OpenRaiser/Intern-Atlas huggingface dataset:
T-POP: Test-Time Personalization with Online Preference Feedback
Venue: arXiv cs.LG 2025
Year: 2025

Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses. However, current personalization methods are ill-suited for new users, as they typically require either slow, resource-intensive fine-tuning or a substantial amount of pre-existing user data, creating a significant cold-start problem. To address this challenge, we introduce a new paradigm for real-time personalization by learning from online pairwise preference feedback collected during text generation. We propose T-POP (Test-Time Personalization with Online Preference Feedback}), a novel algorithm that synergistically combines test-time alignment with dueling bandits. Without updating the LLM parameters, T-POP steers the decoding process of a frozen LLM by learning a reward function online that captures user preferences. By leveraging dueling bandits, T-POP intelligently queries the user to efficiently balance between exploring their preferences and exploiting the learned knowledge to generate personalized text. Extensive experiments demonstrate that T-POP achieves rapid and data-efficient personalization, significantly outperforming existing baselines and showing consistent improvement with more user interactions.
A Robust Multi-Scale Framework with Test-Time Adaptation for sEEG-Based Speech Decoding
Venue: arXiv cs.HC 2025
Year: 2025

Decoding speech from stereo-electroencephalography (sEEG) signals has emerged as a promising direction for brain-computer interfaces (BCIs). Its clinical applicability, however, is limited by the inherent non-stationarity of neural signals, which causes domain shifts between training and testing, undermining decoding reliability. To address this challenge, a two-stage framework is proposed for enhanced robustness. First, a multi-scale decomposable mixing (MDM) module is introduced to model the hierarchical temporal dynamics of speech production, learning stable multi-timescale representations from sEEG signals. Second, a source-free online test-time adaptation (TTA) method performs entropy minimization to adapt the model to distribution shifts during inference. Evaluations on the public DU-IN spoken word decoding benchmark show that the approach outperforms state-of-the-art models, particularly in challenging cases. This study demonstrates that combining invariant feature learning with online adaptation is a principled strategy for developing reliable BCI systems. Our code is available at https://github.com/lyyi599/MDM-TENT.
MemGen: Weaving Generative Latent Memory for Self-Evolving Agents
Venue: arXiv cs.CL 2025
Year: 2025

Agent memory shapes how Large Language Model (LLM)-powered agents, akin to the human brain, progressively refine themselves through environment interactions. Existing paradigms remain constrained: parametric memory forcibly adjusts model parameters, and retrieval-based memory externalizes experience into structured databases, yet neither captures the fluid interweaving of reasoning and memory that underlies human cognition. To address this gap, we propose MemGen, a dynamic generative memory framework that equips agents with a human-esque cognitive faculty. It consists of a \textit{memory trigger}, which monitors the agent's reasoning state to decide explicit memory invocation, and a \textit{memory weaver}, which takes the agent's current state as stimulus to construct a latent token sequence as machine-native memory to enrich its reasoning. In this way, MemGen enables agents to recall and augment latent memory throughout reasoning, producing a tightly interwoven cycle of memory and cognition. Extensive experiments across eight benchmarks show that MemGen surpasses leading external memory systems such as ExpeL and AWM by up to $38.22\%$, exceeds GRPO by up to $13.44\%$, and exhibits strong cross-domain generalization ability. More importantly, we find that without explicit supervision, MemGen spontaneously evolves distinct human-like memory faculties, including planning memory, procedural memory, and working memory, suggesting an emergent trajectory toward more naturalistic forms of machine cognition.
LLM-Handover:Exploiting LLMs for Task-Oriented Robot-Human Handovers
Venue: arXiv cs.RO 2025
Year: 2025

Effective human-robot collaboration depends on task-oriented handovers, where robots present objects in ways that support the partners intended use. However, many existing approaches neglect the humans post-handover action, relying on assumptions that limit generalizability. To address this gap, we propose LLM-Handover, a novel framework that integrates large language model (LLM)-based reasoning with part segmentation to enable context-aware grasp selection and execution. Given an RGB-D image and a task description, our system infers relevant object parts and selects grasps that optimize post-handover usability. To support evaluation, we introduce a new dataset of 60 household objects spanning 12 categories, each annotated with detailed part labels. We first demonstrate that our approach improves the performance of the used state-of-the-art part segmentation method, in the context of robot-human handovers. Next, we show that LLM-Handover achieves higher grasp success rates and adapts better to post-handover task constraints. During hardware experiments, we achieve a success rate of 83% in a zero-shot setting over conventional and unconventional post-handover tasks. Finally, our user study underlines that our method enables more intuitive, context-aware handovers, with participants preferring it in 86% of cases.
Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the Workplace
Venue: arXiv cs.HC 2025
Year: 2025

