model: Causal State–Action Planning with Explicit Reasoning for Speech
Authors: Xuanru Zhou; Jiachen Lian; Henry Hong; Xinyi Yang; Gopala Anumanchipalli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current speech-language models (SLMs) typically use a cascade of speech encoder and large language model, treating speech understanding as a single black box. They analyze the content of speech well but reason weakly about other aspects, especially under sparse supervision. Thus, we argue for explicit reasoning over speech states and actions with modular and transparent decisions. Inspired by cognitive science we adopt a modular perspective and a world model view in which the system learns forward dynamics over latent states. We factorize speech understanding into four modules that communicate through a causal graph, establishing a cognitive state search space. Guided by posterior traces from this space, an instruction-tuned language model produces a concise causal analysis and a user-facing response, enabling counterfactual interventions and interpretability under partial supervision. We present the first graph based modular speech model for explicit reasoning and we will open source the model and data to promote the development of advanced speech understanding.
Fairness-Aware Multi-view Evidential Learning with Adaptive Prior
Authors: Haishun Chen; Cai Xu; Jinlong Yu; Yilin Zhang; Ziyu Guan; Wei Zhao; Fangyuan Zhao; Xin Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-view evidential learning harnesses diverse data sources to improve prediction performance and provide reliable uncertainty estimates. Recent advances have primarily focused on optimizing evidence fusion strategies, assuming that the evidence extracted from each view is naturally reliable for downstream integration. However, our empirical analysis reveals that samples tend to be assigned biased evidence to support data-rich classes, thereby rendering unfair uncertainty estimations. This motivates us to delve into a new Biased Evidential Multi-view Learning (BEML) problem. To this end, we propose Fairness-Aware Multi-view Evidential Learning (FAML) method to rectify biased evidence learning. Specifically, FAML introduces the training-trajectory-based adaptive prior into the construction of Dirichlet parameters, flexibly calibrating the initial support evidence assigned to each class during training. Furthermore, we incorporate a fairness constraint as a regularization term to alleviate bias in the evidence. In the multi-view fusion stage, we propose an opinion alignment mechanism to mitigate view-specific bias across views, thereby encouraging the integration of consistent and mutually supportive evidence. Theoretical analysis shows that FAML effectively achieves less biased evidence allocation. Extensive experiments on real-world multi-view datasets demonstrate the superiority of our FAML, in terms of prediction performance and uncertainty estimation.
Mirage or Method? How Model–Task Alignment Induces Divergent RL Conclusions
Authors: Haoze Wu; Cheng Wang; Wenshuo Zhao; Junxian He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in applying reinforcement learning (RL) to large language models (LLMs) have led to substantial progress. In particular, a series of remarkable yet often counterintuitive phenomena have been reported in LLMs, exhibiting patterns not typically observed in traditional RL settings. For example, notable claims include that a single training example can match the performance achieved with an entire dataset, that the reward signal does not need to be very accurate, and that training solely with negative samples can match or even surpass sophisticated reward-based methods. However, the precise conditions under which these observations hold—and, critically, when they fail—remain unclear. In this work, we identify a key factor that differentiates RL observations: whether the pretrained model already exhibits strong *Model-Task Alignment*, as measured by pass@k accuracy on the evaluated task. Through a systematic and comprehensive examination of a series of counterintuitive claims, supported by rigorous experimental validation across different model architectures and task domains, our findings show that while standard RL training remains consistently robust across settings, many of these counterintuitive results arise only when the model and task already exhibit strong model-task alignment. In contrast, these techniques fail to drive substantial learning in more challenging regimes, where standard RL methods remain effective.
Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling
Authors: Shengyin Sun; Yiming Li; Xing Li; Yingzhao Lian; Weizhe Lin; Hui-Ling Zhen; Zhiyuan Yang; Xianzhi Yu; Chen Chen; Mingxuan Yuan; Chen Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-time scaling has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs) by allocating additional computational resources during inference. However, this paradigm is inherently inefficient due to the generation of different reasoning traces, leading to significant computational overhead. Speculative decoding offers a promising avenue for mitigating this inefficiency, yet its efficacy in the structured and repetition-rich context remains unexplored. To bridge this gap, we introduce the first comprehensive benchmark designed to evaluate speculative decoding methods in LLM test-time scaling. Our benchmark provides consistent experimental protocols across representative test-time scaling paradigms (e.g., Best-of-N sampling and multi-round thinking), enabling a fair comparison of three major categories of speculative decoding: model-based, training-based, and N-gram-based methods. Extensive experiments reveal that simple N-gram-based methods effectively capture repetitive patterns, demonstrating unique potential in accelerating test-time scaling. This phenomenon demonstrates the value of integrating N-gram-based methods with model-based or training-based approaches to benefit both repetitive and diverse reasoning in test-time scaling. We hope this benchmark spurs further research on speculative decoding for test-time scaling, enabling faster and more practical reasoning in LLMs through better handling of repetitive and diverse reasoning paths. Code available at <https://github.com/sunshy-1/SpecTTS-Bench>.
Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
Authors: Jiawen Zhang; Tony He; Kejia Chen; Jian Lou; Jian Liu; Xiaohu Yang; Ruoxi Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning safety-aligned large language models (LLMs) can substantially compromise their safety. Previous approaches require many safety samples or calibration sets, which not only incur significant computational overhead during realignment but also lead to noticeable degradation in model utility. Contrary to this belief, we show that safety alignment can be fully recovered with only a single safety example, without sacrificing utility and at minimal cost. Remarkably, this recovery is effective regardless of the number of harmful examples used in fine-tuning or the size of the underlying model, and convergence is achieved within just a few epochs. Furthermore, we uncover the low-rank structure of the safety gradient, which explains why such efficient correction is possible. We validate our findings across five safety-aligned LLMs and multiple datasets, demonstrating the generality of our approach.
Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts
Authors: Bingqing Zhang; Zhuo Cao; Heming Du; Yang Li; Xue Li; Jiajun Liu; Sen Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world *query shifts*, where the distribution of query data deviates from the training domain, leading to a sharp performance drop. Existing image-focused robustness solutions are inadequate to handle this vulnerability in video, as they fail to address the complex spatio-temporal dynamics inherent in these shifts. To systematically evaluate this vulnerability, we first introduce a comprehensive benchmark featuring 12 distinct types of video perturbations across five severity degrees. Analysis on this benchmark reveals that query shifts amplify the *hubness phenomenon*, where a few gallery items become dominant "hubs" that attract a disproportionate number of queries. To mitigate this, we then propose HAT-VTR (Hubness Alleviation for Test-time Video-Text Retrieval), as our baseline test-time adaptation framework designed to directly counteract hubness in VTR. It leverages two key components: a *Hubness Suppression Memory* to refine similarity scores, and *multi-granular losses* to enforce temporal feature consistency. Extensive experiments demonstrate that HAT-VTR substantially improves robustness, consistently outperforming prior methods across diverse query shift scenarios, and enhancing model reliability for real-world applications.
From atom to space: A region-based readout function for spatial properties of materials
Authors: Jiawen Zou; Weimin Tan; Zhongyao Wang; Hao Qi; Bo Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The message passing–readout framework has become the de facto standard of graph neural networks (GNNs) for material property prediction. However, most existing readout functions are built on an atom-decomposable inductive bias, i.e. the material-level property or feature can be reasonably assigned to contributions of individual atoms. This is a strong bias and may not hold for all properties, limiting the application scenarios (e.g. gas adsorption or separation of Metal Organic Frameworks, MOFs). In this work, we propose a region-based decomposition perspective, reformulating material properties as integrals over space and pooling contributions from spatial regions rather than atoms. Specifically, we propose a novel readout function named SpatialRead. SpatialRead introduces additional spatial nodes to represent a voxelized space, transforming the atomic isomorphic graph into a heterogeneous atom–space graph with unidirectional message flow from atoms to spatial nodes. To combine the two types of inductive bias, multimodal methods can be used to fuse the features of atoms the spatial nodes. Such a region-based readout function is especially suited for spatial properties such as gas adsorption capacity, separation ratio. Extensive experiments demonstrate that a simple PaiNN–Transformer-based SpatialRead trained from scratch outperforms state-of-the-art pre-trained foundation models on these special tasks. Our results highlight the importance of designing physically grounded readout functions tailored to the target property. The code and dataset can be found in github https://github.com/nankusa/SpatialRead.
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
Authors: Hwa Hui Tew; Junn Yong Loo; Leong Fang Yu; Julia K. Lau; Ding Fan; Hernando Ombao; Raphael CW Phan; Chee Pin Tan; Chee-Ming Ting
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the resource-intensive nature of fMRI acquisition limits the availability of high-fidelity samples required for data-driven brain analysis models. While modern generative models can synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals. To address these challenges, we propose Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. Specifically, our framework first converts BOLD signals into a wavelet decomposition map via a discrete wavelet transform (DWT) to capture globalized transient and multi-scale variations, and projects into the discrete cosine transform (DCT) space across brain regions and time to exploit localized energy compaction of low-frequency dominant BOLD coefficients. Subsequently, a spectral flow matching model is trained to generate class-conditioned cosine-frequency representation. The generated samples are reconstructed through inverse DCT and inverse DWT operations to recover physiologically plausible time-domain BOLD signals. This dual-transform approach imposes structured frequency priors and preserves key physiological brain dynamics. Ultimately, we demonstrate the efficacy of our approach through improved downstream fMRI-based brain network classification.
Unified Privacy Guarantees for Decentralized Learning via Matrix Factorization
Authors: Aurélien Bellet; Edwige Cyffers; Davide Frey; Romaric Gaudel; Dimitri Lerévérend; Francois Taiani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Decentralized Learning (DL) enables users to collaboratively train models without sharing raw data by iteratively averaging local updates with neighbors in a network graph. This setting is increasingly popular for its scalability and its ability to keep data local under user control. Strong privacy guarantees in DL are typically achieved through Differential Privacy (DP), with results showing that DL can even amplify privacy by disseminating noise across peer-to-peer communications.
Yet in practice, the observed privacy-utility trade-off often appears worse than in centralized training, which may be due to limitations in current DP accounting methods for DL. In this paper, we show that recent advances in centralized DP accounting based on Matrix Factorization (MF) for analyzing temporal noise correlations can also be leveraged in DL. By generalizing existing MF results, we show how to cast both standard DL algorithms and common trust models into a unified formulation. This yields tighter privacy accounting for existing DP-DL algorithms and provides a principled way to develop new ones. To demonstrate the approach, we introduce MAFALDA-SGD, a gossip-based DL algorithm with user-level correlated noise that outperforms existing methods on synthetic and real-world graphs.