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Chenyu Wang

Publications and source records attributed to Chenyu Wang.

At least 19 recordsLinked to original sources
arXiv2026

Band Alignment of Grafted versus Directly Bonded Si/Ge Heterostructures

The electronic properties of semiconductor heterostructures are influenced by the local bonding and electrostatic environment at the heterointerface. Here, we investigate how the interface-formation mechanism modifies the band alignment of Si/Ge heterostructures by directly comparing Al2O3-mediated semiconductor grafting with direct bonding. Interface-sensitive X-ray photoelectron spectroscopy combined with Kraut analysis reveals type-I band alignment for both structures, but with different band offsets: the valence-band offset increases from ~0.08 eV for directly bonded Si/Ge to ~0.21 eV for grafted Si/Al2O3/Ge, accompanied by a corresponding reduction in the conduction-band offset. Raman spectroscopy further distinguishes the two interfacial configurations. The different core-level shifts and band offsets indicate that the electronic structure is sensitive to the interfacial bonding and electrostatic environment introduced by the two integration schemes. These results demonstrate that, even for an identical Si/Ge material pair, the interface-formation pathway provides an additional degree of freedom for controlling heterojunction band alignment, offering a route toward interface-engineered electronic and optoelectronic devices.

cond-mat.mtrl-sci↗
arXiv2026

On Dependence Measures Based on $f$-Divergence and $Φ$-Entropy

We study dependence measures induced by $Φ$-divergence (also known as $ f$-divergence in the literature) for finite random variables. We define conditional $Φ$-mutual information through an exact chain rule and develop its structural properties and variational representations. But in general it lacks conditional symmetry and deterministic self-reduction. To tackle this, we introduce max-$Φ$-mutual information $\wIPhi$, a variational envelope obtained by optimizing over $\IPhi$ quantities containing auxiliary random variables, which restores both properties and satisfies a subadditive chain rule. Both quantities reduce to Shannon's mutual information when $Φ(t)=t\log t$. For smooth convex generators with $Φ(0)<\infty$ and concave $t\mapsto t^2Φ''(t)$, we establish an optimizer characterization and obtain a closed-form expression for the unconditional quantity in terms of $Φ$-self-information. For the same class, we show that its private-randomness-assisted version is either an affine function of Shannon's mutual information, with coefficients depending only on $Φ$, or identically infinite. This dichotomy is determined by the asymptotic growth of $Φ$ and clarifies the sensitivity of max-$Φ$-mutual information to local randomness. We further derive inner bounds on $Φ$-ribbons from prescribed independence structures and prove that these bounds are optimal when $Φ(t)=t\log t$. We also establish a multipartite $Φ$-generalization of the Zhang--Yeung inequality and derive corresponding ribbon regions. Finally, we give sufficient conditions for reducing the computation of $Φ$-strong data processing constants for Z-channel sources to a boundary optimization.

cs.IT↗
arXiv2026

Matrix $Φ$-Entropic Measures of Dependence

We introduce matrix $Φ$-ribbons, extending the scalar $Φ$-ribbons to the matrix case. For $Φ$ in the subadditivity class, we establish data processing and tensorization properties of matrix $Φ$-ribbons. We establish scalar and matrix $Φ$-entropy inequalities and use them to show that both $Φ$-ribbons characterize the corresponding $Φ$-strong data processing inequality (SDPI) constants. We obtain exact evaluations of the SDPI constants for several sources. We provide examples showing that the matrix $Φ$-SDPI constant can exceed the scalar counterpart. Finally, we derive inner bounds for $Φ$-ribbons from prescribed independence structures and prove that these bounds are optimal when $Φ(t)=t\log t$.

cs.IT↗
arXiv2026

Velocity Ambiguity Profiles: Time-Resolved Bayes-Risk Diagnostics for Flow Matching

