OpenRaiser/Intern-Atlas huggingface dataset:
Symmetry-Driven Asynchronous Forwarding for Reliable Distributed Coordination in Toroidal Networks
Venue: arXiv cs.NI 2025
Year: 2025

The proliferation of large-scale distributed systems, such as satellite constellations and high-performance computing clusters, demands robust communication primitives that maintain coordination under unreliable links. The torus topology, with its inherent rotational and reflection symmetries, is a prevalent architecture in these domains. However, conventional routing schemes suffer from substantial packet loss during control-plane synchronization after link failures. This paper introduces a symmetry-driven asynchronous forwarding mechanism that leverages the torus's geometric properties to achieve reliable packet delivery without control-plane coordination. We model packet flow using a topological potential gradient and demonstrate that symmetry-breaking failures naturally induce a reverse flow, which we harness for fault circumvention. We propose two local forwarding strategies, Reverse Flow with Counter-facing Priority (RF-CF) and Lateral-facing Priority (RF-LF), that guarantee reachability to the destination via forward-flow phase transition points, without protocol modifications or additional in-packet overhead. Through percolation analysis and packet-level simulations on a 16 x 16 torus, we show that our mechanism reduces packet loss by up to 17.5% under a 1% link failure rate, with the RF-LF strategy contributing to 28% of successfully delivered packets. This work establishes a foundational link between topological symmetry and communication resilience, providing a lightweight, protocol-agnostic substrate for enhancing distributed systems.
Beyond the Uncanny Valley: A Mixed-Method Investigation of Anthropomorphism in Protective Responses to Robot Abuse
Venue: arXiv cs.RO 2025
Year: 2025

Robots with anthropomorphic features are increasingly shaping how humans perceive and morally engage with them. Our research investigates how different levels of anthropomorphism influence protective responses to robot abuse, extending the Computers as Social Actors (CASA) and uncanny valley theories into a moral domain. In an experiment, we invite 201 participants to view videos depicting abuse toward a robot with low (Spider), moderate (Two-Foot), or high (Humanoid) anthropomorphism. To provide a comprehensive analysis, we triangulate three modalities: self-report surveys measuring emotions and uncanniness, physiological data from automated facial expression analysis, and qualitative reflections. Findings indicate that protective responses are not linear. The moderately anthropomorphic Two-Foot robot, rated highest in eeriness and "spine-tingling" sensations consistent with the uncanny valley, elicited the strongest physiological anger expressions. Self-reported anger and guilt are significantly higher for both the Two-Foot and Humanoid robots compared to the Spider. Qualitative findings further reveal that as anthropomorphism increases, moral reasoning shifts from technical assessments of property damage to condemnation of the abuser's character, while governance proposals expand from property law to calls for quasi-animal rights and broader societal responsibility. These results suggest that the uncanny valley does not dampen moral concern but paradoxically heightens protective impulses, offering critical implications for robot design, policy, and future legal frameworks.
Lean4Physics: Comprehensive Reasoning Framework for College-level Physics in Lean4
Venue: arXiv cs.AI 2025
Year: 2025

We present **Lean4PHYS**, a comprehensive reasoning framework for college-level physics problems in Lean4. **Lean4PHYS** includes *LeanPhysBench*, a college-level benchmark for formal physics reasoning in Lean4, which contains 200 hand-crafted and peer-reviewed statements derived from university textbooks and physics competition problems. To establish a solid foundation for formal reasoning in physics, we also introduce *PhysLib*, a community-driven repository containing fundamental unit systems and theorems essential for formal physics reasoning. Based on the benchmark and Lean4 repository we composed in **Lean4PHYS**, we report baseline results using major expert Math Lean4 provers and state-of-the-art closed-source models, with the best performance of DeepSeek-Prover-V2-7B achieving only 16% and Claude-Sonnet-4 achieving 35%. We also conduct a detailed analysis showing that our *PhysLib* can achieve an average improvement of 11.75% in model performance. This demonstrates the challenging nature of our *LeanPhysBench* and the effectiveness of *PhysLib*. To the best of our knowledge, this is the first study to provide a physics benchmark in Lean4.
ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems are among the most impactful AI applications, interacting with billions of users every day, guiding them to relevant products, services, or information tailored to their preferences. However, the research and development of recommender systems are hindered by existing datasets that fail to capture realistic user behaviors and inconsistent evaluation settings that lead to ambiguous conclusions. This paper introduces the Open Recommendation Benchmark for Reproducible Research with HIdden Tests (ORBIT), a unified benchmark for consistent and realistic evaluation of recommendation models. ORBIT offers a standardized evaluation framework of public datasets with reproducible splits and transparent settings for its public leaderboard. Additionally, ORBIT introduces a new webpage recommendation task, ClueWeb-Reco, featuring web browsing sequences from 87 million public, high-quality webpages. ClueWeb-Reco is a synthetic dataset derived from real, user-consented, and privacy-guaranteed browsing data. It aligns with modern recommendation scenarios and is reserved as the hidden test part of our leaderboard to challenge recommendation models' generalization ability. ORBIT measures 12 representative recommendation models on its public benchmark and introduces a prompted LLM baseline on the ClueWeb-Reco hidden test. Our benchmark results reflect general improvements of recommender systems on the public datasets, with variable individual performances. The results on the hidden test reveal the limitations of existing approaches in large-scale webpage recommendation and highlight the potential for improvements with LLM integrations. ORBIT benchmark, leaderboard, and codebase are available at https://www.open-reco-bench.ai.
StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglement
Venue: arXiv cs.GR 2025
Year: 2025

