arXiv subjects

Bin Guo

Publications and source records attributed to Bin Guo.

At least 19 recordsLinked to original sources

arXiv2026

Initialization-controlled fault sensitivity and force-balance plateaus in noisy variational quantum optimization

We study deep variational circuits for a periodic Heisenberg chain under local gate depolarization, at depths where noiseless training is coherently exact. Full-width initializations reach low coherent error faster than circuits with confined transverse generators, but incur a larger noise shift and need an order of magnitude more updates for noisy convergence. In many-body-localization-inspired initialization, bounding the initial transverse generator amplitudes keeps the operator support localized and suffices for low-fragility optimization, so longitudinal disorder and Floquet layer repetition are unnecessary. The fault-erasure fraction, the mean fraction of coherent energy removed per fault, sets the effective visibility through a parameter-free exponential model. Full-width circuits start with delocalized operator support, and once coherent energy builds up, each fault erases nearly all of it. Initially, their noise-induced gradient almost uniformly attenuates the coherent gradient, so coherent descent proceeds at the noiseless rate. They then stall on a high-noise force-balance plateau, where finite coherent and noise-induced gradients cancel to first order. At late times all initializations reach the same fault-erasure fraction and noisy-error band, but operator support contracts more slowly, at a rate set by the plateau residence time.

quant-ph↗

arXiv2026

PolicyMediator: Multi-Regional Policy Learning through Mediation Agents with Evolving Skills in OPC Ecosystems

One-person companies (OPCs) are emerging enterprises operated by individual entrepreneurs through digital platforms and LLM-based AI agents. These highly skilled entrepreneurs are characterized by being digital nomads, with limited reliance on local ecosystems. Governments in China use distinct regional support policies to attract mobile OPCs, turning local support into a cross-regional coordination problem. OPCs compare regions to maximize their own utility, but imperfect policy perceptions may cause them to reject otherwise suitable locations. Existing policy-learning methods optimize resources under fixed perceptions, while consensus-formation methods aggregate views without feeding cognitive changes back into policy learning. This disconnect produces mismatched location choices and inefficient regional policy adjustment. We introduce PolicyMediator, a cognition-constrained leader-multi-follower Stackelberg framework with bilateral mediation. The mediator explains verified policies to OPCs to build awareness and trust, and reports their needs and unresolved concerns to governments for policy learning. Its self-evolving skills retain, revise, or prune strategies using verification, allocation, capacity, and objective trajectories rather than mediation success alone. Across six-region simulations in China with five OPC types, five scenarios, and three OPC-simulator backbones, PolicyMediator ranks first in all 15 OPC-backbone--scenario combinations. Over the eight-baseline mean, it improves the aggregate government objective, combining economic performance, employment benefit, and policy-access equality, by 42.4% and average OPC utility by 40.9%.

cs.MA↗

arXiv2026

Oka Geometry of Complex Six-Spheres

Alpöge constructed a complex structure on $S^6$ through a torus fibration with exactly three special fibres. We prove that every member of the associated admissible period family is Oka and that the family projection over each relatively compact parameter disc is an Oka map. The Oka property survives the deletion or simultaneous blow-up of any finite set of distinct points in any member. Blowing up a single point therefore produces an Oka complex manifold diffeomorphic, but not biholomorphic, to $\mathbb{P}^3$. The proofs combine explicit cyclic coverings with analytic extension and the invariance of the Oka property under holomorphic coverings.

math.CV↗

arXiv2026

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase exposure risk. As a result, AUVs primarily rely on passive observation, which yields incomplete local perception and limited task efficiency. Although underwater acoustic communications can mitigate this limitation through information sharing, they are simultaneously constrained by long delays, severe interference, low reliability, and the risk of covert exposure. Existing communications-oriented multi-agent reinforcement learning (MARL) studies often model communication as an ideal information flow, whereas traditional communication optimization primarily focuses on link-level performance. However, both are insufficient to characterize the actual contribution of perceptual information to cooperative tasks under realistic covert physical communication conditions. This paper proposes a Sensed-information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework that leverages practical information to characterize information utility for cooperative tasks and learns distributed cooperative policies under realistic communication and covert constraints. Through a case study of covert multi-AUV cooperative localization and tracking, the potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.

