OpenRaiser/Intern-Atlas huggingface dataset:
Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance
Venue: arXiv cs.CY 2025
Year: 2025

The rise of conversational AI (CAI), powered by large language models, is transforming how individuals access and interact with digital information. However, these tools may inadvertently amplify existing digital inequalities. This study investigates whether differences in formal education are associated with CAI avoidance, leveraging behavioral data from an online experiment (N = 1,636). Participants were randomly assigned to a control or an information-seeking task, either a traditional online search or a CAI (Perplexity AI). Task avoidance (operationalized as survey abandonment or providing unrelated responses during task assignment) was significantly higher in the CAI group (51%) compared to the search (30.9%) and control (16.8%) groups, with the highest CAI avoidance among participants with lower education levels (~74.4%). Structural equation modeling based on the theoretical framework UTAUT2 and LASSO regressions reveal that education is strongly associated with CAI avoidance, even after accounting for various cognitive and affective predictors of technology adoption. These findings underscore education's central role in shaping AI adoption and the role of self-selection biases in AI-related research, stressing the need for inclusive design to ensure equitable access to emerging technologies.
The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web
Venue: arXiv cs.CR 2025
Year: 2025

The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP (Hou et al., 2025), A2A (Habler et al., 2025), ACP (Liu et al., 2025), and Cisco's AGP (Edwards, 2025) -- cannot address at scale. We present the Nanda Unified Architecture, a decentralized framework built around three core innovations: fast DID-based agent discovery through distributed registries, semantic agent cards with verifiable credentials and composability profiles, and a dynamic trust layer that integrates behavioral attestations with policy compliance. The system introduces X42/H42 micropayments for economic coordination and MAESTRO, a security framework incorporating Synergetics' patented AgentTalk protocol (US Patent 12,244,584 B1) and secure containerization. Real-world deployments demonstrate 99.9 percent compliance in healthcare applications and substantial monthly transaction volumes with strong privacy guarantees. By unifying MIT's trust research with production deployments from Cisco and Synergetics, we show how cryptographic proofs and policy-as-code transform agents into trust-anchored participants in a decentralized economy (Lakshmanan, 2025; Sha, 2025). The result enables a globally interoperable Internet of Agents where trust becomes the native currency of collaboration across both enterprise and Web3 ecosystems.
Improving the Price of Anarchy via Predictions in Parallel-Link Networks
Venue: arXiv cs.GT 2025
Year: 2025

We study non-atomic congestion games on parallel-link networks with affine cost functions. We investigate the power of machine-learned predictions in the design of coordination mechanisms aimed at minimizing the impact of selfishness. Our main results demonstrate that enhancing coordination mechanisms with a simple advice on the input rate can optimize the social cost whenever the advice is accurate (consistency), while only incurring minimal losses even when the predictions are arbitrarily inaccurate (bounded robustness). Moreover, we provide a full characterization of the consistent mechanisms that holds for all monotone cost functions, and show that our suggested mechanism is optimal with respect to the robustness. We further explore the notion of smoothness within this context: we extend our mechanism to achieve error-tolerance, i.e. we provide an approximation guarantee that degrades smoothly as a function of the prediction error, up to a predetermined threshold, while achieving a bounded robustness.
A Randomized Rounding Approach for DAG Edge Deletion
Venue: arXiv cs.DS 2025
Year: 2025

In the DAG Edge Deletion problem, we are given an edge-weighted directed acyclic graph and a parameter $k$, and the goal is to delete the minimum weight set of edges so that the resulting graph has no paths of length $k$. This problem, which has applications to scheduling, was introduced in 2015 by Kenkre, Pandit, Purohit, and Saket. They gave a $k$-approximation and showed that it is UGC-Hard to approximate better than $\lfloor 0.5k \rfloor$ for any constant $k \ge 4$ using a work of Svensson from 2012. The approximation ratio was improved to $\frac{2}{3}(k+1)$ by Klein and Wexler in 2016. In this work, we introduce a randomized rounding framework based on distributions over vertex labels in $[0,1]$. The most natural distribution is to sample labels independently from the uniform distribution over $[0,1]$. We show this leads to a $(2-\sqrt{2})(k+1) \approx 0.585(k+1)$-approximation. By using a modified (but still independent) label distribution, we obtain a $0.549(k+1)$-approximation for the problem, as well as show that no independent distribution over labels can improve our analysis to below $0.542(k+1)$. Finally, we show a $0.5(k+1)$-approximation for bipartite graphs and for instances with structured LP solutions. Whether this ratio can be obtained in general is open.
Finding sparse induced subgraphs on graphs of bounded induced matching treewidth
Venue: arXiv cs.DS 2025
Year: 2025

