Search arXiv⌕ Search

arXiv subjects

Jun Wang

Publications and source records attributed to Jun Wang.

At least 73 records · Page 4Linked to original sources
arXiv2026

User Representation via Cross Multi-source Behavior Pre-training for Mobile Games

User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus on single-app or app-level behaviors, overlooking the inherently cross-source and multi-granular nature of user activities on mobile devices. At the device level, user intent emerges from complex interactions among heterogeneous behavior sources and hierarchical action structures, posing challenges that cannot be addressed by conventional app-centric modeling. To tackle this issue, we propose CM-PTM, a novel Cross Multi-source Behavior Pre-Training Model tailored for mobile game user representation learning on device-level behavioral logs. CM-PTM employs hierarchical cascaded mask-then-predict proxy tasks that first infer the source of the next behavior and then progressively refine predictions at the app-action level. This design enables unified modeling of cross-source dependencies and fine-grained behavioral dynamics within a single pre-training paradigm. Extensive experiments on large-scale real-world mobile datasets demonstrate that CM-PTM effectively captures users' endogenous interests and consistently delivers significant performance gains on downstream mobile game recommendation tasks.

cs.AI↗
arXiv2026

Dynamic Water-Wave Tweezers

Following a recent demonstration of stable trapping of floating particles by stationary (monochromatic) structured water waves [Nature 638, 394 (2025)], we report dynamic water-wave tweezers that enable controllable transport of the trapped particle along an arbitrary trajectory on the water surface. Furthermore, we demonstrate simultaneous transport of two trapped particles along different trajectories. We employ a triangular lattice formed by the interference of three plane waves, which can trap particles, depending on the wave frequency and particle parameters, either at intensity maxima or at intensity zeros (vortices). By introducing small frequency detunings of the interfering waves, we control 2D motion of the lattice and trapped particles. This approach is robust and effective over a relatively broad range of particle sizes and wave frequencies, offering remarkable new possibilities for noncontact manipulation of floating (e.g., biological and soft-matter) objects in fluidic environments.

physics.flu-dyn↗
arXiv2026

Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship

LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals. Humanities scholarship, however, requires interpretive, evidence-grounded arguments over primary sources, whose value rests on faithful quotation, verifiable provenance, and deep interpretation. Existing research agents use general planning, tool use, and reflection, leaving these scholarly operations implicit. To address this gap, we introduce SPIRE (Scholarly-Primitives-Inspired Research Engine), a multi-agent framework that realizes Scholarly Primitives, a typology of basic humanities scholarship practices, as cooperating roles over a multi-scale close-reading substrate of passages, intra-context graph communities, and cross-context semantic clusters. On a benchmark of peer-reviewed papers in classical Chinese and Greco-Roman Latin scholarship, SPIRE recovers substantially more cited primary-source evidence and receives higher blind human and LLM ratings on four scholarly quality dimensions than LLM, RAG, and generic agentic baselines. Retrieval-tier and agent ablations, with retrieval-volume and answer-length controls, trace its advantage to targeted retrieval, structured evidence selection, and claim-evidence binding. Code and benchmark data are released at https://github.com/YatingPan/SPIRE.

cs.CL↗
arXiv2026

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models such as large language models (LLMs) provide expressive priors over such spaces, but their likelihoods and self-assessments are unreliable proxies for the objectives and calibrated epistemic uncertainty, especially for novel candidates outside the observed data distribution. We introduce the Large Discovery Model (LDM), an empirically grounded recurrent architecture that couples a generative model with a Bayesian non-parametric reward surrogate model. The generative model proposes and refines candidate designs, while the surrogate predicts their performance and quantifies uncertainty, yielding an uncertainty-aware value that guides candidate generation, refinement, and selection. The discovery memory and the surrogate model are continually updated as each new experimental observation arrives. We evaluate LDM on three scenarios spanning different design modalities and objectives, including neural-network training, antibody design, and molecular optimisation. Compared to LLM-only reflection or traditional statistical search across these domains, LDM achieves a $2.4\times$ greater reduction in validation BPB, an $18.2\%$ relative decrease in binding energy, and more than $60\%$ relative gains in molecular multi-objective performance. These results suggests that LDM could serve as a general-purpose discovery engine for effective search over open-ended hypothesis spaces.

