OpenRaiser/Intern-Atlas huggingface dataset:
Advancements and limitations of LLMs in replicating human color-word associations
Venue: arXiv cs.CL 2025
Year: 2025

Color-word associations play a fundamental role in human cognition and design applications. Large Language Models (LLMs) have become widely available and have demonstrated intelligent behaviors in various benchmarks with natural conversation skills. However, their ability to replicate human color-word associations remains understudied. We compared multiple generations of LLMs (from GPT-3 to GPT-4o) against human color-word associations using data collected from over 10,000 Japanese participants, involving 17 colors and 80 words (10 word from eight categories) in Japanese. Our findings reveal a clear progression in LLM performance across generations, with GPT-4o achieving the highest accuracy in predicting the best voted word for each color and category. However, the highest median performance was approximately 50% even for GPT-4o with visual inputs (chance level of 10%). Moreover, we found performance variations across word categories and colors: while LLMs tended to excel in categories such as Rhythm and Landscape, they struggled with categories such as Emotions. Interestingly, color discrimination ability estimated from our color-word association data showed high correlation with human color discrimination patterns, consistent with previous studies. Thus, despite reasonable alignment in basic color discrimination, humans and LLMs still diverge systematically in the words they assign to those colors. Our study highlights both the advancements in LLM capabilities and their persistent limitations, raising the possibility of systematic differences in semantic memory structures between humans and LLMs in representing color-word associations.
Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
Venue: arXiv cs.LG 2025
Year: 2025

Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we propose Conformal-in-the-Loop (CitL), a novel training framework that addresses both challenges with a conformal prediction-based approach. CitL evaluates sample uncertainty to adjust weights and prune unreliable examples, enhancing model resilience and accuracy with minimal computational cost. Our extensive experiments include a detailed analysis showing how CitL effectively emphasizes impactful data in noisy, imbalanced datasets. Our results show that CitL consistently boosts model performance, achieving up to a 6.1% increase in classification accuracy and a 5.0 mIoU improvement in segmentation. Our code is publicly available: CitL.
Evaluating Creative Short Story Generation in Humans and Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Story-writing is a fundamental aspect of human imagination, relying heavily on creativity to produce narratives that are novel, effective, and surprising. While large language models (LLMs) have demonstrated the ability to generate high-quality stories, their creative story-writing capabilities remain under-explored. In this work, we conduct a systematic analysis of creativity in short story generation across 60 LLMs and 60 people using a five-sentence cue-word-based creative story-writing task. We use measures to automatically evaluate model- and human-generated stories across several dimensions of creativity, including novelty, surprise, diversity, and linguistic complexity. We also collect creativity ratings and Turing Test classifications from non-expert and expert human raters and LLMs. Automated metrics show that LLMs generate stylistically complex stories, but tend to fall short in terms of novelty, surprise and diversity when compared to average human writers. Expert ratings generally coincide with automated metrics. However, LLMs and non-experts rate LLM stories to be more creative than human-generated stories. We discuss why and how these differences in ratings occur, and their implications for both human and artificial creativity.
Evaluating the Ability of Large Language Models to Generate Verifiable Specifications in VeriFast
Venue: arXiv cs.SE 2025
Year: 2025

Static verification is a powerful method for enhancing software quality, but it demands significant human labor and resources. This is particularly true of static verifiers that reason about heap manipulating programs using an ownership logic. LLMs have shown promise in a number of software engineering activities, including code generation, test generation, proof generation for theorem provers, and specification generation for static verifiers. However, prior work has not explored how well LLMs can perform specification generation for specifications based in an ownership logic, such as separation logic. To address this gap, this paper explores OpenAI's GPT-4o model's effectiveness in generating specifications on C programs that are verifiable with VeriFast, a separation logic based static verifier. Our experiment employs three different types of user inputs as well as basic and Chain-of-Thought (CoT) prompting to assess GPT's capabilities. Our results indicate that the specifications generated by GPT-4o preserve functional behavior, but struggle to be verifiable. When the specifications are verifiable they contain redundancies. Future directions are discussed to improve the performance.
One-Way Functions and Polynomial Time Dimension
Venue: arXiv cs.CC 2025
Year: 2025

