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Li Chen

Publications and source records attributed to Li Chen.

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

Emergence of cooperation: A reputation-modulated reinforcement learning

Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information -- it shapes how individuals interpret their own experiences and assess the behavior of others. To bridge this gap, we propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm, in which agents equipped with Q-learning integrate both individual and social information via a locally defined reputation metric to guide their decisions. Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and we observe a discontinuous phase transition from full cooperation to full defection as the temptation increases. Cooperation spreads through the nucleation of cooperative clusters, whereas the disintegration of these clusters drives the system into an absorbing state of complete defection. Overall, this study demonstrates that reputation facilitates cooperation not only by providing direct incentives but also by reshaping the social information landscape that agents rely on for learning and adaptation.

physics.soc-ph↗
arXiv2026

Lipschitz Extension Initialization for Moving Least Squares Reconstruction from Sparse Irregular Samples

The idea of using Lipschitz extensions [1,2], or Gradually Varied Functions (GVFs)[3], for mesh-free scattered data reconstruction was proposed by the author in 2012 [4]. However, its practical application to modern mesh-free reconstruction methods has not been fully explored. Motivated by recent advances in computational tools, including AI-assisted mathematical programming and software development, we revisit this idea and investigate the use of a Lipschitz extension as an initialization step for Moving Least Squares (MLS) reconstruction [5,6]. Our computational experiments indicate that this initialization significantly improves the stability and reconstruction accuracy of MLS under sparse and irregular sampling. This is a preliminary study intended to establish feasibility; a fuller evaluation with additional benchmarks and comparisons is left to future work.

eess.SP↗
arXiv2026

Plexciton-mediated Raman scattering in strongly coupled systems

A series of experimental results demonstrate a distinctive Raman response in plasmon-exciton coupled systems. The enhancement of Raman scattering varies for different phonon modes. We describe the microscopic dynamical process of this Raman scattering using quantum many-body theory. Unlike conventional Raman scattering involving electron-phonon interactions, the process in plasmon-exciton coupled systems is characterized by inelastic scattering between phonons and plasmon-exciton polaritons-formed through the coupling of plasmons and excitons-acting as intermediate states. We derive analytical expressions for the Raman intensity and enhancement factors for various phonon modes, which show excellent agreement with experimental data. Furthermore, experimental fittings indicate a substantial disparity in the linewidths of the upper and lower polariton branches, for which we provide a comprehensive theoretical explanation. Based on linear response theory, we propose a microscopic mechanism for the formation of plasmon-exciton polaritons, enabling the analytical calculation of their dispersions and linewidths. This approach naturally accounts for the significantly asymmetry observed in the linewidths of the upper and lower polariton branches. By characterizing the polariton-phonon scattering process at the quantum level, we reveal the fundamental physical mechanism driving polariton-enhanced Raman scattering. Our work establishes a universal framework for describing the dynamical evolution of coupled systems, providing a versatile paradigm for exploring the interactions between plasmons and other quasiparticles.

cond-mat.mes-hall↗
arXiv2026

When ratios fall: A dynamic approach to contingent convertibles

We propose a novel valuation framework for contingent convertible (CoCo) bonds based on the issuing bank's Common Equity Tier 1 (CET1) ratio, which is widely acknowledged as an indicator of a bank's solvency. Our approach develops a bivariate jump-diffusion model that captures the dynamic relationship linking the CET1 ratios, share prices, and CoCo bond prices, incorporating both continuous market movements and correlated jump risk. The model advances existing literature through three key innovations: (1) a hybrid mechanism for modeling regulatory discretion in trigger decisions, (2) a class of power conversion schemes that generalizes traditional approaches while maintaining analytical tractability, and (3) a method to overcome the temporal discrepancy between high-frequency market data and low-frequency regulatory reporting. We derive semi-closed form formulas for both write-down and equity-convertible CoCo bonds and validate our model through five case studies spanning from 2009 to 2023, including an in-depth analysis of the 2023 Credit Suisse collapse. The results demonstrate a significant improvement in pricing and hedging performance while highlighting the model's data-adaptive nature that enables short-term predictions.