Structured layouts are preferable in many 2D visual contents (\eg, GUIs, webpages) since the structural information allows convenient layout editing. Computational frameworks can help create structured layouts but require heavy labor input. Existing data-driven approaches are effective in automatically generating fixed layouts but fail to produce layout structures. We present StructLayoutFormer, a novel Transformer-based approach for conditional structured layout generation. We use a structure serialization scheme to represent structured layouts as sequences. To better control the structures of generated layouts, we disentangle the structural information from the element placements. Our approach is the first data-driven approach that achieves conditional structured layout generation and produces realistic layout structures explicitly. We compare our approach with existing data-driven layout generation approaches by including post-processing for structure extraction. Extensive experiments have shown that our approach exceeds these baselines in conditional structured layout generation. We also demonstrate that our approach is effective in extracting and transferring layout structures. The code is publicly available at %\href{https://github.com/Teagrus/StructLayoutFormer} {https://github.com/Teagrus/StructLayoutFormer}.
STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments
Venue: arXiv cs.LG 2025
Year: 2025

Human Activity Recognition (HAR) via Wi-Fi Channel State Information (CSI) presents a privacy-preserving, contactless sensing approach suitable for smart homes, healthcare monitoring, and mobile IoT systems. However, existing methods often encounter computational inefficiency, high latency, and limited feasibility within resource-constrained, embedded mobile edge environments. This paper proposes STAR (Sensing Technology for Activity Recognition), an edge-AI-optimized framework that integrates a lightweight neural architecture, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR incorporates a streamlined Gated Recurrent Unit (GRU)-based recurrent neural network, reducing model parameters by 33% compared to conventional LSTM models while maintaining effective temporal modeling capability. A multi-stage pre-processing pipeline combining median filtering, 8th-order Butterworth low-pass filtering, and Empirical Mode Decomposition (EMD) is employed to denoise CSI amplitude data and extract spatial-temporal features. For on-device deployment, STAR is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU), interfaced with an ESP32-S3-based CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human presence detection, utilizing a compact 97.6k-parameter model. INT8 quantized inference achieves a processing speed of 33 MHz with just 8% CPU utilization, delivering sixfold speed improvements over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments.
ReaKase-8B: Legal Case Retrieval via Knowledge and Reasoning Representations with LLMs
Venue: arXiv cs.IR 2025
Year: 2025

Legal case retrieval (LCR) is a cornerstone of real-world legal decision making, as it enables practitioners to identify precedents for a given query case. Existing approaches mainly rely on traditional lexical models and pretrained language models to encode the texts of legal cases. Yet there are rich information in the relations among different legal entities as well as the crucial reasoning process that uncovers how legal facts and legal issues can lead to judicial decisions. Such relational reasoning process reflects the distinctive characteristics of each case that can distinguish one from another, mirroring the real-world judicial process. Naturally, incorporating such information into the precise case embedding could further enhance the accuracy of case retrieval. In this paper, a novel ReaKase-8B framework is proposed to leverage extracted legal facts, legal issues, legal relation triplets and legal reasoning for effective legal case retrieval. ReaKase-8B designs an in-context legal case representation learning paradigm with a fine-tuned large language model. Extensive experiments on two benchmark datasets from COLIEE 2022 and COLIEE 2023 demonstrate that our knowledge and reasoning augmented embeddings substantially improve retrieval performance over baseline models, highlighting the potential of integrating legal reasoning into legal case retrieval systems. The code has been released on https://github.com/yanran-tang/ReaKase-8B.
Similarity-Distance-Magnitude Language Models
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Similarity-Distance-Magnitude (SDM) language models (LMs), which are sequence prediction models fine-tuned to maximize the proportion of generations in the well-calibrated, high-probability region partitioned by a final-layer SDM activation layer used for binary classification of instruction-following. We demonstrate that existing pre-trained decoder-only Transformer LMs can be readily converted into SDM LMs via supervised fine-tuning, using the final-layer SDM activation layer during training to estimate a change-of-base for a supervised next-token loss over a contrastive input encoding scheme, with additional hard negative examples generated online during training. This results in reduced abstentions (i.e., improved statistical efficiency) compared to strong supervised baselines.
PVMark: Enabling Public Verifiability for LLM Watermarking Schemes
Venue: arXiv cs.CR 2025
Year: 2025