The rapid development of artificial intelligence (AI) has fundamentally transformed creative work practices in the design industry. Existing studies have identified both opportunities and challenges for creative practitioners in their collaboration with generative AI and explored ways to facilitate effective human-AI co-creation. However, there is still a limited understanding of designers' collaboration with AI that supports creative processes distinct from generative AI. To address these gaps, this study focuses on understanding designers' collaboration with decision-making AI, which supports the convergence process in the creative workflow, as opposed to the divergent process supported by generative AI. Specifically, we conducted a case study at an online advertising design company to explore how professional graphic designers at the company perceive the impact of decision-making AI on their creative work practices. The case company incorporated an AI system that predicts the effectiveness of advertising design into the design workflow as a decision-making support tool. Findings from interviews with 12 designers identified how designers trust and rely on AI, its perceived benefits and challenges, and their strategies for navigating the challenges. Based on the findings, we discuss design recommendations for integrating decision-making AI into the creative design workflow.
Dynamic Pricing of an Expiring Item under Strategic Buyers with Stochastic Arrival
Venue: arXiv cs.GT 2025
Year: 2025

We study the optimal dynamic pricing of an expiring ticket or voucher, sold by a time-sensitive seller to strategic buyers who arrive stochastically with private values. The expiring nature creates a conflict: the seller's urgency to sell before expiration drives price reductions, which in turn incentivize buyers to wait. We seek the seller's optimal pricing policy that resolves this tension. The main analytical challenge is that buyer type is two-dimensional (valuation and arrival time), which makes equilibrium intractable under general strategies. To address this, we introduce the Value-Based Threshold (VBT) strategy, a tractable framework that decouples these two dimensions. Using this framework, we prove equilibrium existence via an ordinary differential equation and provide a constructive procedure for its characterization. We then derive near-optimal pricing policies for two stylized regimes: a constant price in thin markets and a linear discount in thick markets. Numerical frontier analysis confirms these benchmarks and shows how optimal policy adapts as the seller's time sensitivity changes. Our findings clarify the conflict between quick sales and strategic waiting. Sellers facing thick markets or high time sensitivity benefit from linear discounts, while in thin markets a constant price neutralizes buyers' incentive to wait. We also show this simple policy remains robust across broad conditions. For patient sellers, a quasi-auction schedule that maintains a high price until a sharp final drop is most effective in aggregating demand.
Q-Net: Queue Length Estimation via Kalman-based Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD) that provide segment-wise average speed measurements. However, how to integrate these sources with differing spatial and temporal resolutions for queue length estimation is rather unclear. Addressing this question, we present Q Net: a robust queue estimation framework built upon a state-space formulation. This formulation addresses key challenges in queue modeling, such as violations of traffic conservation assumptions. To overcome the limitations of standard filtering models in integrating diverse data sources, Q-Net employs an AI-augmented Kalman filter for estimation. Q-Net follows the Kalman predict-update framework and maintains physical interpretability, with internal variables linked to real-world traffic dynamics. Q-Net can be implemented in real-time, making it suitable for integration into queue-based traffic control systems. To achieve spatial transferability across road sections, we group aFCD measurements into fixed-size groups. This strategy ensures the dimension of Q-Net's learnable parameters is independent of section length. Evaluations on urban main roads in Rotterdam, the Netherlands, show that Q-Net outperforms baseline methods, accurately tracking queue formation and dissipation while correcting aFCD-induced delays. By combining data efficiency, interpretability, and strong transferability, Q Net makes accurate queue length estimation possible without costly sensing infrastructure like cameras or radar.
IA-VLA: Input Augmentation for Vision-Language-Action models in settings with semantically complex tasks
Venue: arXiv cs.RO 2025
Year: 2025

