arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 19 recordsLinked to original sources

arXiv2026

S$^2$-MAS: A Society-Scale Multi-Agent Simulation Framework with a 500-Million-Persona Resource

Simulating economic responses at society scale requires decision rules that respond to household circumstances and accounting rules that track their consequences. We introduce S$^2$-MAS, a multi-agent simulation framework with a resource of 500 million synthetic household personas. It combines trainable candidate-scoring policies, reusable financial scenarios and explicit state transitions. Trained with SFT + RLCD on 2.93 million examples, YN-mini-MLX achieves 74.55% accuracy and 38.37% question-macro F1 on held-out consumer financial-choice tasks, improving on the training-question majority baseline by 2.78 and 6.21 percentage points. A completed application evaluates 12 million distinct households in two purchase-funding conditions: 24 million one-shot choices feed 288 million monthly ledger states in 3 h 59 min on 14 GPUs. Jointly increasing purchase goals and approved limits produces heterogeneous consumption, cash and repayment paths, with much of the consumption variation arising from transitions between deferral and purchase. In a separate 1,060-household retrospective consumption experiment, standalone YN-master-9B has 3.42% lower annual MAE than the deterministic fully rational benchmark, alongside a 19.7% lower predicted population mean than observed. Information-timing tests show how year labels and later outcome information change decisions. Together, these experiments establish an executable alternative for studying heterogeneous household behavior with learned policies and explicit financial accounting.

cs.MA↗

arXiv2026

Laser wakefield acceleration and x-ray emission in plasma produced by the electrical explosion of an aluminum wire

Underdense plasmas used in relativistic laser-plasma interactions are conventionally produced from gaseous targets, whereas low-density metallic plasmas remain comparatively unexplored. Here, we investigate the interaction of a femtosecond petawatt laser with expanded aluminum plasma using particle-in-cell simulations based on an experimentally motivated exploding-wire density profile. Three-dimensional simulations show that the laser propagates through the low-density metallic plasma, drives a pronounced wake-like structure, accelerates electrons to near-GeV energies, and produces directional broadband x-ray emission. Charge-state-resolved diagnostics further show that electrons associated with higher aluminum ionization stages are disproportionately represented in the energetic population. Complementary two-dimensional simulations indicate that the net electron energy gain is dominated by longitudinal electric-field work and show that relativistic electron generation persists under variations of the assumed initial ionization and density conditions, although the yield remains quantitatively sensitive to the plasma state. These results demonstrate the feasibility of electrically exploded aluminum plasma as a target platform for laser wakefield acceleration and associated x-ray generation.

physics.plasm-ph↗

arXiv2026

Pacifo: An Efficient Fully Homomorphic Encryption-based Lightweight ResNet for Trustworthy Machine Learning as a Service

Machine Learning as a Service (MLaaS) has become a popular paradigm for deploying AI applications. However, it raises significant privacy concerns that the raw client data needs to be uploaded to the server for inference services. Private inference using Fully Homomorphic Encryption (FHE) offers a solution by allowing computations directly on encrypted data, with only the final results decrypted on a client side. However, current practical implementations still suffer from large computational overhead and high latency due to the cost of FHE operations. In this paper, we propose Pacifo, a framework that adapts customized fine-tuning strategies to efficiently evaluate the ResNet20 model in the context of FHE. Specifically, Pacifo introduces a public-private layer partitioning scheme and an efficient ReLU approximation, significantly reducing the number of private layers and costly FHE operations during inference. It also offers flexible security configurations to accommodate varying client privacy requirements. Compared to state-of-the-art FHE implementations of ResNet20, Pacifo achieves a speedup of $3.7-21.7\times$ in runtime while effectively preserving $95\%-99\%$ of the original plaintext model accuracy across downstream tasks. Pacifo is open-sourced at https://github.com/BryceYangLi/Pacifo.

