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Yong Li

Publications and source records attributed to Yong Li.

At least 55 records · Page 3Linked to original sources
arXiv2026

Vitrification-Devitrification Enables Tunable Photonic and Gas Sorption Properties of Zeolitic Imidazolate Frameworks

Zeolitic imidazolate framework (ZIF) glasses represent an emerging family of melt-quenched glasses, which exhibit immense potential for applications in gas separation, energy storage, and optics. However, their intrinsic porosity remains elusive due to the inherent challenges in resolving their disordered atomistic structures. Here, we systematically investigate the porosity of ZIF-4 and ZIF-62 crystals and their corresponding glasses. CO2 sorption at 195 K enables quantitative assessment of microporosity in both crystalline and glassy states, allowing the accessible micropore volume of the ZIF glasses to be determined. Moreover, establishing a direct relationship between photonic properties and structural porosity in Zn-based ZIF glasses remains challenging. Here we demonstrate striking broadband blue-light emission from ZIF-4 glass annealed under optimized conditions. A pronounced red shift is observed when increasing the annealing temperature above the glass transition temperature. By correlating the evolution of photoluminescence with structural porosity, we reveal the interplay between the photonic and gas sorption properties of ZIF glasses. These findings provide new insights into the structure-property relationships of ZIF glasses and offer a pathway toward the rational design of multifunctional MOF glasses.

cond-mat.mtrl-sci↗
arXiv2026

UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks

Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set of provided datasets, and they face challenges in data-intensive scenarios that require discovering and leveraging relevant information from large-scale and heterogeneous data repositories. Urban tasks are representative examples of such scenarios, as urban data are not only large-scale and multi-sourced, but also exhibit complex spatial, temporal, and semantic relationships. To address these challenges, we propose UrbanDS, a graph-guided LLM multi-agent system for data-intensive urban tasks. We first construct a unified dataset graph to organize reusable dataset skills and the relationships among datasets. Specifically, we develop a Data Profiling Agent that constructs a skill for each dataset. Moreover, a Relation Agent identifies relationships among datasets and integrates these relationships into the dataset graph. At runtime, a Planner Agent retrieves task-relevant datasets from the graph and generates execution plans. Multiple Execution Agents then perform data processing and analysis, while their execution progress and intermediate results are shared through a common memory. Finally, a Report Agent synthesizes the experimental logs into a report, which can be further refined based on user feedback. To systematically evaluate the capability of agents in handling data-intensive urban scenarios, we further construct UrbanDS-Bench, an urban data science benchmark covering representative data analysis and modeling tasks. Experiments on both general and urban benchmarks demonstrate that UrbanDS consistently outperforms existing data science agents on data-intensive tasks. Furthermore, UrbanDS has been deployed on the urban operations platform of Dongxihu District, Wuhan, demonstrating its effectiveness in real-world urban applications.

cs.AI↗
arXiv2026

CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration

Large language models (LLMs) have demonstrated strong capabilities in handling long-context tasks, but processing such long contexts remains challenging due to the substantial memory requirements and inference latency. In this work, we discover that certain attention heads exhibit sequential consistency in their attention patterns, which can be persistently identified using a coefficient-of-variation-based algorithm. Inspired by this observation, we propose CateKV, a hybrid KV cache method that retains only critical token information for consistent heads, thereby reducing KV cache size and computational overhead, while preserving the majority of KV pairs in adaptive heads to ensure high accuracy. We show the unique characteristics of our algorithm and its extension with existing acceleration methods. Comprehensive evaluations on long-context benchmarks show that, while maintaining accuracy comparable to full attention, CateKV reduces memory usage by up to $2.72\times$ and accelerates decoding by $2.18\times$ in single-sample inputs, and boosts throughput by $3.96\times$ in batch scenarios.

