arXiv subjects

Jing Huang

Publications and source records attributed to Jing Huang.

At least 19 recordsLinked to original sources

arXiv2026

Feed-Forward Multi-view Multi-person Reconstruction with Contrastive Human-Aware 3D Representation

Multi-view human reconstruction has been extensively studied under simplified settings, yet robust and efficient multi-person reconstruction in unconstrained environments remains challenging. Existing bottom-up methods often rely on accurate camera calibration and explicit cross-view matching, and therefore struggle with severe occlusions and ambiguities. We propose a new top-down paradigm that maintains a unified, instance-centric human-aware 3D space, enabling simultaneous camera calibration, cross-view association, and human reconstruction via cross-modal contrastive learning. Observations from multiple views are lifted and fused into this shared 3D space, where geometric structure, visual appearance, and human-centric semantic cues are jointly encoded at the instance level. We further introduce a spatial contrastive learning strategy that aligns 3D features corresponding to the same human instance across different views and modalities while separating different instances. This enables correspondence reasoning, semantic aggregation, and instance discrimination to be performed natively in 3D, improving cross-view consistency and robustness under severe occlusions. Finally, structured human body models are recovered in a feed-forward manner by regressing SMPL parameters from instance-level 3D human tokens. Extensive experiments demonstrate robust, accurate, and efficient multi-view human reconstruction in challenging real-world scenarios.

cs.CV↗

arXiv2026

Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimization

Sycophantic agreement refers to a behavior in which language models excessively affirm the user, often at the cost of factual accuracy. Although sycophantic agreement is a well-known failure of model alignment, there is limited understanding of how it emerges from model training. In this work, we demonstrate that sycophantic agreement can emerge as an unintended consequence of widely used contrastive preference optimization objectives. Using the OLMo 3 post-training pipeline, we show that, for various pairs of teacher models across three families, there is a strong correlation between the log-ratio of the teacher model sycophantic agreement rates and the resulting student model sycophantic agreement rate. We further demonstrate that this unintended transfer is not limited to DPO but also occurs across 6 other preference optimization objectives. To understand whether this effect can be attributed to particular training examples, we analyze the preference data and find that the sycophancy signal is diffused across the entire dataset rather than concentrated in a sparse set of examples: each example appears neutral, i.e., there are no explicit instances of sycophantic agreement, and filtering based on probe-based data attribution or logit-linear selection fails to mitigate sycophancy without removing a large portion of the dataset. Overall, our findings suggest that the teacher models used to generate preference data can interact with alignment training objectives in unexpected ways, generalizing to undesirable and potentially harmful behaviors like sycophantic agreement.

cs.LG↗

arXiv2026

A Dual-Dimensional LLM Framework for Automated Item Incidental Content Similarity Analysis in Large-Scale Assessments

The rapid expansion of large-scale assessments and the growing adoption of automatic item generation have intensified concerns about incidental content redundancy, where construct-irrelevant elements such as wording or contextual framing become unintentionally repetitive across items. Traditional similarity metrics like BLEU or cosine similarity, often fail to capture the nuanced structural and semantic layers that drive perceived redundancy simultaneously. This study proposes a dual-dimensional framework for Automated Item Similarity Analysis (AISA) powered by Large Language Models (LLMs), operationalizing similarity through Structured Decomposition and Semantic Relatedness. Psychometric validation indicates that LLM-derived metrics align more closely with indicators of construct-irrelevant local dependence and yield more coherent item parameter groupings than traditional text-based measures. The framework is further evaluated through its application in Computerized Adaptive Testing (CAT). Simulations reveal that incorporating LLM-based similarity constraints into item selection improves estimation stability and reduces bias with minimal efficiency trade-offs, outperforming constraints based on conventional metrics. These findings highlight the potential of LLM-powered AISA to support scalable bank curation, content-aware test assembly, and experience-sensitive adaptive testing across diverse assessment contexts.

cs.AI↗

arXiv2026

Do Language Models Consistently Encode the Current Year?

