arXiv subjects

Long Wang

Publications and source records attributed to Long Wang.

At least 19 recordsLinked to original sources

arXiv2026

Economic Evaluation of V2G-Enabled Fast Charging Stations Under Endogenous EV Adoption Dynamics

Building fast charging stations (FCSs) is crucial for transportation electrification, but there exists an indirect network effect: while the increasing number of electric vehicles (EVs) decides the FCS capacity expansion, the spatial locations of these facilities strongly influence drivers' willingness to adopt EVs. Ignoring this interaction can lead to bad capital investments and exacerbate power grid vulnerabilities during tidal traffic peaks. Therefore, we explicitly model the EV adoption dynamics as decision-dependent uncertainties (DDUs) in a new multi-period collaborative planning framework. This framework evaluates the economic viability of V2G-enabled FCSs across both transportation and distribution networks. To simplify the complex calculation, we introduce an aggregated fleet virtual battery model to catch macroscopic vehicle-to-grid (V2G) flexibility. This successfully circumvents the dimension curse inherent in tracking microscopic state-of-charge. To further guarantee calculation speed, the nonlinear infrastructure exposure is transformed into a mixed-integer program by using Special Ordered Set type 2 (SOS2) variables and Second-Order Cone Programming (SOCP) relaxations for grid limits. Finally, numerical studies on a coupled Sioux Falls and IEEE 33-bus testbed prove that our framework achieves superior expected social welfare. Also, macroscopic V2G aggregation is highlighted for its capability to mitigate distribution grid congestion penalties.

eess.SY↗

arXiv2026

Git4Data: Database-Native Version Control for AI Agents

Large Language Model (LLM) agents increasingly explore many candidate states of relational data in parallel, each of which should remain isolated, reproducible, and auditable, preferably through the same SQL interface used for ordinary data work. Existing tools support this requirement only partially: source-code version control does not scale to large datasets, whereas relational databases manage large data efficiently but rarely expose native branching, comparison, and merging. We present Git4Data, a database-native version-control layer for agentic workflows. Git4Data treats a database as a repository and a table as a versioned object, exposing Git-style operations (snapshot/tag, branch, diff, and merge with explicit conflict-resolution policies) through SQL extensions. Implemented in MatrixOne, a cloud-native relational database, Git4Data leverages immutable object storage and MVCC to make the cost of these operations proportional to the size of the change rather than the size of the data. On the BranchBench agentic branching workloads, Git4Data outperforms DoltDB by up to an order of magnitude. Overall, we believe this work sheds light on how relational databases can better support AI agents through efficient versioning.

cs.DB↗

arXiv2026

ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents

Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when to compact history under a hard context-window budget and a byte-sensitive prompt cache. In production agentic systems, this logic is scattered across prompt builders, ad hoc compaction routines, cache-break workarounds, and per-provider shims. We argue that context assembly is structurally isomorphic to query execution in a relational database: both execute under a hard budget, exploit a tiered cache, and leverage statistics. We adopt this discipline in ContextPipe: a five-phase pipeline (Plan Bind Optimize Execute Feedback) backed by a structured data-source catalog, a deterministic cache-aware optimizer, and an EXPLAIN ANALYZE trace. We show that context in ContextPipe is auditable, replayable, and failure-isolated. A preliminary evaluation using the SWE-bench Pro Qutebrowser subset shows that, compared with the append-only context construction policy, ContextPipe reduces total token volume by 31%, LLM calls by 23%, and response time by 9%, at the cost of a lower KV cache-hit ratio.

cs.AI↗

arXiv2026

INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures. Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning. However, post-hoc CoT labels are too coarse to show how intent changes during generation. We introduce INTENT-AS-A-TOOL, an approach that adds intent-targeted tools to give the model a dedicated channel for expressing commitment to a target behavior. The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior. Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention. These findings suggest that action preferences are useful for tracking agentic misalignment during reasoning. Our code and data are accessible: https://github.com/RebeccaZhang22/intent-as-a-tool.

cs.CL↗

arXiv2026

Human-Guided Causal Knowledge Injection for Virtual Cells

Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecting causal graphs into virtual cells can improve the interpretability, but such graphs are usually not available in real-world applications. Recently, many methods have been proposed to construct causal graphs from data, which group genes based on their similarities to form concepts and extract their causal relationships. However, since this automatic process is unsupervised, the causal graphs usually contain errors. In this paper, we propose a human-guided causal knowledge injection method for virtual cells. We developed a gene-similarity-aware causal graph visualization supported by a hybrid optimization algorithm to help explore both the causal relationships between concepts and the similarities between genes. Based on the exploration, we further developed a counterfactual analysis strategy supported by a counterfactual visualization and a causal path visualization to help validate and refine causal graphs. The effectiveness of our method is demonstrated through two real-world case studies, the extraction of scientifically meaningful causal insights, and positive feedback from domain experts.

