GenAI4ELab/papercli-papers huggingface dataset:
Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis
Authors: Junyan Cheng; Kyle Richardson; Peter Chin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet their reasoning suffers from stochastic instability and lacks a verifiable, compositional structure. To address this, we introduce **Analytica**, a novel agent architecture built on the principle of **Soft Propositional Reasoning (SPR)**. SPR reframes complex analysis as a structured process of estimating the soft truth values of different outcome propositions, allowing us to formally model and minimize the estimation error in terms of its bias and variance.
Analytica operationalizes this through a parallel, divide-and-conquer framework that systematically reduces both sources of error. To reduce bias, problems are first decomposed into a tree of subpropositions, and tool-equipped LLM *grounder agents* are employed —including a novel Jupyter Notebook agent for data-driven analysis—that help to validate and score facts. To reduce variance, Analytica recursively synthesizes these grounded leaves using robust linear models that average out stochastic noise with superior efficiency, scalability, and enable interactive "what-if" scenario analysis.
Our theoretical and empirical results on economic, financial, and political forecasting tasks show that Analytica improves 15.84\% accuracy on average over diverse base models, achieving 71.06\% accuracy with the lowest variance of 6.02\% when working with a Deep Research grounder. Our Jupyter Notebook grounder shows strong cost-effectiveness that achieves a close 70.11\% accuracy with 90.35\% less cost and 52.85\% less time. Analytica also exhibits highly noise-resilient and stable performance growth as the analysis depth increases, with a near-linear time complexity, as well as good adaptivity to open-weight LLMs and scientific domains.
On the Computational Limits of AI4S-RL : A Unified $\varepsilon$-$N$ Analysis
Authors: Qili Shen; Kairui Feng; Ang He; Xuanhong Chen; Dake Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent work increasingly adopts AI for Science (AI4S) models to replace expensive PDE solvers as simulation environments for reinforcement learning (RL), enabling faster training in complex physical control tasks. However, using approximate simulators introduces modeling errors that affect the learned policy. In this paper, we introduce a unified $\varepsilon$-$N$ framework that quantifies the minimal computational cost $N^*(\varepsilon)$ required for an AI4S model to ensure that tabular RL can estimate the value function with unbiasedness, with probability at least $1 - \delta$. This characterization allows us to connect surrogate accuracy, grid resolution, and RL policy quality under a shared probabilistic language.  We analyze how the discretization level $K$ of AI4S and RL space governs both PDE surrogate error and RL lattice approximation error, and we employ spectral theory and Sobolev estimates to derive optimal grid strategies that minimize total cost while preserving learning fidelity. Our theory reveals that different systems, such as ODE- and PDE-governed environments, require different allocations of effort between physical simulation and RL optimization. Overall, our framework offers a principled foundation for designing efficient, scalable, and cost-aware AI4S-RL systems with provable learning guarantees.
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization
Authors: Mingda Zhang; Mingli Zhu; Zihao Zhu; Li Shen; Baoyuan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This work investigates Poisoned Sample Detection (PSD), a promising defense approach against backdoor attacks. However, we observe that the effectiveness of many advanced PSD methods degrades significantly under weak backdoor attacks (\eg, low poisoning ratios or weak trigger patterns). To substantiate this observation, we conduct a statistical analysis across various attacks and PSD methods, revealing a strong correlation between the strength of the backdoor effect and the detection performance. Inspired by this, we propose amplifying the backdoor effect through training with Sharpness-Aware Minimization (SAM). Both theoretical insights and empirical evidence validate that SAM enhances the activations of top Trigger Activation Change (TAC) neurons while suppressing others. Based on this, we introduce SAM-enhanced PSD, a simple yet effective framework that seamlessly improves existing PSD methods by extracting detection features from the SAM-trained model rather than the conventionally trained model. Extensive experiments across multiple benchmarks demonstrate that our approach significantly improves detection performance under both strong and weak backdoor attacks, achieving an average True Positive Rate (TPR) gain of +34.3% over conventional PSD methods. Overall, we believe that the revealed correlation between the backdoor effect and detection performance could inspire future research advancements.
