OpenRaiser/Intern-Atlas huggingface dataset:
Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
Venue: arXiv cs.CR 2025
Year: 2025

Watermarking has emerged as a promising technique for detecting texts generated by LLMs. Current research has primarily focused on three design criteria: high quality of the watermarked text, high detectability, and robustness against removal attack. However, the security against spoofing attacks remains relatively understudied. For example, a piggyback attack can maliciously alter the meaning of watermarked text-transforming it into hate speech-while preserving the original watermark, thereby damaging the reputation of the LLM provider. We identify two core challenges that make defending against spoofing difficult: (1) the need for watermarks to be both sensitive to semantic-distorting changes and insensitive to semantic-preserving edits, and (2) the contradiction between the need to detect global semantic shifts and the local, auto-regressive nature of most watermarking schemes. To address these challenges, we propose a semantic-aware watermarking algorithm that post-hoc embeds watermarks into a given target text while preserving its original meaning. Our method introduces a semantic mapping model, which guides the generation of a green-red token list, contrastively trained to be sensitive to semantic-distorting changes and insensitive to semantic-preserving changes. Experiments on two standard benchmarks demonstrate strong robustness against removal attacks and security against spoofing attacks, including sentiment reversal and toxic content insertion, while maintaining high watermark detectability. Our approach offers a significant step toward more secure and semantically aware watermarking for LLMs. Our code is available at https://github.com/UCSB-NLP-Chang/contrastive-watermark.
Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The early diagnosis of various diseases continues to be a significant challenge in healthcare, primarily due to the nonspecific nature of early symptoms, the shortage of expert medical practitioners, and the need for prolonged clinical evaluations, all of which can delay treatment and adversely affect patient outcomes. With impressive accuracy in prediction across a range of diseases, LLMs have the potential to revolutionize clinical pre-screening and decision-making for various medical conditions. In this work, we study the diagnostic capability of LLMs for Rheumatoid Arthritis (RA) with real world patients data. Patient data was collected alongside diagnoses from medical experts, and the performance of LLMs was evaluated in comparison to expert diagnoses for RA disease prediction. We notice an interesting pattern in disease diagnosis and find an unexpected \textit{misalignment between prediction and explanation}. We conduct a series of multi-round analyses using different LLM agents. The best-performing model accurately predicts rheumatoid arthritis (RA) diseases approximately 95\% of the time. However, when medical experts evaluated the reasoning generated by the model, they found that nearly 68\% of the reasoning was incorrect. This study highlights a clear misalignment between LLMs high prediction accuracy and its flawed reasoning, raising important questions about relying on LLM explanations in clinical settings. \textbf{LLMs provide incorrect reasoning to arrive at the correct answer for RA disease diagnosis.}
Wanting to be Understood
Venue: arXiv cs.LG 2025
Year: 2025

This paper explores an intrinsic motivation for mutual awareness, hypothesizing that humans possess a fundamental drive to understand and to be understood even in the absence of extrinsic rewards. Through simulations of the perceptual crossing paradigm, we explore the effect of various internal reward functions in reinforcement learning agents. The drive to understand is implemented as an active inference type artificial curiosity reward, whereas the drive to be understood is implemented through intrinsic rewards for imitation, influence/impressionability, and sub-reaction time anticipation of the other. Results indicate that while artificial curiosity alone does not lead to a preference for social interaction, rewards emphasizing reciprocal understanding successfully drive agents to prioritize interaction. We demonstrate that this intrinsic motivation can facilitate cooperation in tasks where only one agent receives extrinsic reward for the behaviour of the other.
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Multi-modal sequential recommendation systems leverage auxiliary signals (e.g., text, images) to alleviate data sparsity in user-item interactions. While recent methods exploit large language models to encode modalities into discrete semantic IDs for autoregressive prediction, we identify two critical limitations: (1) Existing approaches adopt fragmented quantization, where modalities are independently mapped to semantic spaces misaligned with behavioral objectives, and (2) Over-reliance on semantic IDs disrupts inter-modal semantic coherence, thereby weakening the expressive power of multi-modal representations for modeling diverse user preferences. To address these challenges, we propose a Behavior-Bind multi-modal Quantization for Sequential Recommendation (BBQRec for short) featuring dual-aligned quantization and semantics-aware sequence modeling. First, our behavior-semantic alignment module disentangles modality-agnostic behavioral patterns from noisy modality-specific features through contrastive codebook learning, ensuring semantic IDs are inherently tied to recommendation tasks. Second, we design a discretized similarity reweighting mechanism that dynamically adjusts self-attention scores using quantized semantic relationships, preserving multi-modal synergies while avoiding invasive modifications to the sequence modeling architecture. Extensive evaluations across four real-world benchmarks demonstrate BBQRec's superiority over the state-of-the-art baselines.
RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning via online optimization, they are limited by model inaccuracies. In contrast, learning-based methods offer robustness, but they struggle with precise modulation of interaction forces. We introduce RAMBO, a hybrid framework that integrates model-based whole-body control within a feedback policy trained with reinforcement learning. The model-based module generates feedforward torques by solving a quadratic program, while the policy provides feedback corrective terms to enhance robustness. We validate our framework on a quadruped robot across a diverse set of real-world loco-manipulation tasks, such as pushing a shopping cart, balancing a plate, and holding soft objects, in both quadrupedal and bipedal walking. Our experiments demonstrate that RAMBO enables precise manipulation capabilities while achieving robust and dynamic locomotion.
Efficient Timestamping for Sampling-based Race Detection
Venue: arXiv cs.PL 2025
Year: 2025