This paper demonstrates a duality between the non-robustness of polynomial time dimension and the existence of one-way functions. Polynomial-time dimension (denoted $\mathrm{cdim}_\mathrm{P}$) quantifies the density of information of infinite sequences using polynomial time betting algorithms called $s$-gales. An alternate quantification of the notion of polynomial time density of information is using polynomial-time Kolmogorov complexity rate (denoted $\mathcal{K}_\text{poly}$). Hitchcock and Vinodchandran (CCC 2004) showed that $\mathrm{cdim}_\mathrm{P}$ is always greater than or equal to $\mathcal{K}_\text{poly}$. We first show that if one-way functions exist then there exists a polynomial-time samplable distribution with respect to which $\mathrm{cdim}_\mathrm{P}$ and $\mathcal{K}_\text{poly}$ are separated by a uniform gap with probability $1$. Conversely, we show that if there exists such a polynomial-time samplable distribution, then (infinitely-often) one-way functions exist. Using our main results, we solve a long standing open problem posed by Hitchcock and Vinodchandran (CCC 2004) and Stull under the assumption that one-way functions exist. We demonstrate that if one-way functions exist, then there are individual sequences $X$ whose poly-time dimension strictly exceeds $\mathcal{K}_\text{poly}(X)$, that is $\mathrm{cdim}_\mathrm{P}(X) > \mathcal{K}_\text{poly}(X)$. Further, we show that the gap between these quantities can be made as large as possible (i.e. close to 1). We also establish similar bounds for strong poly-time dimension versus asymptotic upper Kolmogorov complexity rates.
Learning to Assist Humans without Inferring Rewards
Venue: arXiv cs.AI 2025
Year: 2025

Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to help the human reach that goal. This approach requires inferring intentions, which can be difficult in high-dimensional settings. We build upon prior work that studies assistance through the lens of empowerment: an assistive agent aims to maximize the influence of the human's actions such that they exert a greater control over the environmental outcomes and can solve tasks in fewer steps. We lift the major limitation of prior work in this area--scalability to high-dimensional settings--with contrastive successor representations. We formally prove that these representations estimate a similar notion of empowerment to that studied by prior work and provide a ready-made mechanism for optimizing it. Empirically, our proposed method outperforms prior methods on synthetic benchmarks, and scales to Overcooked, a cooperative game setting. Theoretically, our work connects ideas from information theory, neuroscience, and reinforcement learning, and charts a path for representations to play a critical role in solving assistive problems.
Sensitivity Lower Bounds for Approximaiton Algorithms
Venue: arXiv cs.DS 2025
Year: 2025

Sensitivity measures how much the output of an algorithm changes, in terms of Hamming distance, when part of the input is modified. While approximation algorithms with low sensitivity have been developed for many problems, no sensitivity lower bounds were previously known for approximation algorithms. In this work, we establish the first polynomial lower bound on the sensitivity of (randomized) approximation algorithms for constraint satisfaction problems (CSPs) by adapting the probabilistically checkable proof (PCP) framework to preserve sensitivity lower bounds. From this, we derive polynomial sensitivity lower bounds for approximation algorithms for a variety of problems, including maximum clique, minimum vertex cover, and maximum cut. Leveraging the connection between sensitivity and locality in the non-signaling model, which subsumes the LOCAL, quantum-LOCAL, and bounded dependence models, we establish locality lower bounds for several graph problems in the non-signaling model.
DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving
Venue: arXiv cs.MA 2025
Year: 2025

Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles working together. In these scenarios, different models often process inputs that share the same context prefix. Although much work was done in the past to enable the reuse of prefix KV caches across inputs for a single model, how to enable one model to reuse the prefix KV caches of a different model remains an open question. We introduce DroidSpeak, the first distributed LLM inference system that enables KV cache reuse across distributed nodes running inference of different LLMs, so long as the LLMs have the same architecture. We present the first study that aims at understanding the impact of sharing KV caches across different LLMs, and if/when such sharing affects quality. Inspired by the findings, we present DroidSpeak, which selectively recomputes a few layers of the KV cache produced by another LLM and reuses the remaining layers, with negligible quality loss. Moreover, carefully pipelining the layer-wise re-computation and the loading of reused KV cache further improves the inference performance. Experiments on diverse datasets and model pairs demonstrate that DroidSpeak achieves up to 4x throughput improvement and about 3.1x faster prefill (time to first token), with negligible loss of quality in F1 scores, Rouge-L or code similarity score, compared to the baseline which does not allow any sharing across models.
WASHtsApp -- A RAG-powered WhatsApp Chatbot for supporting rural African clean water access, sanitation and hygiene
Venue: arXiv cs.CY 2025
Year: 2025

