GenAI4ELab/papercli-papers huggingface dataset:
Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models
Authors: Anindya Sundar Das; Kangjie Chen; Monowar Bhuyan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when fine-tuned on large, domain-relevant datasets. However, they remain vulnerable to backdoor attacks, where adversaries embed malicious behaviors using trigger patterns in the training data. These triggers remain dormant during normal usage, but, when activated, can cause targeted misclassifications. In this work, we investigate the internal behavior of backdoored pre-trained encoder-based language models, focusing on the consistent shift in attention and gradient attribution when processing poisoned inputs; where the trigger token dominates both attention and gradient signals, overriding the surrounding context. We propose an inference-time defense that constructs anomaly scores by combining token-level attention and gradient information. Extensive experiments on text classification tasks across diverse backdoor attack scenarios demonstrate that our method significantly reduces attack success rates compared to existing baselines. Furthermore, we provide an interpretability-driven analysis of the scoring mechanism, shedding light on trigger localization and the robustness of the proposed defense.
RefTool: Reference-Guided Tool Creation for Knowledge-Intensive Reasoning
Authors: Xiao Liu; Da Yin; Zirui Wu; Yansong Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) can enhance their reasoning capabilities by using external tools. However, many tasks lack predefined tools. Prior works have explored instructing LLMs to generate tools on their own, but such approaches depend heavily on internal knowledge and struggle when tasks fall outside the model’s knowledge scope. To address this limitation, we propose RefTool, a reference-guided framework for automatic tool creation that leverages external materials, such as textbooks and knowledge snippets. RefTool consists of two modules: (1) tool creation, where LLMs generate executable tools from reference content, validate them using illustrative examples, and organize them hierarchically into a toolbox; and (2) tool utilization, where LLMs navigate the toolbox structure to select and apply the appropriate tools to solve problems. Experiments on causality, physics, and chemistry benchmarks demonstrate that RefTool outperforms existing tool-creation and domain-specific reasoning methods by 12.3% on average accuracy, while being cost-efficient and broadly generalizable to non-scientific tasks, e.g., extremely low-resource language translation. Analyses reveal that grounding tool creation in references produces accurate and faithful tools, and that the hierarchical structure facilitates effective tool selection. RefTool enables LLMs to overcome internal knowledge limitations, advancing generalizable reasoning in knowledge-intensive domains.
Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
Authors: Yongchang Hao; Lili Mou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft models. SpS strictly enforces the generated distribution to match that of the verifier LLM. This is unnecessarily restrictive as slight variations of the verifier's distribution, such as sampling with top-$k$ or temperature, would also be acceptable. Typical acceptance sampling (TAS) alleviates this issue by accepting more tokens using entropy-based heuristics. However, this approach distorts the verifier distribution, potentially degrading output quality when the verifier encodes critical information. In this work, we formalize the speculative sampling algorithm through the lens of constrained optimization. Based on this formulation, we propose Cactus (**c**onstrained **ac**cep**t**ance spec**u**lative **s**ampling), a method that guarantees controlled divergence from the verifier distribution and increasing acceptance rates. Empirical results across a wide range of benchmarks confirm the effectiveness of our approach.
VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models
Authors: Weiye Xu; Jiahao Wang; Weiyun Wang; Zhe Chen; Wengang Zhou; Aijun Yang; Lewei Lu; Houqiang Li; Xiaohua Wang; Xizhou Zhu; Wenhai Wang; Jifeng Dai; Jinguo Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Visual reasoning is a core component of human intelligence and a critical capability for advanced multimodal models. Yet current reasoning evaluations of multimodal large language models (MLLMs) often rely on text descriptions and allow language-based reasoning shortcuts, failing to measure genuine vision-centric reasoning. To address this, we introduce VisuLogic: a benchmark of 1,000 human-verified problems across six  categories (e.g., quantitative shifts, spatial relations, attribute comparisons). These various types of questions can be evaluated  to assess the visual reasoning capabilities of MLLMs from multiple perspectives.  We evaluate leading MLLMs on this benchmark and analyze their results to identify common failure modes. Most models score below 30\% accuracy—only slightly above the 25\% random baseline and far below the 51.4\% achieved by humans—revealing significant gaps in visual reasoning.