A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood. We contribute two tasks that probe the current year in conceptually distinct ways: an associative task, which infers the current year from verb tense, and a declarative task, which directly queries for the current year. Both tasks estimate current years within one year of the post-training data cutoff of instruction-tuned language models. For base models, predictions on the associative task serve as a strong proxy for the pre-training data cutoff, with an average error of only 10 months across 13 models. However, their internal mechanisms diverge: the associative task uses mechanisms similar to factual recall, while the declarative task lacks consistent causal pathways. This divergence poses a challenge for updating the current year in language models. None of prompting, SFT, or weight editing succeed in shifting the associative and declarative years simultaneously. Prompting updates the declarative year (94.6% success across 351 target years) but leaves the associative year nearly unchanged (1.7% success). Year-shifted SFT also fails to shift the associative year, matching the target year in only one of eight models. Weight editing, while effective for both tasks individually, does not generalize across both. Overall, our results show that the current year is not consistently encoded in language models: The associative notion, deeply ingrained in linguistic structures learned in pre-training, uses different causal mechanisms and resists the same modifications that easily shift the declarative notion learned in post-training.

cs.CL↗

arXiv2026

Resilience-Oriented Parametric Insurance Design for Power Systems Under Extreme Weather

Extreme weather leaves power systems exposed to residual outage risk even after physical resilience investments. Parametric insurance can provide pre-agreed contingent liquidity, but its physical value depends on how trigger thresholds and payout levels are designed. This paper proposes a resilience oriented parametric insurance framework that couples a three tier wind-index contract with post-event network restoration. Insurance payout expands the budget available to activate emergency resources, so the contract changes the physical restoration feasible set rather than merely offsetting accounting losses. Trigger thresholds and payout levels are jointly designed to balance actuarial premium, expected post-event system cost, and the conditional value-at-risk (CVaR) of scenario energy not supplied (ENS). A response-library method precomputes the restoration mixed-integer linear program for each scenario-payout pair and then evaluates admissible contracts efficiently. On the IEEE RTS-24 with 80 extreme-wind scenarios, the optimized contract reduces expected EENS and CVaR0.90 of ENS by 21.1% and 21.4%, respectively, relative to no insurance, while requiring 48.8% less premium than a fixed parametric contract with comparable resilience. The results show that insurance design should target the nonlinear liquidity-to-resilience response rather than loss compensation alone.

math.OC↗

arXiv2026

Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection

Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, persistently, or silently. Robust recovery therefore requires more than repeated retries: an agent may need to retry the same path, switch to an alternative, or recognize that no viable path remains. We present BENCH2ROBUST, a framework that converts failure-free tool-use benchmarks into controlled stochastic environments with scenario-controlled solvability, where episodes explicitly require retrying, switching, or stopping after available paths are exhausted. We use BENCH2ROBUST to study two complementary interventions: structured runtime recovery context through Bayesian Tool Memory (BTM), and curriculum-controlled reinforcement learning. Across 7 models from 4 families and two multi-turn benchmark families, tool failures produce a near-universal robustness gap. On held-out Retail tasks, BTM improves robustness by up to 16.8 percentage points without retraining, while RL learns complementary recovery behavior that remains beneficial without inference-time BTM. Combining the two reaches 40.8-45.5% under injection while preserving failure-free performance. These results suggest that robust tool use benefits from combining environment-specific recovery knowledge with learned recovery behavior.

cs.AI↗

arXiv2026

PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images

Pose-agnostic Anomaly Detection (PAD) remains challenging as anomalies can appear under arbitrary viewpoints, requiring methods to handle significant pose variations. Existing approaches rely on complex 3D reconstruction, which are computationally expensive and require extensive multi-view data. We propose PADFormer, a novel image-space approach that leverages Vision Transformer (ViT) to directly reconstruct anomaly-free versions of query images while preserving pose information. Our key insight is to adapt cross-view masked reconstruction for anomaly detection through training exclusively on normal data, combined with dynamic patch selection and spatial alignment mechanisms that enable effective learning from sparse reference views under significant pose variations. During inference, we perform multiple forward passes with different masking patterns to generate an ensemble of anomaly-free reconstructions, ensuring comprehensive coverage of the query image. Anomalies are detected by comparing these reconstructions with the query image. PADFormer achieves state-of-the-art results on the PAD benchmark while maintaining comparable performance on classic few-shot anomaly detection (FSAD) tasks, demonstrating superior efficiency and generalization without requiring 3D reconstruction.

