OpenRaiser/Intern-Atlas huggingface dataset:
S-Leak: Leakage-Abuse Attack Against Efficient Conjunctive SSE via s-term Leakage
Venue: arXiv cs.CR 2025
Year: 2025

Conjunctive Searchable Symmetric Encryption (CSSE) enables secure conjunctive searches over encrypted data. While leakage-abuse attacks (LAAs) against single-keyword SSE have been extensively studied, their extension to conjunctive queries faces a critical challenge: the combinatorial explosion of candidate keyword combinations, leading to enormous time and space overhead for attacks. In this paper, we reveal a fundamental vulnerability in state-of-the-art CSSE schemes: s-term leakage, where the keyword with the minimal document frequency in a query leaks distinct patterns. We propose S-Leak, the first passive attack framework that progressively recovers conjunctive queries by exploiting s-term leakage and global leakage. Our key innovation lies in a three-stage approach: identifying the s-term of queries, pruning low-probability keyword conjunctions, and reconstructing full queries. We propose novel metrics to better assess attacks in conjunctive query scenarios. Empirical evaluations on real-world datasets demonstrate that our attack is effective in diverse CSSE configurations. When considering 161,700 conjunctive keyword queries, our attack achieves a 95.15% accuracy in recovering at least one keyword, 82.57% for at least two, 58% for all three keywords, and maintains efficacy against defenses such as SEAL padding and CLRZ obfuscation. Our work exposes the underestimated risks of s-term leakage in practical SSE deployments and calls for a redesign of leakage models for multi-keyword search scenarios.
Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching
Venue: arXiv cs.CL 2025
Year: 2025

Fine-tuning methods such as Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) have demonstrated success in training large language models (LLMs) for single-turn tasks. However, these methods fall short in multi-turn applications, such as diagnostic patient interviewing, where understanding how early conversational turns influence downstream completions and outcomes is essential. In medicine, a multi-turn perspective is critical for learning diagnostic schemas and better understanding conversation dynamics. To address this gap, I introduce Savage Conversation Forests (SCF), a reinforcement learning framework that leverages a branched conversation architecture to fine-tune LLMs for multi-turn dialogue. SCF generates multiple possible conversation continuations at each turn, enabling the model to learn how different early responses affect downstream interactions and diagnostic outcomes. In experiments simulating doctor-patient conversations, SCF with branching outperforms linear conversation architectures on diagnostic accuracy. I hypothesize that SCF's improvements stem from its ability to provide richer, interdependent training signals across conversation turns. These results suggest that a branched training architecture is an important strategy for fine tuning LLMs in complex multi-turn conversational tasks.
Integrated Gaussian Processes for Robust and Adaptive Multi-Object Tracking
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a computationally efficient multi-object tracking approach that can minimise track breaks (e.g., in challenging environments and against agile targets), learn the measurement model parameters on-line (e.g., in dynamically changing scenes) and infer the class of the tracked objects, if joint tracking and kinematic behaviour classification is sought. It capitalises on the flexibilities offered by the integrated Gaussian process as a motion model and the convenient statistical properties of non-homogeneous Poisson processes as a suitable observation model. This can be combined with the proposed effective track revival / stitching mechanism. We accordingly introduce the two robust and adaptive trackers, Gaussian and Poisson Process with Classification (GaPP-Class) and GaPP with Revival and Classification (GaPP-ReaCtion). They employ an appropriate particle filtering inference scheme that efficiently integrates track management and hyperparameter learning (including the object class, if relevant). GaPP-ReaCtion extends GaPP-Class with the addition of a Markov Chain Monte Carlo kernel applied to each particle permitting track revival and stitching (e.g., within a few time steps after deleting a trajectory). Performance evaluation and benchmarking using synthetic and real data show that GaPP-Class and GaPP-ReaCtion outperform other state-of-the-art tracking algorithms. For example, GaPP-ReaCtion significantly reduces track breaks (e.g., by around 30% from real radar data and markedly more from simulated data).
HiPerMotif: Novel Parallel Subgraph Isomorphism in Large-Scale Property Graphs
Venue: arXiv cs.DS 2025
Year: 2025