This paper introduces WASHtsApp, a WhatsApp-based chatbot designed to educate rural African communities on clean water access, sanitation, and hygiene (WASH) principles. WASHtsApp leverages a Retrieval-Augmented Generation (RAG) approach to address the limitations of previous approaches with limited reach or missing contextualization. The paper details the development process, employing Design Science Research Methodology. The evaluation consisted of two phases: content validation by four WASH experts and community validation by potential users. Content validation confirmed WASHtsApp's ability to provide accurate and relevant WASH-related information. Community validation indicated high user acceptance and perceived usefulness of the chatbot. The paper concludes by discussing the potential for further development, including incorporating local languages and user data analysis for targeted interventions. It also proposes future research cycles focused on wider deployment and leveraging user data for educational purposes.
CAD-NeRF: Learning NeRFs from Uncalibrated Few-view Images by CAD Model Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of novel views. Currently, most NeRF methods either require accurate camera poses or a large number of input images, or even both. Reconstructing NeRF from few-view images without poses is challenging and highly ill-posed. To address this problem, we propose CAD-NeRF, a method reconstructed from less than 10 images without any known poses. Specifically, we build a mini library of several CAD models from ShapeNet and render them from many random views. Given sparse-view input images, we run a model and pose retrieval from the library, to get a model with similar shapes, serving as the density supervision and pose initializations. Here we propose a multi-view pose retrieval method to avoid pose conflicts among views, which is a new and unseen problem in uncalibrated NeRF methods. Then, the geometry of the object is trained by the CAD guidance. The deformation of the density field and camera poses are optimized jointly. Then texture and density are trained and fine-tuned as well. All training phases are in self-supervised manners. Comprehensive evaluations of synthetic and real images show that CAD-NeRF successfully learns accurate densities with a large deformation from retrieved CAD models, showing the generalization abilities.
From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice
Venue: arXiv cs.HC 2025
Year: 2025

Creative writing is a deeply human craft, yet AI systems using large language models (LLMs) offer the automation of significant parts of the writing process. So why do some creative writers choose to use AI? Through interviews and observed writing sessions with 18 creative writers who already use AI regularly in their writing practice, we find that creative writers are intentional about how they incorporate AI, making many deliberate decisions about when and how to engage AI based on their core values, such as authenticity and craftsmanship. We characterize the interplay between writers' values, their fluid relationships with AI, and specific integration strategies -- ultimately enabling writers to create new AI workflows without compromising their creative values. We provide insight for writing communities, AI developers and future researchers on the importance of supporting transparency of these emerging writing processes and rethinking what AI features can best serve writers.
Hybrid Rebeca Revisited
Venue: arXiv cs.FL 2025
Year: 2025

Hybrid Rebeca is a modeling framework for asynchronous event-based cyber-physical systems (CPSs). In this work, we extend Hybrid Rebeca to allow the modeling of non-deterministic time behavior. Besides the syntactical extension, we formalize the semantics of the extended language in terms of Timed Transition Systems, and adapt a reachability analysis algorithm originally designed for hybrid automata to be applicable to Hybrid Rebeca models. We prove the soundness of our approach and illustrate its applicability on a case study. The case study demonstrates that our dedicated algorithm is clearly superior to the alternative approach of transforming Hybrid Rebeca models to hybrid automata as an intermediate model and then applying the original reachability analysis method to this intermediate transformed models.
ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal
Venue: arXiv cs.CV 2025
Year: 2025

Image shadow removal is a typical low-level vision task. Shadows cause local brightness shifts, which reduce the performance of downstream vision tasks. Currently, Transformer-based shadow removal methods suffer from quadratic computational complexity due to the self-attention mechanism. To improve efficiency, many approaches use local attention, but this limits the ability to model global information and weakens the perception of brightness changes between regions. Recently, Mamba has shown strong performance in vision tasks by enabling global modeling with linear complexity. However, existing scanning strategies are not suitable for shadow removal, as they ignore the semantic continuity of shadow boundaries and internal regions. To address this, this paper proposes a boundary-region selective scanning mechanism that captures local details while enhancing semantic continuity between them, effectively improving shadow removal performance. In addition, a shadow mask denoising method is introduced to support the scanning mechanism and improve data quality. Based on these techniques, this paper presents a model called ShadowMamba, the first Mamba-based model designed for shadow removal. Experimental results show that the proposed method outperforms existing mainstream approaches on the AISTD, ISTD, and SRD datasets, and also offers clear advantages in parameter efficiency and computational complexity. Code is available at: https://github.com/ZHUXIUJINChris/ShadowMamba
Proxy-informed Bayesian transfer learning with unknown sources
Venue: arXiv cs.LG 2025
Year: 2025

Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sources. Transfer learning is a framework for specifying and refining this knowledge about sets of source (training) and target (prediction) data. A challenging open problem is addressing the empirical phenomenon of negative transfer, whereby the transfer learner performs worse on the target data after taking the source data into account than before. We first introduce a Bayesian perspective on negative transfer, and then a method to address it. The key insight from our formulation is that negative transfer can stem from misspecified prior information about non-transferable causes of the source data. Our proposed method, proxy-informed robust method for probabilistic transfer learning (PROMPT), does not require prior knowledge of the source data (the data sources may be "unknown"). PROMPT is thus applicable when differences between tasks are unobserved, such as in the presence of latent confounders. Moreover, the learner need not have access to observations in the target task (may not have the ability to "fine-tune"), and instead makes use of proxy (indirect) information. Our theoretical results show that the threat of negative transfer does not depend on the informativeness of the proxy information, highlighting the usefulness of PROMPT in cases where only noisy indirect information, such as human feedback, is available.
Concurrent Composition for Differentially Private Continual Mechanisms
Venue: arXiv cs.DS 2025
Year: 2025

Many intended uses of differential privacy involve a $\textit{continual mechanism}$ that is set up to run continuously over a long period of time, making more statistical releases as either queries come in or the dataset is updated. In this paper, we give the first general treatment of privacy against $\textit{adaptive}$ adversaries for mechanisms that support dataset updates and a variety of queries, all arbitrarily interleaved. It also models a very general notion of neighboring, that includes both event-level and user-level privacy. We prove several $\textit{concurrent}$ composition theorems for continual mechanisms, which ensure privacy even when an adversary can interleave queries and dataset updates to the different composed mechanisms. Previous concurrent composition theorems for differential privacy were only for the case when the dataset is static, with no adaptive updates. Moreover, we also give the first interactive and continual generalizations of the "parallel composition theorem" for noninteractive differential privacy. Specifically, we show that the analogue of the noninteractive parallel composition theorem holds if either there are no adaptive dataset updates or each of the composed mechanisms satisfies pure differential privacy, but it fails to hold for composing approximately differentially private mechanisms with dataset updates. We then formalize a set of general conditions on a continual mechanism $M$ that runs multiple continual sub-mechanisms such that the privacy guarantees of $M$ follow directly using the above concurrent composition theorems on the sub-mechanisms, without further privacy loss. This enables us to give a simpler and more modular privacy analysis of a recent continual histogram mechanism of Henzinger, Sricharan, and Steiner. In the case of approximate DP, ours is the first proof showing that its privacy holds against adaptive adversaries.
Chorded cycle facets of the clique partitioning polytope
Venue: arXiv cs.DM 2025
Year: 2025

The $q$-chorded $k$-cycle inequalities are a class of valid inequalities for the clique partitioning polytope. It is known that for $q \in \{2, \tfrac{k-1}{2}\}$, these inequalities induce facets of the clique partitioning polytope if and only if $k$ is odd. Here, we characterize such facets for arbitrary $k$ and $q$. More specifically, we prove that the $q$-chorded $k$-cycle inequalities induce facets of the clique partitioning polytope if and only if two conditions are satisfied: $k = 1$ mod $q$, and if $k=3q+1$ then $q=3$ or $q$ is even. This establishes the existence of many facets induced by $q$-chorded $k$-cycle inequalities beyond those previously known.
Consensus Building in Human-robot Co-learning via Bias Controlled Nonlinear Opinion Dynamics and Non-verbal Communication through Robotic Eyes
Venue: arXiv cs.RO 2025
Year: 2025

Consensus between humans and robots is crucial as robotic agents become more prevalent and deeply integrated into our daily lives. This integration presents both unprecedented opportunities and notable challenges for effective collaboration. However, the active guidance of human actions and their integration in co-learning processes, where humans and robots mutually learn from each other, remains under-explored. This article demonstrates how consensus between human and robot opinions can be established by modeling decision-making processes as non-linear opinion dynamics. We utilize dynamic bias as a control parameter to steer the robot's opinion toward consensus and employ visual cues via a robotic eye gaze to guide human decisions. These non-verbal cues communicate the robot's future intentions, gradually guiding human decisions to align with them. To design robot behavior for consensus, we integrate a human opinion observation algorithm with the robot's opinion formation, controlling its actions based on that formed opinion. Experiments with $51$ participants ($N=51$) in a two-choice decision-making task show that effective consensus and trust can be established in a human--robot co-learning setting by guiding human decisions through nonverbal robotic cues and using bias-controlled opinion dynamics to shape robot behavior. Finally, we provide detailed information on the perceived cognitive load and the behavior of robotic eyes based on user feedback and post-experiment interviews.