OpenRaiser/Intern-Atlas huggingface dataset:
Scalable and Performant Data Loading
Venue: arXiv cs.DC 2026
Year: 2026

We present SPDL (Scalable and Performant Data Loading), an open-source, framework-agnostic library designed for efficiently loading array data to GPU. Data loading is often a bottleneck in AI applications, and is challenging to optimize because it requires coordination of network calls, CPU-bound tasks, and GPU device transfer. On top of that, Python's GIL (Global Interpreter Lock) makes it difficult to gain performance improvement from multi-threading. We found that when data preprocessing functions release the GIL entirely, it is possible to execute them concurrently in a thread pool, thereby improving the workflow performance. Our benchmark shows that compared to the PyTorch DataLoader, SPDL can iterate through the ImageNet dataset 74% faster while using 38% less CPU and 50GB less memory. When training ViT-B/16 model, SPDL can send data to the GPU at a speed that does not starve the training. Additionally, when using SPDL on Python 3.13t, without changing any code, the throughput is further by improved by 33%, thanks to the disabled GIL. SPDL can improve the performance of current AI model training, and receives further performance improvements when Free-Threaded Python is adopted in production systems. SPDL is available at https://github.com/facebookresearch/spdl.
Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors
Venue: arXiv cs.LG 2026
Year: 2026

Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessive refusals, while permissive models risk generating harmful content. Existing approaches, such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), attempt to balance these trade-offs but suffer from performance conflicts, limited controllability, and poor extendability. To address these issues, we propose Preference Vector, a novel framework inspired by task arithmetic. Instead of optimizing multiple preferences within a single objective, we train separate models on individual preferences, extract behavior shifts as preference vectors, and dynamically merge them at test time. This modular approach enables fine-grained, user-controllable preference adjustments and facilitates seamless integration of new preferences without retraining. Experiments show that our proposed Preference Vector framework improves helpfulness without excessive conservatism, allows smooth control over preference trade-offs, and supports scalable multi-preference alignment.
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs
Venue: arXiv cs.AI 2026
Year: 2026

Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts, by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset's diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.
TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability
Venue: arXiv cs.MM 2026
Year: 2026

Diffusion-based speech generation has achieved remarkable fidelity, increasing the risk of misuse and unauthorized redistribution. However, most existing generative speech watermarking methods are developed for GAN-based pipelines, and watermarking for diffusion-based speech generation remains comparatively underexplored. In addition, prior work often focuses on content-level provenance, while support for model-level and user-level attribution is less mature. We propose \textbf{TriniMark}, a diffusion-based generative speech watermarking framework that targets trinity-level traceability, i.e., the ability to associate a generated speech sample with (i) the embedded watermark message (content-level provenance), (ii) the source generative model (model-level attribution), and (iii) the end user who requested generation (user-level traceability). TriniMark uses a lightweight encoder to embed watermark bits into time-domain speech features and reconstruct the waveform, and a temporal-aware gated convolutional decoder for reliable bit recovery. We further introduce a waveform-guided fine-tuning strategy to transfer watermarking capability into a diffusion model. Finally, we incorporate variable-watermark training so that a single trained model can embed different watermark messages at inference time, enabling scalable user-level traceability. Experiments on speech datasets indicate that TriniMark maintains speech quality while improving robustness to common single and compound signal-processing attacks, and it supports high-capacity watermarking for large-scale traceability.
Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection
Venue: arXiv cs.CR 2026
Year: 2026

Side-channel attacks (SCAs) pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electromagnetic emissions. Among existing countermeasures, artificial noise injection is recognized as one of the most effective techniques. However, its high power consumption poses a major challenge for resource-constrained systems such as Internet of Things (IoT) devices, motivating the development of more efficient protection schemes. In this paper, we model SCAs as a communication channel and aim to suppress information leakage by minimizing the mutual information between the secret information and side-channel observations, subject to a power constraint on the artificial noise. We propose an optimal artificial noise injection method that minimizes the mutual information under power constraints for artificial noise. Specifically, we formulate two convex optimization problems: 1) minimizing the total mutual information, and 2) minimizing the maximum mutual information across observations. Our first major contribution is proposing an optimal artificial noise injection framework for the case of Gaussian input, where the mutual information becomes the channel capacity, which is one way to quantify the information leakage. Our second major contribution extends the optimization framework to arbitrary input distributions. We identify conditions ensuring the convexity of the optimization problem and derive the optimal solution using the fundamental relationship between the mutual information and the minimum mean squared error. The simulation results show that the proposed methods significantly reduce both total and maximum mutual information compared to conventional techniques, confirming their effectiveness for resource-constrained, security-critical systems.
Softpick: No Attention Sink, No Massive Activations with Rectified Softmax
Venue: arXiv cs.LG 2026
Year: 2026

