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Jing Tang

Publications and source records attributed to Jing Tang.

At least 19 recordsLinked to original sources
arXiv2026

Mock Observations for the CSST Mission: CPI-C -- Targets for High Contrast Imaging

We introduce CPISM, a simulation program developed for the Cool Planet Imaging Coronagraph (CPI-C) on the China Space Station Telescope (CSST). CPISM supports high-contrast exoplanet imaging by simulating observational conditions and instrumental effects to optimize target selection and observation strategies. The modular design includes target modeling, imaging simulation, observational effects, detector response, and data product generation modules, enabling flexible and realistic synthetic observations. Validation through simulations of a bright star shows strong agreement with theoretical expectations, confirming the program's accuracy. CPISM's modular design allows flexibility, accommodating different stellar and planetary models, and can simulate instrumental noise, cosmic rays, and other observational effects. This tool aids in data processing, signal-to-noise ratio analysis, and high-contrast photometry, contributing to future exoplanet discovery and characterization efforts. The program's outputs will enhance observation planning and scientific return for the CPI-C mission, providing critical insights into exoplanetary systems.

astro-ph.IM↗
arXiv2026

PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers

Scientific agents collaborate with researchers whose expertise and preferences differ, but benchmarks usually hold the collaborator fixed. We introduce PersonaScienceBench: 200 tasks from five benchmarks across four scientific domains. Simulated researchers provide context and feedback according to persona profiles, while task objectives and grading remain fixed. Agents encounter researcher preferences only through conversation. Across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths. Detailed GPT 6 Astra analyses show distinct agent responses across researcher profiles; interaction traces illustrate targeted clar ification and workflow revision. These behaviors can differ even when final scores match. PersonaScienceBench therefore evaluates how agents collaborate with diverse researchers alongside whether they complete the task.

cs.AI↗
arXiv2026

POSEIDON: Scaling Training Environments for Scientific Agents from Code Repositories

Training environments remain a major bottleneck for agents in data-driven scientific discovery. Existing collections of executable and verifiable training environments are small, and their construction remains difficult to scale. To address this limitation, we introduce Poseidon, an automated pipeline for generating diverse, verifiable training environments from scientific code repositories at scale. From the same repository budget, Poseidon extracts nearly 10 times as many training environments as a prior approach by decomposing scientific workflows into four complementary task types: Reproduction, Subflow, Completion, and Correction, where each type targets a distinct capability for scientific research. A recursive improvement loop further refines failed candidates and recovers otherwise discarded environments. Using Poseidon, we construct Pearl, a collection of 97,659 executable and verifiable environments from 7,110 public repositories spanning 10 scientific disciplines. To the best of our knowledge, Pearl is the largest and most broadly multidisciplinary collection of scientific-agent training environments to date. Qwen3.5 models (9B, 27B, and 35B-A3B) trained on Pearl achieve consistent gains across six challenging scientific-agent benchmarks. Overall, the scale and diversity of Pearl offer opportunities for training scientific discovery agents.

cs.AI↗
arXiv2026

Revisiting the $Λ_c^+ \to nπ^+η$ decay in light of the BESIII measurement

Motivated by the recent BESIII measurement, we revisit the $Λ_c^+\to nπ^+η$ decay within the chiral unitary approach, including internal and external $W$-emission mechanisms and final-state interactions. The $a_0(980)^+$ and $N(1535)^{0,+}$ resonances are dynamically generated by meson--meson and meson--baryon interactions, respectively. With the relative weight of internal to external emission fixed at $1/3$, $2/3$, and $1$, all three scenarios reasonably describe the measured $π^+η$ invariant mass distribution, but yield different fractions for the $a_0(980)$ contribution, and the fraction obtained for $γ=2/3$ and $1$ are numerically below the experimental upper limit. The $a_0(980)$ contribution exhibits a threshold cusp, modified by coherent interference in the total spectrum. Its modest prominence, together with the limited statistical precision and coarse binning, offers a plausible explanation for the absence of a clear experimental signal. We also predict the $ηn$ and $π^+n$ invariant mass distributions. Future high-statistics measurements at BESIII, Belle~II, and STCF would enable a joint analysis to constrain the relative production weight and resonance contributions, providing further insight into the nature of the $a_0(980)$ and $N(1535)$.

