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Hao Tang

Publications and source records attributed to Hao Tang.

At least 19 recordsLinked to original sources
arXiv2026

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However, scaling such systems can substantially increase inference cost, especially when multiple agents repeatedly process overlapping or role-irrelevant context. Many existing multi-agent frameworks focus on orchestration and role specialization, but still rely on full-context sharing or static context routing, leading to redundant token consumption, increased runtime, and inefficient memory utilization. We present \emph{Role-Conditioned Context Routing (RCR)}, an execution-time layer that allocates role-relevant shared-memory items to agents under token budgets. Given a structured shared memory and a set of role-specialized agents, RCR selectively routes task-relevant memory items to each agent according to its role, optional task-stage metadata, and token budget. RCR integrates role-conditioned relevance scoring, token-budgeted memory selection, and iterative memory updates to reduce redundant context exposure during multi-agent execution. Experiments on multi-hop QA, embodied reasoning (ALFWorld), and web-based decision-making (WebShop) show that RCR reduces token usage by 35\% on average and runtime by 38\% on average relative to Full-Context, while maintaining or improving task performance.

cs.CL↗
arXiv2026

Photon-loss tolerance of the surface-GKP code with anti-photon-loss decoding

Photon loss somewhat limits fault-tolerant photonic quantum computing. We investigate its effect on finite-energy Gottesman--Kitaev--Preskill (GKP) states by modeling the pure-loss channel directly. We obtain photon-loss threshold estimates for the surface-GKP code using nearest-lattice-point inner GKP decoding and minimum-weight perfect matching for the outer surface code. For GKP states with $ε=0.01$ and $ε=0.05$, corresponding to GKP squeezing of $20$ and $13\,\mathrm{dB}$, the estimated thresholds are $γ^\ast=5.5\%$ ($0.24\,\mathrm{dB}$) and $γ^\ast=5.9\%$ ($0.26\,\mathrm{dB}$), respectively. We then introduce an anti-photon-loss inner decoder that compensates for the contraction of the measured quadrature. It increases the corresponding threshold estimates to $γ^\ast=9.8\%$ ($0.45\,\mathrm{dB}$) and $γ^\ast=6.3\%$ ($0.28\,\mathrm{dB}$). Our results highlight the importance of accounting for loss-induced lattice contraction in inner decoding and demonstrate that quadrature compensation enhances the photon-loss tolerance of surface-GKP codes for both high- and moderate-quality GKP states, providing a route toward more photon-loss-robust optical quantum computing.

quant-ph↗
arXiv2026

Hyperfine spectroscopy and nuclear spin control of dark defects in diamond

Spin defects in diamond serve as powerful building blocks for quantum technologies, especially for quantum sensing and networking. Electron-nuclear defects formed near optically active spins such as the nitrogen-vacancy center provide a resource for multi-qubit quantum registers. However, many remain uncharacterized, limiting their control and integration in quantum devices. Here, we identify the hyperfine components, including their relative sign, and nuclear spin species of two unknown dark spin defects using zero-field double electron-electron resonance spectroscopy and a protocol based on electron-nuclear double resonance. New ab initio calculations assign one defect to a known nitrogen interstitial, while the hydrogen-related defect matches no calculated or previously reported structure. We further use this protocol to demonstrate initialization, unitary control, and long-lived coherence of the hydrogen nuclear spin qubit, with a coherence time of 1.0(3) ms. These characterization and control tools establish a framework for harnessing unknown electron-nuclear defects for quantum registers.

quant-ph↗
arXiv2026

Time-Domain Multiplexing for Resource-Efficient Heralded GKP State Preparation

Gottesman-Kitaev-Preskill (GKP) states are key resources for fault-tolerant photonic quantum computing, but their conditional preparation via Gaussian boson sampling involves a trade-off between source count and photon-number resolution. Distributing heralding photons across more measured modes reduces the resolution required per mode, yet increases the number of simultaneously operated squeezed sources in fully squeezed spatial architectures. Here, we show that time-domain multiplexing (TDM) can reduce these physical source requirements while preserving GKP state quality. Using an end-to-end differentiable Bargmann framework, we find that TDM circuits reusing one or two squeezed sources achieve fidelities comparable to fully squeezed spatial circuits across several configurations. Selected nine-mode configurations retain fidelities above 0.95 with one photon heralded in each measured mode, mitigating detector resolution demands without a proportional increase in source count. Loss-aware reoptimization further shows that, under the investigated spatial loss models, two-mode TDM maintains fidelities near 0.80 at 1.0 dB per channel, whereas the corresponding spatial benchmarks fall below 0.60. These results demonstrate that temporal reuse provides an effective architectural route for balancing source overhead, detection demands, and state preparation performance in the presence of optical loss.

