arXiv subjects

Bin Chen

Publications and source records attributed to Bin Chen.

At least 91 records · Page 5Linked to original sources

arXiv2026

Hua-Chen New Theory of Economic Optimization

Between 1957-1985, Chinese mathematician Loo-Keng Hua pioneered economic optimization theory through three key contributions: establishing economic stability's fundamental theorem, proving the uniqueness of equilibrium solutions in economic systems, and developing a consumption-integrated model 50 days before his death. Since 1988, Mu-Fa Chen has been working on Hua's theory. He introduced stochastics, namely Markov chains, to economic optimization theory. He updated and developed Hua's model and came up with a new model (Chen's model) which has become the starting point of a new economic optimization theory. Chen's theory can be applied to economic stability test, bankruptcy prediction, product ranking and classification, economic prediction and adjustment, economic structure optimization. Chen's theory can also provide efficient algorithms that are programmable and intelligent. {Stochastics} is the cornerstone of Chen's theory. There is no overlap between Chen's theory, and the existing mathematical economy theory and the economics developments that were awarded Nobel Prizes in Economics between 1969 and 2024. The distinguished features of Chen's theory from the existing theories are quantitative, calculable, predictable, optimizable, programmable and can be intelligent. This survey provides a theoretical overview of the newly published monograph \cite{5rw24}. Specifically, the invariant of the economic structure matrix, also known as the Chen's invariant, was first published in this survey.

econ.TH↗

arXiv2026

Exact Relevant Stress-Tensor Flows and a Causality No-Go in Self-Dual Electrodynamics

Can a classically relevant stress-tensor deformation be exactly solvable, duality preserving, and physically causal? We construct an exact power-law family of nonlinear electrodynamics preserving electromagnetic duality, together with a parallel two-dimensional Lax-integrable realization. Its auxiliary geometry yields the full characteristic-cone phase diagram and a universal finite-energy fold. For the Maxwell seed, every nonzero relevant branch is acausal, whereas every causal branch is caustic-free; undeformed Maxwell theory is the only causal point in the relevant regime.

hep-th↗

arXiv2026

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However, this paradigm introduces a critical vulnerability: malicious content injected into memory can be persistently recalled and repeatedly influence agent behavior. In this work, we identify and systematically study multimodal memory poisoning, an overlooked yet practical attack surface in web-agent systems. We propose MemVenom, a unified black-box attack framework that poisons graph-structured external memory with coordinated text-image evidence. Our method consists of a two-stage design: (1) a trigger-conditioned retrieval attack that ensures high-probability recall of malicious memory, and (2) a post-retrieval attack induction that leverages adversarial perturbations and stealthy OCR injection to override the original user objective. Unlike prior attacks that operate on prompts or text-only memory, our approach enables persistent, reusable, and goal-agnostic attacks without modifying model parameters or re-optimizing malicious tasks. Experiments across multiple web-agent frameworks and vision-language models demonstrate that MemVenom achieves strong end-to-end attack success with minimal impact on benign performance, reaching up to 99.15% on GPT-5-family web agents, while transferring effectively across architectures and model scales.

cs.CR↗

arXiv2026

Maximum Coverage Chase Decoder for Optical Interconnects

We propose a low-complexity Chase decoder for optical interconnects that formulates test pattern selection as a generalized maximum coverage problem. For concatenated RS-BCH and oFEC codes, our decoder achieves the standard Chase decoding performance with 25% and 61.3% fewer test patterns, respectively.

cs.IT↗

arXiv2026

Summary of the First Year of the Space Weather Around Young Suns Program: 900 Hours of Low-frequency Radio and Optical Data Dedicated to Young, Solar-type Stars

The Space Weather Around Young Suns (SWAYS) program was introduced in \citet{Davis2025} as a multi-wavelength monitoring program for studying the activity and particle environments of nearby, young, solar-type stars. The SWAYS program currently includes the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) operating between 13--87\,MHz to search for stellar equivalents of solar type~II and III bursts, which are associated with bulk plasma motion in the corona and interplanetary medium. These observations are accompanied by simultaneous photometric data from the high-precision, optical instrument Flarescope to identify associated flare events. These two instruments have collectively acquired nearly 900\,hr of data with $\approx70\%$ overlap between November 2023--June 2024, dedicated to six stars. Here, we present the results of this first season of the SWAYS observing campaign, which include a superflare from the star EK~Draconis with no accompanying low-frequency particle-flux signal. The novelty of the coordination at these specific parts of the spectrum allow us to uniquely evaluate the conditions that may have inhibited a radio detection. We find that the exceptionally hot, dense coronae of incredibly active stars may not be conducive to the development of the instabilities required for type~II and III bursts, or else inspire new expectations for when we should expect to observe a signal relative to the time of the flare. This may represent the plasma-density complement to the magnetospheric limitations to observing space-weather signatures at low frequencies.

