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Tian Li

Publications and source records attributed to Tian Li.

At least 37 records · Page 2Linked to original sources
arXiv2026

Link-Free Multi-Node Timing Synchronization for Scalable Quantum Networking

Precise timing synchronization is essential for distributed quantum networking, enabling entanglement distribution, quantum teleportation, and entanglement swapping across remote nodes. Existing synchronization architectures rely on dedicated timing-distribution infrastructure, most notably White Rabbit networks, which constrain topology, scalability, and deployment in free-space and satellite environments. Here we demonstrate link-free synchronization of quantum network nodes using independently operating miniature rubidium atomic clocks and computational post-processing. We validate the approach on a deployed metropolitan-scale telecom fiber network spanning three geographically separated nodes. Following drift correction, atomic-clock-based synchronization achieves timing performance approaching that of a White Rabbit benchmark and remains stable over continuous 8-hour operation. As a stringent test of quantum-network functionality, we observe Hong-Ou-Mandel interference across spatially separated nodes with visibility exceeding 70%, statistically equivalent to that obtained using dedicated White Rabbit timing links. To the best of our knowledge, this represents the first observation of quantum interference across a deployed metropolitan-scale telecom fiber network synchronized entirely without dedicated timing-transfer infrastructure. These results establish atomic-clock-based synchronization as a scalable, topology-independent alternative to conventional timing-distribution architectures and a practical pathway toward terrestrial, airborne, and space-based quantum networks where dedicated timing links are unavailable.

quant-ph↗
arXiv2026

SLSim: a strong lensing population simulation package

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

astro-ph.CO↗
arXiv2026

Differentially Private Model Merging

In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post-processing techniques, namely random selection and linear combination, to generate final private models satisfying any target privacy parameter. We provide privacy accounting of these approaches from the lens of R'enyi DP and privacy loss distributions on general problems, as well as on private mean estimation, where we precisely characterize the privacy/utility tradeoffs and compare the two mechanisms. Empirically, we demonstrate the effectiveness of our approaches and validate our analyses on several models and both synthetic and real-world datasets.

cs.LG↗
arXiv2026

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learning methods fuse representations only at later stages, missing cross-density interactions. We propose a cross-window knowledge distillation framework in which student encoders learn latent clinical priors from a teacher trained on the most informative window. Evaluated retrospectively on three cohorts - COPD-CT-DF (n=719), RSNA PE (n=1,433), and an in-house CTEPD dataset (n=161) - distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264). Cross-window distillation internalises pathological signatures invisible to supervised approaches, offering a generalisable solution for multi-window pulmonary CT analysis.

eess.IV↗
arXiv2026

PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations

Vision-Language-Action (VLA) models advance robotic control via strong visual-linguistic priors. However, existing VLAs predominantly frame pretraining as supervised behavior cloning, overlooking the fundamental nature of robot learning as a goal-reaching process that requires understanding temporal task progress. We present \textbf{PRTS} (\textbf{P}rimitive \textbf{R}easoning and \textbf{T}asking \textbf{S}ystem), a VLA foundation model that reformulates pretraining through Goal-Conditioned Reinforcement Learning. By treating language instructions as goals and employing contrastive reinforcement learning, PRTS learns a unified embedding space where the inner product of state-action and goal embeddings approximates the log-discounted goal occupancy, the probability of reaching the language-specified goal from the current state-action, quantitatively assessing physical feasibility beyond static semantic matching. PRTS draws this dense goal-reachability supervision directly from offline trajectories without reward annotations, and folds it into the VLM backbone via a role-aware causal mask, incurring negligible overhead over vanilla behavior cloning. This paradigm endows the high-level reasoning system with intrinsic goal reachability awareness, bridging semantic reasoning and temporal task progress, and further benefits goal-conditioned action prediction. Pretrained on 167B tokens of diverse manipulation and embodied-reasoning data, PRTS reaches state-of-the-art performance on LIBERO, LIBERO-Pro, LIBERO-Plus, SimplerEnv, and a real-world suite of 14 complex tasks, with particularly substantial gains on long-horizon, contact-rich, and zero-shot novel-instruction settings, confirming that injecting goal-reachability awareness significantly improves both execution success and long-horizon planning of general-purpose robotic foundation policies.

cs.AI↗
arXiv2026

The Stellar IMF and Dark Matter Halo of ESO0286: Constraints from Strong Lensing and Dynamics