cs.CR↗

arXiv2026

EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation

Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory budgets. In practice, lightweight systems for object detection, instance segmentation, and pose estimation are still dominated by CNN-based architectures such as YOLO, while compact Vision Transformers (ViTs) often struggle to achieve comparable accuracy-efficiency trade-offs, even with large-scale pretraining. We argue that this gap arises primarily from insufficient task-specific representation learning in small-scale ViTs, rather than from an inherent mismatch between ViTs and edge dense prediction. To address this issue, we introduce EdgeCrafter, a unified compact ViT framework for edge dense prediction centered on ECDet, a detection model built on a distilled compact backbone and an edge-friendly encoder-decoder design. The resulting detection-distilled representation transfers directly to instance segmentation and human pose estimation through lightweight task-specific prediction modules. Without using task annotations beyond COCO, ECDet-S achieves 51.7 box AP with fewer than 10M parameters, while ECInsSeg-X and ECPose-X reach 48.4 mask AP and 74.8 keypoint AP, respectively. As a complementary but more compute-intensive setting, Objects365 detection pretraining consistently improves performance across all scales, with the X variants reaching 59.9 box AP, 49.8 mask AP, and 75.9 keypoint AP. These results show that compact ViTs, when combined with task-specialized distillation and edge-aware design, can be a practical and competitive solution for edge dense prediction. Code is available at: https://intellindust-ai-lab.github.io/projects/EdgeCrafter

cs.CV↗

arXiv2026

Replan for Error Recovery: Training Embodied VLMs through Verifiable Interaction with Action-Grouped Turn-GSPO

Embodied VLMs can execute increasingly complex manipulation tasks, yet execution errors can leave the environment in states that require replanning for recovery. Existing recovery methods either rely on inference-time mechanisms with fixed policies or learn from failure examples and reference corrections rather than verified action outcomes. To enable recovery learning through verifiable interaction, we introduce the Embodied Replanning for Recovery Environment (ERR-Env), which provides feedback on task progress and execution errors. We further construct ERR-Bench, a benchmark with held-out tasks spanning nominal, intermediate, and error states for evaluating recovery capability and execution reliability. We then propose Action-Grouped Turn-GSPO (AGT-GSPO), an online reinforcement learning method combining turn-level rewards for task progress and error states with action-grouped advantage estimation, and use it to train ERR-VLM, a compact 2B embodied VLM. Evaluation on ERR-Bench reveals a mismatch between task progress and execution reliability across diverse VLMs. AGT-GSPO improves ERR-VLM by 10.25 percentage points in Strict Success Rate for error-free completion and 22.95 percentage points in Recovery Rate, while ablations and zero-shot transfer further validate the effectiveness of our method.

cs.RO↗

arXiv2026

HARMONY: Hierarchical Anchor Retrieval on Manifold for Oblivious-source Machinery Acoustic Anomaly Detection

In standard acoustic anomaly detection (ASD), models are usually trained separately for each machine or domain using explicit metadata. However, in realistic deployments, machine identifiers are incomplete, unreliable, or simply unavailable. Thus, metadata-free machinery ASD asks a single model to monitor mixed machine populations without machine IDs. In this paper, we argue that the main failure mode is not a weak detector family, but representation entanglement. The heterogeneous normal modes overlap in frozen pre-trained feature spaces, making anomaly scoring and approximate retrieval unreliable. We study this problem directly and propose HARMONY (Hierarchical Anchor Retrieval on Manifold for Oblivious-source acoustic aNomalY detection), an anchor-guided geometric repartitioning framework that reorganizes mixed-source features into compact Voronoi regions and reuses this structure for hierarchical retrieval. On DCASE 2020, HARMONY improves mean AUC from 75.40% to 90.00% (14.60 percentage points) over the strongest unified baseline and from 83.90% to 90.00% over a strong foundation-feature baseline. On a single CPU, its hierarchical retrieval achieves a 7.8x speedup over exhaustive search with only a 0.20 percentage-point AUC drop. These results suggest that incorporating explicit geometric partitioning can improve both detection accuracy and retrieval efficiency for ASD. Code is available at https://github.com/Zzyh0208/HARMONY.

cs.SD↗

arXiv2026

HarnessPAI: An Evolving Harness for Physical AI

Physical AI aims to build embodied agents that perceive the world, understand and reason about it, and decide how to act. Yet the field has focused primarily on the last component: the action model that maps observations to low-level controls. The prevailing training recipe can erode the perceptual and reasoning capabilities needed for robust behavior, leaving even strong action models vulnerable to scene perturbations and long-horizon tasks. We introduce HarnessPAI, a model- and embodiment-agnostic Harness framework for Physical AI that treats code as the executable and evolvable interface that organizes the underlying action primitive. The framework separates two timescales: within a rollout, it executes open-loop at the program level, with a fixed program guiding and checking execution; across rollouts, it evolves closed-loop, using execution feedback to revise the program and distill failures into reusable skills. Across desktop robot arms, household robots, a robot vacuum, and a legged walking agent, HarnessPAI demonstrates consistent improvements against baselines. Beyond the harness, the converged program also acts as a low-cost, reliable source of expert data: it generates training data for fine-tuning VLA and WAM models on tasks where it is hard for these models to obtain successful trajectories on their own. Our results suggest that the frontier of Physical AI depends not only on stronger action models, but also on executable harnesses that integrate perception, task understanding and reasoning, and action execution into a unified, verifiable, and feedback-driven system. Website: https://darwin-agent.github.io/HarnessPAI

cs.RO↗

arXiv2026

KunSet: A Corpus and Benchmark for Lyrics Alignment and Rhyme Retrieval in Kunqu Opera