Dynamic race detection based on the happens before (HB) partial order has now become the de facto approach to quickly identify data races in multi-threaded software. Most practical implementations for detecting these races use timestamps to infer causality between events and detect races based on these timestamps. Such an algorithm updates timestamps (stored in vector clocks) at every event in the execution, and is known to induce excessive overhead. Random sampling has emerged as a promising algorithmic paradigm to offset this overhead. It offers the promise of making sound race detection scalable. In this work we consider the task of designing an efficient sampling based race detector with low overhead for timestamping when the number of sampled events is much smaller than the total events in an execution. To solve this problem, we propose (1) a new notion of freshness timestamp, (2) a new data structure to store timestamps, and (3) an algorithm that uses a combination of them to reduce the cost of timestamping in sampling based race detection. Further, we prove that our algorithm is close to optimal -- the number of vector clock traversals is bounded by the number of sampled events and number of threads, and further, on any given dynamic execution, the cost of timestamping due to our algorithm is close to the amount of work any timestamping-based algorithm must perform on that execution, that is it is instance optimal. Our evaluation on real world benchmarks demonstrates the effectiveness of our proposed algorithm over prior timestamping algorithms that are agnostic to sampling.
"Sorry for bugging you so much." Exploring Developers' Behavior Towards Privacy-Compliant Implementation
Venue: arXiv cs.SE 2025
Year: 2025

While protecting user data is essential, software developers often fail to fulfill privacy requirements. However, the reasons why they struggle with privacy-compliant implementation remain unclear. Is it due to a lack of knowledge, or is it because of insufficient support? To provide foundational insights in this field, we conducted a qualitative 5-hour programming study with 30 professional software developers implementing 3 privacy-sensitive programming tasks that were designed with GDPR compliance in mind. To explore if and how developers implement privacy requirements, participants were divided into 3 groups: control, privacy prompted, and privacy expert-supported. After task completion, we conducted follow-up interviews. Alarmingly, almost all participants submitted non-GDPR-compliant solutions (79/90). In particular, none of the 3 tasks were solved privacy-compliant by all 30 participants, with the non-prompted group having the lowest number of 3 out of 30 privacy-compliant solution attempts. Privacy prompting and expert support only slightly improved participants' submissions, with 6/30 and 8/30 privacy-compliant attempts, respectively. In fact, all participants reported severe issues addressing common privacy requirements such as purpose limitation, user consent, or data minimization. Counterintuitively, although most developers exhibited minimal confidence in their solutions, they rarely sought online assistance or contacted the privacy expert, with only 4 out of 10 expert-supported participants explicitly asking for compliance confirmation. Instead, participants often relied on existing implementations and focused on implementing functionality and security first.
FamilyTool: A Multi-hop Personalized Tool Use Benchmark
Venue: arXiv cs.AI 2025
Year: 2025