Flow matching trains a continuum of velocity regression problems but usually reports a loss averaged over interpolation time, obscuring which regions are limited by intrinsic ambiguity and which remain data-limited. We introduce the Velocity Ambiguity Profile (VAP), $\mathcal{A}_v(t)$, the time-local Bayes-risk floor of velocity prediction, and decompose time-resolved loss as $L_N(t)=\mathcal{A}_v(t)+R(t;N)$, where only the excess term $R(t;N)$ is reducible by more data or a different estimator. For $N$ training samples and a $K$-component mixture in the structured example, we prove two statistical benchmarks. In an intrinsic local-regression benchmark, $\mathcal{A}_v(t)$ enters the time-dependent noise factor of the standard nonparametric rate. In separated isotropic Gaussian mixtures, the Bayes velocity becomes locally affine, and a label-oracle residual-regression benchmark has parametric excess-risk scale $K\mathcal{A}_v(t)/N$, matched within the same separated residual Gaussian-location subproblem. Controlled Gaussian-mixture experiments validate this diagnostic through a dense transition sweep, calibrated oracle-to-blind and empirical-proxy audits, and a local controlled time-sampler stress test in which a frozen reducible-error proxy reduces reducible loss while cross-cell sensitivity delimits aggressive sampling. On CIFAR-100 DINOv2 feature flow, guided time sampling reduces uniformly evaluated velocity loss by $6.78\%$ and global feature Fréchet distance (FD) by $23.6\%$, while class-conditional kernel inception distance (KID) shows no improvement. VAP turns an averaged flow-matching objective into a time-resolved diagnostic for locating ambiguity-limited and data-limited regions.

cs.LG↗
arXiv2026

Hydrogen Ion-Cut Single-Crystal Diamond Membranes for Wide and Ultrawide Bandgap Semiconductor Heterointegration

Single-crystal diamond membranes (SCDms) were created using a hydrogen ion-cut process combined with a two-step post-implantation annealing, electrochemical etching, and micro-transfer printing. Monte Carlo simulations comparing H, He, C, and O implantation predicted a narrower vacancy-distribution profile for hydrogen under the implantation conditions examined. Raman spectroscopy was used to evaluate implantation-induced lattice damage and its recovery during annealing and membrane release, while optical bandgap analysis provided further evidence of restoration of the diamond lattice structure. After delamination and removal of residual graphitic material, freestanding SCDms with improved crystalline quality were obtained. The SCDms were subsequently transferred to GaN, SiC, AlGaN, and AlN substrates without an adhesive layer. The successful formation of diamond/WBG and diamond/UWBG heterostructures demonstrates the feasibility of integrating the SCDms with a range of semiconductor platforms. Overall, these results establish hydrogen implantation as an effective route for producing high-quality SCDms for future diamond-based heterogeneous integration applications.

cond-mat.mtrl-sci↗
arXiv2026

NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our system combines a heterogeneous model pool with intelligent routing, recording the predicted capability demand, selected service tier, and subsequent interaction for each user turn. These records are converted into training examples that preserve interleaved reasoning, tool calls, and harness context, and are admitted through structural validation, six-dimensional semantic evaluation, and subscene-level labeling. Routing signals organize supervised fine-tuning into a three-stage curriculum and extend to routing-guided on-policy distillation, where a teacher supervises student-generated responses under the same progression. Capability-guided allocation then converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop in which what the system learns to do shapes what it learns from next. Across eleven benchmarks covering harness-based agents, tool use, coding, and instruction following, post-training raises the macro-average from 58.94 to 64.87 at 4B and from 65.60 to 69.04 at 9B, substantially narrowing the aggregate gap between the post-trained 4B model and the 9B base model. We further adapt NeoHorse-1-4B into NeoHorse-Jev-4B, a prefill-only model that returns structured decisions and probabilities for agent workflows. NeoHorse-1 provides an initial prototype of this feedback-driven process and a path toward harness-mediated RSI across successive iterations.

cs.CL↗
arXiv2026

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to act, exchange payments, and accumulate wealth from environmental rewards. These simple local incentives induce decentralized credit assignment, driving global planning without explicit orchestration or communication protocols. The population evolves through economic selection: effective agents accumulate wealth and are mutated via exploitation, while ineffective ones go bankrupt and are replaced via exploration. Using modern Large Language Model (LLM) based agents, we show that, initialized with weak agents, the agent economy produces emergent multi-step reasoning strategies and outperforms stronger monolithic baselines across five agentic tasks, including mathematical reasoning, financial research, scientific research, accelerator design, and distributed-system optimization. Our results suggest a new path to multi-agent intelligence: rather than engineering coordination, we can design decentralized incentive structures under which it automatically emerges.