Kunqu is one of the oldest extant forms of Chinese opera, yet its digitization remains scarce and difficult to annotate, with two challenges standing out: first, close analysis of Kunqu performance demands syllable-level lyrics alignment; second, analyzing Kunqu pronunciation requires its phonological attributes (e.g., rhyme) to be linked with each syllable. To address these challenges, we present KunSet, the first Kunqu performance corpus comprising 482,164 fine-grained annotations of syllable-level lyrics and phonological attributes in total media duration of 155.2 h with 106.3 h annotated. To ensure annotation quality, KunSet was preannotated by an iterative ML-assisted pipeline and corrected by over 200 trained annotators via a purpose-built online annotation system under a two-round review process. To assess KunSet as a music information retrieval (MIR) benchmark, we evaluate two tasks: lyrics alignment, by finetuning a forced-alignment model with an acoustic anchor decoder; and same-rhyme retrieval, using frozen Chinese HuBERT features. Models built with KunSet outperform their baselines. We release KunSet and model checkpoints to support MIR research on traditional-music corpora.

cs.SD↗

arXiv2026

On the maximum length of cyclic MDS codes

We study the maximum length of nontrivial simple-root cyclic maximum distance separable (MDS) codes over ${\mathbb F}_q$. We prove $n\le q+1$ if $\gcd(n,q^2-1)>2$, or if the dimension is even and $\gcd(n,q^2-1)>1$. The bound also holds for all lengths whose prime factors lie in $\{2,3,5,7\}$. We obtain further cases involving primes $\ell\ge11$ with $(\ell-1)/2$ prime, under conditions on the exponents and the characteristic. The proofs combine Fourier supports, locality bounds and field descent with arithmetic restrictions on multiplicative orders and prime-adic valuations.

cs.IT↗

arXiv2026

Thermoresponsive self-oscillating hydrogel: spatially lumped model, relaxation dynamics and mechanical output

Thermoresponsive gels with a lower critical solution temperature (LCST) can undergo self-oscillation driven by a thermal flux through repeated cycles of positive feedback, driven by heating-induced collapse, and negative feedback, characterized by deformation recovery due to collapse-induced suppression of heating and heat dissipation to the environment. In order to explore the detailed bifurcation structure of a thermosensitive self-oscillating gel and understand its mechanical output of peristaltic motion and net displacement, we develop a minimal spatially lumped two-variable phenomenological model whose equilibrium parameters are constrained by poly(N-isopropylacrylamide) (PNIPAAm) swelling data. A supercritical Hopf bifurcation creates stable small-amplitude oscillations near a fold of the swollen manifold, and canard-mediated growth transforms this branch into relaxation oscillations. Singular analysis relates the amplitude to fast jumps and the period to drift along slow manifolds, while continuation separates the Hopf boundary from the maximal-canard locus. For the representative maximum-power relaxation cycle considered here, the loaded model predicts greater work than a harmonic volume trajectory with the same stroke, period, and viscous load, with output concentrated in the fast collapse and re-swelling transitions. The net displacement during peristaltic locomotion of the thermoresponsive self-oscillating gel results from the work difference between collapse and re-swelling. These results provide a theoretical basis for designing autonomous thermoresponsive self-oscillating gel actuators whose oscillatory waveform and power output can be controlled through thermal feedback and mechanical loading.

cond-mat.soft↗

arXiv2026

From Outcomes to Steps: Reliable Success-Aware Critic Learning for Long-Horizon Language Agents

Reinforcement learning for long-horizon language agents requires identifying whether intermediate actions make eventual task success more or less likely when supervision is available only at trajectory level. The main difficulty is that a critic with aggregate return-prediction accuracy may still fail to resolve the local value differences needed to assign credit to actions, especially under sparse feedback and function approximation. Conventional critics estimate future return, but policy improvement also depends on knowing whether each intermediate decision makes task success more or less likely. We introduce SCALE, an actor-critic framework that augments conventional return estimation with explicit prediction of eventual task success. At each interaction prefix, the critic predicts a return value and a Success Value, defined as the probability that the task will eventually be completed from that prefix. SCALE converts changes in Success Value into multi-step action credit through Success-GAE and modulates its contribution according to the estimated reliability of the predictor, while retaining the token-level PPO objective. Across five agent benchmarks and three model backbones, SCALE improves task performance over the compared RL baselines in most settings, with gains of up to 4.1 percentage points in success rate. Separate critic-learning analyses show that SCALE also improves critic estimation and training stability.

