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Ke Zhang

Publications and source records attributed to Ke Zhang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Event-Based Token Sequences for Audio-Conditioned Music-Game Level Modeling

Procedural generation of music game levels is an exciting yet challenging problem, as levels must translate musical structure into interactive sequences of timed gameplay events. Most existing approaches formulate this task by frame-based representations, dividing audio into uniform time grids and predicting events at each frame. This makes gameplay events implicit across many frames. As a result, it is hard to describe event-level timing relations and longer-range structure found in human-authored levels. We use procedural generation as a practical setting to study how musical cues map to interactive event sequences. Inspired by event-based symbolic music modeling, we propose a token-level sequence formulation that casts level generation as a multimodal sequence-to-sequence problem. Conditioned on an audio excerpt and level metadata, the model generates a token sequence alternating gameplay-event and beat-shift tokens. This explicitly represents actions and their relative timing in beat space. Based on this formulation, we build a Transformer model. It outperforms representative frame-level baselines under event-level evaluation. It also enables systematic analysis of how audio supports rhythm-aligned event prediction beyond metadata conditioning.

cs.SD↗
arXiv2026

EVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration

The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classification, precisely localizing sparsely distributed forged segments in long-form videos remains a critical challenge. This task is particularly difficult when manipulations are subtly embedded and cross-modal signals are weak and temporally diffuse. To address these challenges, we propose EVAS, an end-to-end multimodal framework for temporal forgery localization. At its core, a Multi-Stage Audio-Visual Synergy mechanism facilitates progressive cross-modal interaction to learn deep multimodal forensic representations and capture high-order semantic traces of sparse manipulations. Furthermore, we introduce a Boundary-Aware Refinement strategy to achieve steered boundary calibration. By incorporating invalid-frame masking, this strategy suppresses ambiguous regions and sharpens transition predictions. We adopt a decoupled training paradigm with auxiliary heads to disentangle representation learning from inference objectives, enhancing model generalization and stability. Additionally, a lightweight HourglassFFN is incorporated to reduce computational overhead. Extensive experiments demonstrate that EVAS achieves state-of-the-art average localization accuracy and average recall across three benchmark datasets, validating its effectiveness for fine-grained temporal forgery localization.

cs.CV↗
arXiv2026

FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning

Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d. lack of refined boundary awareness, which leads to unsatisfactory results in camouflage scenes. To alleviate these issues, we propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD. To mitigate the problem of non-camouflaged object responses, we propose the Frequency-aware Low-rank Adaptation (FoRA) method, which incorporates frequency-aware camouflage scene knowledge into SAM. To overcome the challenges of local and extreme responses, we introduce a gradient-aware contrastive learning approach that effectively delineates precise foreground-background boundaries. Additionally, to address the lack of refined boundary perception, we present a multi-scale frequency-aware representation learning strategy that facilitates the modeling of more refined boundaries. We validate the effectiveness of our approach through extensive empirical experiments on three widely recognized COD benchmarks. The results confirm that our method surpasses both state-of-the-art weakly supervised and even fully supervised techniques.

cs.CV↗
arXiv2026

PHREEQC-MCQ-200: A Diagnostic Benchmark for Tool-Augmented Scientific Simulator Agents

Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely more complex. We introduce PHREEQC-MCQ-200, a benchmark for evaluating tool-augmented agents on deterministic aqueous-geochemistry simulations. The benchmark contains 200 multiple-choice questions derived from 21 validated PHREEQC scenarios, requiring agents to construct simulator inputs, execute PHREEQC, inspect structured outputs, and commit to final answers. Across multiple frontier and mid-tier model families, simulator access substantially improves aggregate accuracy, confirming that grounded execution is necessary for many scientific-computation tasks. However, the gains are not monotonic: tool-augmented agents also lose items they answered correctly without tools, revealing regressions that average accuracy alone hides. We further show that output-access protocol matters. A table-of-contents interface can reduce token cost while preserving or improving accuracy for stronger models, but it degrades performance for mid-tier models that cannot reliably navigate structured simulator outputs. PHREEQC-MCQ-200 therefore frames scientific tool use as an end-to-end diagnostic problem rather than a simple tool-calling capability. We argue that evaluations of scientific agents should report not only accuracy, but also item-level retention, output-access sensitivity, trajectory failures, and where the computation chain breaks.