We introduce softpick, a rectified, not sum-to-one, drop-in replacement for softmax in transformer attention mechanisms that eliminates attention sink and massive activations. Our experiments with 340M and 1.8B parameter models demonstrate that softpick achieves 0\% sink rate consistently. The softpick transformers produce hidden states with significantly lower kurtosis and creates sparse attention maps. Quantized models using softpick outperform softmax on standard benchmarks, with a particularly pronounced advantage at lower bit precisions. Our analysis and discussion shows how softpick has the potential to open new possibilities for quantization, low-precision training, sparsity optimization, pruning, and interpretability. Code: https://github.com/zaydzuhri/softpick-attention.
Toward Efficient Exploration by Large Language Model Agents
Venue: arXiv cs.LG 2026
Year: 2026

A burgeoning area within reinforcement learning (RL) is the design of sequential decision-making agents centered around large language models (LLMs). While autonomous decision-making agents powered by modern LLMs could facilitate numerous real-world applications, such successes demand agents that are capable of data-efficient RL. One key obstacle to achieving data efficiency in RL is exploration, a challenge that we demonstrate many recent proposals for LLM agent designs struggle to contend with. Meanwhile, classic algorithms from the RL literature known to gracefully address exploration require technical machinery that can be challenging to operationalize in purely natural language settings. In this work, rather than relying on finetuning or in-context learning to coax LLMs into implicitly imitating a RL algorithm, we illustrate how LLMs can be used to explicitly implement an existing RL algorithm (Posterior Sampling for Reinforcement Learning) whose capacity for statistically-efficient exploration is already well-studied. We offer empirical results demonstrating how our LLM-based implementation of a known, data-efficient RL algorithm can be considerably more effective in natural language tasks that demand prudent exploration.
SFIBA: Spatial-based Full-target Invisible Backdoor Attacks
Venue: arXiv cs.CR 2026
Year: 2026

Multi-target backdoor attacks pose significant security threats to deep neural networks, as they can preset multiple target classes through a single backdoor injection. This allows attackers to control the model to misclassify poisoned samples with triggers into any desired target class during inference, exhibiting superior attack performance compared with conventional backdoor attacks. However, existing multi-target backdoor attacks fail to guarantee trigger specificity and stealthiness in black-box settings, resulting in two main issues. First, they are unable to simultaneously target all classes when only training data can be manipulated, limiting their effectiveness in realistic attack scenarios. Second, the triggers often lack visual imperceptibility, making poisoned samples easy to detect. To address these problems, we propose a Spatial-based Full-target Invisible Backdoor Attack, called SFIBA. It restricts triggers for different classes to specific local spatial regions and morphologies in the pixel space to ensure specificity, while employing a frequency-domain-based trigger injection method to guarantee stealthiness. Specifically, for injection of each trigger, we first apply fast fourier transform to obtain the amplitude spectrum of clean samples in local spatial regions. Then, we employ discrete wavelet transform to extract the features from the amplitude spectrum and use singular value decomposition to integrate the trigger. Subsequently, we selectively filter parts of the trigger in pixel space to implement trigger morphology constraints and adjust injection coefficients based on visual effects. We conduct experiments on multiple datasets and models. The results demonstrate that SFIBA can achieve excellent attack performance and stealthiness, while preserving the model's performance on benign samples, and can also bypass existing backdoor defenses.
Efficiency of Analysis of Transitive Relations using Query-Driven, Ground-and-Solve, and Fact-Driven Inference
Venue: arXiv cs.DB 2026
Year: 2026