cs.LG↗
arXiv2026

CAER: Causal Action Effect Reweighting for World Model Training

World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agent interventions. Yet existing models are commonly trained with space-time-uniform mean squared error, allowing abundant background tokens to dominate the gradient while sparse interaction dynamics remain under-optimized; such uniform fitting rewards reconstructing appearance rather than learning how actions change the world. We introduce Causal Action Effect Reweighting (CAER), a general training paradigm that redistributes supervision toward the tokens whose predicted future is causally affected by the action. CAER contrasts the model's own predictions with and without action conditioning to localize these tokens online, then normalizes the resulting effect map into a weight that preserves the total coefficient mass and changes only where it is spent. This online signal requires no external annotations or offline preprocessing, avoids additional data-processing time, and scales naturally with model and dataset size. Experiments across heterogeneous action-conditioned world-model tasks show that CAER converges to better solutions than uniform MSE training, with consistent improvements in the physical consistency, controllability, and visual quality of generated videos.

cs.AI↗
arXiv2026

IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training

World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausible interactions. Existing approaches address this limitation by constraining the generation process with external representations encoding motion, geometry, or semantics. Obtaining these spatiotemporally dense representations typically requires auxiliary estimators or manual annotations, limiting training scalability. We instead revisit the training objective and identify a supervision-allocation mismatch under the globally averaged mean squared error (MSE) denoising objective: prevalent static content dominates the optimization signal, leaving sparse dynamic-object regions critical to interaction generation disproportionately under-supervised. Motivated by this observation, we introduce IMPACT, a scalable Interaction-aware Model training framework with Prior-guided Attention Calibration and Targeting. IMPACT uses cross-attention associated with manipulated-object tokens as an internal spatiotemporal prior for action-conditioned changes. It samples candidate regions from this prior, calibrates them with detached local prediction errors to construct an interaction map, and uses the map to reweight denoising supervision, requiring neither external representations nor inference-time modifications. Extensive experiments on robot-arm and human-hand manipulation, spanning diverse control modalities and DiT backbones, show that IMPACT consistently outperforms the corresponding MSE-trained baselines, improving interaction fidelity, physical plausibility, and visual quality.

cs.AI↗
arXiv2026

Ideation Arena: Evaluating LLM Generated Research Ideas with Battle-style Human Expert Assessment

Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies what counts as a good idea. To address this challenge, we introduce Ideation Arena, a battle style platform that evaluates research ideas through pairwise human assessment. Ideation Arena evaluates ideas generated by 14 frontier LLMs and 5 research agent architectures built on 2 base models. To ensure a common starting point, Ideation Arena builds shared literature contexts from papers familiar to the participating researchers and provides the same contexts to all LLMs and agents. We collect over 6,000 double blind pairwise comparisons from 105 active computer science researchers and construct an Elo rating leaderboard of proposal-stage expert preferences in computer science under a shared closed-context protocol. We validate the rankings through interrater agreement and robustness analyses, showing that the leaderboard remains stable under changes in annotator composition and domain coverage. Our results show substantial variation in agent effectiveness, with some frameworks improving ideation quality over their backbones and others offering little benefit or even underperforming their base models. We further construct Ideation Arena Eval, a benchmark for assessing whether automated evaluators align with human preferences in research ideation. Experiments with current LLM judges show that they still cannot reliably reproduce expert preferences, with the best judge reaching 72.56% Soft Accuracy on Overall Quality. Our code, data, and leaderboards are available at https://github.com/foss12138/Research-Ideation-Arena.

cs.AI↗
arXiv2026

Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments

Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs in response to persuasive arguments, as humans do, remains poorly understood. We conduct a systematic comparison using a naturally occurring online persuasion corpus in which original posters explicitly verify whether a reply changed their view. Our results show that LLMs achieve only slight agreement with humans (Cohen's kappa ranging from 0.079 to 0.178). Content-level analyses show that humans and LLMs agree on the strongest persuasion cues but diverge on finer ones: humans are more swayed by novel content and assertive language, whereas LLMs favor topical similarity and surface-level formatting. At the level of persuasion strategy, LLMs underweight emotional appeals and overweight credibility signals relative to humans, while the type of proposition under debate exerts no measurable effect on the degree of divergence. Furthermore, switching from first-person role-playing to third-person observation shifts all models toward greater resistance to persuasion, with the effect varying across persuasion strategies and textual features. These findings highlight the risk of treating LLM judgments as faithful proxies for human belief updating and point to structural differences in how LLMs and humans process persuasive discourse. Our code is available at https://github.com/tsinghua-fib-lab/LLM-belief-update-cmv.

