OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Attacks in Weight-Space Classifiers
Venue: arXiv cs.LG 2026
Year: 2026

Implicit Neural Representations (INRs) have been recently garnering increasing interest in various research fields, mainly due to their ability to represent large, complex data in a compact, continuous manner. Past work further showed that numerous popular downstream tasks can be performed directly in the INR parameter-space. Doing so can substantially reduce the computational resources required to process the represented data in their native domain. A major difficulty in using modern machine-learning approaches, is their high susceptibility to adversarial attacks, which have been shown to greatly limit the reliability and applicability of such methods in a wide range of settings. In this work, we perform an in-depth security analysis of the behavior of weight-space classifiers under adversarial attacks. Our study reveals that parameter-space models trained for classification exhibit increased robustness to standard white-box adversarial attacks compared to standard classifiers that operate in the original signal space. This is achieved without the need of any robust training. We source this robust behavior to the phenomenon of gradient-obfuscation promoted during the INR optimization process, and pinpoint the limitations of this robustness under alternative adversarial approaches. To support our claims, we develop a novel suite of adversarial attacks targeting parameter-space classifiers, and furthermore analyze practical considerations of such attacks.
On Adversarial Attacks In Acoustic Drone Localization
Venue: arXiv cs.SD 2026
Year: 2026

Multi-rotor aerial autonomous vehicles (MAVs, more widely known as "drones") have been generating increased interest in recent years due to their growing applicability in a vast and diverse range of fields (e.g., agriculture, commercial delivery, search and rescue). The sensitivity of visual-based methods to lighting conditions and occlusions had prompted growing study of navigation reliant on other modalities, such as acoustic sensing. A major concern in using drones in scale for tasks in non-controlled environments is the potential threat of adversarial attacks over their navigational systems, exposing users to mission-critical failures, security breaches, and compromised safety outcomes that can endanger operators and bystanders. While previous work shows impressive progress in acoustic-based drone localization, prior research in adversarial attacks over drone navigation only addresses visual sensing-based systems. In this work, we aim to compensate for this gap by supplying a comprehensive analysis of the effect of PGD adversarial attacks over acoustic drone localization. We furthermore develop an algorithm for adversarial perturbation recovery, capable of markedly diminishing the affect of such attacks in our setting.
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
Venue: arXiv cs.LG 2026
Year: 2026

There is strong empirical evidence that the state-of-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small. Prior methods to mitigate the memorization problem often lead to a decrease in image quality. Is it possible to obtain strong and creative generative models, i.e., models that achieve high generation quality and low memorization? Despite the current pessimistic landscape of results, we make significant progress in pushing the trade-off between fidelity and memorization. We first provide theoretical evidence that memorization in diffusion models is only necessary for denoising problems at low noise scales (usually used in generating high-frequency details). Using this theoretical insight, we propose a simple, principled method to train the diffusion models using noisy data at large noise scales. We show that our method significantly reduces memorization without decreasing the image quality, for both text-conditional and unconditional models and for a variety of data availability settings.
KVCrush: Key value cache size-reduction using similarity in head-behaviour
Venue: arXiv cs.CL 2026
Year: 2026

Key-value (KV) caching has emerged as a crucial optimization technique for accelerating inference in large language models (LLMs). By allowing the attention operation to scale linearly rather than quadratically with the total sequence length, KV caching significantly enhances generation throughput. However, due to large context lengths in the modern LLMs, the memory footprint of the KV is a huge bottleneck for model deployment directly impacting the model's batch size, hindering its ability to deliver high-throughput. Existing research addresses this challenge using several techniques, such as discarding low-attention tokens, quantization, and matrix approximation which typically lead to a negative impact on the model accuracy. In this paper, We propose KVCrush technology which can be combined with many KV compression technologies to improve the model accuracy at a much smaller memory. KVCrush provides an alternate representation scheme for key-value states, along with a low-overhead token pruning algorithm that accounts for the token distribution in the KV cache, which in turn allows for a a smaller footprint while maintaining the accuracy of the model. Based on our results, KVCrush reduces LongBench KV Cache size by 4x with less than 1% accuracy drop and achieves state-of-the-art average accuracy with minimal overhead, incurring less than 0.5% total inference latency. KVCrush not only outperforms the accuracy of state-of-the-art importance-based token retention schemes but is also compatible with typical practical LLM deployments using KV cache paging schemes such as vLLM and mixed precision quantization.
Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair
Venue: arXiv cs.SE 2026
Year: 2026

