OpenRaiser/Intern-Atlas huggingface dataset:
F^2TTA: Free-Form Test-Time Adaptation on Cross-Domain Medical Image Classification via Image-Level Disentangled Prompt Tuning
Venue: arXiv cs.CV 2025
Year: 2025

Test-Time Adaptation (TTA) has emerged as a promising solution for adapting a source model to unseen medical sites using unlabeled test data, due to the high cost of data annotation. Existing TTA methods consider scenarios where data from one or multiple domains arrives in complete domain units. However, in clinical practice, data usually arrives in domain fragments of arbitrary lengths and in random arrival orders, due to resource constraints and patient variability. This paper investigates a practical Free-Form Test-Time Adaptation (F$^{2}$TTA) task, where a source model is adapted to such free-form domain fragments, with shifts occurring between fragments unpredictably. In this setting, these shifts could distort the adaptation process. To address this problem, we propose a novel Image-level Disentangled Prompt Tuning (I-DiPT) framework. I-DiPT employs an image-invariant prompt to explore domain-invariant representations for mitigating the unpredictable shifts, and an image-specific prompt to adapt the source model to each test image from the incoming fragments. The prompts may suffer from insufficient knowledge representation since only one image is available for training. To overcome this limitation, we first introduce Uncertainty-oriented Masking (UoM), which encourages the prompts to extract sufficient information from the incoming image via masked consistency learning driven by the uncertainty of the source model representations. Then, we further propose a Parallel Graph Distillation (PGD) method that reuses knowledge from historical image-specific and image-invariant prompts through parallel graph networks. Experiments on breast cancer and glaucoma classification demonstrate the superiority of our method over existing TTA approaches in F$^{2}$TTA. Code is available at https://github.com/mar-cry/F2TTA.
The Gauss-Markov Adjunction Provides Categorical Semantics of Residuals in Supervised Learning
Venue: arXiv cs.AI 2025
Year: 2025

Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of our research is to contribute to this improvement by reformulating machine learning models through the lens of category theory, thereby developing a semantic framework for structuring and understanding AI systems. Our categorical modeling in this paper clarifies and formalizes the structural interplay between residuals and parameters in supervised learning. The present paper focuses on the multiple linear regression model, which represents the most basic form of supervised learning. By defining two Lawvere-enriched categories corresponding to parameters and data, along with an adjoint pair of functors between them, we introduce our categorical formulation of supervised learning. We show that the essential structure of this framework is captured by what we call the Gauss-Markov Adjunction. Within this setting, the dual flow of information can be explicitly described as a correspondence between variations in parameters and residuals. The ordinary least squares estimator for the parameters and the minimum residual are related via the preservation of limits by the right adjoint functor. Furthermore, we position this formulation as an instance of extended denotational semantics for supervised learning, and propose applying a semantic perspective developed in theoretical computer science as a formal foundation for Explicability in AI.
Haptic Biofeedback for Wakeful Rest: Does Stimulation Location Make a Difference?
Venue: arXiv cs.HC 2025
Year: 2025

Wearable haptic interventions offer promising support for relaxation through slow, vibrotactile biofeedback. Despite their potential, current applications focus on stress-inducing procedures and fixed vibration patterns, with limited consideration of body location and dynamic biofeedback during restful states. This study investigates the effects of haptic biofeedback adjusted from real-time heart rate during eyes-closed wakeful rest, comparing four wearable body placements: the wrist, hand, forearm, and shoulder. Heart rate, alpha wave activity on the ear, subjective restfulness, and vibration experience were measured across these conditions. Results show that biofeedback reduced heart rate at the wrist, shoulder, and forearm, while alpha power measured at the ear remained unchanged. Subjective restfulness was rated highest at the shoulder and forearm, which were also the most preferred locations. In addition, participants reported greater comfort, relaxation, and further increased sleepiness at the forearm compared to the wrist, which was more easily recognizable. These findings suggest that the forearm and shoulder are ideal for unobtrusive relaxation feedback for wakeful rest, while the wrist may require design improvements for subjective experience.
MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention
Venue: arXiv cs.CV 2025
Year: 2025

