OpenRaiser/Intern-Atlas huggingface dataset:
Discrete Single-Parameter Optimal Auction Design
Venue: arXiv cs.GT 2025
Year: 2025

We study the classic single-item auction setting of Myerson, but under the assumption that the buyers' values for the item are distributed over finite supports. Using strong LP duality and polyhedral theory, we rederive various key results regarding the revenue-maximizing auction, including the characterization through virtual welfare maximization and the optimality of deterministic mechanisms, as well as a novel, generic equivalence between dominant-strategy and Bayesian incentive compatibility. Inspired by this, we abstract our approach to handle more general auction settings, where the feasibility space can be given by arbitrary convex constraints, and the objective is a convex combination of revenue and social welfare. We characterize the optimal auctions of such systems as generalized virtual welfare maximizers, by making use of their KKT conditions, and we present an analogue of Myerson's payment formula for general discrete single-parameter auction settings. Additionally, we prove that total unimodularity of the feasibility space is a sufficient condition to guarantee the optimality of auctions with integral allocation rules. Finally, we demonstrate this KKT approach by applying it to a setting where bidders are interested in buying feasible flows on trees with capacity constraints, and provide a combinatorial description of the (randomized, in general) optimal auction.
An Industry Interview Study of Software Signing for Supply Chain Security
Venue: arXiv cs.SE 2025
Year: 2025

Many software products are composed of components integrated from other teams or external parties. Each additional link in a software product's supply chain increases the risk of the injection of malicious behavior. To improve supply chain provenance, many cybersecurity frameworks, standards, and regulations recommend the use of software signing. However, recent surveys and measurement studies have found that the adoption rate and quality of software signatures are low. We lack in-depth industry perspectives on the challenges and practices of software signing. To understand software signing in practice, we interviewed 18 experienced security practitioners across 13 organizations. We study the challenges that affect the effective implementation of software signing in practice. We also provide possible impacts of experienced software supply chain failures, security standards, and regulations on software signing adoption. To summarize our findings: (1) We present a refined model of the software supply chain factory model highlighting practitioner's signing practices; (2) We highlight the different challenges-technical, organizational, and human-that hamper software signing implementation; (3) We report that experts disagree on the importance of signing; and (4) We describe how internal and external events affect the adoption of software signing. Our work describes the considerations for adopting software signing as one aspect of the broader goal of improved software supply chain security.
Optimal Sharding for Scalable Blockchains with Deconstructed SMR
Venue: arXiv cs.CR 2025
Year: 2025

Sharding is proposed to enhance blockchain scalability. However, a size-security dilemma where every shard must be large enough to ensure its security constrains the efficacy of individual shards and the degree of sharding itself. Most existing sharding solutions therefore rely on either weakening the adversary or making stronger assumptions on network links. This paper presents Arete, an optimally scalable blockchain sharding protocol designed to resolve the dilemma based on an observation that if individual shards can tolerate a higher fraction of (Byzantine) faults, we can securely create smaller shards in a larger quantity. The key idea of Arete, therefore, is to improve the security resilience/threshold of shards by dividing the blockchain's State Machine Replication (SMR) process itself. Similar to modern blockchains, Arete first decouples SMR in three steps: transaction dissemination, ordering, and execution. However, unlike other blockchains, for Arete, a single ordering shard performs the ordering task while multiple processing shards perform the dissemination and execution of blocks. As processing shards do not run consensus, each of those can tolerate up to half compromised nodes. Moreover, the SMR process in the ordering shard is lightweight as it only operates on the block digests. Second, Arete considers safety and liveness against Byzantine failures separately to improve the safety threshold further while tolerating temporary liveness violations in a controlled manner. Apart from the creation of more optimal-size shards, such a deconstructed SMR scheme also empowers us to devise a novel certify-order-execute architecture to fully parallelize transaction handling, thereby improving the performance of sharding systems. We implement Arete and evaluate it on a AWS environment by running up to 500 nodes, showing that Arete outperforms the state-of-the-art sharding protocols.
Learning interpretable positional encodings in transformers depends on initialization
Venue: arXiv cs.LG 2025
Year: 2025