Smart contracts, predominantly written in Solidity and deployed on blockchains such as Ethereum, are immutable after deployment, making functional correctness critical. However, existing evaluations of Solidity code generation rely largely on surface-level metrics (e.g., BLEU, CrystalBLEU) or manual inspection, which correlate poorly with functional correctness. In contrast to Python, Solidity lacks large-scale, execution-based benchmarks, limiting systematic evaluation of large language models for smart contract development. We introduce SolBench, a comprehensive benchmark and automated testing pipeline for Solidity that emphasizes functional correctness via differential fuzzing. SolBench consists of 28825 functions extracted from 7604 real-world smart contracts collected from Etherscan (genesis-2024), spanning ten application domains. We benchmark 14 diverse LLMs, covering open and closed models, 1.3B-671B parameters, and both general-purpose and code-specialized architectures. The dominant failure mode is missing critical intra-contract information, such as state variables and type definitions. Providing full-contract context improves accuracy but incurs prohibitive inference costs. To address this, we propose Retrieval-Augmented Repair (RAR), a cost-effective framework that integrates execution feedback into code repair. RAR uses compiler and runtime error messages to retrieve only the minimal contract snippets needed to correct a target function, avoiding full-context inference. This significantly reduces input length while improving functional correctness. We further analyze retrieval and repair strategies within RAR, demonstrating consistent gains in accuracy and efficiency. SolBench and RAR enable principled, execution-based evaluation and economical improvement of Solidity code generation. Dataset and code are publicly available at https://github.com/ZaoyuChen/SolBench.
LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains
Venue: arXiv cs.RO 2026
Year: 2026

Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost. This is especially crucial for real-world applications where robots need to navigate diverse terrains in outdoor environments with limited opportunities for recharging or refueling. Despite its practical importance, cost-efficient path planning across heterogeneous terrains has received relatively limited attention in prior work. In this paper, we propose LLM-Advisor, a prompt-based, planner-agnostic framework that leverages large language models (LLMs) as non-decisive post-processing advisors for cost refinement, without modifying the underlying planner. While we observe that LLMs may occasionally produce implausible suggestions, we introduce two effective hallucination-mitigation strategies. We further introduce two datasets, MultiTerraPath and RUGD_v2, for systematic evaluation of cost-efficient path planning. Extensive experiments reveal that state-of-the-art LLMs, including GPT-4o, GPT-4-turbo, Gemini-2.5-Flash, and Claude-Opus-4, perform poorly in zero-shot terrain-aware path planning, highlighting their limited spatial reasoning capability. In contrast, the proposed LLM-Advisor (with GPT-4o) improves cost efficiency for 72.37% of A*-planned paths, 69.47% of RRT*-planned paths, and 78.70% of LLM-A*-planned paths. On the MultiTerraPath dataset, LLM-Advisor demonstrates stronger performance on the hard subset, further validating its applicability to real-world scenarios.
Diffusion Stabilizer Policy for Automated Surgical Robot Manipulations
Venue: arXiv cs.RO 2026
Year: 2026

Intelligent surgical robots have the potential to revolutionize clinical practice by enabling more precise and automated surgical procedures. However, the automation of such robot for surgical tasks remains under-explored compared to recent advancements in solving household manipulation tasks. These successes have been largely driven by (1) advanced models, such as transformers and diffusion models, and (2) large-scale data utilization. Aiming to extend these successes to the domain of surgical robotics, we propose a diffusion-based policy learning framework, called Diffusion Stabilizer Policy (DSP), which enables training with imperfect or even failed trajectories. Our approach consists of two stages: first, we train the diffusion stabilizer policy using only clean data. Then, the policy is continuously updated using a mixture of clean and perturbed data, with filtering based on the prediction error on actions. Comprehensive experiments conducted in various surgical environments demonstrate the superior performance of our method in perturbation-free settings and its robustness when handling perturbed demonstrations.
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
Venue: arXiv cs.LG 2026
Year: 2026

Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are increasingly replaced by general-purpose architectures trained on vast datasets. In line with this trend, several recent studies have questioned the necessity of architectural features commonly found in MD models, such as built-in rotational equivariance or energy conservation. In this work, we contribute to the ongoing discussion by evaluating the performance of an MD model with as few specialized architectural features as possible. We present a recipe for MD using an Edge Transformer, an ``off-the-shelf'' transformer architecture that has been minimally modified for the MD domain, termed MD-ET. Our model implements neither built-in equivariance nor energy conservation. We use a simple supervised pre-training scheme on $\sim$30 million molecular structures from the QCML database. Using this ``off-the-shelf'' approach, we show state-of-the-art results on several benchmarks after fine-tuning for a small number of steps. Additionally, we examine the effects of being only approximately equivariant and energy conserving for MD simulations, proposing a novel method for distinguishing the errors resulting from non-equivariance from other sources of inaccuracies like numerical rounding errors. While our model exhibits runaway energy increases on larger structures, we show approximately energy-conserving NVE simulations for a range of small structures.
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Venue: arXiv cs.LG 2026
Year: 2026

In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced datasets in imitation learning by leveraging Vector Quantized Variational Autoencoders (VQ-VAEs). In this way, we can get structured and interpretable data representations, which help to reveal meaningful patterns in the data. These patterns are used to group the data into clusters, with each sample being assigned a cluster ID. The cluster IDs are then used to re-balance the dataset, ensuring that rare yet valuable samples receive higher priority during training. We evaluate our method through closed-loop experiments in the CARLA simulator. The results on Bench2Drive scenarios demonstrate the effectiveness of CAPS in enhancing model generalization, with substantial improvements in both driving score and success rate.
vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding
Venue: arXiv cs.RO 2026
Year: 2026

Current Visual Simultaneous Localization and Mapping (VSLAM) systems often struggle to create maps that are both semantically rich and easily interpretable. While incorporating semantic scene knowledge aids in building richer maps with contextual associations among mapped objects, representing them in structured formats, such as scene graphs, has not been widely addressed, resulting in complex map comprehension and limited scalability. This paper introduces vS-Graphs, a novel real-time VSLAM framework that integrates vision-based scene understanding with map reconstruction and comprehensible graph-based representation. The framework infers structural elements (i.e., rooms and floors) from detected building components (i.e., walls and ground surfaces) and incorporates them into optimizable 3D scene graphs. This solution enhances the reconstructed map's semantic richness, comprehensibility, and localization accuracy. Extensive experiments on standard benchmarks and real-world datasets demonstrate that vS-Graphs achieves an average of 15.22% accuracy gain across all tested datasets compared to state-of-the-art VSLAM methods. Furthermore, the proposed framework achieves environment-driven semantic entity detection accuracy comparable to that of precise LiDAR-based frameworks, using only visual features. The code is publicly available at https://github.com/snt-arg/visual_sgraphs and is actively being improved. Moreover, a web page containing more media and evaluation outcomes is available on https://snt-arg.github.io/vsgraphs-results/.
$\texttt{SEM-CTRL}$: Semantically Controlled Decoding
Venue: arXiv cs.CL 2026
Year: 2026

Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for real-world deployment. In this paper, we introduce \texttt{SEM-CTRL}, a unified approach that allows for enforcing rich context-sensitive constraints, and task and instance specific semantics directly on the LLM decoder. Our approach integrates token-level MCTS which is guided by specific syntactic and semantic constraints. The constraints over desired outputs are expressed using Answer Set Grammars, which is a logic-based formalism that generalizes context sensitive grammars while incorporating background knowledge to represent task-specific semantics. We show that our approach helps guarantee valid completions for any off-the-shelf LLM without the need for fine-tuning. We evaluate \texttt{SEM-CTRL} on a range of tasks, including synthetic grammar synthesis, combinatorial reasoning, JSON parsing, and planning. Our experimental results demonstrate that \texttt{SEM-CTRL} allows even small pre-trained LLMs to efficiently outperform larger variants and state-of-the-art reasoning models (e.g., \textit{o4-mini}) while simultaneously guaranteeing semantic validity.
Learning a Game by Paying the Agents
Venue: arXiv cs.GT 2026
Year: 2026

