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Chen Liu

Publications and source records attributed to Chen Liu.

At least 19 recordsLinked to original sources
arXiv2026

Prescribed combinatorial curvature beyond positivity for spherical vertex scalings on the sphere

Let $(S,V)$ be a marked 2-sphere with $|V|\ge3$, and fix a spherical discrete conformal class. Izmestiev-Prosanov-Wu proved existence and uniqueness for positive prescribed combinatorial curvature in the spherical discrete conformal class on $(S,V)$. In this paper, we identify a region of prescribed combinatorial curvature vectors, allowing some negative components, for which existence still holds.

math.DG↗
arXiv2026

A lightweight C Code Generator for Heterogeneous TinyML Systems

Over the past decade, the significant advancements in artificial intelligence have been closely tied to increasingly sophisticated training frameworks and steadily powerful computing hardware. However, the entire ecosystem remains fragmented. On one hand, models are represented with frameworks dependent formats, while on the other hand, new tailored hardware for accelerating novel operators has continued to emerge. To effectively diminish the transfer costs from training to inference, we propose a lightweight C code generator designed for bare-metal heterogeneous systems that incorporate domain-specific accelerators. Through a comprehensive case study on a RISC-V based radar DSP, we demonstrate that by employing an accelerator with ahead-of-time optimizations like weight tuning and bias fusion, our approach achieves a 2.3 times improvement in inference speed and a 20% reduction in binary size compared to the microTVM flow registered with the same accelerator, thus offering another compelling choice for resource-constrained edge devices. The code is open-sourced in Gitlab.

cs.SE↗
arXiv2026

LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonlinear payoffs and multi-leg strategy construction, requiring structured decisions rather than simple directional bets. We introduce LiveOption, an evaluation framework for LLM-based agents in option trading. LiveOption formulates the problem as structured sequential decision-making under realistic execution and capital constraints, and provides a reproducible environment with standardized interaction protocols. The framework includes three task suites covering portfolio overlays, event-driven earnings trading, and 0DTE intraday trading. We further propose a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance. Experiments show that current agents often fail to achieve competitive returns in most scenarios. LiveOption offers a principled testbed for evaluating structured decision-making beyond outcome-based metrics. Project Website: https://liveoption.github.io/

cs.AI↗
arXiv2026

Humans in Space: Can MLLMs Construct Human-Centric Spatial Understanding via Active Exocentric Exploration?

While Multimodal Large Language Models (MLLMs) excel at zero-shot reasoning, their spatial capabilities are primarily evaluated through static, passive observation. In complex, human-centric scenes, occlusion often hides crucial geometric evidence, and passive benchmarks force models to guess rather than recognize their own visual uncertainty. To bridge this gap, we introduce SATE-Bench, a novel interactive benchmark comprising 316 real-world scenes and 2,525 multi-turn QA pairs to evaluate visual self-awareness and active exocentric view-exploration. It combines a Passive multi-view control with an Active exploration setting, where models start from a severely occluded view and must iteratively query adjacent viewpoints until they gather enough evidence to fully ``sate'' the question, scored by a step-penalized efficiency reward. Evaluating state-of-the-art MLLMs reveals that they terminate exploration prematurely while reporting high confidence, and struggle with fine-grained 3D geometric tasks; matched-view controls further attribute the Active--Passive gap primarily to insufficient evidence acquisition. Moreover, fine-tuning with Group Relative Policy Optimization (GRPO) shows that rewarding view-seeking increases active exploration but lowers overall accuracy, as models over-explore and fail to fuse expanding multi-view contexts across turns. These findings indicate that scaling passive multi-view perception does not intrinsically yield active spatial reasoning, highlighting the need for models that know where to look, how to integrate what they see, and when to stop. Code and data are available at https://gtc-gh.github.io/SATE-Bench/

cs.CV↗
arXiv2026

Optimization-based Cell Average Limiters as Metric Projections: Constrained Evolution and Convergence Analysis for Convection-diffusion Equations