The induced matching width of a tree decomposition of a graph $G$ is the cardinality of a largest induced matching $M$ of $G$, such that there exists a bag that intersects every edge in $M$. The induced matching treewidth of a graph $G$, denoted by $\mathsf{tree-}μ(G)$, is the minimum induced matching width of a tree decomposition of $G$. The parameter $\mathsf{tree-}μ$ was introduced by Yolov [SODA '18], who showed that, for example, Maximum-Weight Independent Set can be solved in polynomial-time on graphs of bounded $\mathsf{tree-}μ$. Lima, Milanič, Muršič, Okrasa, Rzążewski, and Štorgel [ESA '24] conjectured that this algorithm can be generalized to a meta-problem called Maximum-Weight Induced Subgraph of Bounded Treewidth, where we are given a vertex-weighted graph $G$, an integer $w$, and a $\mathsf{CMSO}_2$-sentence $Φ$, and are asked to find a maximum-weight set $X \subseteq V(G)$ so that $G[X]$ has treewidth at most $w$ and satisfies $Φ$. They proved the conjecture for some special cases, such as for the problem Maximum-Weight Induced Forest. In this paper, we prove the general case of the conjecture. In particular, we show that Maximum-Weight Induced Subgraph of Bounded Treewidth is polynomial-time solvable when $\mathsf{tree-}μ(G)$, $w$, and $|Φ|$ are bounded. The running time of our algorithm for $n$-vertex graphs $G$ with $\mathsf{tree} - μ(G) \le k$ is $f(k, w, |Φ|) \cdot n^{O(k w^2)}$ for a computable function $f$.
Martian World Model: Controllable Video Synthesis with Physically Accurate 3D Reconstructions
Venue: arXiv cs.CV 2025
Year: 2025

Synthesizing realistic Martian landscape videos is crucial for mission rehearsal and robotic simulation. However, this task poses unique challenges due to the scarcity of high-quality Martian data and the significant domain gap between Martian and terrestrial imagery. To address these challenges, we propose a holistic solution composed of two key components: 1) A data curation pipeline Multimodal Mars Synthesis (M3arsSynth), which reconstructs 3D Martian environments from real stereo navigation images, sourced from NASA's Planetary Data System (PDS), and renders high-fidelity multiview 3D video sequences. 2) A Martian terrain video generator, MarsGen, which synthesizes novel videos visually realistic and geometrically consistent with the 3D structure encoded in the data. Our M3arsSynth engine spans a wide range of Martian terrains and acquisition dates, enabling the generation of physically accurate 3D surface models at metric-scale resolution. MarsGen, fine-tuned on M3arsSynth data, synthesizes videos conditioned on an initial image frame and, optionally, camera trajectories or textual prompts, allowing for video generation in novel environments. Experimental results show that our approach outperforms video synthesis models trained on terrestrial datasets, achieving superior visual fidelity and 3D structural consistency.
Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models
Venue: arXiv cs.CV 2025
Year: 2025

CLIP and large multimodal models (LMMs) have better accuracy on examples involving concepts that are highly represented in the training data. However, the role of concept combinations in the training data on compositional generalization is largely unclear -- for instance, how does accuracy vary when a common object appears in an uncommon pairing with another object? In this paper, we investigate how word co-occurrence statistics in the pretraining dataset (a proxy for co-occurrence of visual concepts) impacts CLIP/LMM performance. To disentangle the effects of word co-occurrence frequencies from single-word frequencies, we measure co-occurrence with pointwise mutual information (PMI), which normalizes the joint probability of two words co-occurring by the probability of co-occurring independently. Using synthetically generated images with a variety of concept pairs, we show a strong correlation between PMI in the CLIP pretraining data and zero-shot accuracy in CLIP models trained on LAION-400M (r=0.97 and 14% accuracy gap between images in the top and bottom 5% of PMI values), demonstrating that even accuracy on common concepts is affected by the combination of concepts in the image. Leveraging this finding, we reproduce this effect in natural images by editing them to contain pairs with varying PMI, resulting in a correlation of r=0.75. Finally, we demonstrate that this behavior in CLIP transfers to LMMs built on top of CLIP (r=0.70 for TextVQA, r=0.62 for VQAv2). Our findings highlight the need for algorithms and architectures that improve compositional generalization in multimodal models without scaling the training data combinatorially. Our code is available at https://github.com/helenqu/multimodal-pretraining-pmi.
Assessing the Capabilities and Limitations of FinGPT Model in Financial NLP Applications
Venue: arXiv cs.CL 2025
Year: 2025

