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Ken Chen

Publications and source records attributed to Ken Chen.

At least 19 recordsLinked to original sources
arXiv2026

J-Comm: Jacobian-Guided Latent Communication between LLM Agents

Latent communication requires both an effective continuous reasoning process and an interface through which an external model can leverage its intermediate computation. We present J-Comm, a framework comprising two core components. First, a training-free Geometric Agreement for Latent Reasoning (GEAR) reweights token candidates over existing model embeddings, which monotonically optimizes geometric consensus among candidate tokens in embedding space at each step. Second, we introduce an H+J (Hidden-and-Jacobian) communication interface that gives the Receiver two complementary views of the Sender's reasoning. The H channel supplies continuous inputs derived from feedback distributions, while the J channel conveys intermediate-state cues in vocabulary-indexed Jacobian-lens coordinates. Intuitively, H provides inputs for the Receiver to reason from, while J offers additional guidance within its computation. The Receiver reconstructs these cues using its own dictionary and injects them into intermediate layers during prefill, enabling communication without sharing key-value caches or requiring identical model widths. In Qwen3-8B to Qwen3-4B communication, J-Comm achieves the highest mean score across seven benchmarks, including 94.80% on MATH500, 54.55% on GPQA-Diamond, 73.33% on AIME2024+2025, and 62.93% success on ALFWorld. Compared with TextMAS, it improves performance by 1.29-8.59 percentage points and reduces generated text tokens by 19.2-74.8%.

cs.CL↗
arXiv2026

Electromagnetic Counterparts of Stellar-mass Binary Black Hole Mergers in AGN Accretion Disks: Theoretical Models and Observational Status

The discovery of the binary neutron star merger GW170817 and its electromagnetic counterparts marked the beginning of the era of multimessenger gravitational-wave astronomy. In contrast, binary black hole (BBH) mergers, which constitute the majority of gravitational-wave sources detected to date, are generally expected to be electromagnetically dark. However, if BBH mergers occur within the dense gaseous environments of active galactic nucleus (AGN) accretion disks, interactions between the merging black holes and surrounding gas may produce electromagnetic emission across multiple wavelengths. AGN disks may therefore provide a natural multimessenger laboratory for studying BBH mergers and their astrophysical environments. A key question is whether such a scenario is supported by current observations and whether the associated electromagnetic signatures are sufficiently distinctive and detectable. In this review, we summarize the current theoretical and observational status of BBH mergers in AGN accretion disks, focusing on the evidence for their occurrence, the physical mechanisms and expected signatures of electromagnetic emission, and searches for candidate counterparts. We also discuss the major challenges in identifying and confirming these associations and the prospects for future multimessenger observations with current and forthcoming multiwavelength facilities.

astro-ph.HE↗
arXiv2026

Stepwise Intrinsic Rewards for Reasoning in Large Language Models

Reinforcement learning (RL) has become a widely used paradigm for improving the reasoning abilities of large language models (LLMs) and Vision-language models (VLMs). Sparse binary outcome rewards, however, score only final correctness and cannot identify which intermediate steps contributed to it; in multimodal tasks, they may also reward answers driven by linguistic priors rather than visual evidence. Process reward models (PRMs) densify supervision but usually require process annotations, auxiliary models, or inference-time search. In this paper, we introduce Stepwise Marginal Information Gain (MIG), an intrinsic process reward computed from the policy itself. MIG measures how each structured reasoning prefix changes the length-normalized, teacher-forced log-likelihood of the reference answer. A monotonic historical watermark rewards only new likelihood maxima, avoiding duplicate credit after sub-record detours. We combine this signal with outcome and format rewards and a gated self-distillation objective that retains only structurally valid and correct trajectories. For VLMs, a real-versus-blank likelihood gate down-weights rewards when answers remain predictable without the image. Across eight task-specific benchmarks, the full method exceeds outcome-only GRPO in every single-run comparison. In broad-data transfer, it improves average accuracy by up to 4.8 points over binary-reward training and gains 12.6 points on MathVerse. At 7B, it exceeds an external PRM-BoN@16 baseline by 12.9 points on vision-language transfer without inference-time reranking. These results support policy-derived stepwise credit as an annotation-free alternative to explicit process reward modeling.