cs.LG↗

arXiv2026

A Local Approximation Characterization of Oka Manifolds

We introduce the local approximation propert (LAP) and prove that it characterizes Oka manifolds. We prove that LAP yields convex approximation and, in an $n$-dimensional target, propagates across sets of zero $(2n-1)$-dimensional Hausdorff measure. Consequently, strong dominability characterizes Oka manifolds, convex approximation in the target dimension suffices, the Oka property is local in the Euclidean topology, and it descends under surjective holomorphic maps. Local families of entire curves give geometric applications: smooth projective rationally connected manifolds are Oka, and deleting an algebraic subset of codimension at least two from any such manifold preserves the Oka property. Structure theorems further show that compact Kähler manifolds with nonnegative holomorphic sectional curvature or nef tangent bundle are Oka. Finally, the complement of a smooth degree-$d$ hypersurface in $\mathbb{P}^n$ is Oka exactly when $d\leq n+1$.

math.CV↗

arXiv2026

Oka manifolds are ubiquitous

We prove that a holomorphic family of connected complex Lie groups has Oka total space if and only if its base is Oka. Extension presentations and generating sections give Oka results for stable and parabolic bundle moduli and for holomorphic mapping and embedding spaces. Every nonempty degree component of maps from a compact Oka curve to projective space is Oka; rational-function spaces are also holomorphically elliptic. For a vector bundle on a smooth projective curve, the Oka property of its nested Quot scheme is equivalent to that of the symmetric powers determined by the length increments. Complete Hamiltonian flows give holomorphic ellipticity of irreducible $\mathrm{SL}_2$- and $\mathrm{GL}_2$-character and representation loci, for closed surfaces of genus at least two and for surfaces with fixed regular semisimple boundary classes. The Riemann--Hilbert correspondence yields Oka moduli spaces of nonresonant parabolic connections for arbitrary parabolic weights, and holomorphic ellipticity when reducible monodromy is absent. A proper holomorphic fibration over a Riemann surface, with a section and connected complex tori as smooth general fibres, has Oka total space exactly when its base is Oka. For a Schoen fibre product $X$ of rational elliptic surfaces with disjoint singular values, we construct an entire local biholomorphism $\mathbb C^3\to X$ whose image contains a dense Zariski-open subset. Further applications treat Hilbert schemes, compactified relative Jacobians, and logarithmic complements.

math.CV↗

arXiv2026

Oka complements in algebraic tori and their logarithmic covers

Suppose $n\ge2$ and $K\subset\C^n$ is compact and polynomially convex. Write $q(z)=(e^{-iz_1},\ldots,e^{-iz_n})$, $X_K=(\C^*)^n\setminus(K\cap(\C^*)^n)$, and $Y_K=\C^n\setminus q^{-1}(K)$. We prove that $X_K$ and its logarithmic covering $Y_K$ are Oka. More strongly, $Y_K$ admits normalized holomorphic families of entire embeddings through prescribed holomorphic sections. The proof combines polynomial pair Hamiltonian transport in multiplicative coordinates, compatible with the coordinate hyperplanes, with a fixed Fatou--Bieberbach model. Applications include Oka slab crosses over arbitrary nonempty connected planar domains, complements of periodic equal-radius Euclidean balls centered at products of planar lattices, and complements of sufficiently small closed metric balls in products of elliptic curves.

math.CV↗

arXiv2026

TideKV: Delayed KV Eviction via Layer-Wise Chunked Prefill for Memory-Efficient Long-Context LLM Inference

Long-context large language model (LLM) inference is important for modern intelligent applications but remains constrained by peak memory during prompt prefill. Existing KV cache compression reduces decoding memory but typically requires full-prompt prefill to preserve global context, retaining a high construction peak. Online chunk-level eviction lowers memory earlier but selects before each layer has observed the complete prompt, potentially degrading cache construction and decoding. We present TideKV, an on-GPU cache-construction architecture using layer-major delayed eviction. TideKV processes all prompt chunks within each Transformer layer, retains full-prompt KV only for the active layer, and evicts at layer end. This schedule preserves full-prompt-aware selection while reducing activation and temporary memory. Experiments across multiple computing platforms show that TideKV preserves the inference quality and final cache size of the evaluated compression methods. At 64K on A100, it reduces the construction-time transient footprint by 83.89%. On RTX 4090, it increases time to first token by only 1.06% at 64K and enables 128K prefill. TideKV also completes 128K on Jetson, whereas matched post-prefill compression runs out of memory on both resource-constrained platforms. These results show that delayed layer-end eviction substantially expands feasible context lengths without sacrificing cache utility.