Subgraph isomorphism, essential for pattern detection in large-scale graphs, faces scalability challenges in attribute-rich property graphs used in neuroscience, systems biology, and social network analysis. Traditional algorithms explore search spaces vertex-by-vertex from empty mappings, leading to extensive early-stage exploration with limited pruning opportunities. We introduce HiPerMotif, a novel hybrid parallel algorithm that fundamentally shifts the search initialization strategy. After structurally reordering the pattern graph to prioritize high-degree vertices, HiPerMotif systematically identifies all possible mappings for the first edge (vertices 0,1) in the target graph, validates these edge candidates using efficient vertex and edge validators, and injects the validated partial mappings as states at depth 2. The algorithm then continues with traditional vertex-by-vertex exploration from these pre-validated starting points, effectively pruning the expensive early search tree branches while enabling natural parallelization over edge candidates. Our contributions include the edge-centric initialization paradigm with state injection, a structural reordering strategy achieving up to 5x speedup, rapid edge and vertex validators for attribute-rich graphs, and efficient parallel enumeration over target graph edges. Implemented in the open-source Arachne framework, HiPerMotif achieves up to 66x speedup over state-of-the-art baselines (VF2-PS, VF3P, Glasgow) on diverse datasets where baselines successfully complete execution. Additionally, HiPerMotif successfully processes massive datasets such as the H01 connectome with 147 million edges, which existing methods cannot handle due to memory constraints. Comprehensive evaluation across synthetic and real-world graphs demonstrates HiPerMotif's scalability, enabling advanced analysis in computational neuroscience and beyond.
Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies
Venue: arXiv cs.CL 2025
Year: 2025

Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectiveness on clinical natural language inference (NLI) remains underexplored. This study presents the first controlled evaluation of how prompt structure and efficient fine-tuning jointly shape model performance in clinical NLI. We inspect four classes of prompting strategies to elicit reasoning in LLMs at different levels of abstraction, and evaluate their impact on a range of clinically motivated reasoning types. For each prompting strategy, we construct high-quality demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models (4B parameters) via Low-Rank Adaptation (LoRA). Across different language models fine-tuned on the NLI4CT benchmark, we found that prompt type alone accounts for up to 44% of the variance in macro-F1. Moreover, LoRA fine-tuning yields consistent gains of +8 to 12 F1, raises output alignment above 97%, and narrows the performance gap to GPT-4o-mini to within 7.1%. Additional experiments on reasoning generalisation reveal that LoRA improves performance in 75% of the models on MedNLI and TREC Clinical Trials Track. Overall, these findings demonstrate that (i) prompt structure is a primary driver of clinical reasoning performance, (ii) compact models equipped with strong prompts and LoRA can rival frontier-scale systems, and (iii) reasoning-type-aware evaluation is essential to uncover prompt-induced trade-offs. Our results highlight the promise of combining prompt design and lightweight adaptation for more efficient and trustworthy clinical NLP systems, providing insights on the strengths and limitations of widely adopted prompting and parameter-efficient techniques in highly specialised domains.
LVLM-Composer's Explicit Planning for Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

The burgeoning field of generative artificial intelligence has fundamentally reshaped our approach to content creation, with Large Vision-Language Models (LVLMs) standing at its forefront. While current LVLMs have demonstrated impressive capabilities in text-to-image generation, they often falter when confronted with complex textual descriptions demanding precise compositional understanding and visual planning. This limitation particularly impacts the accurate rendering of multiple objects, their attributes, spatial relationships, and specific poses within intricate scenes, as evidenced by benchmarks like LongBench-T2I. To address these challenges, we introduce LVLM-Composer, a novel 10-billion parameter scale LVLM specifically engineered for enhanced compositional image synthesis. Our method incorporates a Hierarchical Semantic Planning Module for structured prompt decomposition and a Fine-Grained Feature Alignment Mechanism for precise visual guidance during generation. We propose a multi-stage training paradigm, featuring Hierarchical Semantic-Visual Grounding Pre-training and Compositional Planning Reinforcement Learning with Self-Correction, to instill robust compositional reasoning. Extensive experiments on the LongBench-T2I benchmark, utilizing automatic evaluation by Gemini-2.0-Flash and InternVL3-78B, demonstrate LVLM-Composer's superior performance across critical compositional dimensions including object accuracy, composition fidelity, and pose accuracy, significantly outperforming state-of-the-art baselines. An in-depth ablation study further validates the indispensable contribution of our proposed modules, while human evaluations confirm the perceptual superiority of our generated images. LVLM-Composer represents a significant step towards truly controllable and compositionally accurate open-ended text-to-image generation.
Adaptive Two-sided Assortment Optimization: Revenue Maximization
Venue: arXiv cs.GT 2025
Year: 2025