Vision-language-action models (VLAs) have become an increasingly popular approach for addressing robot manipulation problems in recent years. However, such models need to output actions at a rate suitable for robot control, which limits the size of the language model they can be based on, and consequently, their language understanding capabilities. Manipulation tasks may require complex language instructions, such as identifying target objects by their relative positions, to specify human intention. Therefore, we introduce IA-VLA, a framework that utilizes the extensive language understanding of a large vision language model as a pre-processing stage to generate improved context to augment the input of a VLA. We evaluate the framework on a set of semantically complex tasks which have been underexplored in VLA literature, namely tasks involving visual duplicates, i.e., visually indistinguishable objects. A dataset of three types of scenes with duplicate objects is used to compare a baseline VLA against two augmented variants. The experiments show that the VLA benefits from the augmentation scheme, especially when faced with language instructions that require the VLA to extrapolate from concepts it has seen in the demonstrations. For the code, dataset, and videos, see https://sites.google.com/view/ia-vla.
Neural Message-Passing on Attention Graphs for Hallucination Detection
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations. Existing detection methods rely on heuristics or simple models over isolated computational traces such as activations, or attention maps. We unify these signals by representing them as attributed graphs, where tokens are nodes, edges follow attentional flows, and both carry features from attention scores and activations. Our approach, CHARM, casts hallucination detection as a graph learning task and tackles it by applying GNNs over the above attributed graphs. We show that CHARM provably subsumes prior attention-based heuristics and, experimentally, it consistently outperforms other leading approaches across diverse benchmarks. Our results shed light on the relevant role played by the graph structure and on the benefits of combining computational traces, whilst showing CHARM exhibits promising zero-shot performance on cross-dataset transfer.
LatentEvolve: Self-Evolving Test-Time Scaling in Latent Space
Venue: arXiv cs.CL 2025
Year: 2025

Test-time Scaling (TTS) has been demonstrated to significantly enhance the reasoning capabilities of Large Language Models (LLMs) during the inference phase without altering model parameters. However, existing TTS methods are largely independent, implying that LLMs have not yet evolved to progressively learn how to scale more effectively. With the objective of evolving LLMs to learn ``how to scale test-time computation,'' we propose LatentEvolve, a self-evolving latent TTS framework inspired by the complementary learning system (CLS) theory. Analogous to the human brain's dual system of a fast-recall hippocampus and a slow-consolidating neocortex, LatentEvolve comprises two evolutionary components: \textit{daytime scaling}, which rapidly retrieves historical latent representations to better guide current LLM reasoning; and \textit{nighttime scaling}, which integrates past latent optimizations in a manner akin to the human brain's consolidation of experiences during sleep. The alternation of daytime and nighttime processes facilitates a fast and slow evolution of LLM TTS, mirroring human cognitive dynamics in a fully unsupervised manner. Extensive experiments across eight benchmarks and five model backbones demonstrate that our LatentEvolve surpasses state-of-the-art TTS methods such as LatentSeek and TTRL by up to $13.33\%$ and exhibits exceptional cross-domain and cross-backbone generalization.
Large language models for behavioral modeling: A literature survey
Venue: arXiv cs.SE 2025
Year: 2025

In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, particularly focusing on use case and sequence diagrams. Through a term-based search, we filtered and identified 14 relevant primary studies. Our analysis of the selected primary studies reveals that LLMs have demonstrated promising results in automatically generating use case and sequence diagrams. In addition, we found that most of the current literature lacks expert-based evaluations and has mainly used GPT-based models. Therefore, future work should evaluate a broader range of LLMs for behavioral modeling and involve domain experts to evaluate the output of LLMs.
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Cross-view geo-localization aims at establishing location correspondences between different viewpoints. Existing approaches typically learn cross-view correlations through direct feature similarity matching, often overlooking semantic degradation caused by extreme viewpoint disparities. To address this unique problem, we focus on robust feature retrieval under viewpoint variation and propose the novel SkyLink method. We firstly utilize the Google Retrieval Enhancement Module to perform data enhancement on street images, which mitigates the occlusion of the key target due to restricted street viewpoints. The Patch-Aware Feature Aggregation module is further adopted to emphasize multiple local feature aggregations to ensure the consistent feature extraction across viewpoints. Meanwhile, we integrate the 3D scene information constructed from multi-scale UAV images as a bridge between street and satellite viewpoints, and perform feature alignment through self-supervised and cross-view contrastive learning. Experimental results demonstrate robustness and generalization across diverse urban scenarios, which achieve 25.75$\%$ Recall@1 accuracy on University-1652 in the UAVM2025 Challenge. Code will be released at https://github.com/HRT00/CVGL-3D.
Physics-informed learning under mixing: How physical knowledge speeds up learning
Venue: arXiv cs.LG 2025
Year: 2025