cs.AI↗
arXiv2026

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecasting horizons, errors are predominantly driven by cloud induced ramps: relying solely on historical numerical data may struggle to anticipate an incoming cloud, making ground-based sky images a crucial complementary physical signal. Furthermore, forecast performance is highly sensitive to location and local observing conditions, creating a strong need for site-specific data that are often scarce. Recently, large language models (LLMs) have demonstrated competitive performance and high data efficiency in time-series forecasting. Despite their success, existing LLM-based forecasting methods remain predominantly unimodal, relying primarily on historical numerical time-series data. Effectively incorporating sky imagery into an LLM-based forecasting framework remains under-explored and an open challenge. In this paper, we propose SolCloudLLM, an LLM-based multimodal forecasting framework. SolCloudLLM aligns sky-image patches with time-series patches and fuses their corresponding representations through bidirectional multimodal fusion, yielding a unified representation that is subsequently mapped into the embedding space of an LLM. Extensive experiments on the SIRTA and SKIPP'D datasets demonstrate that SolCloudLLM consistently outperforms the best baseline methods in MSE across all forecasting horizons, achieving a maximum relative MSE reduction of 25.4%. Stratified analysis further indicates that the benefits of multimodal fusion are concentrated primarily under cloudy conditions. Notably, SolCloudLLM achieves the best performance in nearly all few-shot settings, whereas other deep learning baselines experience substantial performance degradation and are frequently outperformed by the non-learning physical method.

cs.LG↗
arXiv2026

When Agents Disagree: Bayesian Backward Reasoning as a Label-Free Anchor for Multi-Agent Collective Decision-Making

When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compounds shared errors. Existing collective decision-making methods, including voting, electoral rules, and LLM judges, rely on forward reasoning: they map evidence to labels in one direction. Although these methods can combine diverse forward traces, they still aggregate estimates that share this evidence-to-label factorization and can inherit correlated errors within the forward pool. We therefore construct a reverse posterior for each instance through Bayesian backward reasoning from an explicit likelihood. The forward and reverse posteriors provide differently factorized approximations of the underlying posterior. Because estimates from different factorizations may tend to share the same error less often, we use Jensen-Shannon divergence to rank agents by cross-path consistency. This cross-path consistency signal underlies three strategies: hard selection (MinJS), soft reweighting (FwdJS), and log-linear fusion (LogLin). Evaluated on DDXPlus across five LLM backbones, our proposed strategies show consistent improvements: MinJS outperforms random selection across all backbones, FwdJS generally improves over the strongest baseline, and LogLin achieves the best performance among the evaluated methods, with its largest gains on the subset where the agents disagree. Despite its weaker standalone accuracy, the reverse posterior serves as a more useful anchor than forward-only alternatives, providing complementary information for collective decision-making. When labeled data are available, a lightweight two-stage calibration can further refine the reverse anchor and improve aggregation performance.

cs.AI↗
arXiv2026

Spin Splitter without Spin-Split Bands: A Reconfigurable Altermagnetic Texture

The altermagnetic spin-splitter effect converts an electric field into a transverse pure spin current, with no net magnetization and no charge-Hall counterpart. In established materials this function is tied to crystal-fixed spin-split bands that lock the polarization axis to the lattice. We show that the noncoplanar counter-spiral ground state of a frustrated honeycomb magnet instead carries the altermagnetic operation through a $\mathbf Q$-locked helicity mirror $g$. The mirror selects the spin-current polarization and forbids the perpendicular one, while an antitranslation $Θ$ forbids even-parity spin splitting. Band splitting and spin-splitter response therefore rest on different symmetry elements. Either element alone enforces the charge-Hall zero---a redundancy absent from other spin--orbit-free noncollinear routes---and a charge Hall appears only when both elements are removed. Hole doping then realizes a \emph{spin splitter without spin-split bands}---the symmetry-allowed odd-parity residual below $2\times10^{-7}$ of the hopping $t$ at the Fermi level---with $σ_H^{(s_y)}=0.082\,e^2/h$ without spin--orbit coupling and with zero charge Hall response. Selecting among the three degenerate $\mathbf{Q}$ orientations rotates the polarization axis in exact $120^\circ$ steps at fixed magnitude and charge-Hall zero; the selection rules persist in a $32$-site cell accessible to programmable photonic and circuit lattices.

cond-mat.str-el↗
arXiv2026

Quantum-enhanced estimation of signal field amplitudes with critical squeezed states of photonic modes