quant-ph↗
arXiv2026

HGWM: Hierarchical Graph-Guided World Model for Zero-Shot Object Navigation via Graph Matching

Zero-shot object navigation requires an agent to reason about where a target may be found while retaining evidence from previous observations. We present HGWM, a hierarchical graph-guided world model that combines a category-conditioned room--anchor prior with persistent spatial-semantic memory. A coarse stage uses a vision-language model to score candidate headings, while a fine stage explicitly compares observed objects and relations with the goal prior. Phase-dependent fusion integrates these signals with a language-model recommendation to guide local navigation. HGWM achieves 62.6\% success rate (SR) and 31.5\% success weighted by path length (SPL) on HM3D v0.1, improving over WMNav by 4.5 and 0.3 percentage points, respectively. On MP3D, it achieves 46.8\% SR and 17.9\% SPL, with gains of 1.4 and 0.7 points. On HM3D v0.2, the full model reaches 74.4\% SR and 31.9\% SPL; removing individual components reduces SR by 0.9--2.3 points and SPL by 0.6--1.5 points. These results support the use of relational priors and persistent graph memory for target-directed search.

cs.RO↗
arXiv2026

DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models

Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identifying highly realistic content generated by both modern generation and manipulation pipelines. However, existing detection benchmarks are often built with outdated generative models and primarily emphasize full-image synthesis, creating a growing mismatch between benchmark data and the images encountered in real-world generation and editing scenarios. To bridge this gap, we introduce DailyBench, a high-quality unified benchmark for evaluating whether AI-generated image detectors can generalize across both modern full-image synthesis and object-level manipulation. DailyBench contains two complementary subsets: FakeBench, which includes high-quality images synthesized by recent open-source and commercial generative models, and ManipulationBench, which introduces challenging object-level edits applied to real images using advanced image-conditional models. This design makes DailyBench a realistic testbed for studying both generator-level generalization and manipulation-aware detection under subtle local edits. Experiments on DailyBench reveal substantial robustness gaps in current detectors: methods reporting 91-96% balanced accuracy on GenImage drop to 52-79% on FakeBench and 43-67% on ManipulationBench. These results show that existing detectors remain poorly generalized to realistic synthesis and manipulation, highlighting DailyBench as a rigorous testbed for developing robust and manipulation-aware AI-generated image detection methods. The project is available at https://dailybench.github.io/

cs.CV↗
arXiv2026

IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems

We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (1) stable and efficient pre-training, combining Stable SplitNorm, Muon, and muP with a two-stage curriculum whose second stage increases the coding-data mixture, preserving a wide stability margin between safe and optimal learning rates; (2) fine-grained mid-training, expanding the data mixture across domains and formats and progressively extending the context; and (3) multi-scaffold with multi-version post-training, combining SFT, domain-specialized RL, and MOPD across various scaffolds to integrate complementary training signals and strengthen agentic coding capabilities. Furthermore, model merging combines different experts and checkpoints from different stages while mitigating conflicts among their parameter updates. The resulting IQuest-Q1 demonstrates capabilities in both coding and general tasks. We release IQuest-Q1 as an open-source model to support community research and development.

cs.CL↗
arXiv2026

Uncovering Audio-Text Alignment in Large Audio-Language Models through Cross-Modal Concept Neurons

While the language model backbone is pretrained on text alone, large audio-language models (LALMs) can, to a great extent, recognize acoustic attributes, such as emotion, speaker gender, and spoken language. However, it is unclear how the audio representation is integrated, if at all, with the other text representation before the model produces text. We hypothesize that there is a shared subspace where the audio and text representations are aligned. To test this hypothesis, we identify cross-modal neurons that are responsible for representing both audio and text. We find alignment between audio and text neurons to various degrees across three LALMs and four acoustic attribute types. Causal interventions, steering and deactivating the neurons, confirm that the cross-modal neurons are not just necessary for prediction but can also be manipulated.

