Searcharxiv⌕ Search

arXiv subjects

Yifan Jiang

Publications and source records attributed to Yifan Jiang.

At least 19 recordsLinked to original sources
arXiv2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work makes three contributions. First, we develop benchmark adapters that port more than 80 benchmarks to evaluate arbitrary agents, and validate them through rigorous code review and parity experiments. Second, we conduct a large-scale evaluation of 8 models spanning capability tiers across 54 benchmarks; every model is run with Terminus-2 and with one of 3 native harnesses. This enables a broader analysis of agent capabilities and failure modes than was previously possible. Third, we introduce Harbor-Index, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks, refined from the adapted suite through difficulty filtering, AI and human audit, and an audit-and-fix loop. Harbor-Index preserves the challenge and breadth of large-scale agentic evaluations while being affordable to run; no evaluated model-harness configuration exceeds 30% pass rate, and the strongest (GPT-5.5 with Codex) reaches 28.0%. We release the adapters, evaluation results, in-depth analysis, and Harbor-Index as open-source artifacts to support more reliable and comprehensive evaluation of language-model agents.

cs.AI↗
arXiv2026

Intrinsic anomalous Hall response in the bilayer kagome ferromagnet Co$_3$Sn

Transition-metal kagome magnets provide a rich platform for investigating the interplay between layer stacking, magnetic order, and band topology. Here, we report the molecular beam epitaxy and experimental investigation of high-quality thin films of the kagome metal Co$_3$Sn, which has not been synthesized in bulk form yet. Structural and chemical analyses confirm a hexagonal lattice structure ($P6_3/mmc$) composed of direct A-B stacked Co$_3$Sn kagome bilayers. Magnetometry reveals robust easy-plane ferromagnetism with a Curie temperature exceeding $300\,\text{K}$. Magneto-transport measurements demonstrate metallic behavior (carrier density $n\approx5.01\times10^{22}\,\text{cm}^{-3}$) alongside a temperature-independent anomalous Hall conductivity of $\sigma_{\rm AHE}\approx90\,\Omega^{-1}\cdot{\rm cm}^{-1}$, extending from $2\,\text{K}$ up to room temperature. Results from first-principles density functional theory calculations attribute this anomalous Hall response to intrinsic Berry curvature hotspots near the Fermi level in the spin-split band structure. Our results establish Co$_3$Sn as a room-temperature kagome ferromagnet and highlight the impact of the layer stacking sequence on the material properties of kagome metals from the CoSn family.

cond-mat.mtrl-sci↗
arXiv2026

Renewable high-dimensional expected shortfall regression

Expected Shortfall (ES) has become a core coherent risk measure in finance and statistics, and high-dimensional ES regression is crucial for characterizing heterogeneous tail risk with massive covariates. Existing offline methods for high-dimensional ES regression rely on access to full data, which fails under streaming data scenarios with sequential batch arrival and limited storage. To address this issue, this paper proposes a renewable estimation and inference framework for high-dimensional ES regression tailored to streaming data. By optimizing a surrogate loss function determined only by current data and historical information, the proposed procedure updates the estimator of ES regression coefficients without storing full raw data. Based on the online estimator, we design an online debiased estimator and further construct valid Wald-type confidence intervals using consistent variance estimation. Theoretically, we establish non-asymptotic error bounds for the online high-dimensional ES estimator and verify the asymptotic normality of the online debiased estimator. Extensive simulations show that the proposed method achieves estimation accuracy and inference performance comparable to the offline benchmark. Moreover, an application on the car insurance claim dataset demonstrates strong practical value in insurance risk management.

stat.ME↗
arXiv2026

Competing Chern states revealed by quasiparticle charging in moir\'e rhombohedral graphene

Moir\'e materials realize a versatile platform for exploring the physics of fractional Chern insulators (FCIs). The recently observed evolution from FCIs to an extended quantum anomalous Hall background upon lowering the electronic temperature in moir\'e rhombohedral graphene (mRG)8 raises a fundamental question: Is it caused by a failure to equilibrate the edge states of an FCI or by a genuine phase transition in the bulk from an FCI to a generalized anomalous Hall crystal? Here we address this question by probing quasiparticle charging in a mesoscopic mRG antidot device and by bulk resistance measurements, both of which are bulk-sensitive and free from complications from edge states. Tunneling to the mRG antidot reveals quasiparticles carrying one electron charge for both Chern states at filling factors {\nu}=1 and 2/3 at low temperatures. Temperature dependence measurements of the bulk resistance near {\nu}=2/3 further suggest a thermodynamic phase transition from an FCI to a generalized anomalous Hall crystal at temperatures below about 150mK. The results clearly exclude the edge state equilibration scenario and favor the phase transition scenario. Our work establishes mesoscopic probes as a powerful approach to uncover competing ground states in moir\'e materials and provides a basis for probing fractionalized excitations in FCIs.