Thinking on the Fly: Test-Time Reasoning Enhancement via Latent Thought Policy Optimization
Authors: Wengao Ye; Yan Liang; Lianlei Shan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in Large Language Models (LLMs) have shifted from explicit Chain-of-Thought (CoT) reasoning to more efficient latent reasoning, where intermediate thoughts are represented as vectors rather than text. However, latent reasoning can be brittle on challenging, out-of-distribution tasks where robust reasoning is most critical. To overcome these limitations, we introduce Latent Thought Policy Optimization (LTPO), a parameter-free framework that enhances LLM reasoning entirely at test time, without requiring model parameter updates. LTPO treats intermediate latent "thought" vectors as dynamic parameters that are actively optimized for each problem instance. It employs an online policy gradient method guided by an intrinsic, confidence-based reward signal computed directly from the frozen LLM's own output distributions, eliminating the need for external supervision or expensive text generation during optimization. Extensive experiments on five reasoning benchmarks show that LTPO not only matches or surpasses strong baselines on standard tasks but also demonstrates remarkable robustness where others fail. Most notably, on highly challenging AIME benchmarks where existing latent reasoning baselines collapse to near-zero accuracy, LTPO delivers substantial improvements, showcasing a unique capability for complex reasoning.
MIMIC: Mask-Injected Manipulation Video Generation with Interaction Control
Authors: Tianxiao Chen; Jintao Rong; Huajin Chen; Jingya Wang; Tao Zhou; Jiming Chen; Qi Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Embodied intelligence faces a fundamental bottleneck from limited large-scale interaction data. Video generation offers a scalable alternative, but manipulation videos remain particularly challenging, as they require capturing subtle, contact-rich dynamics. Despite recent advances, video diffusion models still struggle to balance semantic understanding with fine-grained visual details, restricting their effectiveness in manipulation scenarios. Our key insight is that reference videos provide rich semantic and motion cues that can effectively drive manipulation video generation. Building on this, we propose MIMIC, a two-stage image-to-video diffusion framework. (1) We first introduce an Interaction-Motion-Aware (IMA) module to fuse visual features from the reference video, producing coherent semantic masks that correspond to the target image. (2) then utilize these masks as semantic control signals to guide the video generation process. Moreover, considering the ambiguity of the motion attribution,  we introduce a Pair Prompt Control mechanism to disentangle object and camera motion by adding the reference video as an additional input. Extensive experiments demonstrate that MIMIC significantly outperforms existing methods, effectively preserves manipulation intent and motion details, even when handling diverse and deformable objects. Our findings underscore the effectiveness of reference-driven semantics for controllable and realistic manipulation video generation.
Reward Model Routing in Alignment
Authors: Xinle Wu; Yao Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning from human or AI feedback (RLHF/RLAIF) has become the standard paradigm for aligning large language models (LLMs). However, most pipelines rely on a single reward model (RM), limiting alignment quality and risking overfitting. Recent work explores RM routing—dynamically selecting an RM from a candidate pool to exploit complementary strengths while maintaining \(O(1)\) RM calls—but existing methods suffer from cold-start and insufficient exploration. We propose {\name}, a hybrid routing framework that combines offline RM strengths learning with online Bayesian selection. In the offline stage, a multi-task router is trained on preference data to estimate per-RM reliability. In the online stage, a Bayesian Thompson sampling router performs per-query RM selection, initializing RM-specific weight vectors with offline embeddings as Gaussian priors and adaptively updating their posteriors with online rewards to adapt to the evolving policy distribution. Extensive experiments on instruction-following (AlpacaEval-2, Arena-Hard, MT-Bench) and reasoning (GSM8K, MMLU) benchmarks show that {\name} consistently outperforms individual RMs, RM ensembling, and existing routing methods.
Sample-efficient and Scalable Exploration in Continuous-Time RL
Authors: Klemens Iten; Lenart Treven; Bhavya Sukhija; Florian Dorfler; Andreas Krause
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning algorithms are typically designed for discrete-time dynamics, even though the underlying real-world control systems are often continuous in time. In this paper, we study the problem of continuous-time reinforcement learning, where the unknown system dynamics are represented using nonlinear ordinary differential equations (ODEs). We leverage probabilistic models, such as Gaussian processes and Bayesian neural networks, to learn an uncertainty-aware model of the underlying ODE. Our algorithm, COMBRL, greedily maximizes a weighted sum of the extrinsic reward and model epistemic uncertainty. This yields a scalable and sample-efficient approach to continuous-time model-based RL. We show that COMBRL achieves sublinear regret in the reward-driven setting, and in the unsupervised RL setting (i.e., without extrinsic rewards), we provide a sample complexity bound. In our experiments, we evaluate COMBRL in both standard and unsupervised RL settings and demonstrate that it scales better, is more sample-efficient than prior methods, and outperforms baselines across several deep RL tasks.