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
Authors: Silin Gao; Antoine Bosselut; Samy Bengio; Emmanuel Abbe
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in grade school math (GSM)  reasoning. In particular, they tend to experience performance drops when faced with distribution shifts, such as changes to numerical or nominal variables, or insertions of distracting clauses. A possible strategy to address this involves generating synthetic data to further "instantiate" reasoning problems on potential variations. In this work, we instead focus on the strategy of ``abstracting'' reasoning problems. This not only helps counteract distribution shifts but also facilitates the connection to symbolic tools for deriving solutions. Focusing on GSM, we find that this abstraction process is better acquired through reinforcement learning (RL) than just supervised fine-tuning, which often fails to produce faithful abstractions. Our method, AbstRaL---which promotes abstract reasoning in LLMs using RL on granular abstraction data---significantly mitigates performance degradation on recent GSM perturbation benchmarks. Besides, improving GSM robustness via AbstRaL is shown to also implicitly benefit LLMs' capabilities on OOD mathematical and general reasoning tasks, indicating that abstract thinking broadly enables better generalizability.
A Statistical Benchmark for Diffusion-Posterior-Sampling Algorithms
Authors: Martin Zach; Youssef Haouchat; Michael Unser
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a statistical benchmark for diffusion-posterior-sampling (DPS) algorithms in linear inverse problems.
Our test signals are discretized Lévy processes whose posteriors admit efficient Gibbs methods.
These Gibbs methods provide gold-standard posterior samples for direct, distribution-level comparisons with DPS algorithms.
They can also sample the denoising posteriors in the reverse diffusion, which enables the arbitrary-precision Monte Carlo estimation of various objects that may be needed in the DPS algorithms, such as the expectation or the covariance of the denoising posteriors.
In turn, this can be used to isolate algorithmic errors from the errors due to learned components.
We instantiate the benchmark with the minimum-mean-squared-error optimality gap and posterior-coverage tests and evaluate popular algorithms on the inverse problems of denoising, deconvolution, imputation, and reconstruction from partial Fourier measurements.
We release the benchmark code at https://github.com/zacmar/dps-benchmark and invite the community to contribute and report results.
Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks
Authors: Xinxi Lyu; Michael Duan; Rulin Shao; Pang Wei Koh; Sewon Min
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval augmentation has primarily been studied in limited settings, such as factoid question answering; more challenging, reasoning-intensive benchmarks have seen limited success from minimal RAG. In this work, we challenge this prevailing view on a set of established, reasoning-intensive benchmarks: MMLU, MMLU Pro, AGI Eval, GPQA, and MATH. We identify a key missing component in prior work: a usable, web-scale datastore aligned with the breadth of pretraining data. To this end, we introduce CompactDS: a diverse, high-quality, web-scale datastore that achieves high retrieval accuracy and subsecond latency on a single-node deployment, making it suitable for academic use. Its core design combines a compact set of high-quality, diverse data sources with in-memory approximate nearest neighbor (ANN) retrieval and on-disk exact search. Using CompactDS, a minimal RAG pipeline achieves consistent accuracy improvements across all benchmarks and model sizes (8B--70B), with relative gains of 11% on MMLU, 34% on MMLU Pro, 26% on GPQA, and 14% on MATH. No single data source suffices alone, highlighting the importance of diversity of sources (web crawls, curated math, academic papers, textbooks), and a combination of ANN and exact search is shown to be critical for balancing usability and accuracy. Finally, we show that our in-house datastore even outperforms commercial search engines like Google Search. We release CompactDS and our retrieval pipeline as a fully reproducible alternative to commercial search, supporting future research exploring retrieval-based AI systems.
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Authors: Giacomo Turri; Luigi Bonati; Kai Zhu; Massimiliano Pontil; Pietro Novelli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce an end-to-end approach to learn the evolution operators of large-scale non-linear dynamical systems, such as those describing complex natural phenomena. Evolution operators are particularly well-suited for analyzing systems that exhibit spatio-temporal patterns and have become a key analytical tool across various scientific communities. As terabyte-scale weather datasets and simulation tools capable of running millions of molecular dynamics steps per day are becoming commodities, our approach provides an effective tool to make sense of them from a data-driven perspective. The core of it lies in a remarkable connection between self-supervised representation learning methods and the recently established learning theory of evolution operators. We deploy our approach across multiple scientific domains: explaining the folding dynamics of small proteins, the binding process of drug-like molecules in host sites, and autonomously finding patterns in climate data. Our code is available open-source at: https://github.com/pietronvll/encoderops.
