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Zijian Zhang

Publications and source records attributed to Zijian Zhang.

At least 19 recordsLinked to original sources
arXiv2026

(LRDs)$^2$: The Low-ReDshift Little Red Dots Survey. II. DESI DR1 Sample

JWST has revealed a substantial population of "Little Red Dots" (LRDs) at $z>4$, challenging conventional AGN frameworks. However, the low-redshift regime remains largely unexplored. In the second paper of the (LRDs)$^2$ series, we present a systematic selection from DESI DR1 and identify 27 LRDs at $z=0.2-0.9$, yielding a number density lower limit of $7.5\times10^{-10}$ cMpc$^{-3}$. We conducted near-IR spectroscopic follow-up observations for 21 of them, revealing their full SED shapes and emission lines. These low-$z$ LRDs share the hallmark properties of their high-$z$ counterparts: compact morphology, V-shaped UV-optical continua, broad Balmer emission with extreme decrements (median H$α$/H$β\sim15$), frequent Balmer absorption (70%), and blackbody-like optical-to-near-IR continua. All have low metallicity, occupy the same regions in the BPT diagram as high-$z$ LRDs, and have softer ionizing spectra than typical AGNs. The consistency between low-$z$ and high-$z$ LRD properties indicates the same physical processes at work. The correlation between broad-line Balmer luminosity and $L_{5100}$ deviates from that of local type-1 AGNs, limiting the direct application of local BH mass calibrations. Ionized [O III] outflows are ubiquitous (78%). One LRD at $z=0.196$, J1717+3807, shows robust long-term variability in $i$ and WISE bands. The optical-to-NIR continua of LRDs reveal a wide range of temperatures $\sim2000-4700$ K (peak $0.6-1.4$ $μ$m), with a subset showing cooler and larger envelopes than those at high $z$. Low-$z$ LRDs serve not only as proximate laboratories for probing the nature of LRDs, but also trace the cosmic evolution of this population from the cosmic dawn to the present day.

astro-ph.GA↗
arXiv2026

How High Can GPT-6 Astra Climb in Contact-Rich Manipulation? PhysLadder: A Hierarchical, Matched Sim-Real Benchmark for Physical Interaction

We introduce PhysLadder, a hierarchical Sim-Real benchmark for evaluating physical interaction capabilities in contact-rich manipulation. Its 16 household tasks span three cumulative levels: contact selection and geometric alignment (L1), force regulation within sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3). Each task is instantiated in physically and visually matched simulated and real-world environments, with shared task definitions and success criteria. We evaluate GPT-6 Astra zero-shot with three trials per task in each environment. Real-world success rates decline from 80.0% at L1 to 47.6% at L2 and 0.0% at L3, with corresponding simulation rates of 66.7%, 47.6%, and 0.0%. Ten of the 16 tasks have identical success counts across environments; the remaining six differ by one success out of three trials. Across both environments, recurring difficulties in spatial reasoning, contact selection, force regulation, and coordination often prevent local physical progress from accumulating into task completion. These findings characterize Astra's observed capability boundary across increasing interaction demands and, for this model, support matched simulation as a repeatable, scalable testbed for studying the sustained, coordinated physical interactions that limit reliable manipulation.

cs.RO↗
arXiv2026

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this by combining complementary model strengths, yet existing methods rely on fixed rules or black-box models based solely on numerical inputs, failing to leverage LLM reasoning for interpretable weighting decisions. We propose REATS, which leverages LLM reasoning capabilities as an intelligent ensemble router that jointly processes textual temporal pattern descriptions and numerical features to produce interpretable, sample-adaptive ensemble weights through chain-of-thought reasoning. To enable effective LLM-based ensembling, we study its key design choices and propose: (i) a structured input pipeline that transforms raw time series into hybrid textual--numerical representations with fixed token cost, enabling rule-based chain-of-thought construction without API dependency, augmented with retrieved similar-sample priors; (ii) a diverse multi-row weight supervision scheme coupled with a token-efficient percentage-table format that reduces numerical complexity and mitigates LLM hallucinations; and (iii) a two-stage fine-tuning framework combining SFT with GRPO, where a reciprocal reward mapping transforms the continuous unbounded MSE gap into bounded signals with amplified near-oracle sensitivity, addressing the uniform sensitivity and outlier-dominated advantage compression inherent in naive reward designs for regression-based GRPO. Experiments on eight benchmarks demonstrate that REATS outperforms competitive ensemble baselines while providing natural language explanations and demonstrating strong transfer learning and out-of-domain generalization to unseen candidate models.