The internal mass structure of elliptical galaxies offers critical insights into galaxy formation, yet disentangling stellar mass from dark matter and determining the stellar initial mass function (IMF) remains challenging. We present a detailed analysis of ESO0286-G022 ($z=0.0312$), a rare nearby strong-lens system with a fast-rotating elliptical galaxy, combining high-resolution Hubble Space Telescope (HST) imaging with VLT/MUSE integral-field stellar kinematics. We construct axisymmetric and triaxial Schwarzschild orbit-superposition models to reconstruct its intrinsic shape and mass distribution. Despite being a fast rotator, ESO0286 exhibits clear kinematic signatures of intrinsic triaxiality, characterized by rotation along both the major and minor axes, making it only the second such confirmed case. By incorporating the mass enclosed within the Einstein radius from strong lensing as a complementary constraint, we tightly anchor the total mass at large radii. This significantly reduces the uncertainty on the outer mass profile and orbital structure, demonstrating that only models with strong radial anisotropy beyond the IFU field of view are compatible with the data. In the inner regions, we robustly constrain an upper limit for the stellar mass around $r \sim 0.7$ kpc, ruling out an IMF more bottom-heavy than Kroupa, though a gentle gradient toward a slightly heavier central IMF is permitted. This aligns with recent dynamical studies of local massive early-type galaxies but contrasts with heavier IMFs reported for lenses at $z>0.1$. Our work demonstrates the power of combining lensing and dynamical modeling to resolve the detailed inner structure of massive galaxies.

astro-ph.GA↗
arXiv2026

Reconfigurable Four-Photon Interference among Three Nodes on a Field Deployed Metropolitan Fiber Network

Advanced quantum networking protocols beyond bi-photon, point-to-point links rely critically on the ability to perform multi-photon interference across multiple nodes under realistic operating conditions. Yet experimental validation of such higher-order, multi-node interference effects in deployed metropolitan fiber networks remains limited. Here, we report a field demonstration of polarization-controlled reconfigurable four-photon interference over three distant nodes on a deployed metropolitan fiber network. Using a fully fiber-coupled linear-optical platform, we observe a fusion-type four-photon interference signature in presence of real-world impairments, including photon loss, polarization drift, and timing uncertainty. By performing polarization-resolved measurements on two locally retained photons, we conditionally select distinct two-photon coincidence channels that exhibit Bell-like and N00N-like behavior. Rather than pursuing multi-partite entanglement verification, this work focuses on establishing the technical feasibility of multi-photon, multi-node interference and reconfigurable conditional state preparation in the field in a deployed fiber network environment. These results serve as a systems-level validation toward future multi-photon, multi-node quantum networking architectures that require robust interference performance outside the laboratory.

quant-ph↗
arXiv2026

Joint Bayesian Source and Lens Reconstruction for Multi-messenger Binary Black Holes

If a gravitational wave event is lensed by a cluster or galaxy in our line-of-sight, it is expected that its host galaxy would also be lensed. Therefore, connecting lensed gravitational wave events even without direct optical counterpart could be feasible by identifying matching lenses in electromagnetic data and surveys. Seminal work has demonstrated the potential of this approach in LVK, Euclid, HST, JWST, and CSST mock data, motivating the need for a dedicated software package to perform such analyses in practice. Here, we present the alpha-version of silmarel, the first software package designed to bridge these cosmic signals and enable us analysis of real LVK gravitational-wave binaries together with telescope observations from instruments like \textit{Euclid} or \textit{Hubble} Space Telescope, and the future of multimessenger binary black hole lensing.

astro-ph.HE↗
arXiv2026

Tele-Omni: a Unified Multimodal Framework for Video Generation and Editing

Recent advances in diffusion-based video generation have substantially improved visual fidelity and temporal coherence. However, most existing approaches remain task-specific and rely primarily on textual instructions, limiting their ability to handle multimodal inputs, contextual references, and diverse video generation and editing scenarios within a unified framework. Moreover, many video editing methods depend on carefully engineered pipelines tailored to individual operations, which hinders scalability and composability. In this paper, we propose Tele-Omni, a unified multimodal framework for video generation and editing that follows multimodal instructions, including text, images, and reference videos, within a single model. Tele-Omni leverages pretrained multimodal large language models to parse heterogeneous instructions and infer structured generation or editing intents, while diffusion-based generators perform high-quality video synthesis conditioned on these structured signals. To enable joint training across heterogeneous video tasks, we introduce a task-aware data processing pipeline that unifies multimodal inputs into a structured instruction format while preserving task-specific constraints. Tele-Omni supports a wide range of video-centric tasks, including text-to-video generation, image-to-video generation, first-last-frame video generation, in-context video generation, and in-context video editing. By decoupling instruction parsing from video synthesis and combining it with task-aware data design, Tele-Omni achieves flexible multimodal control while maintaining strong temporal coherence and visual consistency. Experimental results demonstrate that Tele-Omni achieves competitive performance across multiple tasks.