Critical phenomena of quantum systems offer a promising strategy to improve measurement precision. So far, many criticality-enhanced quantum metrological schemes have been proposed by using the adiabatically evolved photonic states of composite systems involving a qubit and a field interacting with each other. These schemes focus on the measurement of the system's inherent frequencies. We here propose a criticality-enhanced quantum sensing protocol, aiming to estimate the amplitude of an external signal field with the interacting qubit-photon system. The signal field is coupled to the photonic mode, so that the composite system has a unique dark state, where the photonic mode follows a squeezed vacuum state. The information about the signal field amplitude is encoded in photon number or one quadrature of the quantized photonic mode, which exhibits a divergent behavior near the critical point. The measurement precision can approach the Heisenberg limit with respect to the time to encode the signal and the photon number of the field mode.

quant-ph↗
arXiv2026

Experimental demonstration of entanglement sudden death induced by natural dissipation

Any quantum system inevitably interacts with its natural environment, which can be modeled as a Markovian reservoir consisting of a continuum of electromagnetic field modes. The quantum coherence of qubits in a zero-temperature natural reservoir decays asymptotically, whereas the quantum entanglement of two qubits coupled to such reservoirs may disappear in a finite time. This phenomenon, referred to as entanglement sudden death (ESD), has been simulated with artificially engineered dissipative channels, but ESD induced by natural dissipative channels has not been confirmed. We here present the first demonstration of natural-dissipation-induced ESD for two photonic qubits, each stored in a leaky resonator of a superconducting circuit. The disentanglement dynamics of the two photonic qubits is monitored with two ancilla superconducting qubits, which can be controllably coupled to the corresponding leaky resonators. The techniques developed in our experiment pave the way for experimental exploration of entanglement dynamics in natural environments.

quant-ph↗
arXiv2026

Exploring Hierarchical Merger Scenarios for GW241011 and GW241110

GW241011 and GW241110 are asymmetric binary black hole mergers with rapidly spinning primaries, unequal component masses, and nonzero spin--orbit tilts, making them natural candidates for hierarchical mergers. We use a Bayesian framework to compare a fiducial first-generation (1G+1G) binary black hole population with second-generation plus first-generation (2G+1G) hierarchical merger models in star clusters and active galactic nucleus (AGN) disks. Both events favor the 2G+1G interpretation over the 1G+1G hypothesis, with $\ln\mathcal{B}^{\rm 2G+1G}_{\rm 1G+1G}\simeq6.5$--$8.6$ for GW241011 and $\ln\mathcal{B}^{\rm 2G+1G}_{\rm 1G+1G}\simeq3.0$--$4.5$ for GW241110, depending on the waveform model and assumed environment. The AGN disk models yields slightly larger evidence than the star cluster models, mainly due to their spin tilt distribution, but the data do not provide a decisive environmental classification. We further consider a third-generation plus first-generation (3G+1G) interpretation, but it is not robustly preferred over 2G+1G scenarios. Finally, we also search for optical counterparts by examining AGNs within the three-dimensional localization volumes using ZTF and ATLAS forced photometry, and find one candidate source with weak flare, which might be associated with GW241110 event.

astro-ph.HE↗
arXiv2026

Beyond-adiabatic flat Chern bands from a double-helix skyrmion crystal

A central challenge in flat-band engineering is suppressing kinetic energy without sacrificing Berry curvature. We show that a double-helix skyrmion crystal (DHSKX)--two sublattice-resolved skyrmion textures locked at opposite helicities, obtained here as the classical ground state of a frustrated honeycomb spin model--provides such a route under double exchange. The key mechanism is a single real-space organization, phase clustering: the $π$-locked helicities expel the wave function's phase winding from the skyrmion cores, and the magnetic $C_3$ symmetry pins it into three phase-locked clusters whose distributed destructive interference cancels net transport while preserving the Berry curvature. Ordinary skyrmion crystals, even with the same symmetry, do not develop this organization. Phase clustering yields isolated flat $|C| = 1$ Chern bands over broad coupling windows, one of which surpasses the adiabatic reference in quantum geometry at intermediate coupling. In this beyond-adiabatic window, band-projected exact diagonalization gives finite-size evidence consistent with $ν= 1/3$ Laughlin-type fractional-Chern-insulator physics; the same texture also hosts a higher-Chern ($C = -2$) flat band. Built from site-resolved complex hoppings alone, the DHSKX architecture is directly programmable in topolectric, acoustic, and photonic platforms.