cs.LG↗

arXiv2026

From Evidence to Effect: Authority Semantics and Runtime Infrastructure for Stateful Agents

Stateful agents reuse artifacts after producing executions and permissions change. We formalize authority-sufficient observations and durable effects bound to execution and material identities. WTB implements this interface through runtime adapters, shared evidence, and transactional publication/recovery. Six study families separate the mechanism from its integration. Raw and typed evidence both solve 32/32 authority cases, with model-dependent planning effects. Fixed-intent enforcement blocks six unsafe proposals and executes 12 eligible authorized intents. Complete controls match WTB's capability. Paid integration yields 176/210 accepted benchmark-source stages, including 19/30 publication stages, recovery on 8/8 primary SWE repositories, and the most complete continuous trajectories on each of three source tasks. The findings connect authority information, effect admission, and infrastructure reuse in stateful agents.

cs.MA↗

arXiv2026

Physical AI: Concepts, Techniques, and Challenges

In recent years, physical artificial intelligence (physical AI) has experienced rapid development, capturing significant attention in academia and industry. However, the field has yet to reach consensus on its definition, technical landscape, and core challenges. To address this gap, this monograph seeks to define physical AI, present its technical landscape, and examine each key technical subfield in depth. In doing so, it establishes a unified framework for Physical AI: artificial systems that perceive, model, and act upon the physical world through continuous feedback under real-world constraints. Further, this monograph distinguishes physical AI from conventional digital AI, embodied AI, cyber-physical systems, and digital twins. It develops the foundations of physical AI across physics, control, cognition, learning, and reasoning. It reviews key methodologies, including multimodal perception, physical data and simulation, world models, embodied foundation models, motor-control systems, multi-agent coordination, and hardware-software co-design. Furthermore, it examines applications spanning robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services. Particular attention is given to sim-to-real transfer, reliability, real-time execution, evaluation, safety, governance, and long-term deployment. By connecting perception, prediction, decision-making, action, and adaptation in a closed loop, this monograph identifies the technical challenges and research directions required to advance from narrow automation toward more generalizable physical AI and, ultimately, physical artificial general intelligence (Physical AGI).

cs.AI↗

arXiv2026

Real-time quantum simulation of hadron production in deep inelastic scattering

We develop a quantum simulation framework for meson production in deep inelastic scattering based on light-front QCD Hamiltonian formalism. A quark-antiquark dipole, prepared either as a single-momentum state or as the $q\bar q$ component of a virtual photon, is evolved in real time through the color field of a nuclear target via Trotterized unitary evolution. We consider both proton and gold nuclear targets, each characterized by their respective saturation scale. From the evolved state we extract the transverse momentum broadening, the total and elastic cross sections, and the $J/ψ$ production rate by projecting onto the $J/ψ$ light-front wavefunction. We then extract the nuclear modification factor $R_A$ at different photon virtuality $Q^2$ and analyze nuclear modification effect. Results of classical simulations show good agreement with analytical eikonal predictions across a broad range of parameters. For the single-momentum dipole, quantum simulation is demonstrated on a quantum circuit emulator with a smaller lattice size, and results are consistent with the classical benchmark. This work establishes a first-principles, amplitude-level pathway toward computing exclusive hadron production in deep inelastic scattering, directly relevant to the physics program of the Electron-Ion Collider.

hep-ph↗

arXiv2026

Scene-Adaptive VLA: Efficient Autonomous Driving via Dynamic Layer Routing

Vision-Language-Action (VLA) models offer a promising paradigm for autonomous driving. However, the massive parameter scale of VLAs strains onboard computational resources during real-time inference. This directly increases energy consumption and limits vehicle battery range, making it critical to minimize computational overhead without compromising driving capability and explainability. Current research on efficient VLA-based driving focuses on sequential adaptation (\textit{e.g.}, Chain-of-Thought and token adjustment), leaving structural adaptation underexplored despite parameter redundancy across dynamic driving scenes. To address this gap, we propose Scene-Adaptive VLA, an efficient framework leveraging dynamic layer routing to adjust active parameters based on real-time scenes. Inspired by the temporal continuity of scenes and model predictive uncertainty, our approach characterizes model-perceived scene complexity via frame-level temporal scene variations and decision deviations. To uncover latent scene-to-parameter correspondences, we encode this characterization into scene-aware tokens, alongside two specialized queries. A budget-conditioned layer routing mechanism evaluates these refined queries to allocate a budget that determines the active parameter ratio and subsequently selects appropriate layer combinations. The entire framework is optimized end-to-end. Extensive evaluations on the Bench2Drive benchmark show that when adapted to a state-of-the-art VLA model, our approach significantly reduces inference-time computational overhead by 53.5\% with negligible degradation in driving and language capabilities. Code is available at https:github.com/yang-luvu/Scene-Adaptive-VLA.