In transformers, the positional encoding (PE) provides essential information that distinguishes the position and order amongst tokens in a sequence. Most prior investigations of PE effects on generalization were tailored to 1D input sequences, such as those presented in natural language, where adjacent tokens (e.g., words) are highly related. In contrast, many real world tasks involve datasets with highly non-trivial positional arrangements, such as datasets organized in multiple spatial dimensions, or datasets for which ground truth positions are not known. Here we find that the choice of initialization of a learnable PE greatly influences its ability to learn interpretable PEs that lead to enhanced generalization. We empirically demonstrate our findings in three experiments: 1) A 2D relational reasoning task; 2) A nonlinear stochastic network simulation; 3) A real world 3D neuroscience dataset, applying interpretability analyses to verify the learning of accurate PEs. Overall, we find that a learned PE initialized from a small-norm distribution can 1) uncover interpretable PEs that mirror ground truth positions in multiple dimensions, and 2) lead to improved generalization. These results illustrate the feasibility of learning identifiable and interpretable PEs for enhanced generalization.
Three-dimensional Trajectory Optimization for Quadrotor Tail-sitter UAVs: Traversing through Given Waypoints
Venue: arXiv cs.RO 2025
Year: 2025

Given the evolving application scenarios of current fixed-wing unmanned aerial vehicles (UAVs), it is necessary for UAVs to possess agile and rapid 3-dimensional flight capabilities. Typically, the trajectory of a tail-sitter is generated separately for vertical and level flights. This limits the tail-sitter's ability to move in a 3-dimensional airspace and makes it difficult to establish a smooth transition between vertical and level flights. In the present work, a 3-dimensional trajectory optimization method is proposed for quadrotor tail-sitters. Especially, the differential dynamics constraints are eliminated when generating the trajectory of the tail-sitter by utilizing differential flatness method. Additionally, the temporal parameters of the trajectory are generated using the state-of-the-art trajectory generation method called MINCO (minimum control). Subsequently, we convert the speed constraint on the vehicle into a soft constraint by discretizing the trajectory in time. This increases the likelihood that the control input limits are satisfied and the trajectory is feasible. Then, we utilize a kind of model predictive control (MPC) method to track trajectories. Even if restricting the tail-sitter's motion to a 2-dimensional horizontal plane, the solutions still outperform those of the L1 Guidance Law and Dubins path.
Towards Unsupervised Speech Recognition Without Pronunciation Models
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in supervised automatic speech recognition (ASR) have achieved remarkable performance, largely due to the growing availability of large transcribed speech corpora. However, most languages lack sufficient paired speech and text data to effectively train these systems. In this article, we tackle the challenge of developing ASR systems without paired speech and text corpora by proposing the removal of reliance on a phoneme lexicon. We explore a new research direction: word-level unsupervised ASR, and experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling. Using a curated speech corpus containing a fixed number of English words, our system iteratively refines the word segmentation structure and achieves a word error rate of between 20-23%, depending on the vocabulary size, without parallel transcripts, oracle word boundaries, or a pronunciation lexicon. This innovative model surpasses the performance of previous unsupervised ASR models under the lexicon-free setting.
RMem: Restricted Memory Banks Improve Video Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios, we revisit a simple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of expanding memory banks to accommodate extensive historical information. Our specially designed "memory deciphering" study offers a pivotal insight underpinning such a strategy: expanding memory banks, while seemingly beneficial, actually increases the difficulty for VOS modules to decode relevant features due to the confusion from redundant information. By restricting memory banks to a limited number of essential frames, we achieve a notable improvement in VOS accuracy. This process balances the importance and freshness of frames to maintain an informative memory bank within a bounded capacity. Additionally, restricted memory banks reduce the training-inference discrepancy in memory lengths compared with continuous expansion. This fosters new opportunities in temporal reasoning and enables us to introduce the previously overlooked "temporal positional embedding." Finally, our insights are embodied in "RMem" ("R" for restricted), a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (on the VOST dataset) and long videos (on the Long Videos dataset). Our code and demo are available at https://restricted-memory.github.io/.
A Game Between Two Identical Dubins Cars: Evading a Conic Sensor in Minimum Time
Venue: arXiv cs.RO 2025
Year: 2025

A fundamental task in mobile robotics is keeping an intelligent agent under surveillance with an autonomous robot as it travels in the environment. This work studies a theoretical version of that problem involving one of the most popular vehicle platforms in robotics. In particular, we consider two identical Dubins cars moving on a plane without obstacles. One of them plays as the pursuer, and it is equipped with a limited field-of-view detection region modeled as a semi-infinite cone with its apex at the pursuer's position. The pursuer aims to maintain the other Dubins car, which plays as the evader, as much time as possible inside its detection region. On the contrary, the evader wants to escape as soon as possible. In this work, employing differential game theory, we find the time-optimal motion strategies near the game's end. The analysis of those trajectories reveals the existence of at least two singular surfaces: a Transition Surface (also known as a Switch Surface) and an Evader's Universal Surface. We also found that the barrier's standard construction produces a surface that partially lies outside the playing space.
LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