cond-mat.str-el↗
arXiv2026

Searching for Electromagnetic Counterpart Candidates to GW231123

The detection of GW231123, a gravitational-wave (GW) event with exceptionally massive and rapidly spinning black holes, suggests the possible formation within an active galactic nucleus (AGN) disk, which provides a favorable environment for potentially generating an observable electromagnetic (EM) counterpart. We conduct a search for such a counterpart by crossmatching the GW localization with a comprehensive catalog of AGN flares from the Zwicky Transient Facility. Our analysis yields six plausible optical flare candidates that are spatially and temporally coincident with GW231123 and exhibit significant deviations from their AGN baseline flux. Although these candidates represent a crucial first step, their true nature remains inconclusive. Confirming any one of these flares via future observations would provide a landmark validation of the AGN formation channel and unlock the multi-messenger potential of this extraordinary merger.

astro-ph.HE↗
arXiv2026

Observational Properties of Nonthermal Emission from Relativistic Jets Escaping Active Galactic Nucleus Disks

Relativistic jets launched from stellar-mass compact objects embedded in the accretion disk of an active galactic nucleus (AGN) can produce nonthermal emission upon successfully breaking out of the disk. In this paper, we present a comprehensive study of the long-term propagation dynamics and broadband nonthermal radiation signatures of such jets in a realistic AGN environment, explicitly modeled as wind outflows. Our modeling reveals two distinct features imprinted by the high-density AGN medium: rapid deceleration of the jet ejecta, accompanied by a prompt downshift of the emission spectral energy distribution, and persistently strong synchrotron self-absorption, giving rise to a prominent quasi-thermal hump in the emission spectrum. Crucially, both gamma-ray burst jets and jets powered by accreting binary black hole merger remnants can produce detectable multi-wavelength emissions that substantially outshine the AGN background. Moreover, the short time delays between gravitational wave triggers and these electromagnetic counterparts--typically less than $10^6 s$--greatly facilitate secure multi-messenger associations. Besides, our findings highlight that interaction-induced radiation from AGN-embedded jet systems offers a powerful diagnostic probe of the spatial distribution,density structure, and physical properties of the AGN medium.

astro-ph.HE↗
arXiv2026

Beyond Deepfake vs Real: Facial Deepfake Detection in the Open-Set Paradigm

Facial forgery methods such as deepfakes can be misused for identity manipulation and spreading misinformation. They have evolved alongside advancements in generative AI, leading to new and more sophisticated forgery techniques that diverge from existing ``known" methods. Conventional deepfake detection methods use the closed-set paradigm, thus limiting their applicability to detecting forgeries created using methods that are not part of the training dataset. In this paper, we propose a shift from the closed-set paradigm for deepfake detection. In the open-set paradigm, models are designed not only to identify images created by known facial forgery methods but also to identify and flag those produced by previously unknown methods as `unknown' and not as unforged or real or nmanipulated. In this paper, we propose an open-set deepfake classification algorithm based on supervised contrastive learning. The open-set paradigm used in our model allows it to function as a more robust tool capable of handling emerging and unseen deepfake techniques, enhancing reliability and confidence, and complementing forensic analysis. In the open-set paradigm, we identify three groups, including the `unknown' group that is neither considered a known deepfake nor real. We investigate deepfake open-set classification across three scenarios: classifying deepfakes from unknown methods not as real, distinguishing real images from deepfakes, and classifying deepfakes from known methods, using the FaceForensics++ dataset as a benchmark. Our method achieves state-of-the-art results in the first two tasks and competitive results in the third task.

cs.CV↗
arXiv2026

MSRAMIE: Multimodal Structured Reasoning Agent for Multi-instruction Image Editing