hep-ph↗
arXiv2026

MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents

A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over resources, unequal source exposure, and early execution failures. We construct MC-EvoBench as a controlled Minecraft benchmark with a fixed catalog of 19 registered evolution chains, including 12 primary evaluation chains and seven calibration or probe chains, together with three graph-aligned paired probe families for transfer. The benchmark represents each task as a task-state graph, defines stage-level environment verifiers, assigns matched source and target roles, resets the target world, and permits only a declared agent artifact to cross the source-target boundary. For each probe, no-prior, related, unrelated, and shuffled source conditions are evaluated under matched environment-step budgets and five seeds; we report TSR, WGP, and VMP and retain provenance records. The benchmark reveals a consistent direct-capability gap between controller and planner-backed variants, while related experience yields no stable transfer: it does not improve either tool probe, its small wood-species gain hinges on one seed, and all intervals include zero. Stage audits further show that early wood-collection and crafting bottlenecks often leave later capabilities unobserved. MC-EvoBench thus offers a controlled testbed for durable, transferable embodied capabilities. Code and data are available at https://github.com/xzw-ustc/mc-bench-open-source

cs.RO↗
arXiv2026

Path Complementarity Enables Directional Quantum Light in Hybridized Cavity Polaritons

Directional quantum light requires asymmetric photon statistics without sacrificing coherence between propagation paths, a combination that remains difficult to achieve within a single quantum-optical platform. Here we identify a microscopic path-complementarity mechanism in hybridized cavity polaritons that reconciles these two requirements. In a Zeeman-split ring-cavity QED system, cavity backscattering hybridizes the counterpropagating modes, whereas the Zeeman splitting creates spectrally asymmetric excitation channels. Their interplay gives rise, under weak driving, to directional photon blockade coexisting with strong single-photon path entanglement: one input direction exhibits antibunching while the opposite direction is bunched. Remarkably, switching between the two one-photon polariton resonances reverses both the photon-statistical and intensity asymmetries while nearly preserving the path concurrence over a broad parameter regime. Our results establish path complementarity as a general design principle for directional nonclassical light with retained quantum coherence.

quant-ph↗
arXiv2026

Kerr-Filtered Hybrid States from Double-Squeezed Tripartite Interactions

Multipartite interactions provide a unique route to generate hybrid quantum states, yet their intrinsically weak nonlinear coupling limits the controllable manipulation of the resulting correlations. Here we show that cooperative double squeezing and cavity Kerr nonlinearity together enable controllable engineering of Kerr-filtered hybrid states in a tripartite spin-photon-phonon system. Double squeezing exponentially enhances the spin-mediated photon-phonon pair-generation channel, whereas the Kerr interaction selectively reshapes the photonic component while maintaining the underlying tripartite conversion process. This mechanism reconstructs the composition of the hybrid excitation and gives rise to a fourfold phase-space manifold associated with spontaneous $Z_{2}^{(a)}\times Z_{2}^{(b)}$ parity breaking. Our results reveal a strategy for engineering hybrid quantum states by combining interaction amplification with mode-selective nonlinear filtering.

quant-ph↗
arXiv2026

Interaction-enhanced photon blockade and Bell-state protection in microwave-shielded polar molecules