cs.AI↗
arXiv2026

MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

Driven by Artificial Intelligence-Generated Content (AIGC), the authenticity of audio-visual content is facing severe challenges. Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within untrimmed sequences. However, existing methods are limited by CNNs' local receptive fields or Transformers' quadratic complexity, while emerging linear models often struggle to balance global authentic context compression with local abrupt forgery perception. To address this, we propose MG-RWKV, a multi-granularity framework that leverages the data-dependent state evolution of RWKV to achieve efficient full-sequence processing with O(T) complexity. Our framework features three core innovations: (1) a Bidirectional RWKV architecture that captures bidirectional temporal contexts without quadratic overhead; (2) a Multi-Granularity Mixture of Experts (MG-MoE) that performs dynamic routing over explicit temporal receptive fields, adaptively selecting granularities based on forgery duration to significantly enhance decision interpretability; and (3) Cross-Granularity Consistency (CGC), which aligns adjacent feature pyramid levels through hierarchical scale-wise pairing and spatial boundary-aware weighting, effectively reducing false positives in authentic regions. Extensive experiments on Lav-DF, TVIL, and Psynd datasets demonstrate that MG-RWKV achieves state-of-the-art performance with low computational cost.

cs.CV↗
arXiv2026

CueNet: Robust Audio-Visual Speaker Extraction through Cross-Modal Cue Mining and Interaction

Audio-visual speaker extraction has attracted increasing attention, as it removes the need for pre-registered speech and leverages the visual modality as a complement to audio. Although existing methods have achieved impressive performance, the issue of degraded visual inputs has received relatively little attention, despite being common in real-world scenarios. Previous attempts to address this problem have mainly involved training with degraded visual data. However, visual degradation can occur in many unpredictable ways, making it impractical to simulate all possible cases during training. In this paper, we aim to enhance the robustness of audio-visual speaker extraction against impaired visual inputs without relying on degraded videos during training. Inspired by observations from human perceptual mechanisms, we propose an audio-visual learner that disentangles speaker information, acoustic synchronisation, and semantic synchronisation as distinct cues. Furthermore, we design a dedicated interaction module that effectively integrates these cues to provide a reliable guidance signal for speaker extraction. Extensive experiments demonstrate the strong robustness of the proposed model under various visual degradations and its clear superiority over existing methods.

cs.MM↗
arXiv2026

xGR: Efficient Generative Recommendation Serving at Scale

Recommendation system delivers substantial economic benefits by providing personalized predictions. Generative recommendation (GR) integrates LLMs to enhance the understanding of long user-item sequences. Despite employing attention-based architectures, GR's workload differs markedly from that of LLM serving. GR typically processes long prompt while producing short, fixed-length outputs, yet the computational cost of each decode phase is especially high due to the large beam width. Furthermore, since the beam search involves a vast item space, the sorting overhead becomes particularly time-consuming. We propose xGR, a GR-oriented serving system that meets strict low-latency requirements under high-concurrency scenarios. First, xGR unifies the processing of prefill and decode phases through staged computation and separated KV cache. Second, xGR enables early sorting termination and mask-based item filtering with data structure reuse. Third, xGR reconstructs the overall pipeline to exploit multi-level overlap and multi-stream parallelism. The experiments on real-world datasets demonstrate that xGR achieves at least 2.89x throughput compared to the state-of-the-art baseline under strict latency constraints.

cs.LG↗
arXiv2026

Chemical Divergence and Water Depletion: Gas Properties of Evolved Upper Scorpius Disks Revealed by JWST/MIRI

Tracing the chemical evolution of protoplanetary disks over time requires observations of disks at different ages. However, most JWST/MIRI surveys published to date have targeted younger ($\sim$1-3 Myr) rather than older systems. We present the results of a JWST/MIRI MRS survey of the inner regions of 10 protoplanetary disks (ages $\sim$2-6 Myr, spectral types M0-M4.5) in the Upper Scorpius region previously characterized by the ALMA AGE-PRO large program. Using MCMC slab modeling, we fit to a wide variety of detected molecules, including H$_2$O, CO, C$_2$H$_2$, $^{13}$CCH$_2$, HCN, HC$_3$N, CO$_2$, $^{13}$CO$_2$, C$_2$H$_6$, C$_4$H$_2$, and OH, as well as C$_6$H$_6$, CH$_3$, and H$_2$ visually. We classify each disk along two independent axes-a Water Classification based on H$_2$O line luminosity (Water-Rich, Water-Poor, or Water-Absent) and a Chemotype based on the dominant non-water chemistry (Organic-Rich, CO$_2$-Dominated, or Molecule-Absent)-and find an unexpectedly high diversity of distinct chemical compositions within our population. We leverage the heterogeneity of detected molecules in our sample to present new characteristic "diagnostic" wavelength regions for most species. We find that carbon-based molecules consistently exhibit markedly lower excitation temperatures ($\lesssim$300 K) compared to younger ($\sim$1-3 Myr) star-forming regions ($\sim$600-1000 K), hinting at relatively colder molecular reservoirs. We also determine that Upper Scorpius disks show systematically lower water luminosities by factors of 10-1000. In particular, disks with strong carbon-based molecular features but no observed H$_2$O defy expectations of an inner-disk dust cavity or a low ($\lesssim3$) $R_{\rm gas}/R_{\rm dust}$ ratio, instead suggesting that the presence of a strong outer-disk dust trap largely controls the chemical outcome of the terrestrial planet-forming region.