Medical image recognition serves as a key way to aid in clinical diagnosis, enabling more accurate and timely identification of diseases and abnormalities. Vision transformer-based approaches have proven effective in handling various medical recognition tasks. However, these methods encounter two primary challenges. First, they are often task-specific and architecture-tailored, limiting their general applicability. Second, they usually either adopt full attention to model long-range dependencies, resulting in high computational costs, or rely on handcrafted sparse attention, potentially leading to suboptimal performance. To tackle these issues, we present MedFormer, an efficient medical vision transformer with two key ideas. First, it employs a pyramid scaling structure as a versatile backbone for various medical image recognition tasks, including image classification and dense prediction tasks such as semantic segmentation and lesion detection. This structure facilitates hierarchical feature representation while reducing the computation load of feature maps, highly beneficial for boosting performance. Second, it introduces a novel Dual Sparse Selection Attention (DSSA) with content awareness to improve computational efficiency and robustness against noise while maintaining high performance. As the core building technique of MedFormer, DSSA is designed to explicitly attend to the most relevant content. Theoretical analysis demonstrates that MedFormer outperforms existing medical vision transformers in terms of generality and efficiency. Extensive experiments across various imaging modality datasets show that MedFormer consistently enhances performance in all three medical image recognition tasks mentioned above. MedFormer provides an efficient and versatile solution for medical image recognition, with strong potential for clinical application.
Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model's ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints. We propose GORP (Gradient LOw Rank Projection) for Continual Learning, a novel training strategy that overcomes these limitations by synergistically combining full and low-rank parameters and jointly updating within a unified low-rank gradient subspace. GORP expands the optimization space while preserving efficiency and mitigating catastrophic forgetting. Extensive experiments on continual learning benchmarks demonstrate GORP's superior performance compared to existing state-of-the-art approaches. Code is available at https://github.com/Wcxwcxw/GORP.
Time Resolution Independent Operator Learning
Venue: arXiv cs.CE 2025
Year: 2025

Accurately learning solution operators for time-dependent partial differential equations (PDEs) from sparse and irregular data remains a challenging task. Recurrent DeepONet extensions inherit the discrete-time limitations of sequence-to-sequence (seq2seq) RNN architectures, while neural-ODE surrogates cannot incorporate new inputs after initialization. We introduce NCDE-DeepONet, a continuous-time operator network that embeds a Neural Controlled Differential Equation (NCDE) in the branch and augments the trunk with explicit space-time coordinates. The NCDE encodes an entire load history as the solution of a controlled ODE driven by a spline-interpolated input path, making the representation input-resolution-independent: it encodes different input signal discretizations of the observed samples. The trunk then probes this latent path at arbitrary spatial locations and times, rendering the overall map output-resolution independent: predictions can be queried on meshes and time steps unseen during training without retraining or interpolation. Benchmarks on transient Poisson, elastodynamic, and thermoelastic problems confirm the robustness and accuracy of the framework, achieving almost instant solution prediction. These findings suggest that controlled dynamics provide a principled and efficient foundation for high-fidelity operator learning in transient mechanics.
Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Venue: arXiv cs.HC 2025
Year: 2025

Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. Starting from a small dataset manually crafted by an expert to reflect empathic behavior, we extended the conversations using two LLMs: ChatGPT and Gemini. We analyzed the emotional progression of the dialogues using both sentiment analysis (via VADER) and expert assessments. While the generated conversations often mirrored the intended emotional structure, human evaluation revealed important differences in the perceived empathy and coherence of the responses. These findings suggest that emotion modeling in dialogues requires not only structural alignment in the expressed emotions but also qualitative depth, highlighting the importance of combining automated and humancentered methods in the development of emotionally competent agents.
Clarifying Before Reasoning: A Coq Prover with Structural Context
Venue: arXiv cs.AI 2025
Year: 2025

In this work, we investigate whether improving task clarity can enhance reasoning ability of large language models, focusing on theorem proving in Coq. We introduce a concept-level metric to evaluate task clarity and show that adding structured semantic context to the standard input used by modern LLMs, leads to a 1.85$\times$ improvement in clarity score (44.5\%~$\rightarrow$~82.3\%). Using the general-purpose model \texttt{DeepSeek-V3}, our approach leads to a 2.1$\times$ improvement in proof success (21.8\%~$\rightarrow$~45.8\%) and outperforms the previous state-of-the-art \texttt{Graph2Tac} (33.2\%). We evaluate this on 1,386 theorems randomly sampled from 15 standard Coq packages, following the same evaluation protocol as \texttt{Graph2Tac}. Furthermore, fine-tuning smaller models on our structured data can achieve even higher performance (48.6\%). Our method uses selective concept unfolding to enrich task descriptions, and employs a Planner--Executor architecture. These findings highlight the value of structured task representations in bridging the gap between understanding and reasoning.
Bounded Weighted Edit Distance: Dynamic Algorithms and Matching Lower Bounds
Venue: arXiv cs.DS 2025
Year: 2025