cs.CV↗

arXiv2026

The Exact Maximum of the Spectral Sum of Graphs

For a simple graph $G$ of order $n$, let $S_2(G)=\lambda_1(G)+\lambda_2(G)$ denote its spectral sum. We determine, for every $n\geq5$, the exact maximum of $S_2(G)$ and all equality cases. The unique maximizer, up to isomorphism, is the complement of the disjoint union of a suitably balanced complete bipartite graph and isolated vertices, with the sizes of its three parts determined by $n$ modulo $7$. Denoting this graph by $K_n^\star$, we further show that $ S_2(K_n^\star)\leq\frac{8n}{7}-2,$ with equality exactly when $7\mid n$. This proves a conjecture of Kumar, Liu, Monterde, Pragada and Tait, which strengthens the Aouchiche--Hansen 2010 conjecture by extending it from connected graphs to all graphs and by asserting uniqueness of the extremal graph. The result also subsumes the 2008 conjecture of Ebrahimi B., Mohar, Nikiforov, and Ahmady. The proof combines Ky Fan's variational principle with a spectral inequality for weighted Ferrers quotients to reduce the problem to an explicit family whose complements have incidence rank one. Exact integer optimization and a separate equality analysis then yield the maximum and uniqueness.

math.CO↗

arXiv2026

The Duval--Reiner Conjecture: Counterexamples and the Second Partial-Sum Inequality

Let $F\subseteq\binom{V}{q}$ be a $q$-uniform family on a finite vertex set $V$. Write $s_r(F)$ for the sum of the $r$ largest eigenvalues of its simplicial up-Laplacian and $d_F(v)$ for the degree of $v\in V$. Then $D_r(F)=\sum_{v\in V}\min\{d_F(v),r\}$ is the $r$-th partial sum of the conjugate degree sequence of $F$. The majorization assertion in the Duval--Reiner conjecture [Trans. Amer. Math. Soc., 2002] states that $s_r(F)\le D_r(F)$ for every $q$-uniform family $F$ and every $r\ge1$. We disprove this assertion in two complementary senses: for every $r\ge5$, there is a strict counterexample at index $r$ in some uniformity, while every uniformity $q\ge3$ admits a strict counterexample at some index $r\ge5$. In contrast, we prove the universal inequality $s_2(F)\le D_2(F)$ and classify all equality cases. The counterexamples are obtained from two $3$-uniform seeds with explicitly computed characteristic polynomials through defect-preserving ridge-whiskering and set-complement duality. For the second partial sum, core completion reduces the problem to the boundary matrix of a complete simplex, where Ky Fan variational and compression arguments yield both the inequality and its equality classification.

math.CO↗

arXiv2026

Crowd4D: Scene-Aware Monocular 4D Crowd Reconstruction

Recovering scene-consistent 4D crowd motion from monocular video in large-scale scenes remains challenging due to severe depth ambiguity and complex scene geometry. Existing monocular crowd reconstruction methods typically rely on single-plane assumptions, leading to unreliable metric scale and spatial drift under complex terrain. We propose Crowd4D, the first scene-aware 4D crowd reconstruction framework that jointly optimizes the crowd and scene from a monocular RGB video in large-scale scenes. Crowd4D explicitly incorporates scene geometry and ensures consistency across image and scene spaces via a multi-stage optimization strategy. A key bottleneck of this task lies in accurate human-scene alignment, particularly in scale and position. However, human and scene reconstructions are typically decoupled. To address this, we introduce the Human-Scene Interaction Proxy, abbreviated as HSIP, as an intermediate representation derived from Scene Interaction Point Clouds and a Scene Interaction Surface, abbreviated as SIPC and SIS. These representations encode explicit scene-aware geometric priors and redefine the optimization space for large-scale monocular 4D crowd reconstruction. To further improve temporal stability under occlusions, we introduce Crowd Structural Coherence Regularization, abbreviated as CSCR, which leverages HSIP-based spatial priors to impose soft temporal consistency on pairwise relative displacements and directions within local crowd neighborhoods. Extensive experiments demonstrate that Crowd4D consistently outperforms existing state-of-the-art methods and enables robust monocular 4D crowd reconstruction in complex, large-scale real-world scenes.

cs.CV↗

arXiv2026

A Matching-Number Refinement of Brouwer's Laplacian Eigenvalue Inequality

Let $G=(V,E)$ be a finite simple graph with Laplacian eigenvalues $\lambda_1(L(G))\ge\cdots\ge\lambda_{|V|}(L(G))$, and define \[ \eps_k(G)= \sum_{j=1}^{\min\{k,|V|\}}\lambda_j(L(G))-|E|. \] Let $\nu(G)$ be the matching number of $G$, and let $n(G)$ be the number of non-isolated vertices of $G$. Lew proved that $\eps_k(G)\le k\nu(G)+\lfloor k/2\rfloor$, and conjectured that the additive term can be removed in the non-endpoint range. We prove this conjecture: \[ \eps_k(G)\le k\nu(G) \qquad (1\le k\le n(G)-2). \] We also characterize all equality cases. Up to isolated vertices, equality holds precisely for stars, for $K_1\vee(K_k\cup\overline{K_{n-k-1}})$ with $k$ odd, and for $K_n-E(K_{1,t})$ with $n$ odd, $k=n-2$, and $1\le t\le n-2$. We also analyze the endpoint range $k\ge n(G)-1$, where $\eps_k(G)=|E|$, and determine the specific cases where the inequality $\varepsilon_k(G)\le k\nu(G)$ fails or holds with equality.