cs.LG↗
arXiv2026

$M^2Net$: Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting

Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of spatial dynamics and temporal non-stationarity. Existing approaches, while effective, often overlook two fundamental characteristics of real-world data: (1) Granularity-dependence, where dominant spatial correlations shift significantly across different time scales, and (2) Signal entanglement, where global common patterns are inextricably mixed with node-specific disturbances. In this paper, we propose a novel framework named $M^2Net$ (Multi-Scale Masked attention and Prototype network) to unify scale-specific structural learning and semantic decomposition. First, to capture the evolution of spatial dependencies, we introduce a Multi-Scale Masked attention mechanism. By downsampling temporal features into multiple resolutions, we dynamically generate scale-adaptive spatial masks, allowing the model to distinguish between micro-scale physical neighbors and macro-scale functional correlations. Second, to disentangle unique variations from shared trends, we propose a Prototype Residual Learning module. It retrieves global common patterns from a momentum-updated prototype bank and subtracts them from node representations, explicitly forcing the network to model the residual unique deviations. By integrating these two orthogonal views, i.e., structural multiscaling and semantic decomposition, our approach effectively isolates valid spatial-temporal signals from noise. Extensive experiments on real-world datasets demonstrate that $M^2Net$ outperforms state-of-the-art baselines on extensive empirical verifications.

cs.AI↗
arXiv2026

SpriteStage: Long Horizon Animation Generation via Progressive Scene Programming and Replay

Video generation models synthesize visual sequences from text and image inputs. Extending video generation to long animations requires actions, dialogue, camera coverage, and narrative developments to remain coordinated across scenes. Executable scene programs make these decisions available for inspection. Constructing an entire story before observing its execution can leave later scenes dependent on unrealized events. We present SpriteStage, a progressive scene programming framework that builds animation through iterative enactment and engine reorchestration. Iterative enactment generates each scene from an accepted world checkpoint and narrative snapshot, executes it on a shared spatial stage, and uses runtime and visual feedback to revise the relevant actions or story beats. The accepted outcome becomes the starting state for the next scene. Engine reorchestration maps the resulting draft to production assets and engine capabilities while retaining its events, geometry, and timing. Inspection of the assembled sequence and frozen replay carry the accepted performance into the final video. Three complete animations, ranging from approximately 9.9 to 14.3 minutes, demonstrate the framework's effectiveness in producing extended narratives through scene programming and engine execution.

cs.CV↗
arXiv2026

Hot Molecular Hydrogen in Galaxies at Redshift ~ 0.2-1.5 Revealed by JWST

Molecular hydrogen (H$_2$) is the fundamental component of the molecular gas reservoir that fuels star formation and black hole growth. While prominent hot H$_2$ emission ($\gtrsim 1000$ K) has been explored in nearby galaxies, its excitation mechanisms at higher redshifts remain poorly constrained. We present a systematic study of hot molecular gas traced via ro-vibrational H$_2$ emitters at z~0.2-1.5 using JWST slitless spectroscopy. Exploiting JWST's great sensitivity to near-infrared H$_2$ ro-vibrational transitions such as H$_2$ 1-0 S(1), we build a sample of 144 H$_2$-luminous galaxies and characterize their multi-wavelength properties. About 96.5% and 80% of these galaxies are detected in the radio and far-infrared (FIR) bands, respectively, and their hot H$_2$ emission strongly correlates with the radio and FIR radiation. Based on radio excess, X-ray emission, and optical diagnostics, 62 sources (43%) exhibit significant AGN activity. The H$_2$ line luminosities of the sample show a sublinear dependence on radio luminosity, while the relation between the H$_2$ luminosities and total infrared luminosities is approximately linear after excluding the radio-excess sources. These scaling relations suggest that star formation provides a baseline contribution to hot H$_2$ excitation, while radio-excess sources require an additional contribution from AGN-related processes such as jet- or wind-driven shocks. These results reveal a multi-channel excitation scenario and shed new light on the interplay between star formation, AGN activity, and the interstellar medium during galaxy evolution.

