OpenRaiser/Intern-Atlas huggingface dataset:
Designing The Drive: Enhancing User Experience through Adaptive Interfaces in Autonomous Vehicles
Venue: arXiv cs.HC 2025
Year: 2025

With the recent development and integration of autonomous vehicles (AVs) in transportation systems of the modern world, the emphasis on customizing user interfaces to optimize the overall user experience has been growing expediently. Therefore, understanding user needs and preferences is essential to the acceptance and trust of these technologies as they continue to grow in prevalence. This paper addresses the implementation of HCI principles in the personalization of interfaces to improve safety, security, and usability for the users. This paper explores the way that personalized interfaces can be devised to increase user engagement and satisfaction through various HCI strategies such as adaptive design, multi-modal interaction, and user feedback mechanisms. Moreover, this paper puts emphasis on factors of transparency and user control in the design of an interface; hence, allowing users to design or modify their experience could foster an increase in trust in autonomous systems. In so doing, this research touches on the quite influential role HCI will play in this future scenario of autonomous vehicles while designing to ensure relevance to the diverse needs of users while maintaining high standards of safety and security. Discussing various HCI strategies such as adaptive design, multi-modal interaction, and feedback mechanisms to the user, this paper demonstrates how personalized interfaces can enhance significantly both user engagement and satisfaction. Transparency and user control also in designing an interface are further discussed, pointing out the need for a prerequisite condition of enabling the user to take control of their experience as a state of trust in autonomous systems. In summary, this paper points out the role of HCI in the development of autonomous vehicles and addresses numerous needs with respect to those enforced safety and security standards.
OLR-WAA: Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Averaging
Venue: arXiv cs.LG 2025
Year: 2025

Real-world datasets frequently exhibit evolving data distributions, reflecting temporal variations and underlying shifts. Overlooking this phenomenon, known as concept drift, can substantially degrade the predictive performance of the model. Furthermore, the presence of hyperparameters in online models exacerbates this issue, as these parameters are typically fixed and lack the flexibility to dynamically adjust to evolving data. This paper introduces "OLR-WAA: An Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Average", a hyperparameter-free model designed to tackle the challenges of non-stationary data streams and enable effective, continuous adaptation. The objective is to strike a balance between model stability and adaptability. OLR-WAA incrementally updates its base model by integrating incoming data streams, utilizing an exponentially weighted moving average. It further introduces a unique optimization mechanism that dynamically detects concept drift, quantifies its magnitude, and adjusts the model based on real-time data characteristics. Rigorous evaluations show that it matches batch regression performance in static settings and consistently outperforms or rivals state-of-the-art online models, confirming its effectiveness. Concept drift datasets reveal a performance gap that OLR-WAA effectively bridges, setting it apart from other online models. In addition, the model effectively handles confidence-based scenarios through a conservative update strategy that prioritizes stable, high-confidence data points. Notably, OLR-WAA converges rapidly, consistently yielding higher R2 values compared to other online models.
OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average
Venue: arXiv cs.LG 2025
Year: 2025

Real-world data sets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significantly diminish a model's predictive accuracy. Furthermore, the presence of hyperparameters in online models exacerbates this issue. These parameters are typically fixed and cannot be dynamically adjusted by the user in response to the evolving data distribution. This paper introduces Online Classification with Weighted Average (OLC-WA), an adaptive, hyperparameter-free online classification model equipped with an automated optimization mechanism. OLC-WA operates by blending incoming data streams with an existing base model. This blending is facilitated by an exponentially weighted moving average. Furthermore, an integrated optimization mechanism dynamically detects concept drift, quantifies its magnitude, and adjusts the model based on the observed data stream characteristics. This approach empowers the model to effectively adapt to evolving data distributions within streaming environments. Rigorous empirical evaluation across diverse benchmark datasets shows that OLC-WA achieves performance comparable to batch models in stationary environments, maintaining accuracy within 1-3%, and surpasses leading online baselines by 10-25% under drift, demonstrating its effectiveness in adapting to dynamic data streams.
Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, LLaMA
Venue: arXiv cs.CL 2025
Year: 2025