The edit distance $ed(X,Y)$ of two strings $X,Y\in Σ^*$ is the minimum number of character edits (insertions, deletions, and substitutions) needed to transform $X$ into $Y$. Its weighted counterpart $ed^w(X,Y)$ minimizes the total cost of edits, which are specified using a function $w$, normalized so that each edit costs at least one. The textbook dynamic-programming procedure, given strings $X,Y\in Σ^{\le n}$ and oracle access to $w$, computes $ed^w(X,Y)$ in $O(n^2)$ time. Nevertheless, one can achieve better running times if the computed distance, denoted $k$, is small: $O(n+k^2)$ for unit weights [Landau and Vishkin; JCSS'88] and $\tilde{O}(n+\sqrt{nk^3})$ for arbitrary weights [Cassis, Kociumaka, Wellnitz; FOCS'23]. In this paper, we study the dynamic version of the weighted edit distance problem, where the goal is to maintain $ed^w(X,Y)$ for strings $X,Y\in Σ^{\le n}$ that change over time, with each update specified as an edit in $X$ or $Y$. Very recently, Gorbachev and Kociumaka [STOC'25] showed that the unweighted distance $ed(X,Y)$ can be maintained in $\tilde{O}(k)$ time per update after $\tilde{O}(n+k^2)$-time preprocessing; here, $k$ denotes the current value of $ed(X,Y)$. Their algorithm generalizes to small integer weights, but the underlying approach is incompatible with large weights. Our main result is a dynamic algorithm that maintains $ed^w(X,Y)$ in $\tilde{O}(k^{3-γ})$ time per update after $\tilde{O}(nk^γ)$-time preprocessing. Here, $γ\in [0,1]$ is a real trade-off parameter and $k\ge 1$ is an integer threshold fixed at preprocessing time, with $\infty$ returned whenever $ed^w(X,Y)>k$. We complement our algorithm with conditional lower bounds showing fine-grained optimality of our trade-off for $γ\in [0.5,1)$ and justifying our choice to fix $k$.
LLMREI: Automating Requirements Elicitation Interviews with LLMs
Venue: arXiv cs.SE 2025
Year: 2025

Requirements elicitation interviews are crucial for gathering system requirements but heavily depend on skilled analysts, making them resource-intensive, susceptible to human biases, and prone to miscommunication. Recent advancements in Large Language Models present new opportunities for automating parts of this process. This study introduces LLMREI, a chat bot designed to conduct requirements elicitation interviews with minimal human intervention, aiming to reduce common interviewer errors and improve the scalability of requirements elicitation. We explored two main approaches, zero-shot prompting and least-to-most prompting, to optimize LLMREI for requirements elicitation and evaluated its performance in 33 simulated stakeholder interviews. A third approach, fine-tuning, was initially considered but abandoned due to poor performance in preliminary trials. Our study assesses the chat bot's effectiveness in three key areas: minimizing common interview errors, extracting relevant requirements, and adapting its questioning based on interview context and user responses. Our findings indicate that LLMREI makes a similar number of errors compared to human interviewers, is capable of extracting a large portion of requirements, and demonstrates a notable ability to generate highly context-dependent questions. We envision the greatest benefit of LLMREI in automating interviews with a large number of stakeholders.
SAT-BO: Verification Rule Learning and Optimization for FraudTransaction Detection
Venue: arXiv cs.CR 2025
Year: 2025

Electronic payment platforms are estimated to process billions oftransactions daily, with the cumulative value of these transactionspotentially reaching into the trillions. Even a minor error within thishigh-volume environment could precipitate substantial financiallosses. To mitigate this risk, manually constructed verification rules,developed by domain experts, are typically employed to identifyand scrutinize transactions in production environments. However,due to the absence of a systematic approach to ensure the robust-ness of these verification rules against vulnerabilities, they remainsusceptible to exploitation.To mitigate this risk, manually constructed verification rules, de-veloped by domain experts, are typically employed to identify andscrutinize transactions in production environments. However, dueto the absence of a systematic approach to ensure the robustness ofthese verification rules against vulnerabilities, they remain suscep-tible to exploitation. To ensure data security, database maintainersusually compose complex verification rules to check whether aquery/update request is valid. However, the rules written by ex-perts are usually imperfect, and malicious requests may bypassthese rules. As a result, the demand for identifying the defects ofthe rules systematically emerges.
Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models
Venue: arXiv cs.AI 2025
Year: 2025

Recent Large Reasoning Models (LRMs) excel at complex reasoning tasks but often suffer from overthinking, generating overly long and redundant reasoning trajectories. To explore its essence, our empirical analysis reveals that LRMs are primarily limited to recognizing task properties (i.e., difficulty levels) like humans before solving the problem, leading to a one-size-fits-all reasoning process. Inspired by this, a pressing and natural question emerges: Can we explicitly bootstrap such ability to alleviate overthinking in LRMs? In this paper, we propose Think-How-to-Think (TH2T), a novel two-stage fine-tuning strategy that progressively inspires LRMs' difficulty cognition and redundancy cognition of LRMs. Specifically, we first inject difficulty hypnosis into output prefixes to guide the model toward adaptive reasoning depth, trained on a hybrid dataset mixing short and long reasoning paths. Then, we incorporate redundancy hypnosis, which supervises the intermediate reasoning steps to identify and eliminate unnecessary reasoning patterns. Experiments on 7B/14B/32B models demonstrate that TH2T significantly reduces inference costs by over 70% on easy tasks and 40% on hard tasks while maintaining performance stability. The resulting outputs exhibit clear signs of difficulty-aware capabilities and reduced redundancy (e.g., reflection and looping).
Guided Generation for Developable Antibodies
Venue: arXiv cs.LG 2025
Year: 2025