cs.LG↗

arXiv2026

Federated k-Core Decomposition: A Secure Distributed Approach

As one of the most well-studied cohesive subgraph models, the $k$-core is widely used to find graph nodes that are ``central'' or ``important'' in many applications, such as biological networks, social networks, ecological networks, and financial networks. For Decentralized Online Social Networks (DOSNs), where each vertex is a client as a single computing unit, distributed k-core decomposition algorithms have already been proposed. However, current distributed approaches fail to adequately protect privacy and security. In today's data-driven world, data privacy and security have attracted more and more attention, e.g., DOSNs are proposed to protect privacy by storing user information locally without using a single centralized server. In this work, we are the first to propose the secure version of the distributed $k$-core decomposition.

cs.DC↗

arXiv2026

mmPencil: Toward Writing-Style-Independent In-Air Handwriting Recognition via mmWave Radar and Large Vision-Language Model

In-air handwriting recognition based on radio frequency (RF) signals offers a promising solution for human-computer interaction, yet existing methods are often limited by predefined writing planes, constrained writing styles, and small recognition vocabularies. To address these limitations, we propose m^2VLMs, a modality mapping architecture that bridges millimeter-wave (mmWave) radar sensing and large vision-language models (VLMs), and develop mmPencil, a 3D in-air handwriting word recognition system. mmPencil reconstructs 3D handwriting trajectories from mmWave signals using a multi-stage spatial trajectory reconstruction algorithm and maps them into visual representations through a spatial-to-visual mapping algorithm, enabling VLMs to recognize handwriting while reducing sensitivity to writing styles and environmental factors. We collect and release a 3D mmWave handwriting dataset containing 7,664 samples of 200 words from 12 users across 22 writing scenarios. Extensive experiments demonstrate that mmPencil achieves robust word recognition under variations in writing position, range, angle, speed, size, direction, and user movement. It achieves 97.60% accuracy on 200 word classes for seen users and 92.50% zero-shot accuracy on 50 word classes for unseen users, outperforming state-of-the-art baselines.

cs.HC↗

arXiv2026

Oka realizations, fundamental groups, and holomorphic cofibrations

The homotopy types of Oka manifolds are exactly those of countable finite-dimensional CW complexes. We construct realizations as open subsets of complex Euclidean spaces with explicit dimension bounds. Other realizations have good open covers by sets biholomorphic to $\C^N$, with prescribed finite nerves, or are unions of Fatou--Bieberbach domains whose nonempty finite intersections are again Fatou--Bieberbach domains. Every nonempty Stein manifold admits a proper holomorphic embedding into an Oka open subset of some $\C^N$, and this embedding is a homotopy equivalence. We obtain holomorphic representatives of ordinary and generalized cohomology classes, and realize every countable group as the fundamental group of an Oka domain in $\C^5$. The fundamental groups of algebraically elliptic manifolds are precisely the finite groups, and every virtually abelian Kähler group is the fundamental group of a projective Oka manifold. Finally, we classify holomorphic cofibrations in Lárusson's intermediate model structures and establish factorizations over aspherical targets into proper holomorphic embeddings and holomorphic fibre bundles with Oka fibre.

math.CV↗

arXiv2026

Distance Structure of Insertion Kernels and Local Port Response in Non-Reciprocal Su--Schrieffer--Heeger Chains

Non-reciprocal lattices offer a flexible setting for directional wave transport, raising the question of how enhanced internal scattering translates into transmission between fixed external ports. We address this question for non-reciprocal Su--Schrieffer--Heeger (SSH) chains with staggered gain and loss by relating the reference-dependent insertion kernel $K_p=VG_pV$ to the coherent transmission intensity $T$. On a fixed finite graph, a strong-insertion expansion gives a contribution proportional to $γ^{1-d}$, whose coefficient is the signed sum of shortest directed paths of length $d$. When this sum is nonzero, it sets the leading behavior independently of fixed diagonal losses; when it cancels, those losses can enter the first surviving higher-order term. For a fixed cross-sublattice endpoint channel, transmission has a strictly negative slope at every nonzero differentiable stationary point of the kernel magnitude. In the weak-insertion regime, the reference-system coefficient $χ=-\mathrm{Re}\,\mathrm{Tr}[(G_{0p}Z)^2]$ predicts the initial transmission change for symmetric SSH ports, and $χ>0$ together with a strictly stable baseline guarantees local stable enhancement. Finite-chain calculations and driven dynamics connect these results to observable response and distinguish asymptotic convergence from transient settling. A strictly dissipative short-chain example exhibits local enhancement without net power gain. Together, these results separate graph-path structure, coherent port response, and dynamical establishment, providing channel-specific criteria for interpreting internal scattering.