This work evaluates FinGPT, a financial domain-specific language model, across six key natural language processing (NLP) tasks: Sentiment Analysis, Text Classification, Named Entity Recognition, Financial Question Answering, Text Summarization, and Stock Movement Prediction. The evaluation uses finance-specific datasets to assess FinGPT's capabilities and limitations in real-world financial applications. The results show that FinGPT performs strongly in classification tasks such as sentiment analysis and headline categorization, often achieving results comparable to GPT-4. However, its performance is significantly lower in tasks that involve reasoning and generation, such as financial question answering and summarization. Comparisons with GPT-4 and human benchmarks highlight notable performance gaps, particularly in numerical accuracy and complex reasoning. Overall, the findings indicate that while FinGPT is effective for certain structured financial tasks, it is not yet a comprehensive solution. This research provides a useful benchmark for future research and underscores the need for architectural improvements and domain-specific optimization in financial language models.
Signal or Noise? Evaluating Large Language Models in Resume Screening Across Contextual Variations and Human Expert Benchmarks
Venue: arXiv cs.CL 2025
Year: 2025

This study investigates whether large language models (LLMs) exhibit consistent behavior (signal) or random variation (noise) when screening resumes against job descriptions, and how their performance compares to human experts. Using controlled datasets, we tested three LLMs (Claude, GPT, and Gemini) across contexts (No Company, Firm1 [MNC], Firm2 [Startup], Reduced Context) with identical and randomized resumes, benchmarked against three human recruitment experts. Analysis of variance revealed significant mean differences in four of eight LLM-only conditions and consistently significant differences between LLM and human evaluations (p < 0.01). Paired t-tests showed GPT adapts strongly to company context (p < 0.001), Gemini partially (p = 0.038 for Firm1), and Claude minimally (p > 0.1), while all LLMs differed significantly from human experts across contexts. Meta-cognition analysis highlighted adaptive weighting patterns that differ markedly from human evaluation approaches. Findings suggest LLMs offer interpretable patterns with detailed prompts but diverge substantially from human judgment, informing their deployment in automated hiring systems.
CRISP: Complex Reasoning with Interpretable Step-based Plans
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in large language models (LLMs) underscore the need for stronger reasoning capabilities to solve complex problems effectively. While Chain-of-Thought (CoT) reasoning has been a step forward, it remains insufficient for many domains. A promising alternative is explicit high-level plan generation, but existing approaches largely assume that LLMs can produce effective plans through few-shot prompting alone, without additional training. In this work, we challenge this assumption and introduce CRISP (Complex Reasoning with Interpretable Step-based Plans), a multi-domain dataset of high-level plans for mathematical reasoning and code generation. The plans in CRISP are automatically generated and rigorously validated--both intrinsically, using an LLM as a judge, and extrinsically, by evaluating their impact on downstream task performance. We demonstrate that fine-tuning a small model on CRISP enables it to generate higher-quality plans than much larger models using few-shot prompting, while significantly outperforming Chain-of-Thought reasoning. Furthermore, our out-of-domain evaluation reveals that fine-tuning on one domain improves plan generation in the other, highlighting the generalizability of learned planning capabilities.
The relative importance of being Gaussian
Venue: arXiv cs.CV 2025
Year: 2025

The remarkable results for denoising in computer vision using diffusion models given in \cite{SDWMG,HJA,HHG} yield a robust mathematical justification for algorithms based on crucial properties of a sequence of Gaussian independent $N(0,1)$ random variables. In particular the derivations use the fact that a Gaussian distribution is determined by its mean and variance and that the sum of two Gaussians is another Gaussian. \bigskip The issue raised in this short note is the following: suppose we use the algorithm without any changes but replace the nature of the noise and use, for instance, uniformly distributed noise or noise with a Beta distribution, or noise which is a random superposition of two Gaussians with very different variances. One could, of course, try to modify the algorithm keeping in mind the nature of the noise, but this is not what we do. Instead we study the performance of the algorithm when used with noise that is very far in nature from the Gaussian case, where it is designed to work well. Usually these algorithms are implemented on very powerful computers. Our experiments are all carried out on a small laptop and for the smallest possible image size. Exploring how our observations are confirmed or changed when dealing in different situations remains an interesting challenge.
The State of Computational Science in Fission and Fusion Energy
Venue: arXiv cs.SE 2025
Year: 2025