cs.LG↗
arXiv2026

Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models

Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-experts GP models (GP-pro), which combine local GP models to capture global correlations, alleviate this computational burden. However, training local experts on disjoint data subsets can lead to overestimated posterior variances. We propose GP-pro-c, a product-of-experts GP model that calibrates these variances using an information-based method. The method exploits the monotonicity and submodularity of information gain in GPs to define a calibration ratio that reduces the posterior variance of individual local GP models. We evaluate GP-pro-c using negative log-likelihood (NLL), root mean squared error (RMSE), and expected normalised calibration error (ENCE). Experiments on four synthetic functions and six regression datasets show that GP-pro-c achieves average reductions of 2.3% in NLL and 12.0% in ENCE compared with the uncalibrated GP-pro model. The proposed method mitigates posterior variance overestimation while maintaining predictive accuracy and reducing computational complexity. GP-pro-c provides a promising approach for uncertainty estimation in scalable GP models and may serve as a useful surrogate model for Bayesian optimisation with high-dimensional and large-scale data.

cs.LG↗
arXiv2026

Sharp Lifespan Dichotomies and Threshold Phenomena for Semilinear Heat Equations Driven by the Logarithmic Laplacian

We investigate nonnegative mild solutions of $\partial_t u+(-Δ)^{\ln}u=f(u)$ in $(0,T)\times\mathbb R^N$, with initial datum $u(0,\cdot)=μu_0$, $μ>0$. Unlike the classical and fractional heat semigroups, the positive logarithmic heat kernel exists only for $0<t<N/2$, and the corresponding linear evolution may become singular at its terminal time, with lifespan and growth depending on the spatial decay of $u_0$. The behavior of $f$ near zero determines local solvability: if $\int_{0^+} dσ/f(σ)<\infty$, then no finite nonnegative solution exists on any positive time interval. Under suitable assumptions on $u_0$ and $f$, we establish well-posedness for the nonintegrable logarithmic heat kernel. If $f$ has at most global linear growth, the nonlinear solution attains the full linear lifespan, while the Osgood condition at infinity implies that the maximal existence time tends to zero as $μ\to\infty$. We further distinguish slow-decay, fast-decay, and critical-tail initial data. In the noncritical regimes, a weighted Osgood tail condition yields blow-up strictly before the linear terminal time; if it fails, an amplitude threshold occurs under additional assumptions on $f$. In the critical regime, the dividing power is $3/2$: a square-root weighted Osgood condition yields premature blow-up, while its failure again leads to an amplitude threshold. Finally, we obtain terminal-time blow-up estimates and sharp rates for power nonlinearities.

math.AP↗
arXiv2026

Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching

Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this atomic paradigm fails to capture the complexity of human search intent within personal photo collections, where users often seek compact visual stories bound by structural relations rather than isolated snapshots. To address this limitation, we introduce **Image Bundle Composition (IBC)**, a novel paradigm that shifts the objective from ranking individual images to dynamically composing cohesive image bundles from a massive, unstructured photo pool. Since target bundles are not predefined, IBC presents a severe combinatorial explosion challenge and demands modeling non-decomposable joint relevance. To establish this paradigm, we construct **IBCBench**, the first IBC benchmark dataset containing 109,467 images and 667 verified queries, built via a semi-automated verification pipeline. Furthermore, we propose **BundleWeaver**, an agentic framework that reformulates IBC as query-conditioned incremental hyperedge discovery. By employing a Large Language Model to adaptively search for missing relational roles and utilizing a Vision-Language Model for whole-bundle verification, BundleWeaver effectively navigates the combinatorial space. Extensive experiments demonstrate that while state-of-the-art embedding models and static decompose-and-rerank paradigms suffer from relational blindness, BundleWeaver achieves substantial performance gains, highlighting the necessity of shifting from atomic scoring to dynamic relational composition. Our dataset and code are available.