The optimization-based cell average limiter enforces global conservation and bounds, and has been successfully applied to discontinuous Galerkin (DG) discretizations of a wide range of nonlinear systems, from phase-field models to the compressible Navier-Stokes equations. In this paper, taking the heat and convection-diffusion equations as prototypes, we analyze the convergence of the scheme resulting from cell average limiting. Encoding conservation and admissibility in a differential inclusion identifies the limiter as a backward Euler proximal step in a Lie splitting of the constrained evolution. This proximal step is the metric projection onto the convex admissible set, and we prove that it is nonexpansive and stable in the $L^2$ norm, and that it does not increase the total variation of the cell averages. Combined with the standard error analysis, nonexpansiveness yields an $O(τ+h^{k+1})$ estimate for implicit Euler time stepping with DG discretization of polynomial degree $k$ under a reverse CFL condition, together with an unconditional $O(τ+h^{k})$ estimate. Numerical experiments confirm the predicted convergence rates and the enforcement of the bounds.

math.NA↗
arXiv2026

An optimization-based property-preserving limiter for two-phase flow in porous media on non-matching meshes

Two-phase flow in porous media arises in a wide range of applications in engineering and science. A fundamental challenge in its numerical simulation is that the approximation of the saturation transport equation can violate the maximum principle, and spurious oscillations can appear even when the solution remains within physical bounds. In this work, we propose a bound-preserving anti-oscillation postprocessing limiter for discontinuous Galerkin discretizations of the two-phase flow system on non-matching meshes. An optimization-based cell average limiter enforces the prescribed bounds on the cell averages while preserving global mass by minimizing the $L^2$ distance to the original cell averages. To suppress the remaining oscillations, the cell average limiter is combined with a minmod limiter, which is activated only in the cells identified by a troubled-cell detector to avoid unnecessary loss of accuracy, and a Zhang-Shu scaling limiter subsequently enforces the bounds on point values. In addition, we prove that the cell average limiter preserves the accuracy of the numerical solution at a fixed time on non-matching meshes. Numerical experiments on homogeneous, non-homogeneous, and fractured porous media demonstrate the accuracy and robustness of the proposed method.

math.NA↗
arXiv2026

MOER: Mix-Policy Optimization under Expert Prefix Guidance with Selective Rollback

Mix-policy optimization incorporates off-policy expert data into the reinforcement learning (RL) loop to transcend the capability boundary of on-policy RL. However, existing methods either provide static full-trace guidance that suppresses self-exploration, or adopt expert prefix guidance without correctness guarantees, leading to suboptimal performance or even training collapse. In this paper, we propose MOER, a mix-policy optimization framework that harmonizes expert guidance with self-exploration. MOER injects a decaying expert chain-of-thought prefix into the prompt and lets the model complete the remaining reasoning, with the prefix length progressively annealed to encourage self-exploration. A selective rollback mechanism replaces failed completions with the original full expert trace, guaranteeing the correctness of every off-policy trajectory. Furthermore, a harmonized mix-policy objective optimizes on-policy tokens with a GRPO loss while supervising both expert prefix and completion tokens with a trust-region SFT loss, substantially stabilizing training. Extensive experiments show that MOER achieves the highest average scores in mathematical and general domain reasoning tasks, outperforming all basic and mix-policy baselines, and generalizes consistently across diverse tasks, model scales, and base model categories. Codes are available at https://github.com/CityU-MLO/MOER.

cs.LG↗
arXiv2026

RuleSmith: Multi-Agent LLMs for Automated Game Balancing

Game balancing is a longstanding challenge requiring repeated playtesting, expert intuition, and extensive manual tuning. We introduce RuleSmith, a framework that couples multi-agent LLM self-play with surrogate-based optimization over a parameterized rule space. As a proof of concept, we build CivMini, a simplified civilization-style game with two asymmetric factions governed by 12 tunable parameters. LLM agents interpret textual rulebooks and game states to propose actions that are validated by the game engine, yielding noisy estimates of faction win rates. Our primary implementation uses Gaussian-process Bayesian optimization with Expected-Improvement-based adaptive sampling to allocate more evaluation games to promising candidates. RuleSmith consistently finds near-balanced configurations: three independent optimization runs reach near-even empirical win rates in independent 200- and 300-game evaluations, and additional experiments with NTBEA and TPE show that the same self-play interface supports different search components. Cross-model evaluations and small human studies provide preliminary evidence of transfer across player populations. RuleSmith produces interpretable numerical rule adjustments without task-specific policy training. Project and code: https://adonis-galaxy.github.io/RuleSmith-website/