Logic rules allow analysis of complex relationships to be expressed easily, especially for transitive relations in critical applications. However, understanding and predicting the efficiency of different inference methods remain challenging, even for simplest rules given different kinds of input data. This paper analyzes the efficiency of all three types of well-known inference methods -- query-driven, ground-and-solve, and fact-driven -- along with their respective optimizations, and compares with optimal complexities for the first time, for analyzing transitive graph relations. We also experiment with rule systems widely considered to have the best performance. We analyze all well-known rule variants and widely varying input graphs. The results include precisely calculated optimal time complexities; comparative analysis across different inference methods, rule variants, and graph types; confirmation with performance experiments; as well as discovery of a performance bug.
Sparse-to-Sparse Training of Diffusion Models
Venue: arXiv cs.LG 2026
Year: 2026

Diffusion models (DMs) are a powerful type of generative models that have achieved state-of-the-art results in various image synthesis tasks and have shown potential in other domains, such as natural language processing and temporal data modeling. Despite their stable training dynamics and ability to produce diverse high-quality samples, DMs are notorious for requiring significant computational resources, both in the training and inference stages. Previous work has focused mostly on increasing the efficiency of model inference. This paper introduces, for the first time, the paradigm of sparse-to-sparse training to DMs, with the aim of improving both training and inference efficiency. We focus on unconditional generation and train sparse DMs from scratch (Latent Diffusion and ChiroDiff) on six datasets using three different methods (Static-DM, RigL-DM, and MagRan-DM) to study the effect of sparsity in model performance. Our experiments show that sparse DMs are able to match and often outperform their Dense counterparts, while substantially reducing the number of trainable parameters and FLOPs. We also identify safe and effective values to perform sparse-to-sparse training of DMs.
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation
Venue: arXiv cs.SE 2026
Year: 2026

Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. We formalize this iterative, multi-turn paradigm as codeflow and introduce CodeFlowBench, the first benchmark designed to comprehensively evaluate LLMs' ability to perform codeflow - implementing new functionality by reusing existing functions over multiple turns. CodeFlowBench comprises two complementary components: CodeFlowBench-Comp, a core collection of 5,000+ competitive programming problems from Codeforces updated via an automated pipeline and CodeFlowBench-Repo, which is sourced from GitHub repositories to better reflect real-world scenarios. Furthermore, a novel evaluation framework featured dual assessment protocol and structural metrics derived from dependency trees is introduced. Extensive experiments reveal significant performance degradation in multi-turn codeflow scenarios. Furthermore, our in-depth analysis illustrates that model performance inversely correlates with dependency complexity. These findings not only highlight the critical challenges for supporting real-world workflows, but also establish CodeFlowBench as an essential tool for advancing code generation research.
Agile legged locomotion in reconfigurable modular robots
Venue: arXiv cs.RO 2026
Year: 2026

Legged machines are becoming increasingly agile and adaptive but they have so far lacked the morphological diversity of legged animals, which have been rearranged and reshaped to fill millions of niches. Unlike their biological counterparts, legged machines have largely converged over the past decade to canonical quadrupedal and bipedal architectures that cannot be easily reconfigured to meet new tasks or recover from injury. Here we introduce autonomous modular legs: agile yet minimal, single-degree-of-freedom jointed links that can learn complex dynamic behaviors and may be freely attached to form multilegged machines at the meter scale. This enables rapid repair, redesign, and recombination of highly-dynamic modular agents that move quickly and acrobatically (non-quasistatically) through unstructured environments. Because each module is itself a complete agent, the bodies that contain them can sustain deep structural damage that would completely disable other legged robots. We also show how to encode the vast space of possible body configurations into a compact latent design space that can be efficiently explored, revealing a wide diversity of novel legged forms.
Physics-Conditioned Grasping for Stable Tool Use
Venue: arXiv cs.RO 2026
Year: 2026