We study adaptive two-sided assortment optimization for revenue maximization in choice-based matching platforms. The platform has two sides of agents, an initiating side, and a responding side. The decision-maker sequentially selects agents from the initiating side, shows each an assortment of agents from the responding side, and observes their choices. After processing all initiating agents, the responding agents are shown assortments and make their selections. A match occurs when two agents mutually select each other, generating pair-dependent revenue. Choices follow Multinomial Logit (MNL) models. This setting generalizes prior work focused on maximizing the number of matches under submodular demand assumptions, which do not hold in our revenue-maximization context. Our main contribution is the design of polynomial-time approximation algorithms with constant-factor guarantees. In particular, for general pairwise revenues, we develop a randomized algorithm that achieves a $(\frac{1}{2} - ε)$-approximation in expectation for any $ε> 0$. The algorithm is static and provides guarantees under various agent arrival settings, including fixed order, simultaneous processing, and adaptive selection. When revenues are uniform across all pairs involving any given responding-side agent, the guarantee improves to $(1 - \frac{1}{e} - ε)$. In structural settings where responding-side agents share a common revenue-based ranking, we design a simpler adaptive deterministic algorithm achieving a $\frac{1}{2}$-approximation. Our approach leverages novel linear programming relaxations, correlation gap arguments, and structural properties of the revenue functions.
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
Venue: arXiv cs.CL 2025
Year: 2025

Understanding character relationships is essential for interpreting complex narratives and conducting socially grounded AI research. However, manual annotation is time-consuming and low in coverage, while large language models (LLMs) often produce hallucinated or logically inconsistent outputs. We present SymbolicThought, a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning. The system constructs editable character relationship graphs, refines them using seven types of logical constraints, and enables real-time validation and conflict resolution through an interactive interface. To support logical supervision and explainable social analysis, we release a dataset of 160 interpersonal relationships with corresponding logical structures. Experiments show that SymbolicThought improves annotation accuracy and consistency while significantly reducing time cost, offering a practical tool for narrative understanding, explainable AI, and LLM evaluation.
JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Differentiable programming has emerged as a powerful paradigm in scientific computing, enabling automatic differentiation through simulation pipelines and naturally supporting both forward and inverse modeling. We present JAX-MPM, a general-purpose differentiable meshfree solver based on the material point method (MPM) and implemented in the modern JAX architecture. The solver adopts a hybrid Eulerian-Lagrangian framework to capture large deformations, frictional contact, and inelastic material behavior, with emphasis on geomechanics and geophysical hazard applications. Leveraging GPU acceleration and automatic differentiation, JAX-MPM enables efficient gradient-based optimization directly through its time-stepping solvers and supports joint training of physical models with deep learning to infer unknown system conditions and uncover hidden constitutive parameters. We validate JAX-MPM through a series of 2D and 3D benchmark simulations, including dam-break and granular collapse problems, demonstrating both numerical accuracy and GPU-accelerated performance. Results show that a high-resolution 3D granular cylinder collapse with 2.7 million particles completes 1000 time steps in approximately 22 seconds (single precision) and 98 seconds (double precision) on a single GPU. Beyond high-fidelity forward modeling, we demonstrate the framework's inverse modeling capabilities through tasks such as velocity field reconstruction and the estimation of spatially varying friction from sparse data. In particular, JAX-MPM accommodates data assimilation from both Lagrangian (particle-based) and Eulerian (region-based) observations, and can be seamlessly coupled with neural network representations. These results establish JAX-MPM as a unified and scalable differentiable meshfree platform that advances fast physical simulation and data assimilation for complex solid and geophysical systems.
ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security
Venue: arXiv cs.CR 2025
Year: 2025

Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly injection and real-time anomaly detection using both statistical and machine learning (ML) methods. We simulate timing and fault-based anomalies by injecting execution delays and ciphertext perturbations during encryption, generating labeled datasets for detection model training. Two complementary detection mechanisms are developed: a threshold-based timing anomaly detector and a supervised Random Forest classifier trained on combined timing and ciphertext features. We implement and evaluate the framework on both CPU and FPGA-based SoC hardware (PYNQ-Z1), measuring performance across varying block sizes, injection rates, and core counts. Our results show that ML-based detection significantly outperforms threshold-based methods in precision and recall while maintaining real-time performance on embedded hardware. Compared to existing AES anomaly detection methods, our solution offers a low-cost, real-time, and accurate detection approach deployable on lightweight FPGA platforms.
Mpemba Effect in Large-Language Model Training Dynamics: A Minimal Analysis of the Valley-River model
Venue: arXiv cs.AI 2025
Year: 2025

