arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 181 records · Page 10Linked to original sources

arXiv2026

The afterglow of gamma-ray burst - supernova connections

The X-ray afterglow of several long gamma-ray bursts (LGRBs) associated with broad line type Ib/c supernovae (SNe) exhibits a standard non-thermal afterglow, commonly attributed to synchrotron emission from a relativistic jet, and an evolving thermal component whose physical origin is still debated. We investigate whether these thermal and non-thermal components can be described within a common analytical framework and whether their temporal evolution can provide insight into the physical connection between relativistic jets and SN ejecta. We combine a phenomenological description of the synchrotron emission of the non-thermal energy associated with the relativistic jet with a diffusion model describing the thermal evolution of the jet-affected portion of the SN ejecta. The resulting coupled system is solved analytically, leading to closed-form expressions for both non-thermal and thermal luminosities. We apply the model to the following systems: GRB 060218A/SN 2006aj and GRB 171205A/SN 2017iuk. The proposed formalism reproduces the main features of both thermal and non-thermal light curves. In both systems, the thermal luminosity follows a temporal evolution similar to that of the non-thermal component up to the plateau phase. The inferred thermal energy stored in the jet-affected ejecta is found to be a fraction of the energy coupled to the observed non-thermal emission. The characteristic timescales obtained from the fits suggest a direct link between the evolution of the jet and the thermal response of the expanding ejecta. Although simplified, the proposed framework provides a unified analytical description of LGRB afterglows and their thermal counterparts and offers a useful tool for investigating the physical connection between relativistic jets and SNe Ib/c.

astro-ph.HE↗

arXiv2026

Application-Driven Architecture Exploration for Cross-Layer Heterogeneous Systems

AI and HPC infrastructure increasingly serves workload portfolios that combine dense tensor computation, sparse kernels, large memory footprints, and communication-intensive collectives. Supporting these portfolios requires coordinated choices across accelerators, memory tiers, scale-up fabrics, and cluster networks. The resulting Cross-layer Heterogeneous System (XHS) design space is difficult to explore: hardware choices change legal task mappings, while rack power, switch radix, cabling, and cost constraints invalidate many candidates. We present CHASE, an application-driven framework that searches physically feasible XHS architectures through the workloads they must execute. CHASE represents candidates as hierarchical typed graphs and rejects designs that violate deployment constraints. It avoids intractable joint hardware-mapping search with a decoupled two-level loop: an inner mapper translates hardware-independent workload DAGs into topology-aware event traces, a calibrated event-driven simulator evaluates each mapping, and an outer telemetry-guided optimizer evolves the hardware graph. We evaluate CHASE on sparse-computing and LLM workloads. Its mapper remains within 6.06% of exhaustive optima while reducing mapping time by 60.5% on average relative to PEFT. Compute-model errors average 4.4-7.5%, and communication validation reproduces key trends across physical platforms. The outer search reaches near-global optima within 64 iterations. End-to-end case studies show that sparse workloads favor criticality-aware heterogeneous pods, whereas LLM inference favors scale-up islands; the resulting designs deliver 6.20$\times$ and 2.12$\times$ geomean speedups, respectively, while reducing cost and power relative to the baselines.

cs.DC↗

arXiv2026

The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation

While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like robotic assembly remains largely unexplored. This paper systematically investigates this relationship and introduces a novel scaling law. We find that to achieve a fixed success rate, the required number of demonstrations $N$ grows super-exponentially as the target precision $P$ approaches a limit $c$. This relationship is accurately captured by the model $\log(N) \propto 1/(P-c)$. Crucially, we reveal that the limit precision $c$ is not a static physical constant of the task but an emergent property of the entire agent system, including its sensors and expert policy. Through experiments on canonical manipulation tasks, we validate this law and demonstrate that improving system components, such as adding a wrist camera or using a more effective expert, measurably lowers $c$, thus expanding the system's achievable precision. Our work provides a new theoretical framework for precision in robotics and a quantitative metric to evaluate system capabilities. Furthermore, these findings provide a practical methodology for guiding the development and debugging of high-precision manipulation systems.