cs.CL↗
arXiv2026

From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials

Machine-learned interatomic potentials are commonly fitted by weighted-sum scalarization, which combines energy and force errors in a single loss. A nominal weight, however, identifies a potential only relative to the complete fitting protocol. We therefore treat energy--force balancing as a protocol-dependent problem of physical model selection. For fixed-basis atomic cluster expansion models of molten LiCl, liquid H$_2$O, and Si, the resulting scalarization paths contain dominated states at extreme weights. Their nondominated subsets depend on the solver, and the out-of-distribution Si path is nonmonotone. We replace direct weight selection by minimizing the regularized energy objective subject to an upper bound on the normalized force loss. Projected dual ascent adjusts the Lagrange multiplier from the force-constraint residual. A frozen-multiplier limited-memory quasi-Newton refinement then returns the final model. Across the three systems, the constrained procedure reaches the solver-matched low-error region and gives measured speedups of $6.7$--$8.9$ over completed scalarization scans under the stated timing convention. Physical-property calculations show that first-shell geometry is comparatively insensitive to the selected balance, whereas transport and solid-state observables vary more strongly. These results identify the fitted model--protocol pair as the relevant object of energy--force model selection and support force-constrained fitting as an explicit selection rule.

physics.comp-ph↗
arXiv2026

Randomized Lifting for One-Way Number-on-Forehead Communication

We prove a lifting theorem from two-party public-coin one-way communication to multiparty public-coin one-way number-on-forehead (NOF) communication. For every fixed $k\ge2$ and prime $q>2k$, there is a generalized inner product gadget $\GIP_{q,r}^k:(\F_q^r)^k\to\F_q$ with $r=O_k(q/\log q)$ such that, for every partial Boolean function $f:D\to\bits$, where $D\subseteq\F_q\times\F_q$, \[ R_{1/3}^1(f)-O(1) \le R_{1/6}^{1,\NOF}\bigl(f\circ\GIP_{q,r}^k\bigr) \le R_{1/6}^1(f). \] Thus, composition with the gadget preserves one-way randomized communication complexity up to an additive constant and a change in the error parameter. The lower bound holds in the general one-way NOF model, where the last player sees the entire gadget input. This extends the deterministic one-way NOF lifting theorem of Yang and Zhang to randomized protocols, and extends the randomized lifting result of Wang and Wu from the conservative model to the general one-way NOF model. Our proof introduces a one-way cylinder partition bound that lower bounds public-coin one-way NOF communication complexity. We show that, for the lifted function, this bound is at least half the one-way partition bound of the outer function. The main technical step transfers a dual solution between the two bounds, using Möbius inversion and a discrepancy estimate for generalized inner product to control the loss. Combining this transfer with the characterization of two-party one-way randomized communication complexity by the one-way partition bound yields the lifting theorem.

cs.CC↗
arXiv2026

GRF-Recon: Global Ray-Field Optimization for Long-Sequence Feed-forward Reconstruction

Feed-forward 3D reconstruction provides an efficient paradigm for scene modeling from image sequences. Scaling these models to large monocular scenarios are constrained by excessive GPU memory footprint, degraded local geometry, and long-term trajectory drift. Existing chunk-based optimization strategies provide limited geometric constraints and fail to maintain global consistency over extended trajectories. We present a unified framework for stable and scalable feed-forward 3D reconstruction from long monocular sequences. Our approach builds on coarse-to-fine trajectory alignment augmented by lightweight geometric prior injection. Distilling monocular geometric cues into the feed-forward backbone via LoRA adaptation improves depth accuracy on fine structures while preserving inference efficiency. We introduce a hybrid-weight sparse ray-field optimization that leverages high-frequency geometric features to guide local point-cloud refinement and enforce consistent inter-frame ray constraints. Unlike prior chunk-based methods, this establishes strong cross-frame geometric coupling while maintaining scalability. Finally, an efficient trajectory stitching strategy with joint ray-error optimization explicitly reduces accumulated drift. Extensive experiments show that our approach achieves competitive trajectory accuracy compared with representative SLAM systems, while maintaining globally consistent 3D reconstruction in large-scale scenarios.

cs.CV↗
arXiv2026

Heaps of modules: homological aspects

The definitions of projective objects and Gorenstein projective objects in the category of heaps of $T$-modules are posed, where $T$ is a truss. It is shown that a heap of $T$-modules $P$ is projective if and only if $\mathcal{G}_{e_p}(P)$ is a projective $R(T)$-module for all $e_p\in P$ and a heap of $T$-modules $M$ is BP Gorenstein projective if and only if $\mathcal{G}_{e_m}(M)$ is a Gorenstein projective $R(T)$-module for all $e_m\in M$. Moreover, we give a functorial description of the BP Gorenstein projective dimension. Finally, it is also proven that a unital truss $T$ is a Gorenstein truss if and only if $R(T)$ is an Iwanaga-Gorenstein ring.

math.RT↗
arXiv2026

Guideline-as-Oracle: Zero-Annotation Training of an Ophthalmic Telephone Triage Agent