astro-ph.GA↗
arXiv2026

Large-scale Environmental Dependence of AGN Activity Revealed by JWST Pa$α$ Emitters

The role of the large-scale environment in regulating AGN activity remains an open question. We investigate the Mpc-scale environments of 70 AGNs at $z=1.1$-$1.5$ using a highly complete sample of 3,419 Pa$α$ emitters with accurate spectroscopic redshifts from JWST slitless spectroscopic survey. This dataset allows us to robustly trace the environments and quantify the environmental dependence of AGN properties. We measure the eighth-nearest-neighbor surface density of Pa$α$ emitters around each AGN, probing $\sim$1.5 pMpc scale environments. We find that this environmental density exhibits a weak positive correlation with radio luminosity. Radio-bright AGNs tend to reside in statistically denser environments than their radio-faint counterparts. The environmental density also has a positive correlation with the Eddington ratio and a weak negative correlation with the central black hole mass, implying more efficient black hole accretion in dense environments. We construct Pa$α$ luminosity functions (LFs) in AGN environments and find that they are systematically elevated relative to the field Pa$α$ LF. In addition, their faint-end slopes are shallower, suggesting accelerated galaxy growth and enhanced quenching of star formation. As Pa$α$ emitters trace gas-rich, star-forming galaxies, our results support a scenario in which the large-scale cold gas promotes AGN activity at cosmic noon.

astro-ph.GA↗
arXiv2026

Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning

Scaling test-time reasoning has substantially improved the problem-solving ability of large language models (LLMs), but standard autoregressive decoding still executes long reasoning traces sequentially, creating severe latency for difficult tasks (up to days and weeks). Parallel reasoning offers a natural remedy. However, prior systems primarily focus on Subtask Parallelism, where the model learns to decompose a high-level task into smaller chunks that can be solved independently. This approach overlooks another pervasive form of parallelism: Trial Parallelism, where multiple speculative attempts explore, verify, and aggregate competing hypotheses in parallel. In this paper, we introduce Parason, which reveals and learns both forms of parallelism in LLM reasoning. Our analysis identifies Trial Parallelism as the majority of parallelizable reasoning computation (65.5% in DeepSeek-V4's reasoning steps in HLE), and it becomes increasingly dominant on hard problems. Guided by this taxonomy, Parason converts sequential reasoning traces into structured parallel trajectories with a context-free grammar, then trains models with Parallelism-Aware Group Relative Policy Optimization (PA-GRPO), whose reward jointly balances accuracy, latency, and the two parallelism ratios. At inference time, Parason executes the learned parallel structure through tool calls, translating theoretical savings to real-world wall-clock acceleration. Experiments on mathematical reasoning benchmarks including AIME24 and AIME25 show that Parason achieves an average acceleration about 1.7$\times$ while maintaining competitive accuracy.

cs.AI↗
arXiv2026

A JWST View of Cosmic Star Formation from $z\sim0$ to 9 with Hydrogen Recombination Lines in the COSMOS and GOODS-N fields