Prompt engineering has emerged as a critical factor influencing large language model (LLM) performance, yet the impact of pragmatic elements such as linguistic tone and politeness remains underexplored, particularly across different model families. In this work, we propose a systematic evaluation framework to examine how interaction tone affects model accuracy and apply it to three recently released and widely available LLMs: GPT-4o mini (OpenAI), Gemini 2.0 Flash (Google DeepMind), and Llama 4 Scout (Meta). Using the MMMLU benchmark, we evaluate model performance under Very Friendly, Neutral, and Very Rude prompt variants across six tasks spanning STEM and Humanities domains, and analyze pairwise accuracy differences with statistical significance testing. Our results show that tone sensitivity is both model-dependent and domain-specific. Neutral or Very Friendly prompts generally yield higher accuracy than Very Rude prompts, but statistically significant effects appear only in a subset of Humanities tasks, where rude tone reduces accuracy for GPT and Llama, while Gemini remains comparatively tone-insensitive. When performance is aggregated across tasks within each domain, tone effects diminish and largely lose statistical significance. Compared with earlier researches, these findings suggest that dataset scale and coverage materially influence the detection of tone effects. Overall, our study indicates that while interaction tone can matter in specific interpretive settings, modern LLMs are broadly robust to tonal variation in typical mixed-domain use, providing practical guidance for prompt design and model selection in real-world deployments.
Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
Venue: arXiv cs.LG 2025
Year: 2025

We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provider distributes trained models to multiple clients whose devices support local inference but lack the ability to retrain those models, placing the burden of performance maintenance on the provider. We introduce a model-agnostic framework that captures the interaction between resource allocation, concept drift dynamics, and deployment timing. We show that optimal training policies depend critically on the aging properties of concept durations. Under sudden concept changes, we derive optimal training policies subject to budget constraints when concept durations follow distributions with Decreasing Mean Residual Life (DMRL), and show that intuitive heuristics are provably suboptimal under Increasing Mean Residual Life (IMRL). We further study model deployment under communication constraints, prove that the associated optimization problem is quasi-convex under mild conditions, and propose a randomized scheduling strategy that achieves near-optimal client-side performance. These results offer theoretical and algorithmic foundations for cost-efficient ML model management under concept drift, with implications for continual learning, distributed inference, and adaptive ML systems.
Forgetful but Faithful: A Cognitive Memory Architecture and Benchmark for Privacy-Aware Generative Agents
Venue: arXiv cs.AI 2025
Year: 2025

As generative agents become increasingly sophisticated and deployed in long-term interactive scenarios, their memory management capabilities emerge as a critical bottleneck for both performance and privacy. Current approaches either maintain unlimited memory stores, leading to computational intractability and privacy concerns, or employ simplistic forgetting mechanisms that compromise agent coherence and functionality. This paper introduces the Memory-Aware Retention Schema (MaRS), a novel framework for human-centered memory management in generative agents, coupled with six theoretically-grounded forgetting policies that balance performance, privacy, and computational efficiency. We present the Forgetful but Faithful Agent (FiFA) benchmark, a comprehensive evaluation framework that assesses agent performance across narrative coherence, goal completion, social recall accuracy, privacy preservation, and cost efficiency. Through extensive experimentation involving 300 evaluation runs across multiple memory budgets and agent configurations, we demonstrate that our hybrid forgetting policy achieves superior performance (composite score: 0.911) while maintaining computational tractability and privacy guarantees. Our work establishes new benchmarks for memory-budgeted agent evaluation and provides practical guidelines for deploying generative agents in resource-constrained, privacy-sensitive environments. The theoretical foundations, implementation framework, and empirical results contribute to the emerging field of human-centered AI by addressing fundamental challenges in agent memory management that directly impact user trust, system scalability, and regulatory compliance.
Distillation of Discrete Diffusion by Exact Conditional Distribution Matching
Venue: arXiv cs.LG 2025
Year: 2025