astro-ph.SR↗

arXiv2026

The Lq-Minkowski problem of anisotropic p-torsional rigidity

In this paper, the $L_q$-Minkowski problem of anisotropic $p$-torsional rigidity is considered. The existence of the solution of the $L_q$-Minkowski problem of anisotropic $p$-torsional rigidity with $0<q<1$ and $1<q\neq \frac{p}{p-1}+n$ is given.

math.AP↗

arXiv2026

Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization

With the growing concerns over copyright infringement in diffusion-based customization, adversarial attacks have emerged as a prominent defense strategy to prevent malicious content forgery in personalized image generation. However, current defenses typically introduce persistent perturbations in the latent space of Latent Diffusion Models (LDMs), which remain susceptible to adaptive bypasses by adversaries. In this paper, we introduce Two-Stage Latent Feature Optimization (TS-LFO), an efficient and effective copyright-stealing attack against protected diffusion-based customization. We begin by observing that existing defenses primarily disrupt the mapping between input images and their latent representations, thereby degrading the model's ability to produce personalized outputs. To counteract this, TS-LFO restores the broken mapping through a two-stage optimization process. In the Latent Denoising Stage, we enhance semantic consistency between latent codes and input images by jointly minimizing a Latent-Image Alignment Loss and a Latent Diffusion Loss with timestep-dependent weights, effectively suppressing the high-frequency noise introduced by defenses. In the Latent Reconstruction Stage, we recover low-frequency semantic information using pixel-level constraints to refine the latent features. Extensive experiments show that TS-LFO consistently bypasses state-of-the-art (SOTA) copyright defenses and outperforms SOTA copyright attacks such as DiffPure, GrIDPure and IMPRESS across diverse settings.

cs.CR↗

arXiv2026

All-Sky Ultra-Narrowband Spectral Imaging with the OVRO-LWA: Technosignature Constraints and Axion-Like Particle Prospects

We present an imaging-domain search for technosignatures at decametric wavelengths with the OVRO-LWA, targeting ultra-narrowband continuous-wave signals between 50 and 86 MHz. We implement an offline GPU pipeline that processes raw voltage data with upchannelization to approximately 10 Hz frequency resolution, producing all-sky images for each fine channel and totaling more than 3 x 10^6 images for a single 30 s epoch. Candidate selection is performed using multi-kernel matched filtering across frequency, empirical noise standardization, and false-discovery-rate control. After applying quality cuts that remove extended sources, corrupted images, and obvious RFI, three narrowband candidates with signal-to-noise ratios above 10 sigma were selected for detailed analysis. By re-imaging these candidates with finer temporal and spectral resolution, we resolved their structure and found them to be inconsistent with compact celestial narrowband emitters. Consequently, we report no detection of extraterrestrial technosignatures. The representative sensitivity of the search is ~100 Jy per channel across the entire visible hemisphere. For an unresolved emitter, this corresponds to 10 sigma equivalent isotropic radiated power (EIRP) limits of about 10^14 W at a distance of 10 pc and 10^18 W at 1 kpc. The wide field of view and ultra-fine spectral resolution of this approach enable simultaneous probing of technosignature signals from millions of stellar systems. This method further establishes a scalable framework for deeper integrations and stacked searches toward neutron-star targets relevant to axion-like particle (ALP) line conversion.

astro-ph.IM↗

arXiv2026

On the Nature of Candle-Flame-Shaped Solar Flares and Sub-Alfvénic Supra-Arcade Plasma Downflows

Certain solar flares exhibit a distinctive candle-flame or cusp-shaped feature above the bright flare arcade visible in extreme ultraviolet (EUV) and X-ray channels sensitive to high-temperature plasma. The presence of a cusp-like structure is generally regarded as a key piece of morphological evidence for magnetic reconnection to power explosive energy release in solar flares. In addition, downward-propagating plasma flows above the flare arcade have often been interpreted as outflows driven by magnetic reconnection. However, the relationship between the observed candle-flame-shaped morphology and the underlying magnetic field geometry for reconnection remains unclear. Likewise, the observed speed of the plasma downflows has been found to be too low compared to the upstream Alfvén speed predicted by reconnection theories. With the help of a recently developed three-dimensional magnetohydrodynamics (MHD) model, we examine the locations where magnetic topology changes from antiparallel to closed (Y-points) in a candle-flame-shaped flare, compare the observational emission features with synthetic EUV images generated from the model, and analyze their time evolutions. We also investigate the role of projection effects and line-of-sight integration in the measurements of plasma downflow speeds. Our analysis reveals that the Y-points do not necessarily coincide with the apparent cusp tip. Also, the apparent speeds of the supra-arcade downflows, as derived from tracks in the time-distance plots, underestimate the true Alfvén speeds in the reconnection inflow region by at least a factor of 2 up to an order of magnitude.