Microwave shielding has recently emerged as a powerful tool for engineering interactions in ultracold polar molecules, yet its potential for controlling cavity quantum electrodynamics remains largely unexplored. Here, we investigate a molecular cavity quantum electrodynamics platform in which two microwave-shielded polar molecules are coupled to a single optical cavity mode and demonstrate that shielding-induced interactions provide a unified mechanism for both photon blockade and Bell-state protection. The anisotropic interaction reshapes the few-excitation spectrum by enhancing its anharmonicity, thereby suppressing multiphoton transitions and improving the single-photon purity by more than three orders of magnitude. The enhanced blockade is accompanied by the emergence of negative longitudinal spin correlations, revealing the interaction-induced suppression of simultaneous molecular excitations. We further show that the photon statistics are highly sensitive to the relative molecular configuration, with positional variations on the scale of the relative zero-point fluctuation substantially modifying the blockade performance. Beyond few-photon nonlinear optics, the same interaction protects an initially prepared molecular Bell state by dispersively decoupling molecular excitations from the lossy cavity mode, thereby suppressing cavity-mediated dissipation and slowing the fidelity decay. Our results establish microwave-shielded interactions as a unified interaction resource for engineering few-photon nonlinearities and protecting quantum states in molecular cavity-QED systems.

cond-mat.quant-gas↗
arXiv2026

Dissipation-selected photon-phonon pair and photon-bundle emission in a tripartite Mollow system

Dissipation is generally regarded as detrimental to nonclassical-state generation, yet whether it can determine which quantum resource is emitted from an open hybrid quantum system remains largely unexplored. Here, we show that the symmetry of bosonic dissipation channels can select and switch quantum-emission pathways in a tripartite Mollow system, where a strongly driven two-level emitter simultaneously couples to cavity photons and motional phonons. In the Mollow regime, higher-order tripartite interactions generate resonant photon-phonon pair excitations, whose emitted quantum states are subsequently selected by dissipation. Under symmetric dissipation, the system produces strongly correlated photon-phonon pairs, enabling both single-pair emission and correlated multipair bundles with pronounced nonclassical correlations. Breaking the dissipation symmetry progressively suppresses the hybrid correlations and dynamically decouples the photonic and phononic degrees of freedom. In the strongly asymmetric regime, long-lived phonons accumulate in highly excited Fock states, while rapidly leaking cavity photons are converted into high-purity single-photon and two-photon bundles. The crossover between these distinct emission regimes is characterized through time-resolved correlation functions, Fock-state distributions, and Wigner quasiprobability distributions. Our results establish dissipation symmetry as a resource for steering quantum emission between hybrid and purely photonic nonclassical states, providing a route toward programmable hybrid quantum emitters and dissipative quantum-state engineering.

quant-ph↗
arXiv2026

AgentPersonaBench: Benchmarking Persona-Driven User Simulation

We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deployed for persona-driven user simulation, existing benchmarks primarily evaluate conversational styling or self-reports rather than authentic behavioral fidelity. APB evaluates latent persona adherence one trait at a time, embedding each target trait within a complete synthetic profile without explicitly naming the trait or disclosing the test. Ground-truth adherence is verified strictly from observable actions across four interaction surfaces of increasing realism: survey, chat, web (interactive web environments), and app (desktop software environments). APB comprises 2,460 tasks spanning 867 traits, verified through automated audits and expert review. Our evaluation of 20 frontier model arms demonstrates that high-fidelity user simulation is already attainable: leading models achieve up to 84.7% full-pass adherence under unprompted conditions. At the same time, APB identifies clear behavioral boundaries: adherence drops across interaction modalities (only 37.9-64.3% pass all four surfaces), multi-attribute demands degrade retention, and competing model families exhibit pronounced behavioral divergence.

cs.AI↗
arXiv2026

M2Note: Continual Evolution of Vision Language Models via Mistake Notebook Learning