When humans create sculptures, we are able to reason about how geometrically we need to alter the clay state to reach our target goal. We are not computing point-wise similarity metrics, or reasoning about low-level positioning of our tools, but instead determining the higher-level changes that need to be made. In this work, we propose LLM-Craft, a novel pipeline that leverages large language models (LLMs) to iteratively reason about and generate deformation-based crafting action sequences. We simplify and couple the state and action representations to further encourage shape-based reasoning. To the best of our knowledge, LLM-Craft is the first system successfully leveraging LLMs for complex deformable object interactions. Through our experiments, we demonstrate that with the LLM-Craft framework, LLMs are able to successfully create a set of simple letter shapes. We explore a variety of rollout strategies, and compare performances of LLM-Craft variants with and without an explicit goal shape images. For videos and prompting details, please visit our project website: https://sites.google.com/andrew.cmu.edu/llmcraft/home
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
Venue: arXiv cs.SE 2025
Year: 2025

Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particularly methods utilizing large language models, has become increasingly popular. These neural approaches generate semantically meaningful tests that are more maintainable compared with traditional automatic testing methods like fuzzing. However, the diversity and volume of unit tests in current datasets are limited, especially for newer but important languages. In this paper, we present a novel data augmentation technique, FuzzAug, that introduces the benefits of fuzzing to large language models by introducing valid testing semantics and providing diverse coverage-guided inputs. Doubling the size of training datasets, FuzzAug improves the performance from the baselines significantly. This technique demonstrates the potential of introducing prior knowledge from dynamic software analysis to improve neural test generation, offering significant enhancements in neural test generation.
StructuralSleight: Automated Jailbreak Attacks on Large Language Models Utilizing Uncommon Text-Organization Structures
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are widely used in natural language processing but face the risk of jailbreak attacks that maliciously induce them to generate harmful content. Existing jailbreak attacks, including character-level and context-level attacks, mainly focus on the prompt of plain text without specifically exploring the significant influence of its structure. In this paper, we focus on studying how the prompt structure contributes to the jailbreak attack. We introduce a novel structure-level attack method based on long-tailed structures, which we refer to as Uncommon Text-Organization Structures (UTOS). We extensively study 12 UTOS templates and 6 obfuscation methods to build an effective automated jailbreak tool named StructuralSleight that contains three escalating attack strategies: Structural Attack, Structural and Character/Context Obfuscation Attack, and Fully Obfuscated Structural Attack. Extensive experiments on existing LLMs show that StructuralSleight significantly outperforms the baseline methods. In particular, the attack success rate reaches 94.62\% on GPT-4o, which has not been addressed by state-of-the-art techniques.
Optimizing Large Model Training through Overlapped Activation Recomputation
Venue: arXiv cs.DC 2025
Year: 2025

Large model training often uses recomputation to alleviate memory pressure and pipelines to exploit the parallelism of data, tensors, and devices. However, existing recomputation approaches may incur high overhead when training real-world models, as they are executed on demand in the critical training path. In this paper, we present Lynx, a new recomputation framework to reduce overhead by overlapping recomputation with communication in training pipelines. To reduce the large search space for recomputation strategies, we propose a heuristic-based recomputation scheduling algorithm, which is based on the observation that there are identical structures in large DNN models so that we can apply the same scheduling policy to all such structures. Additionally, we propose a recomputation-aware model partitioning method to balance each stage's execution time for improved training throughput. Our comprehensive evaluation using GPT models with 1.3B-23B parameters shows that Lynx outperforms existing recomputation approaches by up to 1.37x.
Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges
Venue: arXiv cs.SD 2025
Year: 2025

Deep learning models for music have advanced drastically in recent years, but how good are machine learning models at capturing emotion, and what challenges are researchers facing? In this paper, we provide a comprehensive overview of the available music-emotion datasets and discuss evaluation standards as well as competitions in the field. We also offer a brief overview of various types of music emotion prediction models that have been built over the years, providing insights into the diverse approaches within the field. Through this examination, we highlight the challenges that persist in accurately capturing emotion in music, including issues related to dataset quality, annotation consistency, and model generalization. Additionally, we explore the impact of different modalities, such as audio, MIDI, and physiological signals, on the effectiveness of emotion prediction models. Through this examination, we identify persistent challenges in music emotion recognition (MER), including issues related to dataset quality, the ambiguity in emotion labels, and the difficulties of cross-dataset generalization. We argue that future advancements in MER require standardized benchmarks, larger and more diverse datasets, and improved model interpretability. Recognizing the dynamic nature of this field, we have complemented our findings with an accompanying GitHub repository. This repository contains a comprehensive list of music emotion datasets and recent predictive models.
LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions
Venue: arXiv cs.RO 2025
Year: 2025