q-fin.PR↗
arXiv2026

Perturbation Power Selection for First-Error Delay Maximization in Enhanced SC Decoding

In this paper, we analyze the effect of perturbation power in delaying the first error position, i.e., the first information bit incorrectly decoded by the successive cancellation (SC) decoding. It is conducted over the finite-length perturbation-enhanced SC (PE-SC) decoding paradigm. We show that the FEP delaying probability exhibits a non-monotonic dependence on the perturbation power \(σ_{p}^{2}\). Based on this property, an efficient perturbation power selection algorithm that maximizes the delay probability is proposed to enhance the perturbation efficiency. It results in a more efficient perturbation power selection in finite-length PE-SC decoding.

cs.IT↗
arXiv2026

DREAM Technical Report

Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across modules, rely on rigid rules, and have limited awareness of real-time intent, leaving session-level shifts among browsing, comparison, and purchase insufficiently addressed. We present DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them. DREAM has two core components. First, a three-tier Intent Engine fuses on-device signals into structured L0/L1/L2 intent representations; its edge-cloud trigger chain reduces reporting volume to approximately 8.7%. Second, a Meta Engine uses a MetaModel for layered M1-to-M2-to-M3 reasoning: intent summarization, strategy planning informed by Strategy Memory, and parameter translation. It dispatches the resulting parameters through a unified outlet with safety guardrails. A Reward Dual Loop continuously optimizes both components by combining offline simulation for strategy-space exploration with online feedback for outcome calibration, forming a cycle of generation, execution, evaluation, and experience accumulation. Large-scale A/B tests on Taobao's homepage feed show that re-ranking control alone improves IPV by 2.06%, Core IPV by 2.39%, and GMV by 0.88%. Extending control to fine ranking raises these gains to 2.71%, 3.06%, and 1.31%, respectively, while consistently improving PV by more than 1%. These gains require neither replacement of pipeline models nor compromise of serving stability, supporting agentic meta-control as a viable paradigm for industrial recommendation.

cs.IR↗
arXiv2026

HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training. Though existing general image-editing models demonstrate strong capabilities, they lack necessary embodiment-specific priors to fully bridge this gap. In this work, we present HandEdit, a unified large-scale embodiment-aware image-editing dataset and benchmark specifically designed to transform human hands and arms into various dexterous robotic embodiments within egocentric frames. HandEdit comprises over 200M editing instances derived from five diverse source datasets, covering 26 distinct URDFs, including 13 hand-only and 13 hand-arm configurations. Alongside the dataset, we establish a unified benchmark protocol with two tracks: Hand-only and Hand-Arm, supporting URDF-conditioned evaluation. We conduct extensive evaluations of 11 representative image-editing baselines using a multi-dimensional metric suite, including generic similarity metrics, VLM-based judgment, and embodiment-aware metrics. HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.

cs.RO↗
arXiv2026

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times. In this setting, RL training goodput, measured by training throughput, matters more than raw GPU occupancy: GPU waiting and repeated prefill recomputation are pure overhead. We present TideRL, a readiness-aware elastic RL system with Continuous Task Batching, Resource-Aware Ref-Actor Pipelining, and Elastic Resource Scaling. CTB preserves useful rollout state, $\textrm{RA}^2\textrm{P}$ selects between decoupled streaming and colocated aggregation from the ready backlog and arrival interval, and ERS moves ranks between rollout and training using the same readiness signals. Across text-only and multi-modal agentic workloads, TideRL improves RL training goodput by up to 5.6$\times$ over synchronous baselines and over 33% over asynchronous baselines, while reaching similar task performance. It also improves KV cache hit rate by 1.58$\times$, reduces per-step training time by up to 44.3%, and cuts total waiting time by up to 77.6%.

cs.LG↗
arXiv2026

The CodeInverter Suite: Structure- and Data-Aware Binary Decompilation with Efficient LLMs