cs.CV↗
arXiv2026

OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval

Composed image retrieval (CIR) requires complex reasoning over heterogeneous visual and textual constraints. Existing approaches largely fall into two paradigms: unified embedding retrieval, which suffers from single-model myopia, and heuristic agentic retrieval, which is limited by suboptimal, trial-and-error orchestration. To this end, we propose OSCAR, an optimization-steered agentic planning framework for composed image retrieval. We are the first to reformulate agentic CIR from a heuristic search process into a principled trajectory optimization problem. Instead of relying on heuristic trial-and-error exploration, OSCAR employs a novel offline-online paradigm. In the offline phase, we model CIR via atomic retrieval selection and composition as a two-stage mixed-integer programming problem, mathematically deriving optimal trajectories that maximize ground-truth coverage for training samples via rigorous boolean set operations. These trajectories are then stored in a golden library to serve as in-context demonstrations for online steering of VLM planner at online inference time. Extensive experiments on three public benchmarks and a private industrial benchmark show that OSCAR consistently outperforms SOTA baselines. Notably, it achieves superior performance using only 10% of training data, demonstrating strong generalization of planning logic rather than dataset-specific memorization.

cs.AI↗
arXiv2026

S-Equivalence of Band-Twisted Genus One Knots

We add twists to a band of a genus-one Seifert surface, producing a knot $K(\ell,0)$. We prove $K$ and $K(\ell,0)$ have $S$-equivalent Seifert matrices if and only if the $(2,2)$-entry of the Seifert matrix vanishes and the sum of off-diagonal entries divides $\ell$. The necessity follows from the Alexander polynomial and a norm argument proving triviality of the $S$-equivalence subgroup $\mathcal{S}^+$ in the class group of binary quadratic forms (Aka--Feller--Miller--Wieser); sufficiency is an explicit $Λ_1$-operation. The Jones polynomial distinguishes the knots when $V(K)\neq1$, yielding infinite families of $S$-equivalent but inequivalent genus-one knots, illustrated by $9_{46}$. Also in this paper, we provide a partial answer for Problem~1.6 in Kirby's problem list (K3) and Problem~7.7 of Aka--Feller--Miller--Wieser.

math.GT↗
arXiv2026

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-play LoRA adapters through a pretrained hypernetwork. LatentSkill stores skill knowledge in weight space rather than context space, removing per-step skill tokens while preserving modular loading, scaling, and composition. On ALFWorld and Search-QA, LatentSkill outperforms the corresponding in-context skill baseline while using substantially fewer prefill tokens: it improves ALFWorld success by 21.4 and 13.4 points on the seen and unseen splits with 63.9% fewer prefill tokens on average, and improves Search-QA exact match by 3.0 points while using 71.8% fewer tokens per step. Further analysis shows that generated skill LoRAs form a structured semantic geometry, can be continuously modulated via the LoRA scaling coefficient, and can be composed through parameter-space arithmetic when skill components are aligned. These findings suggest that weight-space skills provide an efficient, modular, and less exposed substrate for extending LLM agents.

cs.CL↗
arXiv2026

Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap

Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Current evaluation remains fragmented, prioritizing isolated technical metrics over the holistic, developmental, and societal aspects essential for deployment. Rather than serving merely as a descriptive catalog, this work establishes a diagnostic ontology that causally maps evaluation dimensions to the canonical LLM training pipeline, transforming evaluation from static ranking into a diagnostic tool for root-cause analysis. In this paper, we introduce an anthropomorphic evaluation framework that re-conceptualizes LLM capabilities through a four-dimensional lens: Intelligence Quotient (IQ), Professional Quotient (PQ), Emotional Quotient (EQ), and Value-oriented Quotient (VQ). We operationalize these concepts through a modular evaluation architecture and validate the framework's diagnostic claims through meta-analysis of public benchmark trends. Analyzing over 200 benchmarks, we synthesize key challenges and future directions. This work offers a strategic compass for developing LLMs that are not only technically proficient but also contextually relevant and ethically sound. A curated repository is available at: https://github.com/onejune2018/Awesome-LLM-Eval.