cs.SD↗
arXiv2026

CoEvolve: Self-Evolving Code Policies from Skills to Strategies for Humanoid Robots

Recent advances in Vision-Language-Action (VLA) models and World Action Models (WAMs) have expanded the manipulation and whole body capabilities of individual robots. Yet a single robot remains limited by its embodiment, and many real world tasks require capabilities that can only emerge through cooperation. Existing multi robot frameworks largely emphasize task decomposition, allocation, and replanning, while providing limited support for adapting executable coordination when physical execution deviates from the intended behavior. We present CoEvolve, a framework that represents team coordination as executable code over fixed robot motor skills. CoEvolve introduces Verifier Guided Coordination Evolution, where a multimodal foundation model instantiates coordination strategies through motion primitives, semantic objectives, temporary robot roles, and bounded execution parameters. Physical verification evaluates each executed transition, rejected behaviors trigger structured coordination revision from an accepted checkpoint, and verified experience is retained to guide subsequent decisions. We evaluate CoEvolve with two simulated Unitree G1 humanoids transporting a freely supported rigid payload through constrained corridors. Across 15 base task runs with three multimodal backbone families, CoEvolve completes 8 tasks, with every backbone achieving end to end success. Additional evaluations yield verified placement on two additional corridor layouts and after both same-backbone and cross-backbone experience initialization, while exposing persistent limitations in motion precision and contact stability. These results support executable coordination as a practical interface for adapting long horizon multi robot collaboration under shared physical constraints.

cs.RO↗
arXiv2026

Mitigating Task Objective Conflicts for Continual Learning in Unified Multimodal Models

Recent advances in unified autoregressive (AR) models have enabled multimodal understanding (MMU) and text-to-image generation (T2I) within a single architecture. However, in continual learning settings, their divergent objectives can induce task interference, hindering knowledge preservation and adaptation to new tasks. In this work, we investigate \textbf{T}ask \textbf{O}bjective \textbf{C}onflict (TOC) through theoretical and empirical analyses, revealing conflicting optimization directions at the task level but complementary interactions at the token level. These findings suggest that continual adaptation requires mitigating task interference while preserving beneficial cross-task knowledge sharing. To this end, we propose \textbf{UniDecouple}, a selective decoupling framework based on a \textbf{T}ask-\textbf{A}ware \textbf{M}ixture-\textbf{o}f-\textbf{E}xperts (TA-MoE) architecture. Specifically, UniDecouple retains shared attention layers to preserve common contextual dependencies while introducing task-specific feed-forward network (FFN) experts to mitigate optimization conflicts. Furthermore, hierarchical expert routing enables adaptive expert selection, while hybrid expert collaboration leverages shared experts to facilitate cross-task knowledge transfer. A two-stage training strategy further balances task specialization and coordinated adaptation. Extensive experiments and ablation studies demonstrate that UniDecouple improves both multimodal understanding and image generation performance under a comparable per-token activation budget, achieving a favorable balance between knowledge preservation and task adaptation in continual learning.

cs.CV↗
arXiv2026

Text-to-Image Synthesis: A Decade Survey

Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanning the past decade, with representative advances covered through 2026. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation, and we additionally examine the shared components whose evolution has driven recent progress. Building on this foundation, we discuss the development of these models for T2I and organize cutting-edge research by problem rather than by chronology alone, covering controllability, personalized and identity-preserving generation, instruction-based image editing, preference alignment and post-training with human and AI feedback, efficiency, safety, and consistency in content and spatial relationships. We further review unified multimodal understanding-and-generation models, summarize the datasets and evaluation metrics commonly used in T2I research, and provide a comparative discussion of the three paradigms together with their respective trade-offs. Finally, we discuss the potential applications of T2I within AIGC, along with the challenges and forward-looking research opportunities in this field.