cs.HC↗

arXiv2026

Binary Constraints on the Origin of Nitrogen-rich Field Stars

Recent JWST observations have revealed galaxies with unusually high N/O ratios, suggesting that nitrogen enrichment may be common in intense star-forming environments in the early Universe. In the Milky Way, nitrogen-rich(N-rich) stars in the Galactic field have long served as probes of early Galaxy formation and globular cluster enrichment. However, the identification of binaries among these stars raises the possibility that binary mass transfer could contribute to their origin. In this work, we utilize multi-epoch radial velocities and element abundances from APOGEE DR17 to constrain their formation sites. Among 266 N-rich field stars, 33 exhibit radial velocity variations of $\Delta {\rm RV} > 1\,{\rm km/s}$, including 10 robust spectroscopic binaries identified using the $F_2$ statistic within a well-sampled subset of 46 stars. The resulting close-binary fraction ($21.7\pm6.1\%$) is statistically indistinguishable from that of chemically normal field stars ($18.1\pm0.6\%$), showing no evidence of the excess expected from AGB binary pollution. This is further supported by the absence of correlation between [N/Fe] and [Ce/Fe] and the lack of [C/Fe] enhancement. Crucially, we detect an anti-correlation between binary fraction and [Al/Fe], with strongly Al-enhanced stars ($[\mathrm{Al/Fe}] \gtrsim 0.5$) exhibiting a reduced binary fraction ($< 10\%$). This trend serves as a dynamical fingerprint of high-density environments, consistent with the efficient disruption of binaries via three-body interactions in GC cores. Our results do not support binary mass transfer as the dominant formation channel for N-rich field stars; they are predominantly GC escapees that retain the dynamical memory of their dense birth sites.

astro-ph.GA↗

arXiv2026

Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test

Recent semantic and generative-retrieval recommenders report substantial improvements over ID-only sequential baselines, but it remains unclear whether these gains arise from language-model reasoning, semantic-ID generation, end-to-end semantic architectures, stronger offline item representations, or complementary semantic and collaborative signals. We investigate this attribution ambiguity through LIME-Rec, a lightweight and auditable recovery test. LIME-Rec combines three independent experts: a SASRec sequential expert, an ItemCF co-occurrence expert, and a semantic expert based on frozen BAAI/bge-base-en-v1.5 item embeddings. Their full-catalog scores are normalized per user and combined through auditable score-level fusion followed by bounded history calibration. The fusion gate and calibration head are fitted on validation data only, require no serving-time language-model inference, and keep each expert contribution separately inspectable. On Amazon Beauty, Toys, and Sports, LIME-Rec achieves R@10 scores of 0.0996, 0.1105, and 0.0593, outperforming the strongest comparison baseline by 7.0%-12.0%. Three-expert fusion without history calibration consistently outperforms calibrated SASRec, showing that calibration alone does not explain the recovery. Randomly permuting item-text embeddings across item IDs reduces R@10 by 13.6%-17.5%, indicating that the gains depend on genuine item-text correspondence rather than additional representation capacity. These results suggest that lightweight recovery from offline item representations and transparent fusion should be ruled out before improvements are attributed to serving-time language modeling, semantic-ID generation, or heavier semantic machinery.

cs.IR↗

arXiv2026

Predicting Grasping Compliance in Robotic Hands through Analytical-Model-Informed Neural Networks

In robotic manipulation studies, grasping is often treated as a binary success or failure problem, usually defined by whether the object simply stays in the hand. For forceful tool use, however, this view is insufficient because grasp compliance becomes a critical factor governing how the hand and tool behave under load. Compliance arises from coupled kinematics, grasp configuration, passive mechanics, and contact conditions, producing nonlinear behavior in which deformation and interaction forces influence each other. Understanding this relationship is essential for predictive models of how a grasped tool and a compliant hand jointly respond to external loading. In underactuated hands, these effects are amplified: such designs offer low cost and adaptive grasping, but make compliance behavior more difficult to model and predict. Our goal is therefore to develop a predictive model for grasped tool behavior during forceful interactions. To address this challenge, we introduce an analytical model informed neural network (AMINN), a hybrid predictive model that combines an analytical mechanics layer with data driven learning to estimate grasp stability and in hand tool displacement under external loading. The model is evaluated on a three finger underactuated robotic hand and shows strong predictive capability with mechanically meaningful outputs across diverse loading conditions. Compared with a black box multilayer perceptron baseline, AMINN also achieves better energy based physical consistency. Beyond prediction accuracy alone, this framework advances physically interpretable learning for robotic manipulation and supports more reliable, safer, and more trustworthy autonomous tool use in safety critical settings during forceful interaction.