astro-ph.SR↗

arXiv2026

OctoT2I: A Self-Evolving Agentic Text-to-Image Router

The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns from single-model scaling. Agentic T2I methods emerged to alleviate this bottleneck by using multiple models. However, existing agentic T2I methods suffer from three key challenges: reliance on expensive handcrafted priors or human annotations, rigid single-path decision mechanisms, and a neglect of inference efficiency. To address these challenges, we introduce OctoT2I, a novel agentic framework that reformulates the T2I task as a joint optimization of generation quality and inference efficiency. OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory. This strategy is enabled by a knowledge base built from scratch by our novel Self-Evolving Mechanism. This mechanism, which requires no human supervision, first autonomously defines foundational Conceptual Dimensions (eg, style, color, count) and then intelligently explores their combinations via an iterative" Propose--Solve--Evaluate--Learn"(PSEL) loop. The PSEL loop efficiently discovers each tool's capability frontier, driving continuous improvement without external guidance. Extensive experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval while delivering a 90.3% inference speedup and a 56.6% energy-efficiency gain over the leading baseline (Flow-GRPO), striking an exceptional balance between performance and efficiency. Code and models will be made available.

cs.AI↗

arXiv2026

CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedback

Recent LLM search agents use reinforcement learning with verifiable rewards (RLVR) to learn search-augmented reasoning from outcome rewards. On hard problems, these agents rarely sample end-to-end successful rollouts, leaving outcome-only RLVR with few positive-reward trajectories. We argue that improving learning on such problems requires additional guidance during training, and RLVR already contains verifier-side information that can provide it. This information can identify errors or omissions in the agent's submitted answer and guide revision within the rollout. We propose a training-time mechanism called \textbf{Credit-Attenuated Privileged Feedback} (CAPF), which makes this verifier-side information available through a Privileged Feedback call during training. CAPF lets the policy revise zero-reward attempts into positive-reward repair trajectories and attenuates credit for the feedback call and earlier actions to accommodate deployment without this call. Empirical research demonstrates that CAPF improves Qwen3-4B's average exact-match score from 44.7% under outcome-only RLVR to 48.5% on seven open-domain QA benchmarks.

cs.AI↗

arXiv2026

Modeling the Thermal Low-Frequency Radio Sun with Ray Tracing

Incoherent radio emission at meter--decimeter wavelengths provides a key diagnostic of the coronal thermal plasma, but at frequencies below $\sim$\,1\,GHz coronal refraction can substantially bend ray paths and modify the apparent source size and brightness distribution. We develop a forward-modeling framework that combines refractive ray tracing through a global 3D coronal model with radiative transfer along each ray. The method tracks the ray-tube cross-sectional area $S(s)$ using a step-wise perturbation retracing approach and incorporates a geometric magnification term proportional to $d\ln S/ds$ to enforce flux conservation under focusing/defocusing. Thermal free--free emission and absorption are then computed with the \texttt{GRFF} radiative transfer code to produce synthetic radio maps over 40--800\,MHz. Applying the framework to Carrington rotation 2298, we find that including propagation effects allows the quiet-Sun background spectrum to be well reproduced. However, active region brightness is less accurately modeled, suggesting that additional physical factors should be considered in future work. These results establish a physics-based method for generating low-frequency quiet-Sun synthetic images suitable for quantitative comparison with interferometric observations and for assessing how propagation effects shape the observed morphology.

astro-ph.SR↗

arXiv2026

The Triple $T\bar{T}$-Like Flow in Quantum Field Theories: Irrelevant, Marginal, and Relevant

We introduce a one-parameter root-$T\bar T$-like flow, $ \partial_λ\mathcal{L}=\mathcal{R}_λ^{1/α}$, which organizes stress-tensor deformations into irrelevant, marginal, and relevant branches. Within duality-invariant electrodynamics in four dimensions, and equivalently within two-dimensional integrable sigma models, the flow admits a closed-form solution controlled by an auxiliary equation. The marginal point $α=1$ reproduces the root-$T\bar T$ / ModMax branch, while $α<1$ gives irrelevant deformations distinct from the standard Born-Infeld $T\bar T$ flow. For $α>1$, the same construction yields explicit relevant $T\bar T$-like Lagrangians. These results suggest that root-$T\bar T$ flows provide a common organizing principle for duality-invariant and integrable deformations.