We study the problem of learning the utility functions of no-regret learning agents in a repeated normal-form game. Differing from most prior literature, we introduce a principal with the power to observe the agents playing the game, send agents signals, and give agents payments as a function of their actions. We show that the principal can, using a number of rounds polynomial in the size of the game, learn the utility functions of all agents to any desired precision $ε> 0$, for any no-regret learning algorithms of the agents. Our main technique is to formulate a zero-sum game between the principal and the agents, where the principal chooses strategies among the set of all payment functions to minimize the agent's payoff. Finally, we discuss implications for the problem of steering agents. We introduce, using our utility-learning algorithm as a subroutine, the first algorithm for steering arbitrary no-regret learning agents to a desired equilibrium without prior knowledge of their utility functions.
Latent Domain Modeling Improves Robustness to Geographic Shifts
Venue: arXiv cs.LG 2026
Year: 2026

Geographic distribution shift arises when the distribution of locations on Earth in a training dataset is different from what is seen at inference time. Using standard empirical risk minimization (ERM) in this setting can lead to uneven generalization across different spatially-determined groups of interest such as continents or biomes. The most common approaches to tackling geographic distribution shift apply domain adaptation methods using discrete group labels, ignoring geographic coordinates that are often available as metadata. On the other hand, modeling methods that integrate geographic coordinates have been shown to improve overall performance, but their impact on geographic domain generalization has not been studied. In this work, we propose a general modeling framework for improving robustness to geographic distribution shift. The key idea is to model continuous, latent domain assignment using location encoders and to condition the main task predictor on the jointly-trained latents. On four diverse geo-tagged image datasets with different group splits, we show that instances of our framework achieve significant improvements in worst-group performance compared to existing domain adaptation and location-aware modeling methods. In particular, we achieve new state-of-the-art results on two datasets from the WILDS benchmark.
PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
Venue: arXiv cs.RO 2026
Year: 2026

Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with increased chunking sizes. This reduces the inference efficiency. To tackle this problem, we propose PD-VLA, the first parallel decoding framework for VLA models integrated with action chunking. Our framework reformulates autoregressive decoding as a nonlinear system solved by parallel fixed-point iterations. This approach preserves model performance with mathematical guarantees while significantly improving decoding speed. In addition, it enables training-free acceleration without architectural changes, as well as seamless synergy with existing acceleration techniques. Extensive simulations validate that our PD-VLA maintains competitive success rates while achieving 2.52 times execution frequency on manipulators (with 7 degrees of freedom) compared with the fundamental VLA model. Furthermore, we experimentally identify the most effective settings for acceleration. Finally, real-world experiments validate its high applicability across different tasks.
Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization
Venue: arXiv cs.LG 2026
Year: 2026

Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the process effectively removes specified data without compromising the model's overall performance on the remaining dataset. Many existing machine unlearning methods address this challenge by carefully balancing gradient ascent on the `unlearn' data with the gradient descent on a `retain' set that represents the training data. However, in many cases the training dataset is not fully available when we wish to unlearn some concepts, because models are released without their training datasets, and one may only have access to a $\textit{small part of a training set}$. Here, we propose OrthoGrad, a novel approach that mitigates interference between the unlearn set and a small retain set rather than competing ascent and descent processes. Our method projects the gradient of the unlearn set onto the subspace orthogonal to all gradients in the retain batch, effectively avoiding any gradient interference. We demonstrate the effectiveness of OrthoGrad on multiple machine unlearning benchmarks, including automatic speech recognition, outperforming competing methods.
Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers. We propose a novel Reinforcement Learning approach that allows to directly fine-tune LLMs to express calibrated confidence estimates alongside their answers to factual questions. Our method optimizes a reward based on the logarithmic scoring rule, explicitly penalizing both over- and under-confidence. This encourages the model to align its confidence estimates with the actual predictive accuracy. The optimal policy under our reward design would result in perfectly calibrated confidence expressions. Unlike prior approaches that decouple confidence estimation from response generation, our method integrates confidence calibration seamlessly into the generative process of the LLM. Empirically, we demonstrate that models trained with our approach exhibit substantially improved calibration and generalize to unseen tasks without further fine-tuning, suggesting the emergence of general confidence awareness.