Learning rate (LR) schedules in large language model (LLM) training often follow empirical templates: warm-up, constant plateau/stable phase, and decay (WSD). However, the mechanistic explanation for this strategy remains underexplored, and the choice of plateau height and decay schedule is largely heuristic. In this paper, we connect training dynamics to a thermodynamic analogy via the Mpemba effect - a phenomenon in which a hotter system cools faster than a colder one when quenched into the same bath. We analyze a class of "valley-river" loss landscapes, where sharp (valley) directions equilibrate quickly, while flatter (river) directions govern global descent. The Mpemba effect provides an explanation for the necessity of the warm-up phase and motivates a high plateau - rather than a low one - for accelerating loss decrease during decay. We show that for certain loss landscapes, there exists an optimal plateau learning rate - the "strong Mpemba point" - at which the slowest mode vanishes, resulting in faster convergence during the decay phase. We derive analytical conditions for its existence and estimate decay dynamics required to preserve the Mpemba advantage. Our minimal model and analysis offer a principled justification for plateau-based schedulers and provide guidance for tuning LR in LLMs with minimal hyperparameter sweep.
Context Tuning for In-Context Optimization
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of language models (LLMs) without fine-tuning model parameters. While prompt-based adaptation techniques have demonstrated the effectiveness of lightweight adaptation methods for LLMs, they typically initialize a trainable prompt or prefix with irrelevant tokens for the task at hand. In contrast, Context Tuning initializes the trainable prompt or prefix with task-specific demonstration examples, leveraging the model's inherent In-Context Learning (ICL) ability to extract relevant information for improved few-shot learning performance. Extensive evaluations on benchmarks such as CrossFit, UnifiedQA, MMLU, BIG-Bench Hard, and ARC demonstrate that Context Tuning outperforms traditional prompt-based adaptation methods and achieves competitive accuracy to Test-Time Training with significantly higher training efficiency.
VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Venue: arXiv cs.SI 2025
Year: 2025

Vaccine infodemics, driven by misinformation, disinformation, and inauthentic online behaviours, pose significant threats to global public health. This paper presents our response to this challenge, demonstrating how we developed VaxPulse Vaccine Infodemic Risk Assessment Lifecycle (VIRAL), an AI-powered social listening platform designed to monitor and assess vaccine-related infodemic risks. Leveraging interdisciplinary expertise and international collaborations, VaxPulse VIRAL integrates machine learning methods, including deep learning, active learning, and data augmentation, to provide real-time insights into public sentiments, misinformation trends, and social bot activity. Iterative feedback from domain experts and stakeholders has guided the development of dynamic dashboards that offer tailored, actionable insights to support immunisation programs and address information disorder. Ongoing improvements to VaxPulse will continue through collaboration with our international network and community leaders.
Scaling Context Requires Rethinking Attention
Venue: arXiv cs.LG 2025
Year: 2025

We argue that neither transformers nor sub-quadratic architectures are well suited to training at long sequence lengths: the cost of processing the context is too expensive in the former, too inexpensive in the latter. Approaches such as sliding window attention which reduce the cost-per-token of a transformer impair in-context learning, and so are also unsuitable. To address these limitations, we introduce power attention, an architectural layer for linear-cost sequence modeling whose state size can be adjusted independently of parameters, unlocking the advantages of linear attention on practical domains. We develop and open-source a set of GPU kernels for efficient power attention, identifying a novel pattern of operation fusion to avoid memory and bandwidth bottlenecks. Our experiments on the in-context learning of power attention shows that these models dominate both exponential attention and linear attention at long-context training.
VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learning
Venue: arXiv cs.CR 2025
Year: 2025

The persistent threat of Android malware presents a serious challenge to the security of millions of users globally. While many machine learning-based methods have been developed to detect these threats, their reliance on large labeled datasets limits their effectiveness against emerging, previously unseen malware families, for which labeled data is scarce or nonexistent. To address this challenge, we introduce a novel zero-shot learning framework that combines Variational Graph Auto-Encoders (VGAE) with Siamese Neural Networks (SNN) to identify malware without needing prior examples of specific malware families. Our approach leverages graph-based representations of Android applications, enabling the model to detect subtle structural differences between benign and malicious software, even in the absence of labeled data for new threats. Experimental results show that our method outperforms the state-of-the-art MaMaDroid, especially in zero-day malware detection. Our model achieves 96.24% accuracy and 95.20% recall for unknown malware families, highlighting its robustness against evolving Android threats.