Minimax Optimal Adversarial Reinforcement Learning
Authors: Yudan Wang; Kaiyi Ji; Ming Shi; Shaofeng Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Consider episodic Markov decision processes (MDPs) with adversarially chosen transition kernels, where the transition kernel is adversarially chosen at each episode. Prior works have established regret upper bounds of $\widetilde{\mathcal{O}}(\sqrt{T} + C^P)$, where $T$ is the number of episodes and $C^P$ quantifies the degree of adversarial change in the transition dynamics. This regret bound may scale as large as $\mathcal{O}(T)$, leading to a linear regret. This raises a fundamental question: *Can sublinear regret be achieved under fully adversarial transition kernels?*  We answer this question affirmatively. First, we show that the optimal policy for MDPs with adversarial transition kernels must be history-dependent. We then design an algorithm of Adversarial Dynamics Follow-the-Regularized-Leader (AD-FTRL), and prove that it achieves a sublinear regret of $\mathcal{O}(\sqrt{(|\mathcal{S}||\mathcal{A}|)^K T})$, 
where  $K$ is the horizon length, $|\mathcal{S}|$ is the number of states, and $|\mathcal{A}|$ is the number of actions. Such a regret cannot be achieved by simply solving this problem as a contextual bandit. We further construct a hard MDP instance and prove a matching lower bound on the regret, which thereby demonstrates the **minimax optimality** of our algorithm.
Verifying Chain-of-Thought Reasoning via Its Computational Graph
Authors: Zheng Zhao; Yeskendir Koishekenov; Xianjun Yang; Naila Murray; Nicola Cancedda
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into \textit{why} a computation fails. We introduce a white-box method: \textbf{Circuit-based Reasoning Verification (CRV)}. We hypothesize that attribution graphs of correct CoT steps, viewed as \textit{execution traces} of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier on structural features of these graphs, we show that these traces contain a powerful signal of reasoning errors. Our white-box approach yields novel scientific insights unattainable by other methods. (1) We demonstrate that structural signatures of error are highly predictive, establishing the viability of verifying reasoning directly via its computational graph. (2) We find these signatures to be highly domain-specific, revealing that failures in different reasoning tasks manifest as distinct computational patterns. (3) We provide evidence that these signatures are not merely correlational; by using our analysis to guide targeted interventions on individual transcoder features, we successfully correct the model's faulty reasoning. Our work shows that, by scrutinizing a model's computational process, we can move from simple error detection to a deeper, causal understanding of LLM reasoning.
D&R: Recovery-based AI-Generated Text Detection via a Single Black-box LLM Call
Authors: Yuxia Sun; Ran Zhang; Aoxiang Sun; Xu Li; Zitao Liu; Jingcai Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) generate increasingly human-like text, raising concerns about misinformation and authenticity. Detecting AI-generated text remains challenging: existing methods often underperform, especially on short texts, require probability access unavailable in real-world black-box settings, incur high costs from multiple calls, or fail to generalize across models. 
We propose Disrupt-and-Recover (D\&R), a recovery-based detection framework grounded in posterior concentration. D\&R disrupts text via model-free Within-Chunk Shuffling, performs a single black-box LLM recovery, and measures semantic–structural recovery similarity as a proxy for concentration. This design ensures efficiency, black-box practicality, and is theoretically supported under the concentration assumption. Extensive experiments across four datasets and six source models show that D\&R achieves state-of-the-art performance, with AUROC 0.96 on long texts and 0.87 on short texts, surpassing the strongest baseline by +0.08 and +0.14. D\&R further remains robust under source–recovery mismatch and model variation. Our code and data is available at https://github.com/Yuxia-Sun/D-R.
DynaGuard: A Dynamic Guardian Model With User-Defined Policies
Authors: Monte Hoover; Vatsal Baherwani; Neel Jain; Khalid Saifullah; Joseph James Vincent; Chirag Jain; Melissa Kazemi Rad; C. Bayan Bruss; Ashwinee Panda; Tom Goldstein
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard guardian models are limited to predefined, static harm categories, we introduce DynaGuard, a suite of dynamic guardian models offering novel flexibility by evaluating text based on user-defined policies, and DynaBench, a dataset for training and evaluating dynamic guardian models. Our models provide both rapid detection of policy violations and a chain-of-thought reasoning option that articulate and justify model outputs. Critically, DynaGuard not only surpasses static models in detection accuracy on traditional safety categories, but is competitive with frontier reasoning models on free-form policy violations, all in a fraction of the time. This makes DynaGuard an critical tool for language model guardrails.
How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining
Authors: Kairong Luo; Zhenbo Sun; Haodong Wen; Xinyu Shi; Jiarui Cui; Chenyi Dang; Kaifeng Lyu; Wenguang Chen
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even after sophisticated data curation. A natural approach to better leverage high-quality data is curriculum-based pretraining, where the model is trained on data sorted in ascending order of quality as determined by a quality metric. However, prior studies have reported limited improvements from such curriculum-based pretraining strategies. This work identifies a critical factor constraining these methods: the incompatibility between the ascending data quality order and the decaying learning rate (LR) schedule. We find that while curriculum-based training substantially outperforms random shuffling when using a constant LR, its advantage diminishes under standard LR decay schedules. Our experiments show this incompatibility can be mitigated by two simple strategies: (1) employing a more moderate LR decay schedule, where the final LR is only moderately smaller than the peak LR, and (2) replacing LR decay with model averaging, i.e., computing a weighted average of the final few checkpoints. By combining these strategies, we improve the average score on a suite of standard benchmarks by 1.64% over random shuffling, without additional data refinement. Validated on 1.5B-parameter models trained over 30B tokens with various data-quality metrics, our findings call for a re-evaluation of curriculum-based LLM pretraining and underscore the potential of co-designing data curricula with optimization methods.