math.CO↗

arXiv2026

SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors. We formalize Span-Level Uncertainty Estimation (SLUE), a new task that targets the natural granularity for uncertainty: semantically coherent text spans, each conveying a single assessable unit of meaning. To address this task, we introduce SPANUQ, a lightweight probe that distills the uncertainty knowledge from expensive multi-sample inference into a single forward pass over LLM hidden states. SPANUQ employs a DETR-style span decoder to simultaneously detect spans and estimate their uncertainty via a Mixture of Beta distribution, trained with a principled combination of Beta NLL regression and contrastive ranking objectives. We construct SPANUQ-BENCH, the first span-level uncertainty benchmark comprising 20K prompts, 293K annotated spans, and continuous soft labels derived from multi-sample claim verification. Experiments on five LLM backbones show that SPANUQ consistently achieves the best span-level uncertainty quality, outperforming the strongest probe baseline and all sampling-based methods while being 10-20x faster. Its DETR-based span detector attains 0.910 F1, surpassing the best heuristic by 39.4%, enabling precise error localization that sequence-level methods cannot provide. The framework generalizes across five LLMs spanning two model families.

cs.CL↗

arXiv2026

Unprecedent fast winking of solar flares triggered by bursty magnetic reconnection

Flare ribbons form as a result of energy deposition associated with particles accelerated in low layers of the solar atmosphere. The fine-scale structures of flare ribbons, also called ribbon kernels, offer a potentially powerful diagnostic of the flare reconnection process, however to date the dynamic evolution of ribbon kernels has not been fully characterized in statistical studies. Here, we checked the state-of-the-art observations (cadence $\leq$ 2.5 seconds) of solar flares in the ultraviolet from space by Interface Region Imaging Spectrograph (IRIS) over the past 12 years. Our results showed the first statistical study of multiple spatially-resolved flare kernel quasi-periodic pulsation events for 31 flares, with the period of 6-24 seconds. The ribbon kernels have a spatial scale of 480$-$1200 km and some kernels exhibit unprecedent fast ``winking" process, i.e., quasi-periodic pulsation-like flashing of individual kernels. The shortest heating time reaches about 2$-$3 s, implying that the energy is deposited only in a small localized region within flare ribbons, persisting for only a few seconds. Meanwhile, some ribbon kernels were observed to slip along the ribbon at speeds of 20-1800 km s$^{-1}$. These observations strongly imply a joint picture for the dynamics and the bursty nature of ribbon kernels as being due to coupled effects of plasmoid formation and three-dimensional (3D) magnetic reconnection in the overlaying coronal current sheet. We suggest that the observed flare behaviors provide strong observational evidences of 3D bursty reconnection.

astro-ph.SR↗

arXiv2026

Physical Characteristics of the Asteroid (469219) Kamo'oalewa as a target of the Chinese Tianwen-2 mission

The Near-earth asteroid (469219) Kamo'oalewa, a quasi-satellite of the Earth, is going to be observed in site and sampled by the Chinese space mission Tianwen-2 in near future. Here. we analyze its photometric and spectroscopic data to figure out its basic physical properties, which are very important for the sample return task of the Tianwen-2 mission. With photometry inversion methods, we derived a pole $(276^{o}.79, -21^{o}.43)$ with a spin period of 28.4517 minutes and a slightly flat convex shape. The estimated photometry slope of $0.998 mag/rad$ implies a large albedo of the Kamo'oalewa, i.e. S-type. Using the estimated absolute magnitude of $24.98$ mag, its size could be 27.4m assuming a typical albedo of S-type asteroids. The taxonomy analysis with a constructed ANN tool also supports that the Kamo'oalewa should belong to S-type asreroids, it may be a strong weathering fragment of an A-type or Q-type asteroid. Using derived pole, size and shape information of the target, we estimated its thermal inertia as $163.0 Jm^{-2}K^{-1}s^{-1/2}$ based on the new derived Yarkovski draft $A_2=-13.29349563\times10^{-14}au/day^2$, which means the target has a surface of mixture of grains and small bounds, like the surface of asteroid Bennu.