cs.CV↗
arXiv2026

Periodic conditional measurements on a photonic chip

Quantum measurements not only extract information but also reshape quantum evolution through measurement backaction. Here, we investigate periodic conditional measurements, in which coherent evolution is repeatedly interrupted by local measurements. We show that the bright and dark modes of the conditional evolution operator provide a unified spectral description of both the post-selected final state along no-detection trajectories and the statistics of detection events. Using a three-dimensional photonic chip, we demonstrate on-demand state engineering through dark-mode preservation and bright-mode selection, enabling the protection of topological corner states and excited-state shelving. We further observe the predicted transition of the total detection probability on an effectively infinite one-dimensional lattice and identify its microscopic origin in Floquet near-dark modes. These results establish periodic conditional measurements as a general framework for engineering conditional quantum states and manipulating event-detection statistics in monitored quantum dynamics.

quant-ph↗
arXiv2026

Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation

Recent coding large language models (LLMs) can synthesize dynamic 4D worlds from natural language, but the resulting content remains a visual artifact. It lacks physical semantics such as the collision geometry, mass, friction, inertia, and joint structure, required to enable it to simulate the physical world, and its dynamics are scripted to look plausible rather than designed to execute. What is needed is an executable world: one that a physics engine can load and step, resolving contact physically. We present Code2Worlds-2, an extension of Code2Worlds that turns 4D world generation into an embodied world simulation. We keep the original dual-stream generation pipeline intact and append two modules. First, a SimReady Scene Compiler that turns a generated visual scene into a simulator-compatible representation: it assigns collision meshes via convex decomposition, estimates physical attributes with a vision-language model, infers a lightweight support graph, instantiates the engine's contact, friction, and dynamics constraints, and applies a gravity settling pass, before exporting the scene as MuJoCo MJCF. Second, a Simulator-in-the-Loop 4D Refiner that implements an automated agent harness-a compile, simulate, critique and refine loop-that converts visual dynamics into executable rigid and articulated dynamics and evolves the physical settings rather than only the code, replacing the purely visual motion critic with a simulator critic that inspects penetration depth, collision pairs, stability, contact forces, joint limits, and robot reachability. We further propose SimCode4D, a state-based benchmark with five task templates for evaluating embodied simulation. Experiments on SimCode4D and across natural, indoor, industrial, urban, and service scenes show that Code2Worlds-2 produces physically plausible, simulator-compatible, and robot-interactable scenes.

cs.RO↗
arXiv2026

PWM: Personalized World Models with Online Reinforcement Learning

Pretrained world models can generate diverse environments, yet users often want to explore a particular scene specified by their own video. This requires learning the scene's visual identity while retaining the quality of action-conditioned generation. We introduce Personalized World Models (PWM), a framework for customizing interactive world models from short scene videos through online reinforcement learning. In PWM, the support trajectory and its associated controls provide reward feedback on continuations sampled from the current policy. In the GRPO instantiation, group-relative optimization updates a compact LoRA adapter using a unified reward for scene appearance, visual continuity, and motion, while base-policy anchoring regularizes changes to the pretrained generation prior of a frozen Yume-5B backbone. The same adaptation procedure is applied across real and rendered environments. We also instantiate PWM with DiffusionNFT as an alternative reward-guided optimization method for learning the scene-specific adapter. We also introduce PWM-Bench, comprising 150 customization tasks across Indoor, Outdoor, and Gaming, with paired evaluation on held-out continuations. The GRPO and DiffusionNFT instantiations of PWM improve customization over native Yume in 71.3% and 65.3% of the evaluated scenes, respectively, with positive mean gains across all three domains. For the GRPO instantiation, matched SFT comparisons further demonstrate higher mean customization gains and better mean image-quality scores in every domain, while retaining frame-level visual quality close to the pretrained model.

cs.CV↗
arXiv2026

Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory

Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional subspaces of the representation space. Prior work in cognitive neuroscience has shown that when HuBERT feature sequences are averaged across words of the same duration, they form near-circular trajectories from word onset to word offset in a two-dimensional "temporal subspace." Here we show that this circular structure of word timing is a general property of deep speech representations, by demonstrating its emergence in nine additional speech models covering both supervised and self-supervised. We then move beyond averaged word segments and show that in HuBERT, the circular trajectories are consistent enough at the level of individual words to perform word segmentation. Our approach, Rotary Segmentation (RoSeg), achieves 64.3% boundary F-score on Librispeech by detecting word boundaries when cycles complete in the temporal subspace. Although supervision is needed to identify a good subspace, nevertheless this result demonstrates much stronger implicit word knowledge in HuBERT than has previously been shown.