Memory-Statistics Tradeoff in Continual Learning with Structural Regularization
Authors: Haoran Li; Jingfeng Wu; Vladimir Braverman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the statistical performance of a continual learning problem with two linear regression tasks in a well-specified random design setting. We consider a structural regularization algorithm that incorporates a generalized $\ell_2$-regularization tailored to the Hessian of the previous task for mitigating catastrophic forgetting. We establish upper and lower bounds on the joint excess risk for this algorithm. Our analysis reveals a fundamental trade-off between memory complexity and statistical efficiency, where memory complexity is measured by the number of vectors needed to define the structural regularization. Specifically, increasing the number of vectors in structural regularization leads to a worse memory complexity but an improved excess risk, and vice versa. Furthermore, our theory suggests that naive continual learning without regularization suffers from catastrophic forgetting, while structural regularization mitigates this issue. Notably, structural regularization achieves comparable performance to joint training with access to both tasks simultaneously. These results highlight the critical role of curvature-aware regularization for continual learning.
Probabilistic Kernel Function for Fast Angle Testing
Authors: Kejing Lu; Chuan Xiao; Yoshiharu Ishikawa
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: In this paper, we study the angle testing problem in the context of similarity search in high-dimensional Euclidean spaces and propose two projection-based probabilistic kernel functions, one designed for angle comparison and the other for angle thresholding. Unlike existing approaches that rely on random projection vectors drawn from Gaussian distributions, our approach leverages reference angles and adopts a deterministic structure for the projection vectors. Notably, our kernel functions do not require asymptotic assumptions, such as the number of projection vectors tending to infinity, and can be theoretically and experimentally shown to outperform Gaussian-distribution-based kernel functions. We apply the proposed kernel function to Approximate Nearest Neighbor Search (ANNS) and demonstrate that our approach achieves a 2.5x--3x higher query-per-second (QPS) throughput compared to the widely-used graph-based search algorithm HNSW. Our code and data are available at https://github.com/KejingLu-810/KS.
Bayesian Attention Mechanism: A Probabilistic Framework for Positional Encoding and Context Length Extrapolation
Authors: Arthur S. Bianchessi; Yasmin C. Aguirre; Rodrigo C. Barros; Lucas S. Kupssinskü
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer-based language models rely on positional encoding (PE) to handle token order and support context length extrapolation. 
However, existing PE methods lack theoretical clarity and rely on limited evaluation metrics to substantiate their extrapolation claims. 
We propose the Bayesian Attention Mechanism (BAM), a theoretical framework that formulates positional encoding as a prior within a probabilistic model. 
BAM unifies existing methods (e.g., NoPE and ALiBi) and motivates a new Generalized Gaussian positional prior that substantially improves long-context generalization. 
Empirically, BAM enables accurate information retrieval at $500\times$ the training context length, outperforming previous state-of-the-art context length generalization by more than $25\times$ in retrieval accuracy while maintaining comparable perplexity and introducing minimal additional parameters.
Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
Authors: Zhe Li; Bicheng Ying; Zidong Liu; Chaosheng Dong; Haibo Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Zeroth-order (ZO) optimization enables dimension-free communication in federated learning (FL), making it attractive for fine-tuning of large language models (LLMs) due to significant communication savings. However, existing ZO-FL methods largely overlook curvature information, despite its well-established benefits for convergence acceleration. To address this, we propose **HiSo**, a Hessian-informed ZO federated optimization method that accelerates convergence by leveraging global diagonal Hessian approximations, while strictly preserving scalar-only communication **without transmitting any second-order information**. Theoretically, for non-convex functions, we show that HiSo can achieve an accelerated convergence rate that is independent of the Lipschitz constant $L$ and model dimension $d$ under some Hessian approximation assumptions, offering a plausible explanation for the observed phenomenon of ZO convergence being much faster than its worst-case $O(d)$-bound. Empirically, across diverse LLM fine-tuning benchmarks, HiSo delivers a 1$\sim$5× speedup in communication rounds over existing state-of-the-art ZO-FL baselines. This superior convergence not only cuts communication costs but also provides strong empirical evidence that Hessian information acts as an effective accelerator in federated ZO optimization settings.