cs.LG↗
arXiv2026

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics

Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains challenging due to limited data, complex sequence-function relationships, and the narrow space of biologically viable sequences. Generative methods that drift away from the data manifold can yield sequences that fail to fold, translate poorly, or are otherwise nonfunctional. We present RNAGenScape, a property-guided manifold Langevin dynamics framework for mRNA sequence generation that operates on a learned manifold of real data. By performing iterative local optimization constrained to this manifold, RNAGenScape preserves favorable computational viability proxies, accesses reliable guidance, and discourages excursions into regions poorly supported by the training data. The framework integrates three components: (1) an autoencoder jointly trained with a property predictor to learn a property-organized latent manifold, (2) a denoising autoencoder that projects updates back onto the manifold, and (3) a property-guided Langevin dynamics procedure that performs optimization along the manifold. Across three real-world mRNA datasets spanning two orders of magnitude in size, RNAGenScape increases median oracle-predicted property gain by up to 267% and success rate by up to 31%, while maintaining good biological viability proxy measures and achieving competitive inference efficiency compared to existing generative approaches.

q-bio.QM↗
arXiv2026

Arbitrage-Free Implied Volatility Surface Reconstruction via Additive Martingales

This work focuses on reconstructing a continuous implied volatility (IV) surface from a small number of option quotes, a fundamental task for option pricing and risk management. We propose Additive Martingale (AddMart), a few-shot IV surface reconstruction framework that satisfies static no-arbitrage conditions by construction. Rather than predicting IVs directly, AddMart maps sparse quotes to non-negative increment densities of an exponential additive martingale, from which valid risk-neutral marginals and IVs are recovered. By incorporating the no-arbitrage structure directly into its architecture, rather than enforcing it through regularization in the learning objective, AddMart produces IV surfaces that satisfy static no-arbitrage conditions throughout the domain. Extensive experiments on ten real-option datasets demonstrate that AddMart achieves competitive reconstruction accuracy with no material static arbitrage violations, while directly enabling the estimation of VaR, CVaR, and crash probabilities.

q-fin.CP↗
arXiv2026

Deep Search in Real-World Web Environments

Despite substantial progress in Deep Search agents, their ability to conduct end-to-end research in real-world open Web environments remains insufficiently evaluated. Unlike static retrieval settings, information on the real-world Web is distributed across diverse sources, modalities, and heterogeneous formats, often involving complex interaction mechanisms that require agents to autonomously search for, access, and integrate relevant evidence during execution. To address this gap, we introduce the OpenDS task, designed to evaluate agents' ability to perform multi-step deep search in the live, open Web. This task requires agents to autonomously explore the real Web, discover and connect relevant evidence from multiple independent sources through Web search, browser interaction, and evidence processing, and ultimately determine a unique factual answer. Building on this task formulation, we construct OpenDS-World, a benchmark comprising 200 challenging tasks spanning diverse information modalities, heterogeneous file formats, and interactive access requirements, with independent human verification to ensure evidence validity and answer uniqueness. We evaluate 12 frontier models under a unified tool environment and execution protocol. The best-performing model achieves 39.0% accuracy, while human evaluation across 1,000 independent attempts yields 21.1% accuracy. Further analyses of tool usage, search cost, termination behavior, and failure recovery reveal substantial differences in research strategies across models, as well as persistent challenges in tool coordination and long-horizon search management. OpenDS-World provides a unified evaluation platform for systematically assessing and advancing agents' autonomous deep search capabilities in real-world open Web environments.