astro-ph.EP↗
arXiv2026

From Young to Older Disks: JWST/MIRI Evidence for Fading Molecular Emission and Hints for Elevated C/O in Upper Scorpius

We present JWST/MIRI spectroscopy of 14 disks in the older (~5-10 Myr) Upper Scorpius (USco) association and use slab of gas in local thermal equilibrium to infer basic gas properties. We find that half of these disks are molecular rich, with detections of H$_2$O, CO$_2$, HCN, C$_2$H$_2$, and H$_2$, while the other half are molecular poor, showing no molecular emission other than H$_2$. We further combine this sample with 10 other USco disks from the AGE-PRO program and compare the combined older sample to young (~1-3 Myr) JDISCS Cycle~1 systems, which are analyzed in a similar manner. We find that USco disks have lower detection rates of major molecular species but a significantly higher detection rate of rarer C-bearing molecules such as C$_4$H$_2$. At a given accretion luminosity, molecular line luminosities are systematically lower in USco than in young disks, and the scaling relations with accretion luminosity differ between the two populations. Moreover, we find that about half of the older disks, preferentially the millimeter faint, and likely more compact disks, have observable mass ratios of C- to O-bearing molecules that are higher than the maximum values in the young sample. These results point to reduced inner-disk molecular gas masses, cooler emitting layers, and higher inner gas C/O ratios in older disks, the latter being consistent with pebble drift. Taken together, our findings provide evidence for chemical evolution of inner disk gas from young to older systems, with important implications for the accretion of primordial planetary atmospheres.

astro-ph.EP↗
arXiv2026

Molecular Similarity and Water Diversity in Coeval Binary Disks: JWST/MIRI Observations of Sz 65 and Sz 66

We present JWST/MIRI Medium Resolution Spectrometer spectra of the wide-separation (projected separation $= 980$ au) binary protoplanetary disks Sz 65 (K7; $0.68~M_{\odot}$) and Sz 66 (M3; $0.30~M_{\odot}$), reduced using the uniform pipeline of the JWST Disk Infrared Spectral Chemistry Survey. Both disks show rich molecular emission, including H$_2$O, CO$_2$, HCN, C$_2$H$_2$, and OH. The scaled spectra of the two disks exhibit remarkably similar H$_2$O, CO$_2$, and HCN line emission in the 13--18 $μ$m region, with the only notable difference being stronger C$_2$H$_2$ emission in the primary (Sz 65). Beyond 18 $μ$m, the difference in H$_2$O line emission between the two disks increases. Both the flux ratios and the slab-model-derived mass ratios of cold to hot H$_2$O ($\sim$200 K to $\sim$750 K) and warm to hot H$_2$O ($\sim$450 K to $\sim$750 K) are significantly higher in the secondary (Sz 66). Because binary stars share nearly the same age and metallicity, and as both disks appear compact in millimeter emission ($<30$ au), we suggest that the excess cold H$_2$O in the secondary is best explained by its unstructured dust disk, in contrast to the primary, which shows gaps at 6 and 20 au. The enhanced cold water in the secondary is consistent with efficient pebble drift across the water snow line and increased H$_2$O vapor from the sublimation of icy mantles. Our results demonstrate that wide-separation binaries can serve as powerful control samples for isolating the impact of individual disk properties on inner-disk chemistry and evolution.