cs.AI↗
arXiv2026

Optimizing RNA yield using deep neural networks coupled to massively parallel screening

Messenger RNA (mRNA)-based therapeutics have emerged as a powerful platform for vaccines, protein replacement therapies, and cancer immunotherapy. A critical bottleneck in mRNA development is manufacturing large quantities of RNA economically, as measured by RNA yield emerging from an in vitro transcription (IVT) reaction. However, how promoter-adjacent DNA sequences influence RNA yield remains poorly characterized. Here, we present an integrated deep learning framework that leverages massively parallel next-generation sequencing (NGS) assays to measure RNA yield across large sequence spaces. A library of 10^5 randomized oligonucleotide sequences was designed to systematically explore sequence diversity within a defined structural context. DNA and RNA abundances were quantified in parallel using Illumina sequencing, enabling high-resolution measurement of sequence-to-yield relationships at scale. Sequences were one-hot encoded and used to train deep learning models, using a convolutional neural network architecture. The model achieved a Pearson correlation of 0.94 between predicted and experimentally measured RNA yield on a held-out test set, demonstrating strong generalization across diverse sequence contexts. Importantly, the trained model can be deployed in a production environment to score and rank novel RNA sequence designs by predicted IVT yield, enabling cost-effective, pre-experimental prioritization of the most manufacturable candidates. This framework establishes a scalable, data-driven approach to DNA and RNA sequence optimization, with broad applicability to vaccine antigen design, therapeutic protein delivery, and synthetic biology. By integrating high-throughput experimentation with advanced deep learning modeling, it significantly reduces screening costs and accelerates RNA engineering cycle times.

q-bio.GN↗
arXiv2026

Beyond Over-Refusal: Defending Indirect Prompt Injection via Latent Instruction Manifolds

Large Language Models (LLMs) have been integrated into complex ecosystems (e.g., Code Agents), while Indirect Prompt Injection (IPI) attacks have emerged as critical barriers to their safe deployment. Attackers exploit LLMs' indistinguishability between "instructions" and "data" to manipulate LLMs via maliciously injected instructions. Existing defenses, however, face an intractable safety-utility trade-off: most guardrails either incur high latency or suffer from severe over-refusal. In this paper, we first demonstrate that LLMs can separate instruction from data intrinsically with both theoretical and empirical evidence. Inspired by this insight, we propose AEGIS (Adaptive Ensemble Guard for Injection Shielding). AEGIS extracts instruction-sensitive projectors to identify malicious instructions and leverages a Unified Multi-Layer Consensus mechanism that aggregates topologically distinct signals across the network depth. Empirical evaluations show that AEGIS achieves remarkable detection performance against both heuristic and optimization-based attacks compared to baselines, highlighting its potential to mitigate IPI. Code is available at https://github.com/xaddwell/AEGIS

cs.CR↗
arXiv2026

PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval

Personal photo albums are not merely collections of static images but living, ecological archives defined by temporal continuity, social entanglement, and rich metadata, which makes the personalized photo retrieval non-trivial. However, existing retrieval benchmarks rely heavily on context-isolated web snapshots, failing to capture the multi-source reasoning required to resolve authentic, intent-driven user queries. To bridge this gap, we introduce PhotoBench, the first benchmark constructed from authentic, personal albums. It is designed to shift the paradigm from visual matching to personalized multi-source intent-driven reasoning. Based on a rigorous multi-source profiling framework, which integrates visual semantics, spatial-temporal metadata, social identity, and temporal events for each image, we synthesize complex intent-driven queries rooted in users' life trajectories. Extensive evaluation on PhotoBench exposes two critical limitations: the modality gap, where unified embedding models collapse on non-visual constraints, and the source fusion paradox, where agentic systems perform poor tool orchestration. These findings indicate that the next frontier in personal multimodal retrieval lies beyond unified embeddings, necessitating robust agentic reasoning systems capable of precise constraint satisfaction and multi-source fusion. Our PhotoBench is available.