We combine wide-area COSMOS-3D observations with deeper GOODS-N data from JWST/NIRCam F444W wide-field slitless spectroscopy to trace cosmic star formation over $z\sim0$--9. The observations cover approximately $0.33~\mathrm{deg}^2$ in COSMOS and $60.9~\mathrm{arcmin}^2$ in GOODS-N, with typical $3σ$ integrated line-flux limits of $3.2\times10^{-18}$ and $1.04\times10^{-18}~\mathrm{erg\,s^{-1}\,cm^{-2}}$, respectively. The common $3.9$--$5.0\,μ$m wavelength window probes Br$α$, Br$β$, Pa$α$, Pa$β$, H$α$, and H$β$ across the local Universe, cosmic noon, and reionization. Our final sample contains 9146 galaxies, including 7883 in COSMOS and 1263 in GOODS-N. COSMOS constrains luminous emitters, while GOODS-N extends the luminosity functions toward fainter sources. We measure dust-corrected line luminosities, account for line-detection completeness, and convert the six lines to equivalent H$α$ luminosities assuming Case B recombination. Schechter fits reveal an increase in the characteristic H$α$-equivalent luminosity toward cosmic noon. Integrating the resulting star formation rate functions above $0.24\,M_\odot\,\mathrm{yr}^{-1}$, we trace the cosmic star formation rate density from the local Universe to reionization. The combined Brackett, Paschen, and Balmer measurements are broadly consistent with the canonical UV- and IR-based cosmic star formation history at $z\lesssim6$. Fixed-slope tests show that the H$β$ estimate at $z\sim8$ remains sensitive to the external photometric faint-end constraint and extrapolation below our spectroscopic limit. These measurements provide complementary constraints on the distribution and integrated rate of star formation across cosmic time.

astro-ph.GA↗
arXiv2026

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnesses that manage tools, skills, and accumulated experience. However, we find that as single-session execution lengthens, the agent's reasoning intensity declines alongside slowing progress, summarized as long-horizon deceleration, and the benefits of these custom harnesses vanish as the underlying model improves, summarized as harness staleness. These findings suggest that their benefits may not persist across versions, which motivates a workflow-level approach in which agents build on one another's work across sessions. We model these alternating agent sessions as a context-reset semi-Markov decision process (CR-SMDP) and establish sufficient conditions for alternation to exceed single-agent reference limits, derive an explicit experiment-budget bound, and characterize when session caps preserve this advantage. Guided by this analysis, we introduce Humanize MLE Agents (HMA), which alternates two agents on a shared workspace, limits each session to at most k experiments and resets the context between sessions, without additional task-specific harness or prior knowledge supplements. Across 75 Kaggle tasks in MLE-bench, HMA reaches a state-of-the-art any-medal rate of 78.2%, exceeding the strongest open-source baseline by 8.0 points with a quarter of its time budget. Moreover, among six configurations spanning five heterogeneous agent pairs, HMA improves any-medal rate over the constituent baselines by 6.4 points on average, demonstrating cross-pair generality. A supplementary case on kernel optimization reaches 878 cycles, within 1.6% of the community leaderboard leader, demonstrating applicability beyond MLE.

cs.AI↗
arXiv2026

Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify

Large language model (LLM) agents are rapidly improving on long-horizon scientific and olympiad tasks. Recent systems have produced a Lean-formalized resolution of the Navier--Stokes problem and autonomous solutions to four open questions in the Erdős Conjectures database. Yet neither system releases both its underlying model and complete inference stack. More broadly, a single agent session remains brittle on difficult problems: early errors can propagate, missing domain knowledge can stall progress, and apparent success can mask a misinterpretation or weakening of the original task. We introduce HOA (Humanize Olympiads Agent), an open, flow-level framework that coordinates multiple agent sessions through three complementary feedback channels. First, an independent reviewer evaluates each candidate and provides actionable feedback. Second, a structured wiki supplies task-specific knowledge that may be missing or unreliable in model pretraining. Third, a verifier checks generated Lean proofs against trusted challenge statements. With GPT-5.6 Sol, HOA achieves perfect scores on the IMO, IPhO, and IOI 2026, near-full marks on the IChO 2026 problem set, and solves all 672 problems (100%) on PutnamBench. On the Lean-Eval leaderboard, HOA ranks first, solving 251 of 304 problems (82.6%). We further evaluate HOA with open models. Using Kimi-K3, HOA achieves full scores on the IMO, IPhO, and IOI, and a gold-level score of 97.1% on the IChO. These results closely match the closed-source GPT-5.6 Sol + /Goal baseline on the IMO and IPhO, while exceeding it on the IChO and IOI, demonstrating that systematic feedback can help bridge substantial differences in base-model capability.

cs.AI↗
arXiv2026

A Comprehensive JWST Slitless Spectroscopic Census of $\sim$12,000 Emission-Line Galaxies at Redshift $0\sim9$ in COSMOS