cs.RO↗

arXiv2026

DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning

Chain-of-thought (CoT) supervision exposes intermediate rationales, but flat rationale targets usually optimize a single reasoning sequence and provide limited supervision on how local conclusions should support later decisions. We introduce Dependency-Aware Intermediate QA Supervision (DAIS), a training-time framework that converts filtered teacher rationales into stage-level QA records. Each intermediate record predicts a local answer conditioned on the previous states needed for that decision, while the final-answer record keeps the original task format; evaluation therefore uses only the original input and optional context. Across GDPR, AIACT, MedQA, and FOLIO with multiple Qwen backbones, DAIS improves average final-answer accuracy over answer-only, flat chain-of-thought, and independent-QA baselines. On policy-compliance benchmarks, it achieves a largest gain of 5.6% and an average gain of 4.2% over the strongest non-DAIS baseline. Controlled ablations show that valid previous-state conditioning contributes beyond longer targets or additional intermediate text, supporting dependency-conditioned intermediate QA as a lightweight auxiliary supervision signal for standard final-answer inference.

cs.CL↗

arXiv2026

Convolution for Large Language Models

Large language models (LLMs) largely rely on Transformers, where self-attention provides global token interaction but does not explicitly encode the locality of natural language. We study whether lightweight depthwise convolutions can supply this local inductive bias without materially increasing model size. Our macro-level ablation compares convolution at 17 locations in a Qwen3 Transformer block and finds the best results when convolution is applied to the projected queries, keys, and values before attention. A subsequent micro-level study favors a residual depthwise convolution with kernel size $k=3$, without additional normalization or activation. Across Qwen3 models and several pre-training data budgets, this design improves the average accuracy on seven downstream benchmarks while adding less than $0.01\%$ parameters. A representation-level case study further suggests that the convolution makes repeated token IDs more sensitive to their immediate context. These results support depthwise convolution as a lightweight complement to self-attention for modeling short-range token interactions.

cs.CL↗

arXiv2026

PDD: Unleashing Economical and Flexible Heterogeneous LLM Inference via Cross-Datacenter Prefill-Decode Disaggregation

Interconnecting geographically dispersed clusters over wide-area Ethernet provides a scalable and cost-effective alternative to dedicated intra-datacenter heterogeneous clusters for Large Language Model (LLM) inference. However, this cross-datacenter disaggregation imposes heavy KV-cache transfers between clusters, causing substantial Time-to-First-Token (TTFT) latency, which is especially detrimental for agentic workloads characterized by long contexts, high cache hit rates, and short outputs. We propose PDD, a three-tier disaggregation architecture built upon prefill-decode (PD) disaggregation, consisting of Prefill, RelayDecode (RLD), and MainDecode (MD) instances. On Cluster A, Prefill instances perform the prefill computation, while RLD instances immediately receive the KV cache via high-speed RDMA and begin decoding, thereby overlapping the KV transfer to Cluster B over TCP-based Ethernet. MD instances on Cluster B receive the KV cache along with the tokens produced by RLD, and decoding is then seamlessly handed off from RLD to MD for completion. To maximize overall efficiency, PDD employs three core mechanisms: Decode-side RadixCache to alleviate bandwidth bottlenecks, an Extend-Decode Handoff mechanism for smooth control migration between RLD and MD, and multi-stage pipeline orchestration to manage complex inter-tier dependencies under high concurrency and long-term serving. We further design a low-cost, fine-grained heterogeneous deployment scheme that maximizes latency-masking efficiency at marginal cost. Compared to the intra-DC homogeneous PD baseline, PDD's cross-datacenter mapping of compute-intensive H100s and memory-bandwidth-optimized H200s achieves a Benefit-Cost Ratio (BCR) up to 37.5% higher in SLA-compliant goodput.

cs.AR↗

arXiv2026

Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition

Ambivalence and hesitancy (A/H) are affective states in which individuals express contradictory signals across facial, vocal, and linguistic channels. Automatically recognising A/H in clinical videos requires detecting cross-modal disagreement -- the signal that standard fusion methods suppress. Based on the conflict-aware multimodal fusion framework of Bekhouche et al., we present the Modality Discrepancy Transformer (MDT). MDT enriches the original 6-token design to a 9-token representation comprising three modality embeddings, three absolute-difference features, and three Hadamard-product discrepancy features learned through linear projections. These nine tokens undergo Transformer self-attention, with FiLM-based text-conditioned modulation and LoRA fine-tuning as core architectural components. A text-guided late fusion branch blends a text-only auxiliary head with the full multimodal output at inference. On the BAH dataset from the 3rd ABAW Challenge, MDT achieves 0.7408 Macro F1 on the labelled test split and 0.7368 on the private leaderboard, outperforming the strongest published baseline by over 10 points while training in under 20 minutes on a single GPU.