cs.IR↗
arXiv2026

LightCRS: Lightweight Multi-Domain Conversational Recommendation System

Conversational recommender systems have recently adopted vision-language models to match user dialogue intents with multimodal product information. However, extending these methods to many product domains faces a dilemma: deploying a separately adapted VLM per domain scales cost linearly with the number of domains and requires routing each dialogue to the right model, while naively merging all domains into a single VLM degrades accuracy because product characteristics vary widely across categories. We propose LightCRS, a lightweight, query-conditioned framework for unified multi-domain multimodal conversational recommendation. LightCRS precomputes multimodal product representations once offline with frozen encoders. Online, a compact 5.3M-parameter module fuses this evidence for each dialogue via query-conditioned cross-attention, whose parameters are shared across all domains, and compares candidates jointly with an inter-candidate Transformer. To enable rigorous evaluation, we construct MM-R2A-25, a 25-domain benchmark pairing natural Reddit recommendation-seeking conversations with Amazon product listings. LightCRS achieves state-of-the-art average HR@1, NDCG@5, and MRR, improving HR@1 by 35.4\% over the strongest baseline while reducing per-query latency by 266 times. Joint multi-domain training itself improves HR@1 by 15.2\% over separately trained single-domain models.

cs.AI↗
arXiv2026

OpenGCUA: Toward a Fully Open Stack for Generalist Computer Use

Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programmatic interfaces. We define a generalist computer-use agent (GCUA) as a system in which the model chooses how to observe, act, and organize its work across visual and programmatic interfaces. Yet frontier implementations, such as GPT-6 Astra's computer-use system, remain largely closed, limiting systematic study of their design. We introduce Open Generalist CUA (OpenGCUA), an ongoing project to develop and open the full stack for GCUAs with the community. This report presents the first release of OpenGCUA. Code execution is a key feature of our agent design: it lets intermediate results determine subsequent operations and enables models to apply their existing programming capabilities directly to computer interaction. We also propose harness-to-model decision transfer, moving selected observations and task-state decisions from fixed harness rules to the model. On OSWorld-v2, our full configuration improves task scores across all eight evaluated models, including GPT-6 Astra and Claude Opus 5, raising mean partial score by +19.6 points and reducing reported steps per task by an average of 53.0% relative to batched GUI-only execution. We release our implementation and prompts, evaluation trajectories from all eight models, and preliminary versions of a real-device harness and a cross-device SDK. The harness addresses real-device challenges not fully captured by benchmarks, while the SDK provides a common programming interface across computers, mobiles and browsers. Across four real macOS tasks under our real-device harness, selected Qwen3.8-Max runs use 19.4 times as many reported reasoning tokens as GPT-6 Astra, highlighting substantial differences between models in real-device execution efficiency.

cs.CV↗
arXiv2026

Marrying Optimal Transport and ODEs for Unified Continuous-Time 4D Reconstruction and Tracking

Existing unified 4D reconstruction and point tracking approaches typically rely on heuristic interpolations or just predict at integer timestamps, lacking kinematic coherence and failing to model dynamics at any arbitrary timestamp. In this paper, we propose Uni4R, a framework that unifies these tasks by learning continuous velocity fields through the synergy of Optimal Transport (OT) and Ordinary Differential Equation (ODE). Importantly, this continuous velocity field acts as a kinematic prior that mutually benefits both 4D reconstruction and point tracking. Specifically, we propose the Flow Matching Guided Decoder (FMGD). A global velocity branch first extracts anchor features that capture the global dynamic state of the sequence. Then, FMGD leverages Flow Matching (FM) theory to formulate a probability path defined by OT on the anchor feature manifold, instantiating it as FM-guided velocity features for velocity prediction. This establishes a robust kinematic inductive bias. Meanwhile, a point reconstruction branch provides geometric features. The local velocity prediction module then joint above features and time embeddings, to decode velocities at arbitrary timestamps. To overcome the absence of high-quality ground-truth velocities in fractional frames, we propose an integral-consistency training strategy. This strategy uses an ODE solver to integrate velocities to recover target pointmaps, enabling the model to be supervised end-to-end directly from integer timestamps. Experimental results demonstrate that Uni4R achieves SOTA performance in both 4D reconstruction and point tracking, and achieves SOTA in our new kinematics-aware benchmark at continuous time.