cs.CL↗
arXiv2026

Geometry-Centered 3D Latent World Models for Growing Surfaces

Many physical systems do not merely move or deform; they grow, adding material and changing the geometry that a world model must represent. Existing world models are typically optimized for pixel prediction, reward prediction, or fixed-support physical dynamics, leaving open how to model systems whose underlying physical support expands over time and whose future morphology depends on hidden material response. We introduce FOLIAGE, a geometry-centered latent world model for growing surfaces. Within a fixed state budget, FOLIAGE represents mature regions as a compact scaffold while allocating higher-resolution state to regions predicted to drive near-future growth. This focuses representation and computation where new material and geometric change occur while retaining compact global context. FOLIAGE further separates observation, action, and privileged physics: heterogeneous RGB, point-cloud, and mesh observations are fused into a deployable geometric state; material controls condition the latent dynamics; and hidden physical energies guide training but are not required at deployment. To evaluate this setting, we introduce SURF-GARDEN and SURF-BENCH, providing controlled counterfactual branches, dense cross-modal correspondences, hidden physical signals, and stress tests for growing-geometry state learning. FOLIAGE reduces inverse-material error by $\approx40\%$ and 5-step mesh forecasting Chamfer error by $\approx30\%$ relative to strong baselines, while improving cross-modal retrieval by +14 mAP points. Stress tests show graceful degradation under sensor loss and correspondence corruption. On temporal 3D plant scans, FOLIAGE also improves passive future-geometry forecasting, while transfer experiments show that the learned geometry-centered state remains useful beyond the simulator.

cs.CV↗
arXiv2026

RFPO: Rectified Flow Policy Optimization for Embodied Control

Flow-based policies provide an expressive framework for continuous robot control, but their iterative ODE integration incurs substantial inference cost. Naively reducing the integration budget can severely degrade control, since policies optimized under full-step execution are not explicitly constrained to remain reliable under coarse numerical integration. We refer to this mismatch as the few-step discretization gap. To address this problem, we introduce RFPO, a flow-policy optimization framework for reliable few-step execution. Reward-aware online Reflow rectifies student-induced transport paths during on-policy learning, making the resulting policy more robust to coarse integration. A frozen Gaussian PPO controller supplies complementary action-space supervision at full and intermediate integration budgets, while the deployed policy remains a single flow student executed with one Euler step. Across Unitree Go2, Boston Dynamics Spot, Unitree H1, and Unitree G1, RFPO consistently preserves full-step control performance under one-step execution, with one-step returns remaining within 2.4% of their corresponding 64-step values across both zero and random initialization. On Unitree Go2, one-step execution retains 98.5% of the 64-step reward while reducing onboard mean inference latency from 4.39 ms to 0.08 ms, yielding a 54.9x speedup. Real-robot experiments further validate stable one-step locomotion. Code: https://github.com/AIGeeksGroup/RFPO. Website: https://aigeeksgroup.github.io/RFPO.

cs.RO↗
arXiv2026

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substantial progress in neural scene representations, reconstructing dynamic scenes remains challenging due to non-rigid motion, occlusions, temporal inconsistencies, and the trade-offs between reconstruction fidelity and computational efficiency. Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have introduced diverse approaches to representing and reconstructing dynamic scenes, yet their relationships, underlying design choices, and evaluation protocols remain fragmented. In this paper, we present a unified perspective on 4D scene reconstruction, organizing existing methods around their scene representations, temporal modeling strategies, reconstruction pipelines, and optimization objectives. Through this framework, we examine how different design choices affect geometric fidelity, appearance consistency, motion representation, and computational efficiency. We further consolidate commonly used datasets and evaluation metrics, identify limitations in current experimental practices, and discuss open challenges in reconstructing complex, dynamic real-world environments. By connecting methodological developments with their underlying assumptions and evaluation evidence, this work provides a structured foundation for understanding existing approaches and identifying future research directions. An evolving collection of relevant papers and resources is available at https://github.com/ZiyangYan/Awesome-4D-Scene-Reconstruction.

cs.CV↗