PGRF-Net: A Prototype-Guided Relational Fusion Network for Diagnostic Multivariate Time-Series Anomaly Detection
Authors: Jahoon Jeong; Hyunsoo Yoon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multivariate time-series anomaly detection (MTSAD) faces a critical trade-off between detection performance and model transparency. We propose PGRF-Net, a novel architecture designed to achieve competitive performance while providing structured evidence to support diagnostic insights. At its core, PGRF-Net uses a Multi-Faceted Evidence Extractor that combines prototype learning with the discovery of dynamic relational structures between variables. This extractor generates four distinct types of anomaly evidence: predictive deviation, structural changes in learned variable dependencies, contextual deviation from normal-behavior prototypes, and the magnitude of localized spike events. This evidence is then processed by a Gated Evidence Fusion Network, which learns to weigh each source via data-driven gating. PGRF-Net is trained via a two-stage unsupervised strategy for robust extractor learning and subsequent fusion tuning. Extensive experiments on five public MTSAD benchmarks demonstrate its competitive or superior detection performance. Importantly, by decomposing the final anomaly score into these four evidence types, our model facilitates diagnostic analysis, offering a practical step towards more interpretable, evidence-based MTSAD.
Generalized Spherical Neural Operators: Green’s Function Formulation
Authors: Hao Tang; Hao Chen; Chao Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural operators offer powerful approaches for solving parametric partial differential equations, but extending them to spherical domains remains challenging due to the need to preserve intrinsic geometry while avoiding distortions that break rotational consistency. Existing spherical operators rely on rotational equivariance but often lack the flexibility for real-world complexity. We propose a generalized operator-design framework based on designable Green’s function and its harmonic expansion, establishing a solid operator-theoretic foundation for spherical learning. Based on this, we propose an absolute and relative position-dependent Green’s function that enables flexible balance of equivariance and invariance for real-world modeling. The resulting operator, Green's-function Spherical Neural Operator (GSNO) with a novel spectral learning method, can adapt to non-equivariant systems while retaining spherical geometry, spectral efficiency and grid invariance. To exploit GSNO, we develop SHNet, a hierarchical architecture that combines multi-scale spectral modeling with spherical up-down sampling, enhancing global feature representation. Evaluations on diffusion MRI, shallow water dynamics, and global weather forecasting, GSNO and SHNet consistently outperform state-of-the-art methods. The theoretical and experimental results position GSNO as a principled and generalized framework for spherical operator learning, bridging rigorous theory with real-world complexity. The code is available at: https://github.com/haot2025/GSNO.
Detective SAM:  Adaptive AI-Image Forgery Localization
Authors: Gert Lek; Nicolas van Schaik; Chaoyi Zhu; Pin-Yu Chen; Robert Birke; Lydia Y. Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image forgery localization in the generative AI era poses new challenges, as modern editing pipelines produce photorealistic, semantically coherent manipulations that evade conventional detectors while model capabilities evolve rapidly.
In response, we develop Detective SAM, a framework built on SAM2, a foundation model for image segmentation, that integrates perturbation-driven forensic clues with lightweight feature adapters and a mask adapter to convert forensic clues into forgery masks via automatic prompting.
Moreover, to keep up with the rapidly evolving capabilities of diffusion models, we introduce AutoEditForge: an automated diffusion edit generation pipeline spanning four edit types. This supplies high-quality data to maintain localization accuracy under newly released editors and enables continual fine-tuning for Detective SAM.
Across seven benchmark datasets and seven baselines, Detective SAM delivers stable out-of-distribution performance, averaging 36.99 IoU / 44.19 F1, a 33.67% relative IoU gain over the best baseline. Further, we show that state-of-the-art edits cause localization systems to collapse.
With 500 AutoEditForge samples, Detective SAM quickly adapts and restores performance, enabling practical, low-friction updates as editing models improve.
The pretrained weights, AutoEditForge, and evaluation script are available at https://github.com/Gertlek/DetectiveSAM
Learning for Highly Faithful Explainability
Authors: Yuhan Guo; Lizhong Ding; Shihao Jia; Yanyu Ren; Pengqi Li; Jiarun Fu; Changsheng Li; Ye Yuan; Guoren Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: \textit{Learning to Explain} is a forward-looking paradigm recently proposed in the field of explainable AI, which envisions training explainers capable of producing high-quality explanations for target models efficiently. Although existing studies have made attempts through self-supervised optimization or learning from prior explanation methods, the \textit{Learning to Explain} paradigm still faces three critical challenges: 1) self-supervised objectives rely on assumptions about the target model or task, restricting their generalizability; 2) methods driven by prior explanations struggle to guarantee the quality of the supervisory signals; and 3) depending exclusively on either approach leads to poor convergence or limited explanation quality. To address these challenges, we propose a \textit{faithfulness}-guided amortized explainer that 1) theoretically derives a self-supervised objective free from assumptions about the target model or task, 2) practically generates high-quality supervisory signals by deduplicating and filtering prior explanations, and 3) jointly optimizes both objectives via a dynamic weighting strategy, enabling the amortized explainer to produce more faithful explanations for complex, high-dimensional models. We re-formalize multiple well-validated faithfulness evaluation metrics within a unified notation system and theoretically prove that an explanation mapping can simultaneously achieve optimality across all these metrics. We aggregate prior explanation methods to generate high-quality supervised signals through deduplicating and faithfulness-based filtering. Our amortized explainer leverages dynamic weighting to guide optimization, initially emphasizing pattern consistency with the supervised signals for rapid convergence, and subsequently refining explanation quality by approximating the most faithful explanation mapping. Extensive experiments across various target models and image, text, and tabular tasks demonstrate that the proposed explainer consistently outperforms all prior explanation methods across all faithfulness metrics, highlighting its effectiveness and its potential to offer a systematic solution to the fundamental challenges of the \textit{Learning to Explain} paradigm.