cs.CV↗
arXiv2026

Asynchronous Heavy-Tailed Optimization

Heavy-tailed stochastic gradient noise, commonly observed in transformer models, can destabilize the optimization process. Recent works mainly focus on developing and understanding approaches to address heavy-tailed noise in the centralized or distributed, synchronous setting, leaving the interactions between such noise and asynchronous optimization underexplored. In this work, we investigate two communication schemes that handle stragglers with asynchronous updates in the presence of heavy-tailed gradient noise. We propose and theoretically analyze algorithmic modifications based on delay-aware learning rate scheduling and delay compensation to enhance the performance of asynchronous algorithms. Our convergence guarantees under heavy-tailed noise match the rate of the synchronous counterparts and improve delay tolerance compared with existing asynchronous approaches. Empirically, our approaches outperform prior synchronous and asynchronous methods in terms of accuracy/runtime trade-offs and are more robust to hyperparameters in both image and language tasks.

cs.LG↗
arXiv2026

Amber-Image: Efficient Compression of Large-Scale Diffusion Transformers

Diffusion Transformer (DiT) architectures have significantly advanced Text-to-Image (T2I) generation but suffer from prohibitive computational costs and deployment barriers. To address these challenges, we propose an efficient compression framework that transforms the 60-layer dual-stream MMDiT-based Qwen-Image into lightweight models without training from scratch. Leveraging this framework, we introduce Amber-Image, a series of streamlined T2I models. We first derive Amber-Image-10B using a timestep-sensitive depth pruning strategy, where retained layers are reinitialized via local weight averaging and optimized through layer-wise distillation and full-parameter fine-tuning. Building on this, we develop Amber-Image-6B by introducing a hybrid-stream architecture that converts deep-layer dual streams into a single stream initialized from the image branch, further refined via progressive distillation and lightweight fine-tuning. Our approach reduces parameters by 70% and eliminates the need for large-scale data engineering. Notably, the entire compression and training pipeline-from the 10B to the 6B variant-requires fewer than 2,000 GPU hours, demonstrating exceptional cost-efficiency compared to training from scratch. Extensive evaluations on benchmarks like DPG-Bench and LongText-Bench show that Amber-Image achieves high-fidelity synthesis and superior text rendering, matching much larger models.

cs.CV↗
arXiv2026

Evolving Without Ending: Unifying Multimodal Incremental Learning for Continual Panoptic Perception

Continual learning (CL) is a great endeavour in developing intelligent perception AI systems. However, the pioneer research has predominantly focus on single-task CL, which restricts the potential in multi-task and multimodal scenarios. Beyond the well-known issue of catastrophic forgetting, the multi-task CL also brings semantic obfuscation across multimodal alignment, leading to severe model degradation during incremental training steps. In this paper, we extend CL to continual panoptic perception (CPP), integrating multimodal and multi-task CL to enhance comprehensive image perception through pixel-level, instance-level, and image-level joint interpretation. We formalize the CL task in multimodal scenarios and propose an end-to-end continual panoptic perception model. Concretely, CPP model features a collaborative cross-modal encoder (CCE) for multimodal embedding. We also propose a malleable knowledge inheritance module via contrastive feature distillation and instance distillation, addressing catastrophic forgetting from task-interactive boosting manner. Furthermore, we propose a cross-modal consistency constraint and develop CPP+, ensuring multimodal semantic alignment for model updating under multi-task incremental scenarios. Additionally, our proposed model incorporates an asymmetric pseudo-labeling manner, enabling model evolving without exemplar replay. Extensive experiments on multimodal datasets and diverse CL tasks demonstrate the superiority of the proposed model, particularly in fine-grained CL tasks.

cs.CV↗
arXiv2026

Private Zeroth-Order Optimization with Public Data

One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities compared with DP-SGD, and have only been evaluated in limited application domains. In this work, we propose to leverage public information to guide and improve gradient approximation of private zeroth-order algorithms. We explore a suite of public-data-assisted zeroth-order optimizers (PAZO) with minimal overhead. We provide theoretical analyses of the PAZO framework under an assumption of the similarity between public and private data. Empirically, we demonstrate that PAZO achieves superior privacy/utility tradeoffs across vision and text tasks in both pre-training and fine-tuning settings, outperforming the best first-order baselines (with public data) especially in highly private regimes, while offering up to $16\times$ runtime speedup.