cs.CV↗

arXiv2026

All Roads Lead to Rome: Flow-driven Multi-Anchor Exploration for Open-Environment Active 3D Mapping

To advance the development of embodied intelligence, Open-Environment Active 3D Mapping has attracted increasing attention, aiming to perform a long-horizon and shortest trajectory exploration for reconstructing unseen scenarios. Since only limited information about unseen environments is available, methods built on the closed-set assumption, i.e., assuming that the test environments are similar to those seen during training, cannot generalize satisfactorily. In existing active mapping methods, long-horizon exploration is often guided by predicting a coarse long-range goal and then converting it into an executable path. However, this stage is usually formulated as single-point prediction. Under partial observability, the same local observation may correspond to multiple plausible exploration directions, making such deterministic prediction prone to brittle decisions and degraded performance in unseen scenarios. Our experiments further verify that this is a key factor underlying their weak generalization. To address this issue, we reformulate long-horizon target prediction as conditional multimodal anchor generation using Conditional Flow Matching. Instead of predicting a single goal, our method learns a conditional distribution over coarse exploration anchors from the current mapping state. These anchors are first converted into executable candidate paths through obstacle-aware planning. We then apply exploration-mode clustering to compress geometrically similar trajectories and reduce candidate redundancy. Finally, a hierarchical selection module selects the most promising mode and reranks paths within it to produce the final executable trajectory. Experiments show that our method improves generalization and reconstruction efficiency in open environments.

cs.RO↗

arXiv2026

Filter-Augmented Reinforcement Learning for Battery Fast Charging with State of Health and Plating-Aware Reward Design

Fast charging of lithium-ion batteries is crucial for electric vehicle adoption, yet it increases safety risks and accelerates battery ageing. To address this, we propose a filter-augmented reinforcement learning (RL) framework for lifelong health-aware fast charging, based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. The RL agent is guided by a reward function that balances charging speed and battery longevity through the incorporation of state of health (SoH) information and an SoH-dependent voltage penalty term derived from a constant--current constant--overpotential charging strategy. Since onboard SoH estimates are inevitably corrupted by noise, two filtering strategies are integrated: an exponential moving average (EMA) filter when noise statistics are unknown, and a Kalman filter when they are available, with the filtered estimate embedded in both the agent's state and reward computation. The RL policy is trained using data from high-fidelity life-cycle simulations. The evaluation uses a published electrochemical and degradation parameterisation for a commercial LG M50 cell. Results show that the proposed method achieves up to 21.4\% more equivalent full cycles (EFCs) than conventional constant current--constant voltage (CC--CV) charging under 2\% SoH estimation noise, and maintains this lifetime advantage even under 5\% noise, while sustaining comparable charging speed.

eess.SY↗

arXiv2026

The Safety Relay in Roleplay Jailbreaks: A Component-Resolved Causal Analysis of Harm Recognition and Refusal

Large language models are trained to follow instructions while refusing harmful requests. Jailbreaks exploit this balance to elicit content a model would ordinarily reject. Roleplay jailbreaks are especially concerning: ordinary instructions defining a persona, scenario, and task can induce compliance while the harmful request remains plainly visible. We investigate which components drive this reversal and how they affect the computations leading to the response. Across two benchmarks, three model families, and four authored wrappers, we compare matched harmful and benign requests under controlled combinations of these components. We trace internal harmful--benign contrasts at the request and where generation begins, then evaluate selected internal interventions on held-out requests. A focused study examines influential attention heads and feed-forward computations, testing their functions and downstream connections. Our analysis yields three findings. (1) Successful attacks retain the measured harmful--benign distinction at the request, while its refusal-associated expression weakens where the answer begins, a pattern we call safety-relay attenuation. (2) The contributions of roleplay components depend on the other components present. (3) Reversing selected roleplay-associated internal changes restores refusal, demonstrating their causal influence. Together, these findings connect ordinary roleplay instructions to internal changes in refusal and motivate safeguards that preserve the influence of harm-related information on the response.

cs.LG↗