cs.CV↗
arXiv2026

Tabula Rasa: Monte Carlo estimation of unit-variance noise with controlled spatio-temporal correlation

We suggest a method to generate time-varying Gaussian noise with controlled variance and controlled temporal correlation. This noise is used in several downstream tasks for temporal control and temporal coherence. The core technical idea is to phrase this problem as joint Monte-Carlo estimation of both a classic pixel reconstruction and estimation of variance using the concept of "sketching" from the database literature. We demonstrate that our method allows temporal control for downstream tasks with simpler and faster code than previous methods.

cs.GR↗
arXiv2026

Realization of quantum spin Hall insulator superlattice with emergent multigap-like helical edge states

The functional quantum spin Hall insulators (QSHI), protected by time-reversal symmetry against single-particle backscattering, hold great promise for dissipationless quantum electronics. Realization of QSHI with gapped helical edge states, which would enable deterministic on/off switching of the edge-channel conductance, is a key requirement for programmable topological circuits. Here, we report realization of superlattice-modulated QSHI HfTe5 hosting emergent multigap-like helical edge states. Using scanning tunneling microscopy and spectroscopy, we identify a reconstruction-induced periodic superlattice modulation in epitaxial monolayer HfTe5 and directly observe multiple gap-like features in the edge channel, accompanied by a series of sharp peaks in the density of states. Combined with theoretical modelling, we attribute the observed edge gap to the finite-width coupling between the two edges significantly enhanced by the superlattice modulation, whereas the sharp peaks are the manifestations of mini-gaps opening at the reduced Brillouin zone boundaries by the periodic modulation of spin-orbit coupling. Notably, these sharp peaks exhibit clear Zeeman splitting under magnetic fields, consistent with the helical nature of the topological edge states. Our results establish a viable route to engineering gapped helical edge states in QSHI and provide a promising platform for topological devices with desired on/off switchability.

cond-mat.mtrl-sci↗
arXiv2026

From ASIC to Fleet: Lessons from Building and Operating a Hyperscaler NIC

We describe the operational infrastructure built to deploy and operate fbnic, a custom multi-host NIC, across hundreds of thousands of production hosts at Meta. Vendor multi-host NICs, designed by retrofitting single-host architectures, suffered from shared firmware and buffers that created cascading isolation failures over seven years. fbnic eliminates these through physical isolation, but shifting to in-house hardware shifts the entire operational burden to the hyperscaler. We present a hardware-in-the-loop CI pipeline testing firmware, driver, and kernel cross-products; a unified observability pipeline co-locating NIC and switch counters for cross-layer fault attribution; a driver-first architecture with fewer than ten firmware message types; a targeted firmware upgrade orchestrator at sub-sled granularity; and scoped repair automation confining blast radius to individual host slices. Over ten months, fbnic achieved a 12X reduction in unplanned unavailability, 37% lower mean time to repair, and 2.3X fewer hardware swaps compared to vendor NICs on the same platform.

cs.NI↗
arXiv2026

Temporal-Aware Fusion for Robust Outdoor LiDAR Localization

LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounter limitations in dynamic or ambiguous scenarios, as they typically prioritize single-frame inference, leaving the potential of spatio-temporal consistency across scans not fully explored. In this paper, we propose a Temporal-aware Localization framework (TempLoc) designed to enhance the robustness of outdoor localization by effectively modeling sequential consistency. Specifically, a Global Coordinate Estimation module is first introduced to predict point-wise global coordinates and associated uncertainties for each LiDAR scan. A Prior Coordinate Generation module is then presented to estimate inter-frame point correspondences by the attention mechanism. Lastly, an Uncertainty-Guided Coordinate Fusion module is deployed to integrate both predictions of point correspondence in an end-to-end fashion, yielding a more temporally consistent and accurate global 6-DoF pose. Experimental results on the NCLT and Oxford RobotCar benchmarks show that our TempLoc outperforms state-of-the-art methods by a large margin, demonstrating the effectiveness of temporal-aware correspondence modeling in LiDAR relocalization.

cs.CV↗