Discrete diffusion models (DDMs) are a powerful class of generative models for categorical data, but they typically require many function evaluations for a single sample, making inference expensive. Existing acceleration methods either rely on approximate simulators, such as $τ$-leaping, or on distillation schemes that train new student models and auxiliary networks with proxy objectives. We propose a simple and principled distillation alternative based on \emph{conditional distribution matching}. Our key observation is that the reverse conditional distribution of clean data given a noisy state, $p_{0\mid t}(x_0 \mid x_t)$, admits a Markov decomposition through intermediate times and can be recovered from marginal density ratios and the known forward CTMC kernel. We exploit this structure to define distillation objectives that directly match conditional distributions between a pre-trained teacher and a low-NFE student, both for one-step and few-step samplers.
Sub-$n^k$ Deterministic algorithm for minimum $k$-way cut in simple graphs
Venue: arXiv cs.DS 2025
Year: 2025

We present a \emph{deterministic exact algorithm} for the \emph{minimum $k$-cut problem} on simple graphs. Our approach combines the \emph{principal sequence of partitions (PSP)}, derived canonically from ideal loads, with a single level of \emph{Kawarabayashi--Thorup (KT)} contractions at the critical PSP threshold~$λ_j$. Let $j$ be the smallest index with $κ(P_j)\ge k$ and $R := k - κ(P_{j-1})$. We prove a structural decomposition theorem showing that an optimal $k$-cut can be expressed as the level-$(j\!-\!1)$ boundary $A_{\le j-1}$ together with exactly $(R-r)$ \emph{non-trivial} internal cuts of value at most~$λ_j$ and $r$ \emph{singleton isolations} (``islands'') inside the parts of~$P_{j-1}$. At this level, KT contractions yield kernels of total size $\widetilde{O}(n / λ_j)$, and from them we build a \emph{canonical border family}~$\mathcal{B}$ of the same order that deterministically covers all optimal refinement choices. Branching only over~$\mathcal{B}$ (and also including an explicit ``island'' branch) gives total running time $$ T(n,m,k) = \widetilde{O}\left(\mathrm{poly}(m)+\Bigl(\tfrac{n}{λ_j}+n^{ω/3}\Bigr)^{R}\right), $$ where $ω< 2.373$ is the matrix multiplication exponent. In particular, if $λ_j \ge n^{\varepsilon}$ for some constant $\varepsilon > 0$, we obtain a \emph{deterministic sub-$n^k$-time algorithm}, running in $n^{(1-\varepsilon)(k-1)+o(k)}$ time. Finally, combining our PSP$\times$KT framework with a small-$λ$ exact subroutine via a simple meta-reduction yields a deterministic $n^{c k+O(1)}$ algorithm for $c = \max\{ t/(t+1), ω/3 \} < 1$, aligning with the exponent in the randomized bound of He--Li (STOC~2022) under the assumed subroutine.
Next-generation reservoir computing validated by classification task
Venue: arXiv cs.LG 2025
Year: 2025

An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from the time series inputs. However, benchmark tests so far reported have been one-sided, limited to prediction tasks of temporal waveforms such as Lorenz 63 attractor and Mackey-Glass chaotic signal. We will demonstrate for the first time that NG-RC can perform classification task as good as conventional RC. This validates the versatile computational capability of NG-RC in tasks of both prediction and classification.
OptHQC: Optimize HQC for High-Performance Post-Quantum Cryptography
Venue: arXiv cs.CR 2025
Year: 2025