Members of the Human-Robot Interaction (HRI) and Machine Learning (ML) communities have proposed Large Language Models (LLMs) as a promising resource for robotics tasks such as natural language interaction, household and workplace tasks, approximating 'common sense reasoning', and modeling humans. However, recent research has raised concerns about the potential for LLMs to produce discriminatory outcomes and unsafe behaviors in real-world robot experiments and applications. To assess whether such concerns are well placed in the context of HRI, we evaluate several highly-rated LLMs on discrimination and safety criteria. Our evaluation reveals that LLMs are currently unsafe for people across a diverse range of protected identity characteristics, including, but not limited to, race, gender, disability status, nationality, religion, and their intersections. Concretely, we show that LLMs produce directly discriminatory outcomes- e.g., 'gypsy' and 'mute' people are labeled untrustworthy, but not 'european' or 'able-bodied' people. We find various such examples of direct discrimination on HRI tasks such as facial expression, proxemics, security, rescue, and task assignment. Furthermore, we test models in settings with unconstrained natural language (open vocabulary) inputs, and find they fail to act safely, generating responses that accept dangerous, violent, or unlawful instructions-such as incident-causing misstatements, taking people's mobility aids, and sexual predation. Our results underscore the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes and ensure LLMs only operate on robots when it is safe, effective, and just to do so. We provide code to reproduce our experiments at https://github.com/rumaisa-azeem/llm-robots-discrimination-safety .
Can I introduce my boyfriend to my grandmother? Evaluating Large Language Models Capabilities on Iranian Social Norm Classification
Venue: arXiv cs.SI 2025
Year: 2025

Creating globally inclusive AI systems demands datasets reflecting diverse social norms. Iran, with its unique cultural blend, offers an ideal case study, with Farsi adding linguistic complexity. In this work, we introduce the Iranian Social Norms (ISN) dataset, a novel collection of 1,699 Iranian social norms, including environments, demographic features, and scope annotation, alongside English translations. Our evaluation of 6 Large Language Models (LLMs) in classifying Iranian social norms, using a variety of prompts, uncovered critical insights into the impact of geographic and linguistic context. Results revealed a substantial performance gap in LLMs' comprehension of Iranian norms. Notably, while the geographic context in English prompts enhanced the performance, this effect was absent in Farsi, pointing to nuanced linguistic challenges. Particularly, performance was significantly worse for Iran-specific norms, emphasizing the importance of culturally tailored datasets. As the first Farsi dataset for social norm classification, ISN will facilitate crucial cross-cultural analyses, shedding light on how values differ across contexts and cultures.
Empirical Networks are Sparse: Enhancing Multi-Edge Models with Zero-Inflation
Venue: arXiv cs.SI 2025
Year: 2025

Real-world networks are sparse. As we show in this article, even when a large number of interactions is observed, most node pairs remain disconnected. We demonstrate that classical multi-edge network models, such as the $G(N,p)$, configuration models, and stochastic block models, fail to accurately capture this phenomenon. To mitigate this issue, zero-inflation must be integrated into these traditional models. Through zero-inflation, we incorporate a mechanism that accounts for the excess number of zeroes (disconnected pairs) observed in empirical data. By performing an analysis on all the datasets from the Sociopatterns repository, we illustrate how zero-inflated models more accurately reflect the sparsity and heavy-tailed edge count distributions observed in empirical data. Our findings underscore that failing to account for these ubiquitous properties in real-world networks inadvertently leads to biased models that do not accurately represent complex systems and their dynamics.
GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
Venue: arXiv cs.LG 2025
Year: 2025

The rapid advancement of large language model (LLM) agents has raised new concerns regarding their safety and security. In this paper, we propose GuardAgent, the first guardrail agent to protect target agents by dynamically checking whether their actions satisfy given safety guard requests. Specifically, GuardAgent first analyzes the safety guard requests to generate a task plan, and then maps this plan into guardrail code for execution. By performing the code execution, GuardAgent can deterministically follow the safety guard request and safeguard target agents. In both steps, an LLM is utilized as the reasoning component, supplemented by in-context demonstrations retrieved from a memory module storing experiences from previous tasks. In addition, we propose two novel benchmarks: EICU-AC benchmark to assess the access control for healthcare agents and Mind2Web-SC benchmark to evaluate the safety policies for web agents. We show that GuardAgent effectively moderates the violation actions for different types of agents on these two benchmarks with over 98% and 83% guardrail accuracies, respectively. Project page: https://guardagent.github.io/