Binary decompilation plays a vital role in various cybersecurity and software engineering tasks. Recently, end-to-end decompilation methods powered by large language models (LLMs) have attracted increasing attention for their ability to generate highly readable source code with minimal human intervention. However, existing LLM-based methods still struggle with reconstructing program structure and logic, achieving accurate data recovery, ensuring data security and privacy, and maintaining computational efficiency. To address these challenges, we propose the CodeInverter Suite, with three main pieces: (1) the CodeInverter Workflow (CIW) is a novel prompt engineering method that incorporates control flow graphs (CFG) and explicit data mappings to enhance structure reconstruction and data recovery during decompilation; (2) building upon CIW, we construct the CodeInverter Dataset (CID), a large-scale domain-specific dataset containing 8.69 million samples enriched with CFGs and data mapping information; (3) we develop CodeInverter Models (CIMs), two lightweight LLMs with 1.3B and 6.7B parameters, enabling efficient inference in privacy-sensitive and resource-constrained environments. Extensive experiments on two benchmark datasets demonstrate that CIW significantly enhances the decompilation performance of various LLMs, with average improvements of 13.07% in re-executability and 23.94% in re-compilability. For our proposed decompilation model, CIM-6.7B achieves state-of-the-art performance in terms of re-executability and readability, outperforming existing LLMs-even with over 100 times more parameters-by an average of 11.03% and 6.27%, respectively.

cs.SE↗
arXiv2026

Beyond the PPAD hardness of Auto-bidding Auctions

Computing certain autobidding equilibria is PPAD complete in the worst case. Yet such instances rarely arise in practice, where advertisers running simple, decentralized learning strategies usually converge quickly. We show there is no contradiction: the hardness requires atomicity and vanishes once the value distribution is nonatomic, as it is in real world markets. To bridge worst case hardness and practical convergence, we introduce diffuse analysis, a beyond worst case framework that studies equilibrium computation when bidder values are drawn from general nonatomic distributions. Under this framework, the autobidding equilibrium becomes a separately monotone generalized Nash equilibrium (GNE). For this GNE, we give the first solver with last iterate linear convergence. Thus, the equilibrium has polynomial diffuse complexity, matching the convergence observed in real-world markets. Concretely, our framework subsumes the budget pacing and the throttling equilibrium as special cases when the payment rule is a convex combination of first and second price.

cs.GT↗
arXiv2026

Hierarchical Clustering of Networks via Hierarchical Distance Matrices

Clustering populations of networks while recovering their latent hierarchical organization is a fundamental yet largely unexplored problem in network analysis. To formalize this, we introduce the Hierarchical Distance Matrix, a specific class of population-level distance matrices that encodes latent hierarchical organization through recursively nested distance separation, accommodating unbalanced tree depths. Building on this framework, we propose a fully data-driven top-down procedure: network hierarchical clustering based on two-sample testing (NHC-TST). The algorithm recursively splits networks via spectral clustering and uses a graph-based two-sample stopping rule. The procedure adaptively determines the branching structure without requiring prior knowledge of the number of clusters or tree depth. Theoretically, we establish exact recovery of the population-level hierarchical structure and statistical consistency in the empirical procedure. Simulation studies demonstrate highly accurate recovery of both cluster memberships and hierarchical relationships across a wide range of settings. Applied to a global migration dataset, NHC-TST uncovers interpretable multi-resolution temporal structures that are not revealed by conventional flat clustering approaches.

stat.ME↗
arXiv2026

An Empirical Study of Coordination Mode as the First-Class Citizen in From-Scratch Multi-Agent Coding

Multi-agent vibe coding promises to accelerate software development, yet existing benchmarks rely on synthetic environments that ignore practical time and monetary costs, conflate reasoning with communication, and reward only superficial completion. We introduce multi-agent from-scratch evaluation benchmark, MSEval, evaluating multi-agent coding on real-world tasks. Grounded in 10 authentic, full-stack projects across 10 domains, MSEval scores performance using hierarchical requirements and deterministic rubrics. Its execution engine, LegoGent, tests 10 collaboration topologies where agents coordinate via periodic sync intervals and deploy through native CI/CD pipelines. Concurrently, the automated grader TAgent dynamically probes implementations to jointly measure functional success, latency, and prefix-cached token cost. Across 100 runs, MSEval reveals that organizational topology rivals model capability in shaping the speed--cost--quality trade-off. For identical tasks and models, varying the topology shifts scores by over 30 points and doubles wall-clock time. Structured pipelines converge fastest with the highest quality, whereas heavy managerial oversight degrades performance. Ultimately, MSEval establishes a rigorous, reproducible standard for measuring how multi-agent teams actually build software. The benchmark is released at https://github.com/robinren03/MSEval.