A major challenge in physics-informed machine learning is to understand how the incorporation of prior domain knowledge affects learning rates when data are dependent. Focusing on empirical risk minimization with physics-informed regularization, we derive complexity-dependent bounds on the excess risk in probability and in expectation. We prove that, when the physical prior information is aligned, the learning rate improves from the (slow) Sobolev minimax rate to the (fast) optimal i.i.d. one without any sample-size deflation due to data dependence.
TACO-Net: Topological Signatures Triumph in 3D Object Classification
Venue: arXiv cs.CV 2025
Year: 2025

3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds sampled on CAD models of the objects and/or captured by LiDAR or RGBD cameras have achieved remarkable success in recent years, achieving high classification accuracy remains a challenging problem due to the unordered point clouds and their irregularity and noise. To this end, we propose a novel state-of-the-art (SOTA) 3D object classification technique that combines topological data analysis with various image filtration techniques to classify objects when they are represented using point clouds. We transform every point cloud into a voxelized binary 3D image to extract distinguishing topological features. Next, we train a lightweight one-dimensional Convolutional Neural Network (1D CNN) using the extracted feature set from the training dataset. Our framework, TACO-Net, sets a new state-of-the-art by achieving $99.05\%$ and $99.52\%$ accuracy on the widely used synthetic benchmarks ModelNet40 and ModelNet10, and further demonstrates its robustness on the large-scale real-world OmniObject3D dataset. When tested with ten different kinds of corrupted ModelNet40 inputs, the proposed TACO-Net demonstrates strong resiliency overall.
Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Venue: arXiv cs.CR 2025
Year: 2025

Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classification via SVM, MLP, and XGB. Results show that the system maintains reliable performance across varying LLM usages and cognitive contexts, with Equal Error Rates ranging from 5.1% to 10.4%. These findings demonstrate the feasibility of behavioral authentication under modern writing conditions and offer insights into designing more context-resilient models.
Query Circuits: Explaining How Language Models Answer User Prompts
Venue: arXiv cs.AI 2025
Year: 2025

Explaining why a language model produces a particular output requires local, input-level explanations. Existing methods uncover global capability circuits (e.g., indirect object identification), but not why the model answers a specific input query in a particular way. We introduce query circuits, which directly trace the information flow inside a model that maps a specific input to the output. Unlike surrogate-based approaches (e.g., sparse autoencoders), query circuits are identified within the model itself, resulting in more faithful and computationally accessible explanations. To make query circuits practical, we address two challenges. First, we introduce Normalized Deviation Faithfulness (NDF), a robust metric to evaluate how well a discovered circuit recovers the model's decision for a specific input, and is broadly applicable to circuit discovery beyond our setting. Second, we develop sampling-based methods to efficiently identify circuits that are sparse yet faithfully describe the model's behavior. Across benchmarks (IOI, arithmetic, MMLU, and ARC), we find that there exist extremely sparse query circuits within the model that can recover much of its performance on single queries. For example, a circuit covering only 1.3% of model connections can recover about 60% of performance on an MMLU questions. Overall, query circuits provide a step towards faithful, scalable explanations of how language models process individual inputs.
Efficient Sketching and Nearest Neighbor Search Algorithms for Sparse Vector Sets
Venue: arXiv cs.DS 2025
Year: 2025

Sparse embeddings of data form an attractive class due to their inherent interpretability: Every dimension is tied to a term in some vocabulary, making it easy to visually decipher the latent space. Sparsity, however, poses unique challenges for Approximate Nearest Neighbor Search (ANNS) which finds, from a collection of vectors, the k vectors closest to a query. To encourage research on this underexplored topic, sparse ANNS featured prominently in a BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on large benchmark datasets by throughput and accuracy. In this work, we introduce a set of novel data structures and algorithmic methods, a combination of which leads to an elegant, effective, and highly efficient solution to sparse ANNS. Our contributions range from a theoretically-grounded sketching algorithm for sparse vectors to reduce their effective dimensionality while preserving inner product-induced ranks; a geometric organization of the inverted index; and the blending of local and global information to improve the efficiency and efficacy of ANNS. Empirically, our final algorithm, dubbed Seismic, reaches sub-millisecond per-query latency with high accuracy on a large-scale benchmark dataset using a single CPU.