astro-ph.EP↗

arXiv2026

Hybrid Electronic-Ionic Ferroelectricity in Superlubric van der Waals Heterostructures

One strategy to lower the switching barrier in a sliding ferroelectric (sFE) is to insert an incommensurate spacer to reduce sliding friction, creating a superlubric sliding ferroelectric (SL-sFE). However, how polarization survives across the effectively decoupled outer layers remains an open question. We show that SL-sFEs are fundamentally different from conventional sFEs: polarization is not driven by sliding alone, but by an intricate coupling between interlayer sliding and the out-of-plane buckling of the spacer layer. This coupling results in a unique hybrid electronic-ionic polarization arising from asymmetric orbital hybridization. The interplay of these order parameters generates several distinct types of ferroelectric hysteresis, including mixed first- and second-order transitions, multi-step switching, and antiferroelectric-like behavior, establishing SL-sFEs as a distinct class of ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead. Latent reasoning moves deliberation into continuous space, but existing methods mostly learn deterministic or reward-maximizing paths, lacking a principled way to allocate probability across trajectories with different correctness and costs. We propose Latent Thought Flow (LTF), which models reasoning as variable-length continuous trajectories and trains a sampler to match a reward-induced posterior over answer quality and computation cost. We instantiate this with a continuous GFlowNet using stochastic latent transitions. To handle sparse answer supervision, we introduce an Entropy-Weighted Subtrajectory Balance objective for intermediate rewards and a reference-prior regularizer to anchor exploration. Experiments under finetuning and transfer learning settings show that LTF outperforms explicit CoT and latent reasoning baselines, improving accuracy by 9.5% while reducing reasoning length by 27.2% on average compared with strong latent reasoning baselines.

cs.AI↗

arXiv2026

Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence

While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, charts, and videos. These tasks require models to connect visual perception to executable programs, as correctness depends not only on syntax but also on layout, data semantics, and domain-specific constraints that apply after execution. This survey reviews Multimodal Code Intelligence, covering systems that generate, edit, refine, or reason with code under visually grounded inputs and outputs. We first formulate the field by the role that code plays in each task, distinguishing code as a rendered artifact, an editable structure, an intermediate reasoning trace, or an executable tool interface. Then we organize benchmarks and methods into four domains: Graphical User Interface, Scientific Visualization, Structured Graphics, and Frontier Tasks and Frameworks. This taxonomy connects artifact-generation problems to agentic and unified settings and allows us to compare how different tasks treat evidence of correctness. Across the literature, we argue that reliable evaluation requires evidence about semantics and interaction beyond visual fidelity. Looking ahead, future research may benefit from four verification-centered directions. Multi-signal validation can combine complementary evidence of correctness, multi-state verification can test behavior across execution trajectories, cross-task transfer testing can probe reusable visual-code skills, and verifiable agent traces can reveal whether agent actions are grounded in visual evidence. Together, these directions may move this field from single-output imitation toward evidence-grounded executable systems. An ongoing project and resources are available on \href{https://github.com/xjywhu/Awesome-Multimodal-LLM-for-Code}{GitHub}.

cs.CL↗

arXiv2026

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents

Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use. However, training reliable tool-using agents remains challenging in practice. While reinforcement learning provides an on-policy paradigm for improving agents from their own environment interactions, its effectiveness depends heavily on the training task distribution. When tasks are fixed before training, the task distribution can become increasingly mismatched with the policy's evolving capabilities, causing many rollouts to be spent on uninformative tasks. We propose SENTINEL, a failure-driven reinforcement learning framework that turns the Solver's rollout failures into targeted training tasks. SENTINEL follows a Controller--Proposer--Solver loop: the Controller analyzes failed trajectories and summarizes recurring error patterns, the Proposer generates executable tasks that stress these weaknesses, and the Solver is trained on the targeted tasks. On Tau2-Bench Retail with Qwen3-4B-Thinking-2507, SENTINEL improves Pass\^{}1 from 66.4 to 74.9 and outperforms RL on general synthetic tasks across Pass\^{}k metrics. These results demonstrate that model failures provide an effective and scalable source of targeted training signal for improving tool-using language model agents.

cs.CL↗