Scaling supervision for multi-turn medical agents is difficult because expert dialogue annotation is costly and clinical conversations are privacy-restricted. We introduce Guideline-as-Oracle (GAO), which compiles American Academy of Ophthalmology guidance into a 70-row operational rule table and uses it as the sole source of instance-level supervision for 3,000 training dialogues, reserving human labeling for evaluation. Because converting rules into dialogues is itself a design problem, we catalog eight construction strategies, including cited-row tier assignment, one-fact boundary pairs, metadata-only repair, and label repair, and characterize the evidential status of each: labeling mechanism, null, confounded, or evaluated only as a package. Fine-tuning a 9B backbone on this corpus yields GAO-Triage, improving agreement with a 201-case operational reference from 61.7% to 74.1% (exact McNemar p=0.0046) and emergent-case recall from 9.5% to 69.0%; the gains persist across a second seed and patient simulator. None of the seven general-purpose systems we test dominates GAO-Triage on both metrics, and GAO-Triage requires no frontier model at inference time. Permuting label-dialogue assignments collapses the model to a constant-routine predictor, indicating that the signal lies in guideline-derived assignment rather than dialogue surface form. Label repair coincides with the disappearance of a late-training safety degradation.

cs.CL↗
arXiv2026

COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models

Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded inputs. This shift introduces a new safety risk: in many multimodal jailbreaks, neither the prompt nor the image is harmful in isolation. Unsafe behavior emerges only when the model binds an apparently benign operation, such as summarizing, translating, or following, to a localized visual target. This reveals a structural weakness in current multimodal defenses, which largely moderate the prompt-image pair as a whole even though the true security-relevant unit is the grounded operation-target pair produced during dereference. In this work, we identify and analyze this reference-dependent failure mode and show that existing defenses degrade when harmful semantics are localized, activated only after grounding, and dependent on visual reference resolution. To address this problem, we propose COMIC (Context-Operation-Modality-Image-Classifier), a reference-aware pre-generation safety gate for MLLMs. COMIC first infers the requested operation and reference type, constructs candidate targets from OCR and open-vocabulary proposals, grounds plausible referents, and evaluates safety over explicit operation-target pairs. To handle ambiguity conservatively, COMIC combines max-risk aggregation with quality-aware routing before deciding whether to forward or block a request. We evaluate COMIC across multiple open-source MLLMs, localized and broader multimodal jailbreak benchmarks, and benign reference-sensitive settings. The results show that COMIC consistently improves robustness while preserving benign utility and practical efficiency. More broadly, our findings suggest that multimodal safety cannot be enforced reliably without modeling the requested operation, the visual target to which it applies, and the confidence of that grounding.

cs.CR↗
arXiv2026

WitCert: Sound Runtime Risk Observability and Gating for KV-Cache Quantization

KV-cache quantization is validated today by offline benchmark averages; a deployed system cannot tell whether compression is damaging the request it is serving right now. We give it a provably sound runtime meter -- a "DTrace for KV quantization": a per-(layer, head, step) upper bound on the total variation between exact and compressed attention. The meter has two tiers: a deterministic band-norm-witness bound, sound for any cache-preserving black-box quantizer and for any query (adaptive-safe, worst-case Cauchy--Schwarz plus RoPE band-unitarity), and a tighter probabilistic certificate for a controlled subtractively-dithered INT8 quantizer under an explicit request-level failure budget (stated for non-adaptive queries; core theorems machine-checked in Lean 4). Three results. Observability: the meter enters SGLang through an env-guarded patch, and any scheme registered as one tensor function is measured in live serving. Repair: meter-driven gating -- risk-ranked where the witness is saturated, certified where it is informative -- empirically restores the quality floor at benchmark scale, e.g. raw-cast fp8 from 22.8 back to 79.7 on hard RULER tasks with the difference from uncompressed bounded at $[+0.0,+0.8]$ by a paired test. Analysis: aggressive schemes survive on cross-layer error cancellation, not per-step fidelity -- in a 28-layer sweep, no single layer's pollution alone loses anything (0/28) -- and the certified int8 cache serves $1.88\times$ more KV tokens at the same memory in SGLang. All artifacts, guards, and the Lean development are released at https://github.com/metask-ai/witcert-kv-certificates; every number regenerates from the shipped artifacts by one command.