Vision Language Models (VLMs) have demonstrated remarkable capabilities in multimodal reasoning tasks, yet they still suffer from recurring failures, such as skipping key visual checks, misapplying domain rules, and hallucinating unsupported concepts. Most existing solutions rely on supervised fine-tuning (SFT) and reinforcement learning (RL), which are expensive to iterate and can be brittle under distribution shift. To this end, we propose Multimodal Mistake Notebook Learning (M2Note), a training-free continual evolution framework that externalizes learning into an editable memory. M2Note transforms failed trajectories into compact subject-guidance notes: the subject summarizes the underlying domain and concept, while the guidance provides actionable verification steps that can be reused in future inference. At test time, M2Note retrieves relevant notes via multimodal retrieval-augmented generation (RAG) and appends them to the model context, steering reasoning away from previously observed pitfalls. To stabilize continual evolution, we adopt batch-level post-verification with rollback, which commits notebook edits only if they improve performance on the same batch, reducing noisy updates and preventing regressions. M2Note supports both self-evolving, where the same VLM acts as solver and supervisor, and cross-model evolving, where a stronger supervisor guides a weaker solver, enabling capability transfer without weight updates. Experiments on six multimodal reasoning benchmarks show consistent improvements across domains and backbones, while achieving strong cost and sample efficiency and remaining complementary to Chain-of-Thought (CoT) prompting.

cs.MA↗
arXiv2026

The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models

Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving unclear what these features should preserve for control. Our representation diagnostics show that representations with more predictable future changes need not make linear action decoding easier. Observed future changes provide additional action information beyond the present, and linearly readable action information is spatially concentrated. These findings motivate Action-Relevant Predictive States (ARPS), a compact predictive interface between the video and action experts. ARPS uses a horizon-conditioned state predictor to aggregate intermediate video features into a compact state that supplies all visual context to the action expert. Future-representation supervision trains different parts of this state to predict visual representations at different future times, together with their changes relative to the present. At inference, the supervision branch is removed, and the action expert only uses the learned predictive state computed from current observations. Controlled ablations show that future supervision substantially improves generalization under distribution shift. ARPS achieves 99.2% success on LIBERO and transfers to LIBERO-Plus without adaptation, reaching 87.3% and exceeding Fast-WAM by 39.2 percentage points.

cs.CV↗
arXiv2026

EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading

Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.

cs.AI↗
arXiv2026

Do Video Generators Track the World Across Segments? A Benchmark and Method for World-State Reasoning in Video Continuation

Video generators build long videos by composing shorter parts, either by generating segments one after another or by autoregressively extending chunks. Each new part usually depends on memories of historical observations, such as recent frames, selected key frames, memory banks, or cached features. These memories preserve visible evidence from the past, but current generators do not reliably turn such evidence into a world-state interface: what holds in the video world after previous actions and how it should change under the next prompt. A past frame remains valid history, but it may not describe the state needed by the next segment; some states must instead be inferred from occluded or implicit changes rather than copied from a directly observed frame. This creates a simple but overlooked question for video continuation: given a previous video, its prompt, and a new prompt, can a model generate a continuation that reflects the state determined by both the historical video and the new prompt? To answer this question, we introduce Statebench, a benchmark that targets this gap by testing continuations over three state categories: past-visible states, occluded-process states, and complex-transition states. We further propose Stateagent, which explicitly maintains an entity-state representation, updates it under the new prompt, grounds the predicted post-action state as a future end frame, and renders the next video. Experiments show that our method improves controlled video continuation by raising the all-case state score (SCS-All) from 45.2 to 69.3, and also benefits story generation at the one-minute scale. Code is avaliable at https://github.com/AMAP-ML/StateAgent.