cs.IR↗
arXiv2026

Code as Representation: A Compilable Parsing Paradigm for Academic Documents

Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For Multimodal Large Language Models (MLLMs), the core challenge is not only perception, but representation: scientific pages interleave text with Structured Academic Elements (SAEs) such as tables, formulas, charts, and pseudocode, whose structure, data, and logic are poorly preserved by common surrogates like Markdown. We therefore propose Compilable Academic Document Parsing (CADP), a paradigm that reconstructs a full page as contextual \LaTeX{} plus executable Python, so that structure-preserving elements and executable chart representations can be reconstructed, recompiled, and directly verified against the source page. To support this setting, we introduce CADP-Bench, an expert-verified benchmark of full academic pages containing tightly coupled text and multiple SAE types, evaluated through a re-injection compilation protocol. We further study current capabilities using SOTA MLLMs and an exploratory multi-agent baseline that incorporates common agentic techniques. Results show that even frontier models still struggle to produce high-fidelity executable reconstructions, highlighting substantial room for improvement in structure-aware scientific document parsing. CADP-Bench is released for future research.

cs.CV↗
arXiv2026

Bifurcation analysis of Stokes waves with piecewise smooth vorticity in deep water

In this paper, we establish the existence of Stokes waves with piecewise smooth vorticity in a two-dimensional, infinitely deep fluid domain. These waves represent traveling water waves propagating over sheared currents in a semi-infinite cylinder, where the vorticity may have jump discontinuities across internal streamlines. The analysis is carried out by applying a hodograph transformation, which reformulates the original free boundary problem into an abstract elliptic boundary value problem. Compared to previously studied steady water waves, the present setting introduces several novel features: the presence of an internal interface, an unbounded spatial domain, and a non-Fredholm linearized operator. To address these difficulties, we introduce a height function formulation, casting the problem as a transmission problem with suitable transmission conditions. A singular bifurcation approach is then employed, combining global bifurcation theory with Whyburn's topological lemma. The singular limit is taken in a natural deep-water topology based on the surface trace and the derivatives of the height function. An exact mean-flux identity controls the closing zero Fourier mode uniformly as the regularization is removed. This yields a connected global continuum of exact waves together with explicit norm, ellipticity, surface-obliqueness, and parameter-boundary alternatives.

math.AP↗
arXiv2026

Rethinking Normalization Placement for LLMs: Post-Norm under Curriculum Depth Growing

Pre-norm is the standard normalization placement in modern Transformers because it facilitates joint optimization of full-depth models. We ask whether this preference persists when depth is introduced through a curriculum. In curriculum depth growth, each appended block receives the boundary representation produced by a trained prefix, making normalization placement relevant to forward conditioning. We therefore test whether placement and training curriculum interact. In a controlled distillation study with a Qwen3-8B teacher and a nine-layer student, pre-norm and post-norm are indistinguishable under joint training, differing by $0.0004$ validation CE, while post-norm improves over pre-norm by $0.0328$ under curriculum growth, an order of magnitude larger. A post-joint control matched by student active-layer tokens remains worse than post-grow, which rules out compute as the sole explanation. The ranking crosses over during the curriculum: post-norm takes the lead once blocks are appended. Single-block and freeze controls localize the ranking change to block appending rather than shallow-block quality or retraining. Boundary diagnostics associate post-norm with stable residual scales and pre-norm with structural-token scale drift; on a fixed batch, the final pre-grow block is also nearly identity-mapped. Together with the phase-wise crossover, these observations are consistent with boundary-scale conditioning after new blocks are appended. The results motivate treating normalization placement and training curriculum as coupled design choices in this distillation setting.

cs.AI↗
arXiv2026

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm's commanded path, we inject per-arm $\mathrm{SE}(3)$ transformations into attention via \textbf{PRoPE-style geometric encoding}, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight \textbf{depth branch} for scene-level geometry and use \textbf{SAM3 masks} with a frozen \textbf{V-JEPA teacher} to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, \model{} achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.

cs.CV↗