cs.CY↗
arXiv2026

Towards World Models in Biomedical Research

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

cs.AI↗
arXiv2026

Tlow: Flow-based Item Tokenizer for Recommendation

Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems of excessive parameters and cold starts. However, the most common tokenizer, RQ-VAE, suffers from low decoding efficiency due to the inherent dependencies among its codebooks. Meanwhile, efficient independent tokenizers such as optimized product quantization (OPQ) still struggle with dimensional correlations and distribution complexity of semantic embeddings. In this work, we propose a f\underline{low}-based item \underline{T}okenizer (Tlow) to transform raw semantic embeddings into a latent space where embeddings conform to a unified standard normal distribution, achieving dual advantages of dimensional independence and distributional simplicity. Independent tokenization performed on these latent embeddings yields semantically clear token IDs. Additionally, we introduce a novel codebook guidance to align the codebook space with the token embedding space, further aiding the learning of more semantically distinct token embeddings. Offline experiments on four public datasets demonstrate that Tlow's tokenization and codebook guidance significantly improve recommendation performance. The improvement on cross-domain and multi-modal recommendations also proves the effectiveness of item tokenization in a simplified embedding space. Online experiments for a multi-modal retrieval task on China's largest social media platform WeChat validate Tlow's powerful distribution transformation capability. The retrieval model based on token IDs improves user CTR by 10.32\% globally and by 11.64\% for new items. Our codes are available at https://github.com/wjjln/Tlow.

cs.IR↗
arXiv2026

Phonon-Mediated Chirality Transfer from Organic Cation to Inorganic lattice in Hybrid Perovskites

Two-dimensional hybrid metal halide perovskites combine spin-orbit coupling, soft lattice dynamics, and molecular tunability, making them promising platforms for chiral optoelectronics. While chiral organic spacers are known to induce optical activity in the inorganic framework, the microscopic mechanism by which molecular chirality couples to the inorganic lattice is actively studied. Here, we investigate coherent vibrational dynamics in chiral ($R$-MBA)$_2$PbI$_4$ and its racemic analogue using circular-polarized transient absorption spectroscopy. A vibrational mode at ~5.7 meV is present in the chiral material and not observed in the racemic counterpart, confirmed as a lattice phonon by agreement between time-domain and steady-state measurements. Ab-initio calculations reveal correlated libration of the organic spacer and bending displacement of the Pb-I framework analogous to a classical Wilberforce pendulum whose sense is locked to the structural handedness of the lattice. Polarization-resolved measurements reveal a transient circular dichroism whose exact sign reversal between ($R$-MBA)$_2$PbI$_4$ and ($S$-MBA)$_2$PbI$_4$ is consistent with a periodic modulation of the excitonic rotational strength driven by the coupled lattice displacement. These results establish dynamic chirality transfer through the coupled librational-bending mechanism, with direct implications for phonon-driven chiral optical responses and spin-selective transport.

cond-mat.mtrl-sci↗
arXiv2026

TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction

World Action Models (WAMs) combine future-state prediction with robot action generation, but existing approaches largely rely on visual futures. Visual prediction captures scene structure and object motion, yet provides limited supervision for force, deformation, shear, and slip during contact-rich manipulation. This creates two design requirements: tactile futures should carry meaningful physical information, and they should not become privileged cues for action generation. We present TacWAM, a mechanics-aware tactile WAM that addresses this challenge in three steps. First, a Spatially Aligned Fusion (SAF) Tactile Encoder maps tactile appearance, dense force fields, and deformation flow into a shared latent prediction space, with bilateral force and torque reconstruction preserving global contact information. Second, a tactile history encoder provides temporal context so future tactile prediction reflects how force and deformation change beyond the current tactile observation. Third, Anchor-Guided Tri-Modal (AGT) Attention separates current visual and tactile anchors, future prediction tokens, and action tokens, allowing future tactile states to supervise training without being directly read by the action branch. We evaluate TacWAM on four real-world contact-rich manipulation tasks covering fragile grasping, sustained surface contact, and dynamic in-hand manipulation. TacWAM achieves an average success rate of 75.0%, exceeding the strongest evaluated baseline by 37.5 percentage points. Staged ablations show consistent degradation when tactile history is removed and access to future prediction targets is relaxed. These results indicate that future tactile supervision can improve contact-aware action learning when combined with informative tactile representations and deployment-consistent information constraints.