cs.AI↗
arXiv2026

The ALMA-QUARKS Survey: Properties of Hot Molecular Fragments in the Massive Protocluster IRAS 17233-3606

To investigate the physical mechanisms of fragmentation within the hot molecular core of the massive protocluster IRAS 17233-3606 (G351.78-0.54), we carried out a detailed analysis of continuum and lines, using the ALMA Band 3 data from the ATOMS survey and Band 6 data from the QUARKS survey. The low-resolution 3 mm data reveal a massive hot core MM1 with a mass of ~81.3 Msun, and a prominent ultracompact (UC) HII region MM2, while the high-resolution data resolve MM1 into 11 hot molecular fragments (HMFs). These HMFs exhibit hot (Trot = 100-310 K) CH3CN and CH3OH emission and high column densities (NH2 > 10^23 cm^-2), indicating their potential to form massive stars. Based on outflows, masers, HII regions, and f[CH3CN/CH3O] abundance ratios, the evolutionary sequences of the 11 HMFs are categorized as phases I to IV. The mean minimum-spanning tree (MST) separation (~1.8 x 10^3 au) of the HMFs is nearly half of the thermal Jeans length (~3.3 x 10^3 au). Together with the Q parameter Q = 0.77 and virial parameter alpha_vir = 0.84 of MM1, these results suggest an evolutionary scenario in which fragmentation is initially driven by thermal instability, followed by global gravitational contraction and growth through active accretion. Meanwhile, feedback from the B2-type zero-age main-sequence (ZAMS) star and the UC HII region significantly influence the morphology and chemical properties of MM1 and MM2. This heterogeneity highlights the role of diverse physical processes taking place in high-mass protoclusters.

astro-ph.GA↗
arXiv2026

Fewer Paths, Better Performance: Understanding the ZCube Topology through Braess's Paradox

Datacenter networks follow a multipath doctrine: provision many paths between endpoints, hash flows across them, and let redundancy absorb both failures and load imbalance. The ZCube topology violates this doctrine. It removes the Spine layer, eliminates path multiplicity, and cuts one third of switching hardware, yet delivers better performance for both large model training and inference. We explain this anomaly through a structural connection to Braess's paradox, first observed in 1968: both phenomena trace to congestion-oblivious routing over competing paths. Braess showed that adding paths under this condition can hurt; ZCube shows that removing paths under the same condition can help. First, we show that multipath fabrics under structured LLM traffic operate in Braess's shadow: static ECMP hashing is strictly more fragile than greedy routing. Greedy routing reaches an equilibrium within 4/3 of optimal for affine latencies; static hashing admits unbounded imbalance in the worst case. Second, we prove that ZCube is immune to Braess's paradox and that its orthogonal dual partition provably balances load for arbitrary traffic matrices; AllReduce in training and KV cache transfers in disaggregated inference fall out as two corollaries. Third, we quantify the price of this immunity: ZCube trades microsecond hash recovery for millisecond control plane recovery, a trade that upper-layer resilience in LLM serving makes favorable. Production measurements from a cluster serving GLM-5.1 coding inference report 33% lower network cost, 15% higher GPU throughput, and 40.6% lower P99 time to first token. Our analysis suggests that for workloads driven by model structure, matching topology to traffic matters more than path multiplicity.