As post-quantum cryptography (PQC) becomes increasingly critical for securing future communication systems, the performance overhead introduced by quantum-resistant algorithms presents a major computing challenge. HQC (Hamming Quasi-Cyclic) is a newly standardized code-based PQC scheme designed to replace classical key exchange methods. In this paper, we propose OptHQC, an optimized implementation of the HQC scheme to deliver high-performance cryptographic operations. Our approach provides a comprehensive analysis of each computational blocks in HQC and introduces optimizations across all three stages: key generation, encryption, and decryption. We first exploit data-level sparsity in vector multiplication to accelerate polynomial operations during vector generation. We then leverage instruction-level acceleration (e.g., AVX2) in hash computation to further improve performance. Last, we transform multiplication into lookup table indexing and optimize memory access patterns in syndrome computation and error vector recovery, which are the most computationally intensive operations in HQC. Overall, OptHQC achieves an average 55% speedup over the reference HQC implementation on CPU.
A Direct Second-Order Method for Solving Two-Player Zero-Sum Games
Venue: arXiv cs.GT 2025
Year: 2025

We introduce, to our knowledge, the first direct second-order method for computing Nash equilibria in two-player zero-sum games. To do so, we construct a Douglas-Rachford-style splitting formulation, which we then solve with a semi-smooth Newton (SSN) method. We show that our algorithm enjoys local superlinear convergence. In order to augment the fast local behavior of our SSN method with global efficiency guarantees, we develop a hybrid method that combines our SSN method with the state-of-the-art first-order method for game solving, Predictive Regret Matching$^+$ (PRM$^+$). Our hybrid algorithm leverages the global progress provided by PRM$^+$, while achieving a local superlinear convergence rate once it switches to SSN near a Nash equilibrium. Numerical experiments on matrix games demonstrate order-of-magnitude speedups over PRM$^+$ for high-precision solutions.
Open Source Software and Data for Human Service Development: A Case Study on Predicting Housing Instability
Venue: arXiv cs.CY 2025
Year: 2025

Open-source data and tools are lauded as essential for replicable and usable social science, though little is known about their use in resource constrained human service provision. This paper examines the challenges and opportunities of open-source tools and data in human service development by using both to forecast failure to pay eviction filings in Bronx County, NY. We use zip code level data from the Housing Data Coalition, the American Community Survey 5-year estimates, and DeepMaps Model of the Labor Force to forecast rates through July 2021. We employ multilevel (MLM) and exponential smoothing (ETS) models using the R project for Statistical Computing, an oft used open-source statistical software. We compare our results to what happened during the same period, to illustrate the efficacy of the open-source tools and techniques employed. We argue open-source data and software may facilitate rapid analysis of public data - a much-needed ability in human service intervention development under increasingly constrained resources - but find public data are limited by the information they reliably capture, limiting their utility by a non-trivial margin of error. The manuscript concludes by considering lessons for human service organizations with limited analytical resources and a vested interest in low-resourced communities.
3-Query RLDCs are Strictly Stronger than 3-Query LDCs
Venue: arXiv cs.CC 2025
Year: 2025

We construct $3$-query relaxed locally decodable codes (RLDCs) with constant alphabet size and length $\tilde{O}(k^2)$ for $k$-bit messages. Combined with the lower bound of $\tildeΩ(k^3)$ of [Alrabiah, Guruswami, Kothari, Manohar, STOC 2023] on the length of locally decodable codes (LDCs) with the same parameters, we obtain a separation between RLDCs and LDCs, resolving an open problem of [Ben-Sasson, Goldreich, Harsha, Sudan and Vadhan, SICOMP 2006]. Our RLDC construction relies on two components. First, we give a new construction of probabilistically checkable proofs of proximity (PCPPs) with $3$ queries, quasi-linear size, constant alphabet size, perfect completeness, and small soundness error. This improves upon all previous PCPP constructions, which either had a much higher query complexity or soundness close to $1$. Second, we give a query-preserving transformation from PCPPs to RLDCs. At the heart of our PCPP construction is a $2$-query decodable PCP (dPCP) with matching parameters, and our construction builds on the HDX-based PCP of [Bafna, Minzer, Vyas, Yun, STOC 2025] and on the efficient composition framework of [Moshkovitz, Raz, JACM 2010] and [Dinur, Harsha, SICOMP 2013]. More specifically, we first show how to use the HDX-based construction to get a dPCP with matching parameters but a large alphabet size, and then prove an appropriate composition theorem (and related transformations) to reduce the alphabet size in dPCPs.
Challenges and Enablers: Remote Work for People with Disabilities in Software Development Teams
Venue: arXiv cs.SE 2025
Year: 2025