Therapeutic antibodies require not only high-affinity target engagement, but also favorable manufacturability, stability, and safety profiles for clinical effectiveness. These properties are collectively called `developability'. To enable a computational framework for optimizing antibody sequences for favorable developability, we introduce a guided discrete diffusion model trained on natural paired heavy- and light-chain sequences from the Observed Antibody Space (OAS) and quantitative developability measurements for 246 clinical-stage antibodies. To steer generation toward biophysically viable candidates, we integrate a Soft Value-based Decoding in Diffusion (SVDD) Module that biases sampling without compromising naturalness. In unconstrained sampling, our model reproduces global features of both the natural repertoire and approved therapeutics, and under SVDD guidance we achieve significant enrichment in predicted developability scores over unguided baselines. When combined with high-throughput developability assays, this framework enables an iterative, ML-driven pipeline for designing antibodies that satisfy binding and biophysical criteria in tandem.
Real-time Image-based Lighting of Glints
Venue: arXiv cs.GR 2025
Year: 2025

Image-based lighting is a widely used technique to reproduce shading under real-world lighting conditions, especially in real-time rendering applications. A particularly challenging scenario involves materials exhibiting a sparkling or glittering appearance, caused by discrete microfacets scattered across their surface. In this paper, we propose an efficient approximation for image-based lighting of glints, enabling fully dynamic material properties and environment maps. Our novel approach is grounded in real-time glint rendering under area light illumination and employs standard environment map filtering techniques. Crucially, our environment map filtering process is sufficiently fast to be executed on a per-frame basis. Our method assumes that the environment map is partitioned into few homogeneous regions of constant radiance. By filtering the corresponding indicator functions with the normal distribution function, we obtain the probabilities for individual microfacets to reflect light from each region. During shading, these probabilities are utilized to hierarchically sample a multinomial distribution, facilitated by our novel dual-gated Gaussian approximation of binomial distributions. We validate that our real-time approximation is close to ground-truth renderings for a range of material properties and lighting conditions, and demonstrate robust and stable performance, with little overhead over rendering glints from a single directional light. Compared to rendering smooth materials without glints, our approach requires twice as much memory to store the prefiltered environment map.
Learning few-step posterior samplers by unfolding and distillation of diffusion models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models (DMs) have emerged as powerful image priors in Bayesian computational imaging. Two primary strategies have been proposed for leveraging DMs in this context: Plug-and-Play methods, which are zero-shot and highly flexible but rely on approximations; and specialized conditional DMs, which achieve higher accuracy and faster inference for specific tasks through supervised training. In this work, we introduce a novel framework that integrates deep unfolding and model distillation to transform a DM image prior into a few-step conditional model for posterior sampling. A central innovation of our approach is the unfolding of a Markov chain Monte Carlo (MCMC) algorithm - specifically, the recently proposed LATINO Langevin sampler (Spagnoletti et al., 2025) - representing the first known instance of deep unfolding applied to a Monte Carlo sampling scheme. We demonstrate our proposed unfolded and distilled samplers through extensive experiments and comparisons with the state of the art, where they achieve excellent accuracy and computational efficiency, while retaining the flexibility to adapt to variations in the forward model at inference time.
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
Venue: arXiv cs.LG 2025
Year: 2025

This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied reinforcement learning to pricing, most implementations remain single-agent and fail to model the interdependent nature of real-world supply chains. This study addresses that gap by evaluating the performance of three MARL algorithms: MADDPG, MADQN, and QMIX against static rule-based baselines, within a simulated environment informed by real e-commerce transaction data and a LightGBM demand prediction model. Results show that rule-based agents achieve near-perfect fairness (Jain's Index: 0.9896) and the highest price stability (volatility: 0.024), but they fully lack competitive dynamics. Among MARL agents, MADQN exhibits the most aggressive pricing behaviour, with the highest volatility and the lowest fairness (0.5844). MADDPG provides a more balanced approach, supporting market competition (share volatility: 9.5 pp) while maintaining relatively high fairness (0.8819) and stable pricing. These findings suggest that MARL introduces emergent strategic behaviour not captured by static pricing rules and may inform future developments in dynamic pricing.