cs.AR↗
arXiv2026

Runtime Observability for Heterogeneous Attention Memory

Modern models no longer keep a plain KV cache: latent caches, learned sparse selectors and recurrent states each carry the model's memory in a different form, and each fails differently under compression. We give a runtime observability contract that covers all four memory classes with three operators, instantiate it on six model configurations across five architecture families, and compose the per-stage bounds into an executable request-level risk ledger. Contracts carry their error metric as a type -- composition is only defined when metrics match, and this check rejected our own first composed chain; the repaired chain crosses metrics through two proved bridges, and whatever no formal system can certify is measured instead, dropping the composed tier to empirical automatically: every claim is certified, partially certified, or empirical, composition inherits the weakest tier, and the tier is decided by the machine. Replayed over $12.4$M entry reads and run under eight-way concurrency with per-request budgets and fail-closed identity attribution, the ledger quantifies the honest trade-off on today's witness and holds its risk budget with zero violations. A fused always-on probe observes a declared one-layer subset under CUDA graphs inside the serving noise floor. Applied to a served DeepSeek-V4 stack with a packed compressed-KV prototype, the same machinery localizes a silent corruption to a precise structural boundary -- exact in the eviction-free, identity-isolated regime, with every observed failure in an eviction or slot-reuse regime -- through a machine-adjudicated discrimination campaign whose calculus rejected two of our own confounded inferences along the way. All artifacts, guards, and the Lean development are released at https://github.com/metask-ai/witprobe-attention-memory; every number in this paper regenerates from the shipped artifacts by one command.

cs.AI↗
arXiv2026

Priority based inter-twin communication in vehicular digital twin networks

With the advancement and boom of autonomous vehicles, vehicular digital twins (VDTs) have become an emerging research area. VDT can solve the issues related to autonomous vehicles and provide improved and enhanced services to users. Recent studies have demonstrated the potential of using priorities in acquiring improved response time. However, since VDT is comprised of intra-twin and inter-twin communication, it leads to a reduced response time as traffic congestion grows, which causes issues in the form of accidents. It would be encouraging if priorities could be used in inter-twin communication of VDT for data sharing and processing tasks. Moreover, it would also be effective for managing the communication overhead on the digital twin layer of the cloud. This paper proposes a novel priority-based inter-twin communication in VDT to address this issue. We formulate the problem for priorities of digital twins and applications according to their categories. In addition, we describe the priority-based inter-twin communication in VDT in detail and algorithms for priority communication for intra-twin and inter-twin are designed, respectively. Finally, experiments on different priority tasks are conducted and compared with two existing algorithms, demonstrating our proposed algorithm's effectiveness and efficiency.

cs.NI↗
arXiv2026

QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture

The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) evaluations. To this end, we present QuArch (pronounced 'quark'), the first benchmark designed to facilitate the development and evaluation of LLM knowledge and reasoning capabilities specifically in computer architecture. QuArch v1.0 provides a comprehensive collection of 2,671 expert-validated question-answer (QA) pairs covering various aspects of computer architecture, including processor design, memory systems, and interconnection networks. Our evaluation reveals that while frontier models possess domain-specific knowledge, they struggle with skills that require higher-order thinking in computer architecture. Frontier model accuracies vary widely (from 34% to 73%) on these advanced questions, highlighting persistent gaps in architectural reasoning across analysis, design, and implementation QAs. Furthermore, via fine-tuning we find that QuArch can translate to improved performance on a realistic memory hierarchy design task, resulting in up to 1.99x more area-efficient solutions and up to 40% more viable solutions overall. By holistically assessing fundamental skills, QuArch provides a foundation for building and measuring LLM capabilities that can accelerate innovation in computing systems. The QuArch benchmark and leaderboard are publicly available at: https://quarch.ai/.

cs.AR↗
arXiv2026

Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning

While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be attributed to the miscalibration of post-trained language models, and the lack of calibration in popular sampling techniques. Here, we present Online Reasoning Calibration (ORCA), a framework for calibrating the sampling process that draws upon conformal prediction and test-time training. Specifically, we introduce a meta-learning procedure that updates the calibration module for each input. This allows us to provide valid confidence estimates under distributional shift, e.g. in thought patterns that occur across different stages of reasoning, or in prompt distributions between model development and deployment. ORCA not only provides theoretical guarantees on conformal risks, but also empirically shows higher efficiency and generalization across different reasoning tasks. At risk level $δ=0.1$, ORCA improves Qwen2.5-32B efficiency on in-distribution tasks with savings up to 47.5% with supervised labels and 40.7% with self-consistency labels. Under zero-shot out-of-domain settings, it improves MATH-500 savings from 24.8% of the static calibration baseline to 67.0% while maintaining a low empirical error rate, and the same trend holds across model families and downstream benchmarks. Our code is publicly available at https://github.com/wzekai99/ORCA.

cs.LG↗