cs.RO↗

arXiv2026

Disentangling the Morphology of Palomar~5: Effects of the Bar, Spiral Arms, LMC, and Halo Flattening

The Palomar~5 (Pal~5) globular cluster and its tidal tails provide a sensitive probe of globular-cluster evolution in the time-dependent Milky Way potential. We study the past 3~Gyr evolution of Pal~5 using collisional direct $N$-body simulations with \texttt{PeTar}, adopting Galactic potential models that include spiral arms, the Galactic bar, halo flattening, the Large Magellanic Cloud (LMC), and bar deceleration. We find that halo shape strongly influences the projected stream track, reflecting Pal~5's sensitivity to Galactic force-field flattening. The LMC causes only modest direct changes to the present-day projected stream morphology, but can alter the pericentric distance and hence the progenitor's mass evolution. The Galactic bar strongly affects stream length and debris redistribution along the tails, producing model-dependent density structures and leading--trailing asymmetries. Comparison with observations from Erkal et al. (2017) and Xiao et al. (2025) shows that no single model simultaneously reproduces all observed properties of Pal~5, including cluster evolution, stream length, track, width, and line-density profile. Although our simulations capture several global properties, the remaining discrepancies indicate that a more precise match likely requires better constraints on the initial properties of the Pal~5 progenitor and a more complex Galactic potential, including perturbations from small-scale perturbers such as dark matter subhalos and giant molecular clouds. Future work may combine self-consistent direct $N$-body simulations with particle-spray methods to investigate these discrepancies more efficiently.

astro-ph.GA↗

arXiv2026

CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training

Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents CLAP (Closed-Loop Agent Post-training), a closed-loop method that converts business data into structured SFT samples, decision-preference samples, holdout sets, risk diagnostics, and release-gate records. CLAP combines data validation, target/evidence normalization, reward/KL diagnosis, offline gates, and application-chain replay to decide whether an adapter is suitable for the target application chain. On five anonymized manufacturing-scenario batches, QLoRA-style LoRA-SFT yields modest average gains: overall score increases by 0.0098, pass rate by 0.0240, and evidence accuracy by 0.0280, while hallucination and wrong facts decrease. Yet only 3 of 5 batches improve, some batches regress, and GRPO exposes high KL risks. Application-chain replay further shows that RAG is necessary for factual extraction; under the same 3B backbone and 100 replay cases, an application-RAG-oriented LoRA-SFT adapter improves value, core fields, and answer-evidence doc/page matching over base+RAG, but increases latency. These results support managing domain-agent post-training through an integrated data-training-evaluation-release loop rather than relying on training completion or a single offline score.

cs.AI↗

arXiv2026

Evolution of Compact Stellar Systems in Ultralight Dark Matter Halos: Dependence on Stellar and Dark Matter Parameters

Compact stellar systems are often used to place stringent constraints on the particle mass of ultralight dark matter (ULDM), as the heating effect induced by wave interference can drive system expansion, potentially bringing them into tension with observations. In a recent study, we pointed out that internal two-body relaxation in these stellar systems may have a significant impact on their evolution in ULDM halos, an effect overlooked in previous studies. Here, we further investigate the influence of stellar metallicity, the Milky Way's tidal field, and the ULDM particle mass on the long-term fate of compact stellar populations. We find that metal-richer systems are generally more resistant to disruption. The tidal field of the Milky Way, by altering the orbital motion of the stellar systems within host ULDM halos, can significantly affect their stability. Furthermore, we find in our simulations that the heating effect becomes stronger with increasing ULDM particle mass when the system size is much smaller than the ULDM de Broglie wavelength $R_{\rm h} \ll \lambda_{\rm dB} $, in contrast to the $\lambda_{\rm dB}\lesssim R_{\rm h}$ case. These results highlight the complexity of the evolution of compact stellar systems in ULDM halos, and suggest that existing constraints derived from the systems, such as ultrafaint dwarf galaxies, may require careful revision.