Tool use often fails not because robots misidentify tools, but because grasps cannot withstand task-induced wrench. Existing vision-language manipulation systems ground tools and contact regions from language yet select grasps under quasi-static or geometry-only assumptions. During interaction, inertial impulse and lever-arm amplification generate wrist torque and tangential loads that trigger slip and rotation. We introduce inverse Tool-use Planning (iTuP), which selects grasps by minimizing predicted interaction wrench along a task-conditioned trajectory. From rigid-body mechanics, we derive torque, slip, and alignment penalties, and train a Stable Dynamic Grasp Network (SDG-Net) to approximate these trajectory-conditioned costs for real-time scoring. Across hammering, sweeping, knocking, and reaching in simulation and on hardware, SDG-Net suppresses induced torque up to 17.6%, shifts grasps below empirically observed instability thresholds, and improves real-world success by 17.5% over a compositional baseline. Improvements concentrate where wrench amplification dominates, showing that robot tool use requires wrench-aware grasp selection, not perception alone.
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
Venue: arXiv cs.CR 2026
Year: 2026

Gradient sparsification, while mitigating communication bottlenecks in Federated Learning (FL), fundamentally alters the geometric landscape of model updates. We reveal that the resultant high-dimensional orthogonality renders traditional Euclidean-based robust aggregation metrics mathematically ambiguous, creating a 'sparsity-robustness trade-off' that adversaries exploit to bypass detection. To resolve this structural dissonance, we propose SafeSparse, a consensus restoration framework that decouples defense into topological and semantic dimensions. Unlike prior arts that treat sparsification and security orthogonally, SafeSparse introduces: (1) a Structure-Aware Calibration mechanism utilizing Jaccard similarity to filter topological outliers induced by index poisoning; and (2) a Directional Semantic Alignment module employing density-based clustering on update signs to neutralize magnitude-invariant attacks. Theoretically, we establish convergence guarantees for SafeSparse. Extensive experiments across multiple datasets and attack scenarios demonstrate that SafeSparse recovers up to 25.7% global accuracy under coordinated poisoning, effectively closing the vulnerability gap in communication-efficient FL.
HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning[Technical Report]
Venue: arXiv cs.DB 2026
Year: 2026

Enterprise deployments of vector databases require access control policies to protect sensitive data. These systems often implement access control through hybrid vector queries that combine nearest-neighbor search with relational predicates based on user permissions. However, existing approaches face a fundamental trade-off: dedicated per-user indexes minimize query latency but incur high memory redundancy, while shared indexes with post-search filtering reduce memory overhead at the cost of increased latency. This paper introduces HONEYBEE, a dynamic partitioning framework that leverages the structure of Role-Based Access Control (RBAC) policies to create a smooth trade-off between these extremes. RBAC policies organize users into roles and assign permissions at the role level, creating a natural ``thin waist`` in the permission structure that is ideal for partitioning decisions. Specifically, HONEYBEE produces overlapping partitions where vectors can be strategically replicated across different partitions to reduce query latency while controlling memory overhead. To guide these decisions, HONEYBEE develops analytical models of vector search performance and recall, and formulates partitioning as a constrained optimization problem that balances memory usage, query efficiency, and recall. Evaluations on RBAC workloads demonstrate that HONEYBEE achieves up to 13.5X lower query latency than row-level security with only a 1.24X increase in memory usage, while achieving comparable query performance to dedicated, per-role indexes with 90.4% reduction in additional memory consumption, offering a practical middle ground for secure and efficient vector search.
BadPatches: Routing-aware Backdoor Attacks on Vision Mixture of Experts
Venue: arXiv cs.CR 2026
Year: 2026

Mixture of Experts (MoE) architectures have gained popularity for reducing computational costs in deep neural networks by activating only a subset of parameters during inference. While this efficiency makes MoE attractive for vision tasks, the patch-based processing in vision models introduces new methods for adversaries to perform backdoor attacks. In this work, we investigate the vulnerability of vision MoE models for image classification, specifically the patch-based MoE (pMoE) models and MoE-based vision transformers, against backdoor attacks. We propose a novel routing-aware trigger application method BadPatches, which is designed for patch-based processing in vision MoE models. BadPatches applies triggers on image patches rather than on the entire image. We show that BadPatches achieves high attack success rates (ASRs) with lower poisoning rates than routing-agnostic triggers and is successful at poisoning rates as low as 0.01% with an ASR above 80% on pMoE. Moreover, BadPatches is still effective when an adversary does not have complete knowledge of the patch routing configuration of the considered models. Next, we explore how trigger design affects pMoE patch routing. Finally, we investigate fine-pruning as a defense. Results show that only the fine-tuning stage of fine-pruning removes the backdoor from the model.