astro-ph.EP↗
arXiv2026

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models

Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either follow a monolithic design that assigns uniform parallelism to heterogeneous modules or adopt a disaggregated deployment that separates modules while executing them as a batch-synchronized pipeline. In this paper, we highlight that the above solutions are still not sufficient, and VLM training can be further decoupled. To this end, we present FlowTrain, a flow-based decoupled training framework that reformulates VLM training as a producer-consumer dataflow coordinated through a unified memory pool. The encoder and backbone can progress independently over a global virtual address space. Since this execution decoupling fundamentally changes the optimization objective of allocation and scheduling, FlowTrain further introduces a heterogeneous parallel allocator that assigns module-specific parallelism strategies by solving a throughput matching problem. The dynamic packing scheduler is used to construct balanced microbatches at runtime according to the actual LLM-side computation cost. Extensive experiments on real-world workloads show that FlowTrain achieves over 50% MFU and up to 1.7x throughput improvement, narrowing the efficiency gap to LLM-only training.

cs.LG↗
arXiv2026

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training

As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive learning trajectories. Beyond static heuristic filtering, advanced data selection methods for LLM training largely follow two paradigms, each with fundamental limitations. Influence-based methods provide principled bi-level objectives but require intractable inverse-Hessian computations, while excess-loss methods are computationally efficient but rely on a static reference model that becomes misaligned with the evolving proxy model during training. We propose BLADE (Bi-Level Adaptive Data sElection), a Hessian-free framework for data selection. BLADE reformulates the bi-level optimization problem underlying influence-based methods as a penalized single-level objective via Lagrange multipliers, avoiding inverse-Hessian computation while revealing a principled connection to excess-loss based data selection. The resulting objective recovers an excess-loss form but replaces the static reference model with a dynamic one that stays synchronized with training. Theoretically, we prove that this penalized formulation guarantees first-order convergence. For efficient online batch selection, we instantiate BLADE as a memoryless randomized block-coordinate Frank-Wolfe algorithm. Extensive experiments show that BLADE consistently outperforms state-of-the-art data selection baselines, providing a practical recipe for LLM training.

cs.LG↗
arXiv2026

3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation

Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically treat video frames independently or rely on weak temporal regularization. These methods, lacking a holistic perception of the underlying 3D scene, inevitably suffer from geometrically inconsistent predictions and severe cross-frame drift. To address these limitations, we introduce a new paradigm that recasts sequential video depth estimation as an unconstrained multi-view 3D reconstruction problem, enabling full exploitation of the powerful geometric priors embedded in recent 3D foundation models. The core of our approach is a 3D consistency optimization framework driven by three constraints: image-level photometric rendering, explicit world-coordinate geometric alignment, and multi-scale temporal gradient consistency. Such unified optimization elegantly anchors isolated frames to a globally coherent 3D structure. Our method has been validated in both the self-supervised training scenarios and challenging zero-shot clinical environments. Results show that the proposed approach achieves state-of-the-art spatial accuracy, outperforming the frame-based, video-based depth estimators and the multi-view 3D reconstruction baselines.

cs.CV↗
arXiv2026

Multi-channel high-speed flip-chip packaging platform for thin-film lithium niobate photonic circuits

To address the urgent need for multi-channel high-speed electrical interfacing of thin-film lithium niobate (TFLN) photonic circuits, we realize a flip-chip packaging platform capable of simultaneously delivering 13 high-speed and 32 low-speed electronic signals to a centimeter-sized TFLN chip. The platform exhibits low flip-chip bonding loss and low inter-channel crosstalk over a broad bandwidth up to 50 GHz. Leveraging this packaging platform, we demonstrate high-speed electrical interfacing with two proof-of-concept TFLN photonic circuits, namely a 2x8 optical switch and an electro-optic comb-based transmitter. The switch achieves arbitrary 8-channel routing with ~3 dB insertion loss, < -20 dB crosstalk, and an equipment-limited switching time of <= 34 ps. The transmitter circuit includes a 50 GHz electro-optic comb generator with 2.8-dB flatness, a tunable microring to arbitrarily filter one comb line, and a modulator for data transmission at 20 Gbit/s. The packaging platform could significantly advance large-scale TFLN circuits in optical communications, microwave photonics, and photonic computing.