Watermarking schemes for large language models (LLMs) have been proposed to identify the source of the generated text, mitigating the potential threats emerged from model theft. However, current watermarking solutions hardly resolve the trust issue: the non-public watermark detection cannot prove itself faithfully conducting the detection. We observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public, or the adversary may launch removal attacks provided the key; nor can it be private, or the watermarking detection is opaque to the public. To resolve the dilemma, we propose PVMark, a plugin based on zero-knowledge proof (ZKP), enabling the watermark detection process to be publicly verifiable by third parties without disclosing any secret key. PVMark hinges upon the proof of `correct execution' of watermark detection on which a set of ZKP constraints are built, including mapping, random number generation, comparison, and summation. We implement multiple variants of PVMark in Python, Rust and Circom, covering combinations of three watermarking schemes, three hash functions, and four ZKP protocols, to show our approach effectively works under a variety of circumstances. By experimental results, PVMark efficiently enables public verifiability on the state-of-the-art LLM watermarking schemes yet without compromising the watermarking performance, promising to be deployed in practice.
Thor: Towards Human-Level Whole-Body Reactions for Intense Contact-Rich Environments
Venue: arXiv cs.RO 2025
Year: 2025

Humanoids hold great potential for service, industrial, and rescue applications, in which robots must sustain whole-body stability while performing intense, contact-rich interactions with the environment. However, enabling humanoids to generate human-like, adaptive responses under such conditions remains a major challenge. To address this, we propose Thor, a humanoid framework for human-level whole-body reactions in contact-rich environments. Based on the robot's force analysis, we design a force-adaptive torso-tilt (FAT2) reward function to encourage humanoids to exhibit human-like responses during force-interaction tasks. To mitigate the high-dimensional challenges of humanoid control, Thor introduces a reinforcement learning architecture that decouples the upper body, waist, and lower body. Each component shares global observations of the whole body and jointly updates its parameters. Finally, we deploy Thor on the Unitree G1, and it substantially outperforms baselines in force-interaction tasks. Specifically, the robot achieves a peak pulling force of 167.7 N (approximately 48% of the G1's body weight) when moving backward and 145.5 N when moving forward, representing improvements of 68.9% and 74.7%, respectively, compared with the best-performing baseline. Moreover, Thor is capable of pulling a loaded rack (130 N) and opening a fire door with one hand (60 N). These results highlight Thor's effectiveness in enhancing humanoid force-interaction capabilities.
Linear Causal Discovery with Interventional Constraints
Venue: arXiv cs.LG 2025
Year: 2025

Incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interventional constraints, which differs fundamentally from interventional data. While interventional data require direct perturbations of variables, interventional constraints encode high-level causal knowledge in the form of inequality constraints on causal effects. For instance, in the Sachs dataset (Sachs et al.\ 2005), Akt has been shown to be activated by PIP3, meaning PIP3 exerts a positive causal effect on Akt. Existing causal discovery methods allow enforcing structural constraints (for example, requiring a causal path from PIP3 to Akt), but they may still produce incorrect causal conclusions such as learning that "PIP3 inhibits Akt". Interventional constraints bridge this gap by explicitly constraining the total causal effect between variable pairs, ensuring learned models respect known causal influences. To formalize interventional constraints, we propose a metric to quantify total causal effects for linear causal models and formulate the problem as a constrained optimization task, solved using a two-stage constrained optimization method. We evaluate our approach on real-world datasets and demonstrate that integrating interventional constraints not only improves model accuracy and ensures consistency with established findings, making models more explainable, but also facilitates the discovery of new causal relationships that would otherwise be costly to identify.
UniTok-Audio: A Unified Audio Generation Framework via Generative Modeling on Discrete Codec Tokens
Venue: arXiv cs.SD 2025
Year: 2025