The increasing adoption of remote and hybrid work modalities in the technology sector has brought new opportunities and challenges for the inclusion of people with disabilities (PWD) in software development teams (SDT). This study investigates how remote work affects PWDs' experience in mixed-ability SDT, focusing on the unique challenges and strategies that emerge in remote environments. We conducted an online survey with \totalSurveyResponses valid responses, encompassing PWD, their leaders, and teammates, to capture sociotechnical aspects of their experiences with remote collaboration. To deepen our understanding, we carried out 14 structured interviews with software developers who self-identified as having disabilities (six autistic individuals, six with physical disabilities, and two who are d/Deaf). Our analysis combines quantitative data with qualitative coding of open-ended survey responses and interview transcripts. The results reveal that, despite the barriers faced by team members with disabilities, their teammates and leaders have a limited perception of the daily challenges involved in sustaining collaborative remote work. These findings highlight opportunities for improvement in accessibility tools, communication strategies, and adaptive management approaches.
Towards Open Standards for Systemic Complexity in Digital Forensics
Venue: arXiv cs.AI 2025
Year: 2025

The intersection of artificial intelligence (AI) and digital forensics (DF) is becoming increasingly complex, ubiquitous, and pervasive, with overlapping techniques and technologies being adopted in all types of scientific and technical inquiry. Despite incredible advances, forensic sciences are not exempt from errors and remain vulnerable to fallibility. To mitigate the limitations of errors in DF, the systemic complexity is identified and addressed with the adoption of human-readable artifacts and open standards. A DF AI model schema based on the state of the art is outlined.
Scaling Up AI-Generated Image Detection via Generator-Aware Prototypes
Venue: arXiv cs.CV 2025
Year: 2025

The pursuit of a universal AI-generated image (AIGI) detector often relies on aggregating data from numerous generators to improve generalization. However, this paper identifies a paradoxical phenomenon we term the Benefit then Conflict dilemma, where detector performance stagnates and eventually degrades as source diversity expands. Our systematic analysis, diagnoses this failure by identifying two core issues: severe data-level heterogeneity, which causes the feature distributions of real and synthetic images to increasingly overlap, and a critical model-level bottleneck from fixed, pretrained encoders that cannot adapt to the rising complexity. To address these challenges, we propose Generator-Aware Prototype Learning (GAPL), a framework that constrain representation with a structured learning paradigm. GAPL learns a compact set of canonical forgery prototypes to create a unified, low-variance feature space, effectively countering data heterogeneity.To resolve the model bottleneck, it employs a two-stage training scheme with Low-Rank Adaptation, enhancing its discriminative power while preserving valuable pretrained knowledge. This approach establishes a more robust and generalizable decision boundary. Through extensive experiments, we demonstrate that GAPL achieves state-of-the-art performance, showing superior detection accuracy across a wide variety of GAN and diffusion-based generators. Code is available at https://github.com/UltraCapture/GAPL
VoroLight: Learning Quality Volumetric Voronoi Meshes from General Inputs
Venue: arXiv cs.CG 2025
Year: 2025

We present VoroLight, a differentiable framework for 3D shape reconstruction based on Voronoi meshing. Our approach generates smooth, watertight surfaces and topologically consistent volumetric meshes directly from diverse inputs, including images, implicit shape level-set fields, point clouds and meshes. VoroLight operates in three stages: it first initializes a surface using a differentiable Voronoi formulation, then refines surface quality through a polygon-face sphere training stage, and finally reuses the differentiable Voronoi formulation for volumetric optimization with additional interior generator points. Project page: https://jiayinlu19960224.github.io/vorolight/