astro-ph.GA↗

arXiv2026

A pulsar escaping an ancient open cluster via tidal stripping

Open clusters are the primary birthplaces of stars in the Milky Way disk, yet their neutron star progeny are rarely found within them, presumably due to supernova-induced kicks that eject them at birth. Here we report the arcsec-level localization of the pulsar PSR J1921+3745 to the tidal tail of NGC 6791, one of the oldest and most massive open clusters. Our N-body simulation shows that more than 95% of neutron stars formed in such clusters have been ejected. This pulsar's location in the tidal tail indicates it was retained for billions of years before being stripped by Galactic tides. This long-term retention requires low natal kicks, consistent with formation via electron-capture supernova. Our findings capture a rare snapshot of a neutron star transitioning into the Galactic field, identifying tidal stripping of ancient clusters as a verified source of the Galactic neutron star population.

astro-ph.HE↗

arXiv2026

Rapid intermediate-mass black hole formation via runaway mergers of black holes

Observations indicate that supermassive black holes (SMBHs) in high-redshift galaxies formed on timescales far shorter than classical growth models allow. One hypothesis suggests intermediate-mass black hole (IMBH) seeds as an efficient growth channel. Using N-body simulations, we demonstrate that in dense stellar-mass black hole (BH) clusters ($\ge 5\times10^9 M_{\odot}/{\rm pc}^3$), runaway gravitational-wave binary BH (BBH) mergers can produce a $\sim 10^3 M_\odot$ IMBH within 10 Myr from the formation of the BH subsystem. This scenario is simple and avoids large uncertainties regarding stellar mergers and evolution in the IMBH formation via very massive stars channel. We find that the runaway GW-merger mechanism relies on hard BBH formation through a chain of exchanged soft BBHs with accumulated hardening, which is far more efficient than three-body scattering. We analyze how IMBH formation depends on cluster density, total mass, initial mass function, and stellar halo potential. We find that due to cluster expansion, the systems forming IMBHs have densities consistent with present-day nuclear star clusters, such as those in the Milky Way and M33. Furthermore, we show that IMBH spin remains low due to repeated mergers, and we estimate the rate of GW190521 and GW231123-like events within the first 100 Myr to be $2.27-247.52$ and $3.23-63.63 $ per Gyr per cluster.

astro-ph.GA↗

arXiv2026

The complex kinematics of the young stars orbiting the supermassive black hole in the Galactic center can be explained by the presence of an intermediate mass companion of Sgr A$^\star$

The sub-parsec proximity around the Sgr A$^\star$ supermassive black hole (SMBH) in the center of the Milky Way contains an inner cluster of eccentric S-stars with randomly oriented orbits, a midway-disk of clockwise-rotating stars (CWSs), and a surrounding population of off-the-disk stars (ODSs). Despite their diverse kinematic properties, all three-populations appear to be massive (WR/O/B types) and have similarly limited life span $\tau_\star \sim 6-15$ Myr. Several scenarios, including star formation induced by SMBH's close encounters with one or more gas clouds as well as impulsive close scattering by a putative intermediate-mass companion (IMC) of Sgr A$^\star$ possible an intermediate-mass black hole (IMBH), have been proposed to explain piecemeal for the origin and dynamical evolution of S-stars, CWSs, ODSs, as well as hyper-velocity stars in the Galaxy. But, their coexistence and the origin of a recently discovered zone of avoidance in S-stars' eccentricity-peri-centric-distance distribution remain enigmatic. Here, we construct a unified model to comprehensively take into account these stars' interaction with each other, their single natal disk, and an independent IMC. We show their disparate present-day orbits would only be concurrently attainable, within their multi-Myr age, under the combined influence of IMC's secular perturbation and these stars' resonant relaxation in a depleting gaseous-disk environment.

astro-ph.GA↗

arXiv2026

Evolution of the stellar mass function in open clusters from a universal and unsegregated initial state

The stellar mass function (MF) and its spatial variation (mass segregation) within star clusters encode signatures of early formation physics and subsequent secular evolution. Yet, a coherent evolutionary picture remains elusive due to conflicting reports regarding the universality of the initial mass function (IMF) and the prevalence of primordial mass segregation. These discrepancies often arise from unresolved binaries, field contamination, and completeness bias. Here, we resolve these issues by analyzing 163 high-fidelity open clusters via a Bayesian forward-modeling framework. We reveal a remarkably simple initial state: young clusters ($\lesssim 300$ Myr) share a mean IMF slope of $-2.29$ in the mass range $M \geq 0.5 M_\odot$, consistent with the Salpeter slope but with an intrinsic scatter of 0.17, and exhibit minimal mass segregation at the onset of gas-free evolution ($\sim$10 Myr). This broadly universal "zero-point" for secular evolution disfavors star-forming scenarios that predict strong primordial segregation or significant IMF variations, and suggests that chaotic cluster assembly and gas expulsion efficiently erase any mild primordial inhomogeneities. By tracing the evolutionary sequence from $10^7$ to $10^{9.8}$ yr, we demonstrate that dynamical processing operates on distinct timescales: mass segregation proceeds rapidly via internal relaxation, whereas global MF flattening due to tidal evaporation becomes dominant only after $\sim$600 Myr. These findings impose robust observational constraints on the physics of star formation and early feedback and establish an empirical baseline for modeling secular stellar dynamics.