cs.CV↗
arXiv2026

R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models

High similarity between first-visit and return frames does not necessarily show that a video world model remembered the scene; the intervening rollout may simply have changed very little. This ambiguity makes absolute revisit scores sensitive to rendering stability, repetitive content, and failed motion. We introduce \emph{R2M-Bench} (\textbf{R}elative \textbf{R}evisit \textbf{M}emory Benchmark), a benchmark of observable revisit-selective consistency. For every detected return, R2M-Bench compares the revisit pair with two controls from the same rollout: a gap-matched non-revisit pair that measures generic temporal stability and a short-range pair that estimates short-horizon consistency. These comparisons produce \emph{MemoryGain} (MG), the revisit advantage over the temporal baseline, and the \emph{Normalized Memory Ratio} (NMR), which normalizes this advantage by the short-to-baseline dynamic range. R2M-Bench combines 100 reference scenes with three leave-and-return trajectories to form 300 instances and evaluates appearance fidelity, scene and object identity, local geometry, and persistent state. Across seven action-conditioned video world models, Overall NMR correlates with human consistency judgments at Spearman's $ρ=0.547$ (95\% CI $[0.45,0.63]$). Its within-model correlation magnitude with generated motion is $0.072$, compared with $0.207$ for raw revisit similarity, indicating that relative calibration substantially reduces the slow-motion shortcut. DreamX-World-Memo achieves the highest Overall NMR among the evaluated video models. Together, these results support same-rollout relative calibration as a practical way to distinguish revisit-specific consistency from generic temporal stability.

cs.CV↗
arXiv2026

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm's commanded path, we inject per-arm $\mathrm{SE}(3)$ transformations into attention via \textbf{PRoPE-style geometric encoding}, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight \textbf{depth branch} for scene-level geometry and use \textbf{SAM3 masks} with a frozen \textbf{V-JEPA teacher} to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, \model{} achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.

cs.CV↗
arXiv2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions. Records are either sampled from a dependency graph that preserves correlated attributes or derived from human-authored profiles. We release a quality-filtered coreset of approximately 1 million personas, comprising 599,847 human-grounded and 400,000 synthetic records. Second, the MatrAIx Playground provides four environments in which diverse users evaluate and interact with digital products: Survey, AI Chatbot, Web, and App. Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare. We conducted 18,189 evaluation trials across eight representative tasks. Persona agents were powered by three LLMs: Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The resulting feedback captures how decisions and preferences vary across persona backgrounds, including hesitation after a price increase, willingness to continue after an AI assistant fails, and latency tolerance. We conducted two main validation studies: First, a 400-trial controlled study evaluated persona adherence across ten behavioral attributes and all four environments. The declared behavior was expressed or correctly suppressed in 366 trials (91.5%). Second, human and LLM judges evaluated the extraction quality of human-grounded personas. Overall, MatrAIx provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.

cs.AI↗
arXiv2026

402Pilot: An x402 Decision Layer for Autonomous Agent Micropayments

Programmable-payment protocols such as x402 enable per-request micropayments, but they do not determine which payable service an autonomous agent should buy under a finite wallet. We formulate this buyer-side problem as agent-native payment decision-making: contextual provider selection under wallet pressure, chosen-only paid feedback, and changing market conditions. We propose 402Pilot, a protocol-agnostic buyer-side decision layer between autonomous agents and payment execution that implements purchasing policies for selecting among payable providers. We instantiate it with PA-DCT, a payment-aware discounted contextual Thompson-sampling policy that adapts purchasing decisions under wallet pressure while learning from post-payment feedback. To evaluate buyer-side payment policies, we introduce 402Pilot-Bench, a frozen-replay benchmark spanning 823 tasks, five heterogeneous provider pipelines, and three market regimes, each evaluated over 30 paired seeds. PA-DCT achieves the strongest fixed-wallet adaptive trade-off among non-oracle policies: it maintains competitive service quality while spending only 39 to 43 percent of the wallet and reallocates spending as market conditions change. It attains the best non-oracle PA-gap/T under the price shock and the best mean and worst-case ranks across the nine scenario-metric combinations of quality, ROI, and PA-gap/T. Comparisons with learning baselines and component ablations further support the effectiveness and design of the proposed decision policy. These results suggest that programmable payment must be complemented by buyer-side decision-making capable of learning service value and adapting purchasing decisions accordingly.

cs.AI↗