cs.CL↗

arXiv2026

Orbital Dynamics and Gravitational-Wave Signatures of EMRIs in Self-Dual Loop Quantum Gravity Black Holes

Loop quantum gravity (LQG) predicts quantum modifications to classical black-hole spacetimes, which may leave imprints on the dynamics and gravitational-wave signals of compact objects in the strong-field regime. In this work, we investigate the orbital dynamics and gravitational-wave signatures of extreme mass-ratio inspirals (EMRIs) in a self-dual loop quantum gravity black hole spacetime. We analyze test-particle motion in the static, spherically symmetric self-dual LQG geometry characterized by two quantum parameters: the polymeric parameter $P$ and the minimal area parameter $a_0$. The effective potential and orbital structure are systematically studied, and we quantify the influence of quantum corrections on circular-orbit stability and strong-field orbital behavior. Compared with the classical Schwarzschild spacetime, LQG corrections modify the near-horizon orbital dynamics. Based on the orbital evolution, we construct gravitational-wave waveforms and investigate the impact of quantum corrections on waveform morphology. We find that LQG effects accumulate during the long inspiral phase, leading to noticeable signal deviations from the classical case. To incorporate rotation, we construct a rotating extension of the self-dual spacetime using the Newman--Janis algorithm. The resulting LQG-corrected Kerr geometry is used to analyze orbital motion, revealing the interplay between spin and quantum corrections in strong-field trajectories. Finally, we perform a Fisher matrix analysis to estimate potential constraints on quantum parameters from future space-based gravitational-wave detectors. Our results indicate that EMRI observations provide a promising avenue to probe quantum gravitational effects in black-hole spacetimes.

gr-qc↗

arXiv2026

Quasinormal Modes of Self-Dual Loop Quantum Gravity Corrected Kerr Black Holes

We investigate scalar quasinormal modes and ringdown signals of a rotating loop quantum gravity black hole constructed via the revised Newman--Janis algorithm. The spacetime incorporates two quantum parameters, the polymeric parameter $P$ and the minimal area parameter $a_0$, and reduces to the Kerr spacetime in the classical limit. Using the sixth-order WKB approximation and time-domain integration, we analyze the effective potential, quasinormal mode spectrum, and ringdown dynamics. The polymeric parameter $P$ systematically reduces the oscillation frequency $\mathrm{Re}(ω)$, while the spin parameter $a$ enhances it. The time-domain signals exhibit clear dependence on the multipole number $\ell$ and overtone index $n$. Our results provide a theoretical framework for understanding the effects of loop quantum gravity corrections on black hole perturbations and gravitational-wave ringdown signals.

gr-qc↗

arXiv2026

Infinity-Parser2 Technical Report

We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing, addressing the persistent scarcity of faithfully annotated parsing corpora. Our contributions are threefold. First, we build a scalable synthesis engine, pairing a controllable rendering framework with an iterative refinement loop, and use it to construct and open-source Infinity-Doc2-5M: a 5-million-sample bilingual (Chinese/English) corpus spanning diverse document types, annotated with element bounding boxes, canonical content forms (Markdown, HTML, LaTeX, SMILES, structured charts), and full-page reading order. Second, we introduce a verifiable, multi-task reward system that enables Joint Reinforcement Learning across eight co-trained objectives (document parsing, layout analysis, table parsing, math formula parsing, chart parsing, chemical formula parsing, document VQA, and general multimodal understanding), unifying perception, structure, and reasoning in a single optimization signal. Third, we release two variants under a shared architecture: Infinity-Parser2-Flash, optimized for low-latency inference with a 3.68x throughput gain over Infinity-Parser-7B, and Infinity-Parser2-Pro, engineered for precision-critical settings. Infinity-Parser2-Pro reaches state-of-the-art 87.6% on olmOCR-Bench and 74.3% on ParseBench, surpassing DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU2.5, with strong generalization to charts, chemical formulas, and document VQA.