physics.optics↗
arXiv2026

ProServe: Unified Multi-Priority Request Scheduling for LLM Serving

The widespread deployment of large language models (LLMs) for interactive applications necessitates serving systems that can handle thousands of concurrent requests with diverse Service Level Objective (SLO) requirements. A critical yet often overlooked dimension in this context is the inherent priority difference among clients; for instance, business-critical functions demand higher performance guarantees, as fulfilling such requests yields significantly greater business value. However, existing LLM serving schedulers fail to jointly optimize for both SLO attainment and client-level priorities. To bridge this gap, we first formalize multi-priority request scheduling as a service gain maximization problem, where satisfying latency requirements for requests of different priorities contributes varying gain. We propose ProServe, a unified two-tier scheduling framework designed to maximize overall service gain. At the engine layer, SlideBatching dynamically adapts batch formation under varying loads, employing a sliding boundary mechanism to balance latency and priority differentiation. Considering potential preemption, efficient block management adopts asynchronous offloading, pipelined reloading, and adaptive copy-budget control to overlap computation with host-device block transfers. At the service layer, GoRouting performs gain-oriented and capability-aware dispatching across distributed instances, proactively reserving capacity for future high-priority or long requests. Extensive evaluation on four open-source and one industrial dataset shows that ProServe outperforms state-of-the-art baselines, improving system gain by up to 35% and SLO attainment by up to 52%.

cs.DC↗
arXiv2026

TANDEM: Bi-Level Data Mixture Optimization with Twin Networks

The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-level optimization problem, which we simplify into a single-level penalized form and solve with twin networks: a proxy model trained on primary data and a dynamically updated reference model trained with additional data. Our proposed method, Twin Networks for bi-level DatA mixturE optiMization (TANDEM), measures the data efficacy through the difference between the twin models and up-weights domains that benefit more from the additional data. TANDEM provides theoretical guarantees and wider applicability, compared to prior approaches. Furthermore, our bi-level perspective suggests new settings to study domain reweighting such as data-restricted scenarios and supervised fine-tuning, where optimized mixture ratios significantly improve the performance. Extensive experiments validate TANDEM's effectiveness in all scenarios.

cs.LG↗
arXiv2026

The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): Formaldehyde (H$_2$CO) emission and its links to disk properties

Protoplanetary disks are rotating structures of gas and dust surrounding young stars, serving as the birth places of planets. Understanding the chemical evolution of organic materials in these disks is key for tracing the origins of organics in planetary systems. Formaldehyde (H$_2$CO) is the most commonly detected organic molecule in protoplanetary disks. In this study, we investigate the emission of H$_2$CO and its link to disk properties, using a sample of 20 Class II disks in the Lupus and Upper Sco star-forming regions spanning over 1-6 Myr. We analyze the H$_2$CO lines at 218.222 and 290.623 GHz observed as part of the AGE-PRO ALMA Large Program. Within this sample we achieve a detection rate of H$_2$CO of 45% (9/20), and set robust upper limits for the non-detections. We measure the excitation temperature and column density of the H$_2$CO gas in the sources with H$_2$CO detections. We combine our sample with 13 additional disks with archival H$_2$CO detections and search for correlations between H$_2$CO properties and disk parameters. Notably, we find strong correlations between H$_2$CO line luminosity and dust radius, gas radius, dust mass, gas mass, stellar mass, and stellar luminosity. This suggests that H$_2$CO emission is brighter for extended massive dust disks where H$_2$CO can form via CO ice hydrogenation on grain surfaces. We find that the H$_2$CO excitation temperature is also correlated with stellar mass and stellar luminosity, so more massive and luminous stars could increase H$_2$CO excitation.

astro-ph.EP↗
arXiv2026

Revisiting Model Stitching In the Foundation Model Era

Model stitching, connecting early layers of one model (source) to later layers of another (target) via a light stitch layer, has served as a probe of representational compatibility. Prior work finds that models trained on the same dataset remain stitchable (negligible accuracy drop) despite different initializations or objectives. We revisit stitching for Vision Foundation Models (VFMs) that vary in objectives, data, and modality mix (e.g., CLIP, DINOv2, SigLIP 2) and ask: Are heterogeneous VFMs stitchable? We introduce a systematic protocol spanning the stitch points, stitch layer families, training losses, and downstream tasks. Three findings emerge. (1) Stitch layer training matters: conventional approaches that match the intermediate features at the stitch point or optimize the task loss end-to-end struggle to retain accuracy, especially at shallow stitch points. (2) With a simple feature-matching loss at the target model's penultimate layer, heterogeneous VFMs become reliably stitchable across vision tasks. (3) For deep stitch points, the stitched model can surpass either constituent model at only a small inference overhead (for the stitch layer). Building on these findings, we further propose the VFM Stitch Tree (VST), which shares early layers across VFMs while retaining their later layers, yielding a controllable accuracy-latency trade-off for multimodal LLMs that often leverage multiple VFMs. Taken together, our study elevates stitching from a diagnostic probe to a practical recipe for integrating complementary VFM strengths and pinpointing where their representations align or diverge.

cs.CV↗