Polynomial, trigonometric, and tropical activations
Authors: Ismail Khalfaoui-Hassani; Stefan Kesselheim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Which functions can be used as activations in deep neural networks? This article explores families of functions based on orthonormal bases, including the Hermite polynomial basis and the Fourier trigonometric basis, as well as a basis resulting from the tropicalization of a polynomial basis. Our study shows that, through a simple variance-preserving initialization and without additional clamping mechanisms, these activations can successfully be used to train deep models, such as GPT-2 for next-token prediction on OpenWebText and ConvNeXt for image classification on ImageNet. Our work addresses the issue of exploding and vanishing activations and gradients, particularly prevalent with polynomial activations, and opens the door for improving the efficiency of large-scale learning tasks. Furthermore, our approach provides insight into the structure of neural networks, revealing that networks with polynomial activations can be interpreted as multivariate polynomial mappings. Finally, using Hermite interpolation, we show that our activations can closely approximate classical ones in pre-trained models by matching both the function and its derivative, making them especially useful for fine-tuning tasks. These activations are available in the torchortho library.
Spectral Attention Steering for Prompt Highlighting
Authors: Weixian Waylon Li; Yuchen Niu; Yongxin Yang; Keshuang Li; Tiejun Ma; Shay B Cohen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Attention steering is an important technique for controlling model focus, enabling capabilities such as prompt highlighting, where the model prioritises user-specified text. However, existing attention steering methods require explicit storage of the full attention matrix, making them incompatible with memory-efficient implementations like FlashAttention. We introduce Spectral Editing Key Amplification (SEKA), a training-free steering method that tackles this by directly editing key embeddings before attention computation. SEKA uses spectral decomposition to steer key embeddings towards latent directions that amplify attention scores for certain tokens. We extend this to Adaptive SEKA (AdaSEKA), a query-adaptive variant that uses a training-free routing mechanism to dynamically combine multiple expert subspaces based on the prompt's semantic intent. Our experiments show both methods significantly outperform strong baselines on standard steering benchmarks while adding much lower latency and memory overhead, in compatibility with optimised attention.
FRABench and UFEval: Unified Fine-grained Evaluation with Task and Aspect Generalization
Authors: Shibo Hong; Jiahao Ying; Haiyuan Liang; Mengdi Zhang; Jun Kuang; Jiazheng Zhang; Yixin Cao
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Evaluating open-ended outputs of Multimodal Large Language Models has become a bottleneck as model capabilities, task diversity, and modality rapidly expand. Existing ``MLLM-as-a-Judge'' evaluators, though promising, remain constrained to specific tasks and aspects (i.e., specific evaluation criteria such as fluency for text and image quality for images). In this paper, we argue that, on one hand, based on the interconnected nature of criteria, learning specific aspects can generalize to unseen aspects; on the other hand, jointly learning to assess multiple visual criteria and tasks may foster a synergistic effect. To this end, we propose UFEval, the first unified fine-grained evaluator with task and aspect generalization for four evaluation tasks --- Natural Language Generation, Image Understanding, Image Generation, and Interleaved Text-and-Image Generation. However, training such a unified evaluator is hindered by the lack of a large-scale, multi-modal, and aspect-level resource. To address this gap, we introduce FRABench, a comprehensive fine-grained evaluation dataset. Specifically, (1) We first construct a hierarchical aspect taxonomy encompassing 112 distinct aspects across the aforementioned four tasks. (2) Based on this taxonomy, we create FRABench, comprising 60.4k pairwise samples with 325k evaluation labels obtained from a combination of human and GPT-4o annotations. (3) Finally, leveraging FRABench, we develop UFEval, a unified fine-grained evaluator. Experiments show that learning on specific aspects enables UFEval to generalize to unseen aspects, and joint learning to assess diverse visual tasks and aspects can lead to substantial mutual benefits.