astro-ph.GA↗

arXiv2026

Multiple populations detection with the Chinese Space Station Survey Telescope main survey camera

Multiple stellar populations (MPs), characterized by star-to-star light-element abundance variations, are ubiquitous in globular clusters (GCs). Spectroscopy directly reveals these anomalies, while photometric studies, especially with the \textit{Hubble Space Telescope} (\textit{HST}), have been essential for tracing MP sequences in colour-magnitude diagrams (CMDs). However, the limited field of view of \textit{HST} confines most studies to cluster centres. The upcoming \textit{Chinese Space Station Survey Telescope} (CSST), with its wide field of view and UV-optical coverage, will enable systematic MP studies over entire clusters. We assess the capability of the CSST wide-field camera to detect and characterize MPs in GCs using realistic simulations. Synthetic stellar population models with different helium abundances ($\Delta Y$) and CNO variations were used to simulate CSST observations of GCs at distances of 9.6 and 20~kpc under different exposure times. MP detectability was evaluated using CMDs in seven CSST bands and UV-optical pseudo-colour diagrams. For a GC at 9.6~kpc, the $NUV-u$ colour is highly sensitive to $\Delta Y$ and CNO variations, with separations of $\Delta(NUV-u)\approx0.16$ mag for red giants and up to 0.44 mag for dwarfs. MPs can be resolved when the total UV exposure exceeds $\sim1000$~s and the optical exposure exceeds $\sim300$~s. At 20~kpc, encompassing $\sim80\%$ of Galactic GCs, CSST still retains strong diagnostic power, resolving populations with $\Delta Y\geq0.06$ and $\delta[\mathrm{N/Fe}]\geq0.64$, and separating MPs down to $i\sim19.5$ mag in clusters with large chemical spreads. The $NUV$-$u$-$g$ combination provides diagnostic performance comparable to the \textit{HST} F275W--F336W--F438W system. CSST will enable homogeneous MP surveys across the full spatial extent of star clusters in the Milky Way and nearby galaxies.

astro-ph.SR↗

arXiv2026

The formation of the C-19 progenitor: a primordial cluster heated by gas expulsion

The extremely metal-poor nature of the C-19 stream indicates that its progenitor was a primordial stellar system born in the very early Universe. Current observations show that it has a small metallicity dispersion (0.18 at the 95% confidence level), which is the signature of a globular cluster origin, while at the same time displaying an unusually large velocity dispersion ($\sim10$ km/s) typical of dwarf galaxies. To reconcile this conflicting observational evidence, previous simulations have focused on potential interactions with dark matter subhalos, which can efficiently make a cluster stream dynamically hot. In this work, we explore internal dynamical processes in star cluster formation, focusing on initial conditions shaped by gas expulsion and a top-heavy initial mass function. We find that the large observed velocity dispersion and broad stream morphology can be reproduced by a cluster that underwent severe gas expulsion and expansion during its birth phase, which is potentially a typical formation scenario of extremely metal-poor star clusters. A top-heavy IMF and binaries can also increase the velocity dispersion. The formation of C-19 may involve a combination of these effects.

astro-ph.GA↗

arXiv2026

Optimal network structure for collective performance with strategic information sharing

Information sharing between individuals is crucial to improve performance in collective tasks. However, in a competitive world, individuals may be reluctant to share information with the others, and it is still unclear how the presence of strategic behaviors affects the collective performance of a group. In this study, we introduce an evolutionary game modeling the dynamics of individual behaviors in a collective estimation task. The individuals are organized in a network and have to guess the distribution of ball colors in a box. Each of them samples a given number of balls and can strategically decide whether to share or not this information with its neighbors. We develop a framework that allows to investigate analytically how the collective performance depends on the network structure. We find that the optimal network results from a trade-off between the sharing rate and the way the information is integrated in the network. We further reveal that there exists an intermediate average degree for each type of network maximizing the collective performance. In addition to the uniform case, we consider the case of non-homogeneous allocations of the number of individual samples, showing that the largest collective performance is obtained when the number of ball extracted by an individual is inversely proportional to its degree.

physics.soc-ph↗