cs.NI↗
arXiv2026

LH-AVLN: A Benchmark for Long-Horizon Audio-Visual-Language Navigation

Embodied navigation is moving toward long-horizon missions, yet existing long-horizon benchmarks are largely acoustically silent, and audio-visual navigation tasks typically focus on a single goal. We introduce LH-AVLN, a benchmark for Long-Horizon Audio-Visual-Language Navigation that combines multi-goal mission execution, heterogeneous goal specifications, and persistent spatialized acoustic cues. In LH-AVLN, an agent receives a global mission of two to four goals specified by category, language description, or reference image, and navigates with RGB-D observations, pose, and binaural audio in indoor 3D environments. The benchmark supports both ordered and unordered missions, where alternating goal-associated sounds can guide non-line-of-sight search but may also become distractors as mission progress changes. We further develop PAG-Nav, a training-free reference agent that maintains a temporal uniform semantic map and performs progressive goal-state planning, using sound for search while reserving completion for visual-semantic verification. Experiments show that existing vision-language, memory-based, and audio-visual agents struggle to complete full LH-AVLN missions, and that PAG-Nav provides a stronger diagnostic baseline while leaving substantial room for future progress.

cs.RO↗
arXiv2026

GII-Polar Codes for Block Fading Channels

Polar codes are proven to be capacity-achieving codes, being gradually practiced in wireless communications. However, their successive cancellation (SC) and successive cancellation list (SCL) decoding incur latency challenge especially for long codes. This paper proposes the generalized integrated interleaved (GII)-polar codes for block Rayleigh fading channels, yielding both reduced decoding latency and competent decoding performance. Under the GII paradigm, two consecutive polar codewords of length $N$ are virtually coupled through a nested codebook which has a lower dimension, capable of correcting more errors. The component polar codes are known as interleaves of a GII-polar code. If decoding of an interleave fails, it can be projected into the decoding of the nested one, enabling richer error patterns to be corrected by each interleave. Since decoding of the interleaves can be performed in parallel, GII-polar codes yield a reduced decoding latency over a single polar code of length $2N$. Our simulation results validate both the decoding latency and performance merits of the proposed coding scheme under block Rayleigh fading channel.

cs.IT↗
arXiv2026

Achievable-Rate Analysis of MISO Systems with Transmit-Side Multiport Matching Networks

Characterizing communication performance under the physical constraints imposed by radio frequency front-end circuits is essential for bridging communication-theoretic analysis and practical circuit design. In this work, we investigate the achievable-rate upper bound of a multiple-input single-output (MISO) system with a transmitter-side interconnected multiport matching network (MMN) under multiport Bode--Fano constraints and develop a rate-oriented MMN circuit realization method. First, based on a circuit-theoretic communication model and the concept of directional gain from multivariable control theory, we reveal how the directional characteristics of MMN power transmission interact with wireless propagation to affect communication performance. Then, building on this directional-gain interpretation, we reformulate the matrix-valued functional optimization problem that characterizes the achievable-rate upper bound and derive the optimal transmission-coefficient structure. Further, a greedy constraint-wise repair method is developed to obtain a feasible suboptimal solution. Finally, through a rate-oriented MMN circuit realization method, we demonstrate that the derived theoretical insights provide effective guidance for practical MMN design. Numerical results validate the theoretical analysis of the achievable-rate upper bound and the effectiveness of the proposed MMN circuit realization method.

eess.SP↗
arXiv2026

UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies

Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical. Since such dense labels are rarely available at scale, normalized time within a demonstration is often used as a scalable substitute: later frames are treated as higher progress. However, this time-derived label is only a noisy proxy for physical task progress. In contact-rich manipulation, a robot may make progress and then lose it through slips, failed grasps, or partial undoing, while the time-derived label continues to increase monotonically. We introduce Unsupervised Robotic Value Correction (UR-VC), an offline, training-free method for correcting time-derived progress labels. UR-VC exploits a simple regularity in demonstration data: similar states often recur across different episodes, but at different timestamps. Instead of trusting the timestamp from a single trajectory, UR-VC retrieves similar states from other episodes and aggregates their time-derived labels to obtain a corrected progress estimate. UR-VC requires no manual progress labels, reward annotations, or additional value model. We evaluate UR-VC on real bimanual cloth flatten-and-fold data, a long-horizon deformable-object manipulation task with visible intermediate progress. The corrected labels capture local regressions and non-uniform progress that normalized time cannot represent, while preserving the overall task trend. We further use the corrected signal to construct advantage labels for VLA training, following recent advantage-conditioned policy learning. UR-VC shows a positive trend in real-robot task success under matched data, model, and training settings.

cs.RO↗