quant-ph↗

arXiv2026

Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

Large language model (LLM) agents are highly vulnerable to memory-poisoning attacks. Existing defenses primarily rely on external modules for either static memory isolation or continuous online auditing, resulting in low memory efficiency and high computational overhead. To address these limitations, inspired by neuroscientific mechanisms that weak reactivation can destabilize memories and induce decay in the absence of reinforcement, we propose an agent-intrinsic training-free memory repair framework, DREAM (Dynamic Reactivation for Engram Attenuation in Memory), that enables selective functional forgetting of poisoned memories. Specifically, DREAM implements a three-stage pipeline: perturbation-induced implicit reactivation to reveal the structural fragility of malicious memories, adaptive anomaly diagnosis based on multi-dimensional activation patterns to detect topological anomalies, and a dynamic memory repair module that selectively suppresses harmful memories while reinforcing benign ones. Extensive experiments across diverse attackers and real-world tasks demonstrate that DREAM reduces attack success rates by over 95\% against backdoor poisoning while preserving strong benign utility. DREAM also achieves competitive robustness against injection attacks. In terms of runtime and token consumption, DREAM achieves up to a 2.13$\times$ speedup and a 37.7\% reduction in token consumption compared with A-MemGuard. Furthermore, DREAM also achieves a task success rate of 92.96\% on poisoned multi-agent systems.

cs.AI↗

arXiv2026

Fundamental groups of Calabi-Yau spaces

We study the fundamental groups of a normal projective Calabi-Yau variety $X$ with klt singularities and of its regular locus $X_{reg}$. We prove that both groups are almost abelian, and that their ranks are determined by the irregularities of finite etale and quasi-etale covers, respectively. Our approach is differential geometric, and relies on recent advances in the geometric theory of complex Monge-Ampere equations.

math.DG↗

arXiv2026

PB-GRPO: Learning Socially Adaptive LLM Agents from Persona-Driven Simulation with Preference-Batched GRPO

Building LLMs that behave well socially, not merely correctly, requires Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent agent must infer users' unstated goals, respect their preferences, and adapt as the conversation unfolds. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather than directly observable. To address these challenges, we build on a persona-driven social simulation environment (consisting of a persona library, LLM-based user simulators, and a user-satisfaction scoring system ranging from [0, 1]), to introduce preference-batched GRPO (PB-GRPO), a post-training algorithm that learns socially adaptive policies from conversation-level feedback. Compared to vanilla GRPO, PB-GRPO computes advantages using a normalization estimated across a bucket of users with similar preferences, stabilizing training across a diverse social population. Empirical evidence shows that PB-GRPO improves models' social behavior over strong reinforcement learning baselines in our simulated environment.

cs.CL↗

arXiv2026

Analytic Construction of Rational Curves on Fano Manifolds

Inspired by constructions of entire curves in Oka geometry, we construct rational curves on complex Fano manifolds analytically, by alternately deforming a holomorphic disk to reduce its area and enlarging its source. Positive Ricci curvature yields an area-decreasing deformation, while an affine-lift perturbation enlarges the source at a controlled area cost. The two operations change the area and the weighted derivative by amounts we estimate explicitly, and analytic compactness passes from the resulting disks to a holomorphic sphere. The main result produces a sphere that meets a prescribed compact fiber without being contained in it, with an explicit area bound.

math.CV↗

arXiv2026

Periodic transport and Oka complements in $\C^n$

For every $n\geq2$, we prove that the complement in $\C^n$ of a periodic closed set with compact holomorphically convex quotient in $(\C^*)^n$ is Oka. The proof combines periodic divergence-free transport with Fatou--Bieberbach basins. Applications include complements of convex tubes $\R^n+\ii B$, products of closed annuli, and, more generally, products of planar compact sets whose complements have at most one bounded component. In particular, $\C^2\setminus\R^2$ is Oka.

math.CV↗

arXiv2026

Stability and gradient estimates for fully nonlinear elliptic equations on hermitian manifolds

The known proofs of the gradient estimate for complex Hessian equations rely on the second order estimate of Hou-Ma-Wu and on a Liouville theorem of Dinew-Kolodziej. In this paper, we give a direct proof of the gradient estimate for concave fully nonlinear elliptic equations on compact Hermitian manifolds, which uses neither the Hou-Ma-Wu estimate nor the Liouville theorem. Instead, the proof combines a stability estimate with a comparison argument against nearby smooth admissible functions. We also give an independent proof of the complex Hessian estimate on compact Kähler manifolds, based on Dirichlet Green's functions, under additional assumptions on the operator.

math.AP↗