cs.RO↗
arXiv2026

Optical Theorem for Measuring the Acoustic Extinction Cross Section of Helmholtz Resonators

The optical theorem is a powerful tool of scattering theory that directly relates the extinction cross section of a scatterer to its forward scattering amplitude. While widely used in electromagnetism and optics, its application in acoustics has remained limited, primarily due to experimental challenges. These include the finite size of practical sound sources and the stringent requirements for detecting weak scattered signals. In this work, we analyze these limitations and develop a robust methodology for measuring the acoustic extinction cross section under realistic conditions, including non-ideal anechoic environments. The approach is applied experimentally to a Helmholtz resonator in an imperfect anechoic environment with standing-wave resonances. The retrieved extinction cross section exhibits a single pronounced resonance near 2000 Hz. The resonance position, peak magnitude, and spectral lineshape are consistent with full-wave numerical simulations. The results demonstrate that, when combined with appropriate data processing, the optical theorem provides a simple and reliable tool for characterizing acoustic resonators, opening new opportunities for quantitative analysis of acoustic scattering and absorption phenomena.

physics.class-ph↗
arXiv2026

Most Probable KAM Tori in Stochastic Hamiltonian Systems Driven by Multiplicative Noise

This paper investigates the effect of state-dependent multiplicative noise on the integrable structure of Hamiltonian systems. Under a local high-dimensional Lamperti-type condition, we derive the Onsager--Machlup functional in the corresponding flattening coordinates. When the diffusion vector fields are Hamiltonian, the resulting action is minimized uniquely by the deterministic Hamiltonian trajectory. Combining this characterization with a finitely differentiable KAM theorem, we prove that, under suitable assumptions, the invariant tori with Diophantine frequencies of an integrable Hamiltonian system persist, in the sense of most probable paths, under a small deterministic perturbation and multiplicative noise. Furthermore, as the intensity of the multiplicative noise tends to zero, we establish a localized large deviation principle that characterizes the asymptotic probability of solution trajectories deviating from these invariant tori.

math.DS↗
arXiv2026

CacheSpec: Finding the Sweet Spot for Small Models in Large Language Models

Large language models (LLMs) are increasingly used for program-aided reasoning, agentic decision making, and structured task execution, but these settings often incur substantial inference cost. Many such requests share similar computational structures while differing in variables, constraints, or contexts, creating opportunities for program-level caching. Since program caches need to reapply reusable computation logic to new requests, their key steps often involve lightweight and structured operations such as variable extraction, program binding, and generation acceleration, which are well suited for small models. We propose CacheSpec, an inference optimization framework centered on reusable program caches. The framework converts Program-of-Thoughts (PoT)-style programs from one-time reasoning artifacts into reusable cache objects, and reuses the same small model for two roles: semantic variable extraction on the cache-hit path and speculative drafting during target-LLM generation. Experiments on shopping-style request datasets, WebShop, Formula, and CodeTAT-QA show that CacheSpec reduces inference latency and improves effective cache reuse while preserving comparable or better task quality than existing caching and generation baselines, achieving up to about 3.1$\times$ latency speedup; in parallel serving experiments, it improves throughput by about 2.8$\times$ over PoT-style methods. These results suggest that the sweet spot for small models in large-model inference systems lies not in solving complex tasks independently, but in performing lightweight, structured, and verifiable auxiliary operations.