cond-mat.mes-hall↗
arXiv2026

Sensing for Reliable UAV Communication: Robust Trajectory and Resource Optimization in Low-Altitude Networks

In low-altitude wireless networks, sensing-aided communication has emerged as a promising integrated sensing and communication (ISAC) paradigm for unmanned aerial vehicle (UAV) tracking and communication. This paper investigates reliable sensing-aided communication for multiple cellular-connected UAVs under mobility uncertainties. Specifically, we maximize the minimum outage capacity among UAVs by jointly optimizing their real-time predicted positions, as well as the base station (BS) transmit power and bandwidth allocations. To address the non-convex and intractable maximum tolerable outage probability (OP) constraints, two robust optimization schemes are proposed based on a continuous confidence ellipse (CE) and discretized inverse-whitened sectors (IWSs), respectively. For the CE-based scheme, an efficient algorithm is proposed to optimize the predicted UAV positions individually via block successive convex approximation, followed by convex resource allocation. For the IWS-based scheme, an IWS-based OP approximation is proposed to facilitate the robust optimization, based on which a low-complexity IWS selection method is proposed to decouple the optimization variables. Then, a similar sequential optimization algorithm is proposed based on the projected gradient descent approach. The two algorithms are further unified into a common trajectory-resource optimization framework, revealing a low-complexity structure for robust UAV trajectory and resource management. Simulation results validate the effectiveness of our proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.

eess.SP↗
arXiv2026

AFFORDANCE20Q: Evaluating Affordance Reasoning from Physical Properties

Affordance reasoning, the inference of an object's action possibilities from its physical properties (e.g., shape and material), is fundamental to human physical understanding and increasingly critical for Large Language Models (LLMs). However, existing affordance benchmarks largely expose explicit object identities in the evaluation setup, allowing models to rely on memorized object-affordance mappings rather than reasoning over physical properties. To address this gap, we introduce Affordance20Q, a novel affordance reasoning benchmark formulated as a 20-Questions game without exposing the object's identity. In each game, the model identifies a hidden object's affordance from a candidate set by asking yes/no questions about its physical properties. Affordance20Q comprises 1,009 games over 454 objects and 59 affordances, all manually filtered, refined, and annotated. We conduct comprehensive experiments with 15 state-of-the-art LLMs and find a substantial gap (~20 points) compared to human performance. A KL-based information-gain (IG) analysis further shows that models fail to ask discriminating questions as the game progresses. To close the gap, we develop KB-Anchored Rule Induction (KARI), a pipeline based on LLMs that generates affordance rules grounded in evidence from knowledge bases (KBs). KARI improves open-source LLMs by up to 15.2 points, while the limited coverage of KBs hinders further gains. We release all our code and data at https://github.com/1171-jpg/Affordance20Q.git.

cs.AI↗
arXiv2026

saCI: An R Package for Stochastic Approximation Confidence Intervals for Correlation Coefficients

This paper presents saCI, an R package that implements the stochastic approximation method for constructing nonparametric confidence intervals for Pearson's correlation coefficient. The package is based on the algorithm proposed by Garthwaite (1996) and further developed by Xiong & Xu (2016). The implementation provides both the stochastic approximation (SA) method and the bootstrap BCa method for comparison, along with an interactive Shiny application for exploratory analysis. The package has been successfully published on CRAN, demonstrating its compliance with R package standards and reproducibility.

stat.ME↗
arXiv2026

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models

Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but vision-language models (VLMs) can often reach solutions through shortcuts or prior familiarity with a chart or question. To strictly evaluate visual reasoning, we propose counterfactual charts where the chart-question task remains fixed, but the underlying data and the corresponding answer are varied. We introduce Chartographer, a framework to reverse engineer charts into executable code, validate reconstruction fidelity, generate counterfactual variants, and derive new answers from executable QA logic. We apply this framework to existing chart QA datasets and evaluate proprietary and open-source VLMs, measuring variant sensitivity and generalizability. Counterfactual charts reveal failures hidden by single-chart performance: VLMs often fail to generalize after answering the original chart correctly. We find that failures are most prevalent when updated charts require novel visual reasoning pathways.