cs.AI↗

arXiv2026

AgentSociety 2: An Integrated Research Environment for Executable Social Science

AI scientist systems are beginning to automate parts of scientific research, but social science poses a distinct challenge: its objects of inquiry are not merely datasets or laboratory protocols, but integrated social processes involving situated participants, interaction contexts, interventions, and outcomes. Yet a critical link is missing: existing systems either assist isolated research tasks or simulate agents as experimental subjects, leaving the research workflow and simulated society decoupled. Here we introduce AgentSociety 2, an Integrated Research Environment for executable social science. It couples two roles of LLM agents in the same runtime: AI social scientists that coordinate literature grounding, hypothesis generation, experiment design, simulation execution, result interpretation, and manuscript drafting; and silicon participants that generate behavioral responses within configurable social environments. This dual-role design turns hypotheses into auditable agent behaviors, environment rules, interventions, and measurements, thereby supporting an end-to-end workflow. Across seven illustrative studies spanning micro-level social-science laboratory experiments, meso-level dynamics in social media, and macro-level urban scenarios, we demonstrate its capacity to support diverse disciplinary questions, reproduce major qualitative patterns from prior studies, identify informative deviations, and enable large-scale simulations through optimized agent-environment interactions. By preserving human researchers' high-level agency while delegating procedural orchestration to agentic systems, it provides a human-in-the-loop and controllable infrastructure for next-generation computational social science, with broader applications in scalable computational social experimentation and AI-enabled social governance platforms.

cs.CY↗

arXiv2026

Reward-Free Evolving Agents via Pairwise Validator

A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal. Designing that signal is often the costly part of the project: a reliable scalar reward requires domain expertise and labeled examples that are themselves as expensive to assemble as the agent's underlying task. We propose replacing the scalar at the accept/reject gate with a pairwise validator: a frozen LLM that, given the parent and child candidate, returns a binary verdict on which is better. Pairwise judgment is generally easier and more stable than absolute scoring, due to its contrastive nature, which mitigates the need for strict scale calibration. The validator also requires no training of its own. We integrate the validator into three published self-evolving engines (GEPA, ADRS, ShinkaEvolve) and report two flavors: Adaptive Focus, which retains the engine's existing val-set parent selection, and Soft Elo, which lets the validator's verdicts drive parent selection so that val-set rewards drop as well. Across multiple agents and two artifact substrates (prompt and code), our method matches or exceeds the full-reward baseline on the majority of settings we evaluate, and the pattern survives a cross-family validator swap. The pairwise gate is thus a drop-in replacement for per-step reward design at competitive task accuracy without the labeling cost.

cs.AI↗

arXiv2026

PM-KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMs

Recently, significant progress has been made in developing reasoning-capable Large Language Models (LLMs) through long Chain-of-Thought (CoT) techniques. However, this long-CoT reasoning process imposes substantial memory overhead due to the large Key-Value (KV) Cache memory overhead. Post-training KV Cache quantization has emerged as a promising compression technique and has been extensively studied in short-context scenarios. However, directly applying existing methods to long-CoT LLMs causes significant performance degradation due to the following two reasons: (1) Large cumulative error: Existing methods fail to adequately leverage available memory, and they directly quantize the KV Cache during each decoding step, leading to large cumulative quantization error. (2) Short-context calibration: Due to Rotary Positional Embedding (RoPE), the use of short-context data during calibration fails to account for the distribution of less frequent channels in the Key Cache, resulting in performance loss. We propose Progressive Mixed-Precision KV Cache Quantization (PM-KVQ) for long-CoT LLMs to address the above issues in two folds: (1) To reduce cumulative error, we design a progressive quantization strategy to gradually lower the bit-width of KV Cache in each block. Then, we propose block-wise memory allocation to assign a higher bit-width to more sensitive transformer blocks. (2) To increase the calibration length without additional overhead, we propose a new calibration strategy with positional interpolation that leverages short calibration data with positional interpolation to approximate the data distribution of long-context data. Extensive experiments on 7B-70B long-CoT LLMs show that PM-KVQ improves reasoning benchmark performance by up to 8% over SOTA baselines under the same memory budget and achieves 2.73-5.18x throughput over the original 16-bit LLMs.