The integration of tool learning with Large Language Models (LLMs) has expanded their capabilities in handling complex tasks by leveraging external tools. However, existing benchmarks for tool learning inadequately address critical real-world personalized scenarios, particularly those requiring multi-hop reasoning and inductive knowledge adaptation in dynamic environments. To bridge this gap, we introduce FamilyTool, a novel benchmark grounded in a family-based knowledge graph (KG) that simulates personalized, multi-hop tool use scenarios. FamilyTool, including base and extended datasets, challenges LLMs with queries spanning from 1 to 4 relational hops (e.g., inferring familial connections and preferences) and 2 to 6 hops respectively, and incorporates an inductive KG setting where models must adapt to unseen user preferences and relationships without re-training, a common limitation in prior approaches that compromises generalization. We further propose KGETool: a simple KG-augmented evaluation pipeline to systematically assess LLMs' tool use ability in these settings. Experiments reveal significant performance gaps in state-of-the-art LLMs, with accuracy dropping sharply as hop complexity increases and inductive scenarios exposing severe generalization deficits. These findings underscore the limitations of current LLMs in handling personalized, evolving real-world contexts and highlight the urgent need for advancements in tool-learning frameworks. FamilyTool serves as a critical resource for evaluating and advancing LLM agents' reasoning, adaptability, and scalability in complex, dynamic environments. Code and dataset are available at \href{https://github.com/yxzwang/FamilyTool}{https://github.com/yxzwang/FamilyTool}.
Beware of "Explanations" of AI
Venue: arXiv cs.LG 2025
Year: 2025

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations.
On a Characterization of Spartan Graphs
Venue: arXiv cs.DM 2025
Year: 2025

The eternal vertex cover game is played between an attacker and a defender on an undirected graph $G$. The defender identifies $k$ vertices to position guards on to begin with. The attacker, on their turn, attacks an edge $e$, and the defender must move a guard along $e$ to defend the attack. The defender may move other guards as well, under the constraint that every guard moves at most once and to a neighboring vertex. The smallest number of guards required to defend attacks forever is called the eternal vertex cover number of $G$, denoted $evc(G)$. For any graph $G$, $evc(G)$ is at least the vertex cover number of $G$, denoted $mvc(G)$. A graph is Spartan if $evc(G) = mvc(G)$. It is known that a bipartite graph is Spartan if and only if every edge belongs to a perfect matching. We show that the only König graphs that are Spartan are the bipartite Spartan graphs. We also give new lower bounds for $evc(G)$, generalizing a known lower bound based on cut vertices. We finally show a new matching-based characterization of all Spartan graphs.
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Venue: arXiv cs.CV 2025
Year: 2025

Cognitive demands of fetal ultrasound examinations pose unique challenges among clinicians. With the goal of providing an assistive tool, we developed an automated pipeline for predicting fetal orientation from ultrasound videos acquired following a simple blind sweep protocol. Leveraging on a pre-trained head detection and segmentation model, this is achieved by first determining the fetal presentation (cephalic or breech) with a template matching approach, followed by the fetal lie (facing left or right) by analyzing the spatial distribution of segmented brain anatomies. Evaluation on a dataset of third-trimester ultrasound scans demonstrated the promising accuracy of our pipeline. This work distinguishes itself by introducing automated fetal lie prediction and by proposing an assistive paradigm that augments sonographer expertise rather than replacing it. Future research will focus on enhancing acquisition efficiency, and exploring real-time clinical integration to improve workflow and support for obstetric clinicians.
Grouping Strategies on Two-Phase Methods for Bi-objective Combinatorial Optimization
Venue: arXiv cs.DS 2025
Year: 2025

Two-phase methods are commonly used to solve bi-objective combinatorial optimization problems. In the first phase, all extreme supported nondominated points are generated through a dichotomic search. This phase also allows the identification of search zones that may contain other nondominated points. The second phase focuses on exploring these search zones to locate the remaining points, which typically accounts for most of the computational cost. Ranking algorithms are frequently employed to explore each zone individually, but this approach leads to redundancies, causing multiple visits to the same solutions. To mitigate these redundancies, we propose several strategies that group adjacent zones, allowing a single run of the ranking algorithm for the entire group. Additionally, we explore an implicit grouping approach based on a new concept of coverage. Our experiments on the Bi-Objective Spanning Tree Problem demonstrate the beneficial impact of these grouping strategies when combined with coverage.
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
Venue: arXiv cs.CR 2025
Year: 2025

Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy guarantees. These models approximate low-dimensional marginals or query workloads; crucially, they require the training data to be pre-discretized, i.e., continuous values need to first be partitioned into bins. However, as the range of values (or their domain) is often inferred directly from the training data, with the number of bins and bin edges typically defined arbitrarily, this approach can ultimately break end-to-end DP guarantees and may not always yield optimal utility. In this paper, we present an extensive measurement study of four discretization strategies in the context of DP marginal generative models. More precisely, we design DP versions of three discretizers (uniform, quantile, and k-means) and reimplement the PrivTree algorithm. We find that optimizing both the choice of discretizer and bin count can improve utility, on average, by almost 30% across six DP marginal models, compared to the default strategy and number of bins, with PrivTree being the best-performing discretizer in the majority of cases. We demonstrate that, while DP generative models with non-private discretization remain vulnerable to membership inference attacks, applying DP during discretization effectively mitigates this risk. Finally, we improve on an existing approach for automatically selecting the optimal number of bins, and achieve high utility while reducing both privacy budget consumption and computational overhead.
ShadowBinding: Realizing Effective Microarchitectures for In-Core Secure Speculation Schemes
Venue: arXiv cs.CR 2025
Year: 2025

Secure speculation schemes have shown great promise in the war against speculative side-channel attacks, and will be a key building block for developing secure, high-performance architectures moving forward. As the field matures, the need for rigorous microarchitectures, and corresponding performance and cost analysis, become critical for evaluating secure schemes and for enabling their future adoption. In ShadowBinding, we present effective microarchitectures for two state-of-the-art secure schemes, uncovering and mitigating fundamental microarchitectural limitations within the analyzed schemes, and provide important design characteristics. We uncover that Speculative Taint Tracking's (STT's) rename-based taint computation must be completed in a single cycle, creating an expensive dependency chain which greatly limits performance for wider processor cores. We also introduce a novel michroarchitectural approach for STT, named STT-Issue, which, by delaying the taint computation to the issue stage, eliminates the dependency chain, achieving better instructions per cycle (IPC), timing, area, and performance results. Through a comprehensive evaluation of our STT and Non-Speculative Data Access (NDA) microarchitectural designs on the RISC-V Berkeley Out-of-Order Machine, we find that the IPC impact of in-core secure schemes is higher than previously estimated, close to 20% for the highest performance core. With insights into timing from our RTL evaluation, the performance loss, created by the combined impact of IPC and timing, becomes even greater, at 35%, 27%, and 22% for STT-Rename, STT-Issue, and NDA, respectively. If these trends were to hold for leading processor core designs, the performance impact would be well over 30%, even for the best-performing scheme.
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety
Venue: arXiv cs.CL 2025
Year: 2025

Applications of generative Large Language Models LLMs are rapidly expanding across various domains, promising significant improvements in workflow efficiency and information retrieval. However, their implementation in specialized, high-stakes domains such as hazardous materials transportation is challenging due to accuracy and reliability concerns. This study evaluates the performance of three fine-tuned generative models, ChatGPT, Google's Vertex AI, and ORNL Retrieval Augmented Generation augmented LLaMA 2 and LLaMA in retrieving regulatory information essential for hazardous material transportation compliance in the United States. Utilizing approximately 40 publicly available federal and state regulatory documents, we developed 100 realistic queries relevant to route planning and permitting requirements. Responses were qualitatively rated based on accuracy, detail, and relevance, complemented by quantitative assessments of semantic similarity between model outputs. Results demonstrated that the RAG-augmented LLaMA models significantly outperformed Vertex AI and ChatGPT, providing more detailed and generally accurate information, despite occasional inconsistencies. This research introduces the first known application of RAG in transportation safety, emphasizing the need for domain-specific fine-tuning and rigorous evaluation methodologies to ensure reliability and minimize the risk of inaccuracies in high-stakes environments.
Buffer Centering for bittide Synchronization via Frame Rotation
Venue: arXiv cs.DC 2025
Year: 2025

Maintaining consistent time in distributed systems is a fundamental challenge. The bittide system addresses this by providing logical synchronization through a decentralized control mechanism that observes local buffer occupancies and controls the frequency of an oscillator at each node. A critical aspect of bittide's stability and performance is ensuring that these elastic buffers operate around a desired equilibrium point, preventing data loss due to overflow or underflow. This paper introduces a novel method for centering buffer occupancies in a bittide network using a technique we term frame rotation. We propose a control strategy utilizing a directed spanning tree of the network graph. By adjusting the frequencies of nodes in a specific order dictated by this tree, and employing a pulsed feedback controller that targets the buffer occupancy of edges within the spanning tree, we prove that all elastic buffers in the network can be driven to their desired equilibrium. This ordered adjustment approach ensures that prior centering efforts are not disrupted, providing a robust mechanism for managing buffer occupancy in bittide synchronized systems.