cs.CL↗
arXiv2026

Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery

Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language models (VLMs) can distinguish useful visual evidence from incidental image context in lexical judgments. We use human concreteness and imagery ratings because they span words with varying expected visual relevance, from abstract and low-imagery words to concrete and high-imagery words. We find that real-image contexts do not yield consistent gains and often hurt alignment with human ratings, most sharply when visual evidence is least relevant. Through probing and canonical correlation analysis, complemented by an attribution case study, we find that real-image contexts are associated with representational shifts and greater sensitivity to spurious visual cues, coinciding with weaker recoverability of the targeted lexical properties. We further show that instructing models to focus solely on textual content at inference time can reduce this degradation, with the clearest gains on these vulnerable subsets. Our findings suggest that current instruction-tuned VLMs need better calibration of when visual context should inform lexical judgments.

cs.CL↗
arXiv2026

Miller-Index-Based Latent Crystallographic Fracture Plane Reasoning and generation with Vision-Language Models

We study whether multimodal large language models (MLLMs) can leverage crystallographic plane indices (Miller indices) as a structured latent representation for reasoning about fracture geometry. We formulate Miller indices $z = (h,k,l)$ as a latent variable governing idealized planar fracture and evaluate two complementary capabilities: (i) latent inference, where the model maps visual observations to plane hypotheses under physically valid conditions, and (ii) latent applicability assessment, where the model determines whether such a representation is meaningful for a given fracture image. Through extensive experiments spanning synthetic data, controlled 2D--3D geometric pairs, and real-world fracture images across multiple material classes -- including ceramics, glass, metals, and concrete -- we show that MLLMs can reliably perform latent inference in idealized settings and, critically, can reject the latent representation when the underlying physics does not support it. As an exploratory extension, we further examine AI-generated fracture sequences and observe qualitatively plausible brittle-fracture progression behaviors, suggesting that multimodal generative models may encode partial implicit physical priors related to material failure dynamics. These results suggest that MLLMs can act as physics-aware reasoning systems conditioned on structured latent priors, provided that the domain of validity is explicitly modeled.

cs.LG↗
arXiv2026

Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models

Optical character recognition (OCR) and multilingual scene-text understanding remain challenging for multimodal large language models (MLLMs), particularly in real-world images containing small or degraded text, cluttered layouts, occlusion, handwriting, and complex typography. We present an OCR-aware multilingual post-training framework that improves visual-text grounding in a general-purpose MLLM without requiring an external OCR engine, OCR-extracted text, or text bounding boxes at inference time. The framework combines large-scale multilingual OCR supervision, approximately 5M additional multilingual training samples, controlled synthetic OCR generation and in-image text translation, LoRA-based supervised fine-tuning (SFT), and lightweight OCR-oriented Chain-of-Thought prompting. On a held-out real-world multilingual OCR benchmark, OCR-SFT improves OCR completeness from 71.3 to 84.6, reduces hallucination rate from 18.3\% to 5.5\%, and improves translation BLEU-1 from 52.3 to 80.2, with substantial hallucination reductions under blur and rotation. Evaluation on public benchmarks further shows gains on OCR-intensive tasks while largely preserving broader multimodal capabilities; ablations show that SFT provides the primary improvement, with prompting offering smaller complementary gains. These results demonstrate that data-centric OCR-aware post-training provides a practical and scalable approach to improving multilingual visual-text grounding in general-purpose MLLMs.

cs.CV↗
arXiv2026

Cryogenic shock exfoliation for ultrahigh mobility rhombohedral graphite nanoelectronics

Rhombohedral multilayer graphene (RMG) offers a highly tunable platform for correlated electron physics, featuring field-effect control of magnetic, superconducting, and topological phases[1-24]. The promise of these materials has been held back by the limited abundance of rhombohedral stacking in natural graphite, which constrains both sample yield and useful area. Here we introduce 'cryogenic shock exfoliation' to produce large area rhombohedral graphene flakes which, combined with a low-pressure van der Waals assembly technique that preserves stacking order, enable highly uniform devices exceeding 1300 $\mu m^2$ with fabrication yields of 90%. Using scanning nanoSQUID-on-tip imaging, we demonstrate uniform spin magnetism over the full central 10 times 10 $\mu m^2$ area of our devices. Transverse magnetic focusing reveals a disorder mean free path exceeding 200 $\mu m$ at low temperatures. Within the flat surface bands of RMG[20], we observe a size-driven crossover from Poiseuille to porous electron flow in the intermediate-temperature regime of strong electron-electron hydrodynamics[16, 25], providing a further signature of ultrahigh device quality. Our approach overcomes a key materials bottleneck in the fabrication of mesoscopic rhombohedral graphene devices, paving the way for incorporating strongly correlated phases into two-dimensional nanoelectronics.

cond-mat.mes-hall↗
arXiv2026

VIDEOP2R: Video Understanding from Perception to Reasoning

Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning. In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning. Our project page is available at https://videop2r.github.io/videop2r/.