Emission-line galaxies provide key diagnostics of star formation, chemical enrichment, and AGN activity across cosmic time. We present a systematic census of emission-line sources using JWST/NIRCam wide-field slitless spectroscopy (WFSS) from the COSMOS-3D program, which covers a large contiguous area of 0.33 deg$^2$ and provides F444W grism spectroscopy over $3.9$--$5.1\ μ$m. By combining slitless spectroscopy with deep multi-band imaging data, we construct a homogeneous spectroscopic sample of 12,275 objects at redshift 0--9. The typical $3σ$ line-flux limit reaches $3.2\times10^{-18}\ {\rm erg\ s^{-1}\ cm^{-2}}$. To address source confusion inherent to slitless spectroscopy, we develop a multi-step identification framework that combines information from spectra, images, spectral energy distributions, and spatial distributions. We introduce convolutional neural network and large language model to emulate the human identification process and improve efficiency. The resulting catalog provides spectroscopic redshifts, emission-line measurements, and derived physical properties for all sources. The F444W WFSS enables the detection of multiple emission lines, including primarily [O III], H$α$, He I $\lambda10830$, [S III] $λ\lambda9531,9069$, Pa$β$, Pa$α$, Br$β$, PAH, Br$α$, allowing a continuous census of galaxies across cosmic time. We construct composite spectra in several redshift bins and detect a variety of faint spectral features that are difficult to identify in individual spectra, including higher-order hydrogen recombination lines, CO absorption bands, and [Fe II] lines. This catalog provides a key reference dataset for studies of galaxy evolution and AGN activity, and demonstrates the power of WFSS for constructing large, unbiased spectroscopic samples over wide areas. The full catalog will be publicly available online upon publication.

astro-ph.GA↗
arXiv2026

Humanize: Judgement Engineering for Agentic Coding

Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a multi-agent orchestration workflow for agentic coding built around judgement engineering: explicit, mechanically enforced decisions at the boundaries between planning, implementation, review, and learning. A human approves a plan contract, a builder agent implements it in rounds, and a reviewer agent from another vendor decides completion; deterministic hooks, not a model, route work between these roles and enforce 72 mechanical gates. Viewed as a Markov chain over repository states, alternating builder and reviewer samples jointly from two models, so a defect survives only if both miss it. We study Humanize through its deployment, 118 public postmortems of real loops, and its applications. Over 68 versions in 108 days, it gathered 1,468 GitHub stars. Applications include a 567-file gem5 build-system migration under upstream review; Kernel Design Agents, which extend the loop with a kernel knowledge base and profiling feedback and placed in the top three of all three Full-Agent tracks of the MLSys 2026 FlashInfer contest; and, through Humanize Olympiad Agents (HOA), full scores in IOI 2026, IMO 2026, IPhO 2026, and IBO 2024, 418.5/437 in IChO 2026 (gold-medal). Humanize also achieves 672/672 on PutnamBench and ranks first (251/303) on Lean-Eval's leaderboard even competiting with professional mathematicians. The postmortems show that independent review catches unsupported builder claims, but stopping remains a key weakness. In reports that separate rounds by phase, two thirds of rounds occurred after implementation was accepted. This evidence is observational, not a controlled comparison of workflows.

cs.AI↗
arXiv2026

Hybrid-Precision Block-Jacobi Preconditioned GMRES Solver for Linear System in Circuit Simulation

As integrated circuits become increasingly complex, the demand for efficient and accurate simulation solvers continues to rise. Traditional solvers often struggle with large-scale sparse systems, leading to prolonged simulation times and reduced accuracy. In this paper, a hybrid-precision block-Jacobi preconditioned GMRES solver is proposed to solve the large sparse system in circuit simulation. The proposed method capitalizes on the structural sparsity and block properties of circuit matrices, employing a novel hybrid-precision strategy that applies single-precision arithmetic for computationally intensive tasks and double-precision arithmetic for critical accuracy-sensitive computations. Additionally, we use the graph partitioning tools to assist in generating preconditioners, ensuring an optimized preconditioning process. For large-scale problems, we adopt the restart strategy to increase the computational efficiency. Through rigorous mathematical reasoning, the convergence and error analysis of the proposed method are carried out. Numerical experiments on various benchmark matrices demonstrate that our approach significantly outperforms existing solvers, including SuperLU, KLU, and SFLU, in terms of both preconditioning and GMRES runtime. The proposed hybrid-precision preconditioner effectively improves spectral clustering, leading to faster solutions.