hep-th↗

arXiv2026

Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization

Multimodal Large Reasoning Models introduce the reasoning paradigm, demonstrating strong capabilities on complex vision-language tasks. However, they still suffer from severe hallucinations. Existing training-based methods typically mitigate hallucinations through response-level direct preference optimization (DPO), where the Chain-of-Thought (CoT) and the final answer are treated as a monolithic output and optimized jointly. We reveal that this formulation performs similarly to answer-only optimization, suggesting that it primarily learns answer-level preference, while leaving CoT-level supervision insufficiently exploited. To address this issue, we explicitly formulate a CoT-oriented preference term and derive Reasoning-Conditioned Direct Preference Optimization (RC-DPO), which models the CoT as a condition for answer generation and contrasts the preference for the same preferred answer under different CoT conditions, promoting answer-supportive reasoning chain alignment. To further improve optimization, we introduce a reasoning-enhanced preference data generation strategy that employs Monte Carlo Tree Search to discover visually grounded and logically consistent CoTs as positive samples, and attention-guided CoT token pruning to construct negative ones. Extensive experiments across various models and benchmarks show that RC-DPO effectively mitigates hallucinations and improves the reliability of the multimodal reasoning process.

cs.AI↗

arXiv2026

FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models

Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the generation of harmful or undesirable content. Existing concept erasure methods are either inference-time interventions with limited effectiveness or rely on supervised fine-tuning (SFT), which requires precisely aligned data and struggles with scalability and multi-concept settings. In this paper, we propose \emph{FlowErase-RL}, the first GRPO-based framework for concept erasure in flow matching models. We reformulate concept erasure as a reward optimization problem and introduce a \textbf{dynamic dual-path reward mechanism} that jointly optimizes (i) a Concept Erasure (CE) reward to suppress target concepts and (ii) a Non-target Space (NS) reward to preserve generative fidelity. The two reward paths are adaptively balanced during training via a performance-driven switching strategy, enabling stable optimization without explicit supervision. Extensive experiments on nudity, object, and artistic style erasure demonstrate that our method achieves state-of-the-art erasure performance while maintaining strong image quality and semantic alignment. Moreover, it exhibits robust resistance to adversarial attacks and scales effectively to multi-concept scenarios. Our results establish a new paradigm for safe and controllable generation in flow matching models.

cs.CV↗

arXiv2026

CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception

High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods struggle with the trade-off between coverage and efficiency. Visual expert-assisted search is efficient but prone to blind spots when proposals fail, whereas scan-based search guarantees coverage at the cost of computational redundancy and semantic fragmentation. To address this dilemma, we introduce CVSearch, a training-free adaptive framework that dynamically schedules search strategies via an Assess-then-Search workflow. Specifically, CVSearch first invokes expert-assisted search when global information is insufficient, and only triggers a novel semantic-aware scanning mechanism upon failure. Distinct from rigid grid partitioning, this efficient scanning paradigm incorporates Semantic Guided Adaptive Patching to decompose images into semantically consistent regions, effectively mitigating object fragmentation. Furthermore, we devise a Dynamic Bottom-Up Search strategy driven by a Visual Complexity prior to enable efficient and precise iterative exploration of local details. Extensive experiments on HR benchmarks demonstrate that CVSearch achieves state-of-the-art accuracy while substantially improving search efficiency. Code is released at https://github.com/liliupeng28/ICML26-CVSearch.

cs.CV↗

arXiv2026

Molecular Lead Optimization via Agentic Tool Planning

Drug discovery is a lengthy and resource-intensive process composed of multiple stages. Among these stages, lead optimization plays a critical role in transforming early hit compounds into viable drug candidates. This stage requires improving ADMET-related properties through subtle structural refinement while preserving key molecular substructures responsible for binding affinity to disease targets. Recent advances in artificial intelligence have shown promise in accelerating various aspects of drug discovery; however, most existing approaches to lead optimization rely on one-step molecular optimization, which fail to account for the long-term consequences of sequential design decisions. To address this limitation, we propose TRACE, a trajectory-aware, LLM-reasoning agent for molecular lead optimization that formulates tool selection as a sequential decision-making problem over action trajectories. Given a lead molecule and an optimization objective, TRACE makes trajectory-aware decisions over molecular optimization tools, enabling forward-looking refinement under structural constraints. Experiments on multiple ADMET optimization tasks show that our agent achieves higher optimization success, larger property improvements, and higher validity, while preserving molecular similarity compared to baseline models.

cs.LG↗

arXiv2026

C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, they also introduce various risks, like phishing and academic dishonesty. Numerous research efforts have been dedicated to developing algorithms for detecting AI-generated text and constructing relevant datasets. However, in the domain of Chinese corpora, challenges remain, including limited model diversity and data homogeneity. To address these issues, we propose C-ReD: a comprehensive Chinese Real-prompt AI-generated Detection benchmark. Experiments demonstrate that C-ReD not only enables reliable in-domain detection but also supports strong generalization to unseen LLMs and external Chinese datasets-addressing critical gaps in model diversity, domain coverage, and prompt realism that have limited prior Chinese detection benchmarks. We release our resources at https://github.com/HeraldofLight/C-ReD.

cs.CL↗