cs.AI↗
arXiv2026

Resonant state expansion for acoustic resonators. Part I. Eigenvalue problem

Resonant-state expansion (RSE) is a powerful modal framework for the perturbative analysis of open resonant systems, providing direct access to complex eigenfrequencies and eigenmodes. While RSE is well developed in electromagnetism, a comparably systematic formulation for acoustics remains less established. Here, we develop a general Green-function-based formalism for acoustic RSE and illustrate it for a class of two-dimensional acoustic resonators. Using the resonant states of an analytically solvable cylindrical reference system as a basis, we derive explicit perturbation matrix elements for uniform, radial, and sectoral variations of density and compressibility, representing homogeneous tuning, graded profiles, and symmetry-induced modal coupling. The resulting complex eigenfrequencies and eigenmodes are validated against exact analytical solutions and finite-element simulations, showing excellent quantitative agreement. The framework provides a systematic and physically transparent approach for analyzing perturbed open acoustic resonators and establishes a basis for resonant-state methods in acoustic metamaterials and non-Hermitian acoustics.

physics.comp-ph↗
arXiv2026

Resonant state expansion for acoustic resonators. Part II. Scattering problem

We develop a resonant-state expansion formulation for acoustic scattering by individual resonators. The scattered pressure and particle-velocity fields are expanded over the resonant states of the system, with excitation amplitudes determined by overlap integrals between the incident field and the resonant states over the resonator volume. Using the acoustic energy flux, we derive expressions for the extinction, scattering, and absorption cross-sections and show that the extinction spectrum can be resolved into contributions from individual resonant states. The formulation is first validated for a homogeneous two-dimensional cylinder, where it reproduces the analytical Mie-theory solution. We then consider a sectorally perturbed cylinder with coupled azimuthal modes and demonstrate agreement with finite-element simulations. Finally, we combine the eigenvalue and scattering formulations for a material-programmed hard-wall annular metaatom and reproduce its scattering spectra and near fields. The developed framework provides a physically transparent modal approach to acoustic scattering by open resonators with reduced symmetry and spatially structured material parameters.

physics.comp-ph↗
arXiv2026

Learning Canonical Register Automata over Ordered Data Domains

Register automata are finite automata equipped with memory that recognize data languages over infinite alphabets. In this work, we investigate active learning algorithms for deterministic register automata (DRAs) over ordered data domains--covering both dense domains, such as the rationals, and non-dense domains such as the integers. We show that the active learning problem for DRAs over both dense and non-dense ordered domains can be treated within a single unified framework. More specifically, we develop and implement a polynomial-time active learning procedure for DRAs over ordered domains, using oracles for membership, equivalence and memorability queries. The memorability queries were originally introduced for learning DRAs over domains with identity tests. Our unified framework also leads to a new consequence: minimization of DRAs over the non-dense ordered domain of integers is decidable, extending a result previously known only for dense domains. Finally, we give improved complexity bounds of several decision problems for DRAs over ordered domains that are closely related to the queries used in active learning.

cs.CL↗
arXiv2026

UrbanWorld2.0: A Multimodal Agentic Framework for Reality-Aligned 3D World Generation at City-Scale

The automated generation of high-fidelity, city-scale 3D environments remains a formidable challenge with profound academic and industrial implications. However, existing methods struggle to achieve the necessary quality, fidelity, and scalability. To address this, we propose UrbanWorld2.0, a reality-aligned intelligent multimodal synthesis engine that creates detailed, city-scale 3D worlds of high fidelity. We introduce an agentic framework that leverages diverse multimodal foundation tools to acquire real-world knowledge, maintain robust intermediate representations, and construct complex 3D scenes. This agentic design, featuring dynamic data processing, iterative self-reflection and refinement, and the invocation of advanced multimodal tools, minimizes cumulative errors and enhances overall performance. Extensive quantitative experiments and qualitative analyzes validate the superior performance of UrbanWorld2.0 in real-world alignment, shape precision, texture fidelity, and aesthetics level, achieving a win rate of over 86\% against existing baselines for overall perceptual quality. This combination of 3D quality, reality alignment, scalability, and seamless compatibility with computer graphics pipelines makes UrbanWorld2.0 a promising foundation for applications in immersive media, embodied intelligence, and world models.

cs.CV↗