Existing instruction-based image editing models perform well with simple, single-step instructions but degrade in realistic scenarios that involve multiple, lengthy, and interdependent directives. A main cause is the scarcity of training data with complex multi-instruction annotations. However, it is costly to collect such data and retrain these models. To address this challenge, we propose MSRAMIE, a training-free agent framework built on Multimodal Large Language Model (MLLM). MSRAMIE takes existing editing models as plug-in components and handle multi-instruction tasks via structured multimodal reasoning. It orchestrates iterative interactions between an MLLM-based Instructor and an image editing Actor, introducing a novel reasoning topology that comprises the proposed Tree-of-States and Graph-of-References. During inference, complex instructions are decomposed into multiple editing steps which enable state transitions, cross-step information aggregation, and original input recall, which enables systematic exploration of the image editing space and flexible progressive output refinement. The visualizable inference topology further provides interpretable and controllable decision pathways. Experiments show that as the instruction complexity increases, MSRAMIE can improve instruction following over 15% and increases the probability of finishing all modifications in a single run over 100%, while preserving perceptual quality and maintaining visual consistency.

cs.CV↗
arXiv2026

Geometric criticality in the driven Jaynes-Cummings model

When the photonic mode in the Jaynes-Cummings model is driven by an external classical field, the system can undergo the photon-blockade breakdown phase transition at a critical point. Such a phase transition has been detailedly investigated, but the critical properties of the eigenstates remain largely unexplored so far. We here study the geometric criticality associated with these eigenstates. The amplitude and phase of the drive serve as the control parameter of the governing Hamiltonian. We find the quantum metric and Berry curvature tensors for each eigenstate display divergent behaviors in the critical region. More importantly, the divergence associated with bright eigenstates is much more pronounced than that for the unique dark state. Our theoretical results can be experimentally confirmed in circuit quantum electrodynamics systems, where the driven Jaynes-Cummings model has been realized.

quant-ph↗
arXiv2025

Critical quantum metrology robust against dissipation and non-adiabaticity

Critical systems near quantum phase transitions were predicted to be useful for improvement of metrological precision, thanks to their ultra-sensitive response to a tiny variation of the control Hamiltonian. Despite the promising perspective, realization of criticality-enhanced quantum metrology is an experimentally challenging task, mainly owing to the extremely long time needed to encode the signal to some physical quantity of a critical system. We here circumvent this problem by making use of the critical behaviors in the Jaynes-Cummings model, comprising a single qubit and a photonic resonator, to which the signal field is coupled. The information about the field amplitude is encoded in the qubit's excitation number in the dark state, which displays a divergent changing rate at the critical point. The most remarkable feature of this critical sensor is that the performance is insensitive to the leakage to bright eigenstates, caused by decoherence and non-adiabatic effects. We demonstrate such a metrological protocol in a superconducting circuit, where an Xmon qubit, interacting with a resonator, is used as a probe for estimating the amplitude of a microwave field coupled to the resonator. The measured quantum Fisher information exhibits a critical quantum enhancement, confirming the potential of this system for quantum metrology.

quant-ph↗
arXiv2025

BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner

We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale numerical experimental data with theoretical text corpora; Binary Patch Encoding replaces byte pair encoding(BPE) tokenization; Monte Carlo Attention substitutes traditional transformer architectures. Through next-word-prediction pretraining on cross-discipline scientific datasets of real-world problems mixed with general textual corpus, followed by fine-tuning and inference on downstream tasks, BigBang-Proton demonstrates 100\% accuracy in up to 50-digit arithmetic addition operations, performance on par with leading specialized models in particle physics jet tagging, matching MAE of specialized models in inter-atomic potential simulation, performance comparable to traditional spatiotemporal models in water quality prediction, and benchmark-exceeding performance in genome modeling. These results prove that language-guided scientific computing can match or exceed the performance of task-specific scientific models while maintaining multitask learning capabilities. We further hypothesize to scale the pretraining to the universe scale as a fundamental step toward developing material world foundational model.

cs.LG↗
arXiv2025

Observation of photonic dynamics in dissipative quantum Rabi models

The quantum Rabi model (QRM), composed of a qubit interacting with a quantized photonic field, is a cornerstone of quantum optics. The QRM with dominant unitary dynamics has been demonstrated in circuit quantum electrodynamics (QED) systems, but an open QRM with a strong photonic dissipation has not been experimentally explored. We here present the first experimental demonstration of such an open system in circuit QED, featuring a controlled competition between the coherent qubit-field interaction and the photonic dissipation. We map out the photon number distributions of the dissipative resonator for different coupling strengths in the steady state. We further observe the variation of the photon number during the system's evolution toward the steady state with fixed control parameters. The results demonstrate that the system's behavior is significantly modified by photonic dissipation.

quant-ph↗