math.NA↗
arXiv2026

Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study

Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model predictions are directly translated into physical actions. This tight coupling between model behavior and physical execution makes hidden security vulnerabilities particularly consequential. While backdoor attacks have been widely studied in conventional AI models, their effects on deployed learning-based robotic arm manipulation systems remain less understood: a backdoored robot can behave normally during benign operation while inducing attacker-specified behaviors only when specific triggers are present, posing potentially serious risks in physical environments. In this work, we present a preliminary empirical security study of backdoor attacks and defenses in learning-based robotic manipulation on two real commercial industrial robotic arms (FANUC and xArm). We investigate whether a backdoor can reliably induce semantically incorrect manipulation behaviors while remaining stealthy under nominal task execution. We then develop an online defense pipeline that detects and neutralizes triggers at runtime, and compare its effectiveness against an offline fine-tuning defense. Beyond defense effectiveness, we further evaluate the computational latency and execution overhead introduced by the defense pipeline to assess its suitability for high-throughput industrial operation.

cs.RO↗
arXiv2026

From Charts to Code: A Hierarchical Benchmark for Multimodal Models

We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (LMMs). Chart2Code is explicitly designed from a user-driven perspective, capturing diverse real-world scenarios and progressively increasing task difficulty. It consists of three levels: Level 1 (Chart Reproduction) reproduces charts from a reference figure and user query; Level 2 (Chart Editing) involves complex modifications such as changing chart types or adding elements; and Level 3 (Long-Table to Chart Generation) requires models to transform long, information-dense tables into faithful charts following user instructions. To our knowledge, this is the first hierarchical benchmark that reflects practical chart2code usage while systematically scaling task complexity. In total, Chart2Code contains 2,023 tasks across 22 chart types, paired with multi-level evaluation metrics that assess both code correctness and the visual fidelity of rendered charts. We benchmark 25 state-of-the-art (SoTA) LMMs, including both proprietary and the latest open-source models such as GPT-5, Qwen2.5-VL, InternVL3/3.5, MiMo-VL, and Seed-1.6-VL. Experimental results demonstrate that even the SoTA model GPT-5 averages only 0.57 on code-based evaluation and 0.22 on chart-quality assessment across the editing tasks, underscoring the difficulty of Chart2Code. We anticipate this benchmark will drive advances in multimodal reasoning and foster the development of more robust and general-purpose LMMs. Our code and data are available on Chart2Code.

cs.SE↗
arXiv2026

$α$-Wasserstein Mechanism for Rényi Pufferfish Privacy

This paper introduces the $α$-Wasserstein mechanism for achieving Rényi Pufferfish Privacy using Laplace and Gaussian noise. By leveraging Hölder's inequality, we demonstrate that the scale parameter of the Laplace mechanism can be calibrated via an upper bound on the $W_α$ metric to satisfy $(α, ε)$-Rényi Pufferfish Privacy for $α\in (1, \infty]$. We show that at the limit $α= \infty$, this framework recovers the established $W_\infty$ mechanism for $ε$-pufferfish privacy. This result is subsequently extended to the exponential mechanism. Furthermore, we propose a $W_α$ mechanism for Gaussian noise for $α\in (1, \infty)$, demonstrating that it generalizes existing results within the Rényi Differential Privacy framework. Experimental evaluations reveal that our $α$-Wasserstein mechanism significantly reduces noise power compared to the conventional $W_\infty$-based approach, with the Gaussian mechanism providing superior utility over the Laplace mechanism. Notably, the mechanisms derived in this work achieve exact $(α, ε)$-Rényi Pufferfish Privacy without requiring additional relaxations, such as $δ$-approximations.

cs.CR↗