cs.LG↗
arXiv2026

iPTF16geu through the lens of thermonuclear explosion models

The magnification resulting from strong gravitational lensing is a powerful tool to add new constraints to the cosmic evolution of supernova progenitors by enabling the study of distant supernovae that would otherwise not be observable. iPTF16geu is the most well-observed gravitationally lensed supernova (glSN) to date. At a redshift of $z = 0.409$ and magnified by a factor of $\sim$68, extensive photometric and spectroscopic observations have been obtained. The explosion mechanism producing this rare event and differences compared to lower redshift supernovae however have not been explored in detail. Here we compare observations of iPTF16geu to existing radiative transfer simulations of type Ia supernova explosion models selected from the literature. We find that overall the DDC6, PDDEL1, and N10 models produce the closest match to the light curves and many absorption features, providing some evidence in favour of the delayed detonation scenario. All models struggle however to replicate the observed colours and in particular the rest-frame UV. We also investigate the magnification and reddening values required to improve agreement with the selected models. Upcoming surveys will significantly increase the samples of SNe discovered at high redshifts due to strong gravitational lensing. These glSNe will enable tighter constraints on the explosion physics of type Ia supernovae and how this has evolved throughout the Universe.

astro-ph.HE↗
arXiv2025

TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model

World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent video generation models have demonstrated impressive visual quality, they remain limited in real-time interaction, long-horizon consistency, and persistent memory of dynamic scenes, hindering their evolution into practical world models. In this report, we present TeleWorld, a real-time multimodal 4D world modeling framework that unifies video generation, dynamic scene reconstruction, and long-term world memory within a closed-loop system. TeleWorld introduces a novel generation-reconstruction-guidance paradigm, where generated video streams are continuously reconstructed into a dynamic 4D spatio-temporal representation, which in turn guides subsequent generation to maintain spatial, temporal, and physical consistency. To support long-horizon generation with low latency, we employ an autoregressive diffusion-based video model enhanced with Macro-from-Micro Planning (MMPL)--a hierarchical planning method that reduces error accumulation from frame-level to segment-level-alongside efficient Distribution Matching Distillation (DMD), enabling real-time synthesis under practical computational budgets. Our approach achieves seamless integration of dynamic object modeling and static scene representation within a unified 4D framework, advancing world models toward practical, interactive, and computationally accessible systems. Extensive experiments demonstrate that TeleWorld achieves strong performance in both static and dynamic world understanding, long-term consistency, and real-time generation efficiency, positioning it as a practical step toward interactive, memory-enabled world models for multimodal generation and embodied intelligence.

cs.CV↗
arXiv2025

Exploring the Security Threats of Retriever Backdoors in Retrieval-Augmented Code Generation

Retrieval-Augmented Code Generation (RACG) is increasingly adopted to enhance Large Language Models for software development, yet its security implications remain dangerously underexplored. This paper conducts the first systematic exploration of a critical and stealthy threat: backdoor attacks targeting the retriever component, which represents a significant supply-chain vulnerability. It is infeasible to assess this threat realistically, as existing attack methods are either too ineffective to pose a real danger or are easily detected by state-of-the-art defense mechanisms spanning both latent-space analysis and token-level inspection, which achieve consistently high detection rates. To overcome this barrier and enable a realistic analysis, we first developed VenomRACG, a new class of potent and stealthy attack that serves as a vehicle for our investigation. Its design makes poisoned samples statistically indistinguishable from benign code, allowing the attack to consistently maintain low detectability across all evaluated defense mechanisms. Armed with this capability, our exploration reveals a severe vulnerability: by injecting vulnerable code equivalent to only 0.05% of the entire knowledge base size, an attacker can successfully manipulate the backdoored retriever to rank the vulnerable code in its top-5 results in 51.29% of cases. This translates to severe downstream harm, causing models like GPT-4o to generate vulnerable code in over 40% of targeted scenarios, while leaving the system's general performance intact. Our findings establish that retriever backdooring is not a theoretical concern but a practical threat to the software development ecosystem that current defenses are blind to, highlighting the urgent need for robust security measures.

cs.CR↗
arXiv2025

Hold Onto That Thought: Assessing KV Cache Compression On Reasoning

Large language models (LLMs) have demonstrated remarkable performance on long-context tasks, but are often bottlenecked by memory constraints. Namely, the KV cache, which is used to significantly speed up attention computations, grows linearly with context length. A suite of compression algorithms has been introduced to alleviate cache growth by evicting unimportant tokens. However, several popular strategies are targeted towards the prefill phase, i.e., processing long prompt context, and their performance is rarely assessed on reasoning tasks requiring long decoding. In particular, short but complex prompts, such as those in benchmarks like GSM8K and MATH500, often benefit from multi-step reasoning and self-reflection, resulting in thinking sequences thousands of tokens long. In this work, we benchmark the performance of several popular compression strategies on long-reasoning tasks. For the non-reasoning Llama-3.1-8B-Instruct, we determine that no singular strategy fits all, and that performance is heavily influenced by dataset type. However, we discover that H2O and our decoding-enabled variant of SnapKV are dominant strategies for reasoning models, indicating the utility of heavy-hitter tracking for reasoning traces. We also find that eviction strategies at low budgets can produce longer reasoning traces, revealing a tradeoff between cache size and inference costs.

cs.CL↗