cs.CV↗
arXiv2026

Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ensuring their reliability in practical deployment necessitates robust confidence estimation. Prior works have predominantly focused on text-only LLMs, often relying on computationally expensive self-consistency sampling. In this paper, we extend this to multimodal settings and conduct a comprehensive evaluation of MLLMs' response confidence estimation. Our analysis reveals a significant instinct-reflection misalignment: the model's implicit token-level support frequently diverges from its verbal self-assessment confidence. To address this misalignment, we propose a monotone confidence fusion framework to merge dual-channel signals and cross-channel consistency to estimate correctness. Subsequently, an order-preserving mean alignment step is applied to correct global bias, which improves calibration while preserving the risk-coverage trade-off for selective prediction. Experiments on diverse open-source and closed-source MLLMs show that our method consistently yields more reliable confidence estimates and improves both calibration and failure prediction. Code will be available at https://github.com/Yunkaidang/Instinct-vs.-Reflection.

cs.CV↗
arXiv2026

CLASP: Class-Adaptive Layer Fusion and Dual-Stage Pruning for Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing approaches typically address this issue using single-layer Vision Transformer (ViT) features and static pruning strategies. However, such fixed configurations are often brittle under diverse instructions. To overcome these limitations, we propose CLASP, a plug-and-play token reduction framework based on class-adaptive layer fusion and dual-stage pruning. Specifically, CLASP first constructs category-specific visual representations through multi-layer vision feature fusion. It then performs dual-stage pruning, allocating the token budget between attention-salient pivot tokens for relevance and redundancy-aware completion tokens for coverage. Through class-adaptive pruning, CLASP enables prompt-conditioned feature fusion and budget allocation, allowing aggressive yet robust visual token reduction. Extensive experiments demonstrate that CLASP consistently outperforms existing methods across a wide range of benchmarks, pruning ratios, and MLLM architectures. Code will be available at https://github.com/Yunkaidang/CLASP.

cs.CV↗
arXiv2026

Evolution of effective magnetic exchange interaction under spin dilution in SrIr$_{1-x}$Sn$_x$O$_3$

Resonant inelastic X-ray scattering measurements reveal robust magnetic excitations in the perovskite iridates SrIr$_{1-x}$Sn$_x$O$_3$. We analyzed the dispersions of the magnetic excitation with spin-dilution ratio $x$ = 0, 0.03, 0.06, 0.1, and 0.2, crossing from semi-metal to spin-diluted while antiferromagnetically ordered insulators. The extracted effective magnetic exchange interactions decrease continuously upon increasing spin dilution, and their evolution follows a simple spin-dilution scaling law. These results not only verify the strong electron-correlation nature of the metallic parent SrIrO$_3$, but also reveal the entanglement of the charge and spin dynamics in this system.

cond-mat.str-el↗
arXiv2026

Suppression of the tendency toward antiferromagnetic order in the Dirac semimetal SrIrO$_3$

The entangled charge and spin dynamics in strongly electron correlated system has been a fruitful playground for exploring new physical phenomena. Here with resonant inelastic X-ray scattering we studied the spin dynamics of SrIrO$_3$, a half-filled paramagnetic semimetal hosting highly itinerant Dirac Fermions due to its topological band structure. Our results show that its magnetic excitations share much similarity to the ordered compounds upon Sn substitution in exchange strength and AFM instability, while the system maintains spin non-ordered. Further, the non-ordered pristine SrIrO$_3$ hosts even longer lifetime magnetic excitations near the AFM zone center comparing to the Sn substituted ordered compounds, contrary to general expectation. These observations indicate an interesting connection between band topology and electron correlation in SrIrO$_3$.

cond-mat.str-el↗
arXiv2026

Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives

Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of surface similarity between narratives. This gap motivates a key question: What is the impact of enhancing structural mapping with LLM-derived abstractions on their analogical reasoning ability in narratives? To that end, we propose a modular framework named YARN (Yielding Abstractions for Reasoning in Narratives), which uses LLMs to decompose narratives into units, abstract these units, and then passes them to a mapping component that aligns elements across stories to perform analogical reasoning. We define and operationalize four levels of abstraction that capture both the general meaning of units and their roles in the story, grounded in prior work on framing. Our experiments reveal that abstractions consistently improve model performance, resulting in competitive or better performance than end-to-end LLM baselines. Closer error analysis reveals the remaining challenges in abstraction at the right level, in incorporating implicit causality, and an emerging categorization of analogical patterns in narratives. YARN enables systematic variation of experimental settings to analyze component contributions, and to support future work, we make the code for YARN openly available.

cs.CL↗