cs.CL↗

arXiv2026

GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version

Hybrid programs combine a quantum circuit with a classical host program that consumes measurement outcomes. In such programs, an outcome may be syntactically read by the host but semantically non-contributory: changing the outcome cannot change the returned value. Such outcomes obscure gates that are dead only relative to the host semantics, and are therefore invisible to circuit-local optimizers. We present a semantics-aware host-side static analysis that identifies non-contributory measurement outcomes by abstract interpretation, and prove its soundness. We implement the analysis and evaluate it on $24$ application-faithful hybrid workloads across quantum chemistry, optimization, quantum machine learning, and quantum finance. Compared with a syntactic liveness baseline, our analysis identifies more than $4\times$ as many non-contributory measurements, and it standalone enables the removal of $37.98\%$ of total gates on average. Even after the state-of-the-art optimizers like Qiskit, t|ket$\rangle$, and PyZX have already optimized the circuits, our analysis still enables removal of more than $30\%$ of the post-optimized gates, showing that the host-semantic opportunities exposed by our analysis are not subsumed by circuit-local optimization. To scale our analysis, we further lower host programs to an SSA-style levelized intermediate representation that exposes level-wise parallelism for GPU execution, and implement a CUDA backend. We prove that this lowering preserves the analysis result, and the evaluation shows speedups of up to $6.53\times$ over a sequential baseline as structural parallelism increases.

quant-ph↗

arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗

arXiv2026

Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer

Photorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image while strictly preserving its structural integrity. However, existing deep learning-based methods inherently suffer from semantic entanglement caused by pre-trained image encoders, leading to unnatural spatial distortions. Moreover, current pixel-level mapping paradigms often ignore color gamut topology, resulting in color banding, while also lacking the multimodal capability for intuitive text-driven control. To address these bottlenecks, we propose StatLUT, an innovative multimodal framework for 3D LUT generation. First, we bypass traditional encoders and introduce a Lab-Extractor to derive spatially-agnostic statistical features, fundamentally decoupling color distributions from structural semantics to ensure artifact-free rendering. Second, we formulate LUT generation as a Transformer-based Seq2Seq translation task, utilizing a Multi-dimensional Residual Mapper (MR-Mapper) to predict topologically smooth 3D LUTs. Finally, to break the single-modal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from natural language prompts, enabling flexible text-driven color grading. Extensive experiments on standard benchmarks demonstrate that StatLUT significantly outperforms state-of-the-art methods in both visual quality and quantitative metrics, pioneering a highly robust and flexible paradigm for multimodal photorealistic style transfer.

cs.CV↗

arXiv2026

Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations

Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement. Traditional precipitation multi-model blending algorithms perform pixel-by-pixel blending on the forecast field based on weights, which may lead to the expansion of precipitation areas and the smoothing of extreme values. This study proposes an U-Net based two-stage framework: probability classification followed by value reconstruction, to blend forecasts from six major NWP models. A novel station-grid joint supervision mechanism is introduced by integrating observations from 2411 national meteorological stations in China into the loss function, simultaneously constraining spatial structures and peak intensities. Evaluations using independent samples from the 2025 flood season demonstrate that our model significantly outperforms both individual NWPs and current operational products. For rainstorms (>=50 mm), the Threat Score (TS) improved by 38.4% compared to the best NWP. Notably, for extreme events (>=100 mm) driven by extratropical cyclones and the subtropical high, the model successfully elevated the TS to above 0.1, transforming forecasts from having negligible reference value into those with certain operational utility. Furthermore, the model exhibits data-driven spatial correction capabilities, effectively realigning systematic rainbelt displacements with actual precipitation centers. The inclusion of station observations specifically enhanced the TS for rainstorms by 10.4% and effectively balanced the Bias. These results highlight the efficacy of multi-source joint supervision in enhancing the capture of extreme precipitation events.

cs.LG↗

arXiv2026

A Bayesian Approach for the Network Reconstruction of Interdependent Critical Infrastructure Systems from Cascading Failures

Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks. For many applications such as critical infrastructure systems, understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions. However, data on the topology of individual networks are often publicly unavailable due to privacy and security concerns. Additionally, interdependent links are often only revealed in the aftermath of a disruption as a result of cascading failures. We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures. Metropolis-Hastings algorithm coupled with the infrastructure-dependent proposal are employed to increase the efficiency of sampling possible graphs. Results of reconstructing a synthetic system of interdependent infrastructure networks demonstrate that the proposed approach outperforms existing methods in both accuracy and computational time. We further apply this approach to reconstruct the topology of one synthetic and two real-world systems of interdependent infrastructure networks, including gas-power-water networks in Shelby County, TN, USA, and an interdependent system of power-water networks in Italy, to demonstrate the general applicability of the approach.

cs.LG↗