The tools used to engineer something are just as important as the thing that is actually being engineered. In fact, in many cases, the tools can indeed determine what is engineerable. In fusion and fission1 energy engineering, software has become the dominant tool for design. For that reason, in 2024, for the first time ever, we asked 103 computational scientists developing the codes used in fusion and fission energy about the problems they are attempting to solve with their codes, the tools available to them to solve them, and their end to end developer experience with said tools. The results revealed a changing tide in software tools in fusion and fission, with more and more computational scientists preferring modern programming languages, open-source codes, and modular software. These trends represent a peek into what will happen 5 to 10 years in the future of nuclear engineering. Since the majority of our respondents belonged to US national labs and universities, these results hint at the most cutting-edge trends in the industry. The insights included in the State of Computational Science in Fission and Fusion Energy indicate a dramatic shift toward multiphysics codes, a drop-off in the use of FORTRAN in favor of more modern languages like Python and C++, and ever-rising budgets for code development, at times reaching $50M in a single organization. Our survey paints a future of nuclear engineering codes that is modular in nature, small in terms of compute, and increasingly prioritized by organizations. Access to our results in web form are available online.
An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images
Venue: arXiv cs.CV 2025
Year: 2025

Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data.
Photonic Rails in ML Datacenters
Venue: arXiv cs.NI 2025
Year: 2025

Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-to-all connectivity in rails imposes massive power, cost, and complexity overheads. We propose a rethinking of the rail abstraction by retaining its communication semantics, but realizing it using optical circuit switches. The key challenge is that optical switches support only one-to-one connectivity at a time, limiting the fan-out of traffic in ML workloads using hybrid parallelisms. We introduce parallelism-driven rail reconfiguration as a solution that leverages the sequential ordering between traffic from different parallelisms. We design a control plane, Opus, to enable time-multiplexed emulation of electrical rail switches using optical switches. More broadly, our work discusses a new research agenda: datacenter fabrics that co-evolve with the model parallelism dimensions within each job, as opposed to the prevailing mindset of reconfiguring networks before a job begins.
Just Read the Question: Enabling Generalization to New Assessment Items with Text Awareness
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning has been proposed as a way to improve educational assessment by making fine-grained predictions about student performance and learning relationships between items. One challenge with many machine learning approaches is incorporating new items, as these approaches rely heavily on historical data. We develop Text-LENS by extending the LENS partial variational auto-encoder for educational assessment to leverage item text embeddings, and explore the impact on predictive performance and generalization to previously unseen items. We examine performance on two datasets: Eedi, a publicly available dataset that includes item content, and LLM-Sim, a novel dataset with test items produced by an LLM. We find that Text-LENS matches LENS' performance on seen items and improves upon it in a variety of conditions involving unseen items; it effectively learns student proficiency from and makes predictions about student performance on new items.
An Embedded Real-time Object Alert System for Visually Impaired: A Monocular Depth Estimation based Approach through Computer Vision
Venue: arXiv cs.CV 2025
Year: 2025

Visually impaired people face significant challenges in their day-to-day commutes in the urban cities of Bangladesh due to the vast number of obstructions on every path. With many injuries taking place through road accidents on a daily basis, it is paramount for a system to be developed that can alert the visually impaired of objects at close distance beforehand. To overcome this issue, a novel alert system is proposed in this research to assist the visually impaired in commuting through these busy streets without colliding with any objects. The proposed system can alert the individual to objects that are present at a close distance. It utilizes transfer learning to train models for depth estimation and object detection, and combines both models to introduce a novel system. The models are optimized through the utilization of quantization techniques to make them lightweight and efficient, allowing them to be easily deployed on embedded systems. The proposed solution achieved a lightweight real-time depth estimation and object detection model with an mAP50 of 0.801.
On the Parallel Complexity of Finding a Matroid Basis
Venue: arXiv cs.DS 2025
Year: 2025

A fundamental question in parallel computation, posed by Karp, Upfal, and Wigderson (FOCS 1985, JCSS 1988), asks: \emph{given only independence-oracle access to a matroid on $n$ elements, how many rounds are required to find a basis using only polynomially many queries?} This question generalizes, among others, the complexity of finding bases of linear spaces, partition matroids, and spanning forests in graphs. In their work, they established an upper bound of $O(\sqrt{n})$ rounds and a lower bound of $\widetildeΩ(n^{1/3})$ rounds for this problem, and these bounds have remained unimproved since then. In this work, we make the first progress in narrowing this gap by designing a parallel algorithm that finds a basis of an arbitrary matroid in $\tilde{O}(n^{7/15})$ rounds (using polynomially many independence queries per round) with high probability, surpassing the long-standing $O(\sqrt{n})$ barrier. Our approach introduces a novel matroid decomposition technique and other structural insights that not only yield this general result but also lead to a much improved new algorithm for the class of \emph{partition matroids} (which underlies the $\widetildeΩ(n^{1/3})$ lower bound of Karp, Upfal, and Wigderson). Specifically, we develop an $\tilde{O}(n^{1/3})$-round algorithm, thereby settling the round complexity of finding a basis in partition matroids.