Generative modeling has recently achieved remarkable success across text, image, and audio domains, demonstrating powerful capabilities for unified representation learning. However, audio generation models still face challenges in terms of audio quality and generalization ability across tasks. This fragmentation results in redundant development efforts, inconsistent performance, and limited extensibility. To address these issues, we propose \textbf{UniTok-Audio}, a scalable and extensible framework for unified audio generation tasks. Specifically, 1) UniTok-Audio extracts continuous feature of conditions to generates discrete tokens of target audio in an autoregressive manner; 2) a special task identifier token unifies different learning patterns of multiple tasks in a single framework; 3) a dual-stream audio codec involving acoustic and semantic branch is developed for high-fidelity waveform reconstruction. Experimental results demonstrate that UniTok-Audio achieves competitive performance in comparation with state-of-the-art task-specific or multi-task systems across five time-aligned tasks: speech restoration, target speaker extraction, speech separation, voice conversion, and language-queried audio source separation. To foster future research, we will open-source our codebase. The demo page of our work can be found here: https://alibaba.github.io/unified-audio.
EEG-Driven Image Reconstruction with Saliency-Guided Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Existing EEG-driven image reconstruction methods often overlook spatial attention mechanisms, limiting fidelity and semantic coherence. To address this, we propose a dual-conditioning framework that combines EEG embeddings with spatial saliency maps to enhance image generation. Our approach leverages the Adaptive Thinking Mapper (ATM) for EEG feature extraction and fine-tunes Stable Diffusion 2.1 via Low-Rank Adaptation (LoRA) to align neural signals with visual semantics, while a ControlNet branch conditions generation on saliency maps for spatial control. Evaluated on THINGS-EEG, our method achieves a significant improvement in the quality of low- and high-level image features over existing approaches. Simultaneously, strongly aligning with human visual attention. The results demonstrate that attentional priors resolve EEG ambiguities, enabling high-fidelity reconstructions with applications in medical diagnostics and neuroadaptive interfaces, advancing neural decoding through efficient adaptation of pre-trained diffusion models.
Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
Venue: arXiv cs.LG 2025
Year: 2025

Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is critical in precision medicine. However, the costly trial data limit the prediction performance. To address this issue, we propose a cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance predictions on scarce but high-fidelity trial data. CFKD-AFN incorporates a dual-channel knowledge distillation module to extract complementary knowledge from the low-fidelity model, along with an attention-guided fusion module to dynamically integrate multi-source information. Experiments on treatment outcome prediction for the chronic obstructive pulmonary disease demonstrates significant improvements of CFKD-AFN over state-of-the-art methods in prediction accuracy, ranging from 6.67\% to 74.55\%, and strong robustness to varying high-fidelity dataset sizes. Furthermore, we extend CFKD-AFN to an interpretable variant, enabling the exploration of latent medical semantics to support clinical decision-making.
Wireless Memory Approximation for Energy-efficient Task-specific IoT Data Retrieval
Venue: arXiv cs.NI 2025
Year: 2025

The use of Dynamic Random Access Memory (DRAM) for storing Machine Learning (ML) models plays a critical role in accelerating ML inference tasks in the next generation of communication systems. However, periodic refreshment of DRAM results in wasteful energy consumption during standby periods, which is significant for resource-constrained Internet of Things (IoT) devices. To solve this problem, this work advocates two novel approaches: 1) wireless memory activation and 2) wireless memory approximation. These enable the wireless devices to efficiently manage the available memory by considering the timing aspects and relevance of ML model usage; hence, reducing the overall energy consumption. Numerical results show that our proposed scheme can realize smaller energy consumption than the always-on approach while satisfying the retrieval accuracy constraint.
Metacognition and Confidence Dynamics in Advice Taking from Generative AI
Venue: arXiv cs.HC 2025
Year: 2025

Generative Artificial Intelligence (GenAI) can aid humans in a wide range of tasks, but its effectiveness critically depends on users being able to evaluate the accuracy of GenAI outputs and their own expertise. Here we asked how confidence in self and GenAI contributes to decisions to seek and rely on advice from GenAI ('prospective confidence'), and how advice-taking in turn shapes this confidence ('retrospective confidence'). In a novel paradigm involving text generation, participants formulated plans for events, and could request advice from a GenAI (Study 1; N=200) or were randomly assigned to receive advice (Study 2; N=300), which they could rely on or ignore. Advice requests in Study 1 were related to higher prospective confidence in GenAI and lower confidence in self. Advice-seekers showed increased retrospective confidence in GenAI, while those who declined advice showed increased confidence in self. Random assignment in Study 2 revealed that advice exposure increases confidence in GenAI and in self, suggesting that GenAI advice-taking causally boosts retrospective confidence. These results were mirrored in advice reliance, operationalised as the textual similarity between GenAI advice and participants' responses, with reliance associated with increased retrospective confidence in both GenAI and self. Critically, participants who chose to obtain/rely on advice provided more detailed responses (likely due to the output's verbosity), but failed to check the output thoroughly, missing key information. These findings underscore a key role for confidence in interactions with GenAI, shaped by both prior beliefs about oneself and the reliability of AI, and context-dependent exposure to advice.