arXiv subjects

Li Chen

Publications and source records attributed to Li Chen.

At least 55 records · Page 3Linked to original sources

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↗

arXiv2026

Technology Fundamentals and False Bubble Detection: Evidence from Dot-Com and AI Episodes

We show that widely used bubble tests, most prominently the PSY framework, suffer severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, thereby altering the asymptotic null distribution of the test statistic and causing the standard bubble test to overreject. We propose a technology-adjusted diagnostic that removes an estimated technology component from measures of productivity, IT-investment, and patents before testing the residual. The adjustment is conservative: because a boom can itself raise these technology measures, a rejection remains robust to such feedback, whereas a non-rejection only bounds residual explosiveness. Dot-com residual explosiveness concentrates in December 1999-March 2000; the 2020-2025 AI rally shows no residual explosiveness in our sample across baseline and sensitivity checks.

econ.GN↗

arXiv2026

Revisiting the Radial Velocities of Nearby Open Clusters using Gaia DR3

Open clusters (OCs) are essential laboratories for probing stellar dynamics and tracing the structure and evolution of the Milky Way. Accurate measurements of their average radial velocities (RVs) and RV dispersions are crucial for estimating dynamical masses, orbital evolution, and overall kinematic states. Gaia Data Release 3 (DR3) provides an unprecedented volume of high-precision RVs. However, when applied to OCs, Gaia DR3 RVs often yield unusually large and overestimated RV dispersions. This inflation is primarily driven by RV measurement systematics for hot and faint stars, as well as unresolved binary contamination. To mitigate this, we revisit the average RVs and RV dispersions of OCs within 500 pc of the Sun using Gaia DR3. We evaluate the reliability of RV measurements and employ a color-based filtering method. By selecting member stars within an intrinsic color range of $0.2 \le (BP-RP)_{0} \le 1.2$ mag, we exclude hot and cool stars with less reliable RVs. This filtering significantly reduces the inferred RV dispersions while maintaining average RVs consistent with previous literature. Specifically, the median RV dispersion decreases by 26%, dropping from 3.76 km s$^{-1}$ prior to filtering to 2.79 km s$^{-1}$ afterward. These filtered RV dispersions remain systematically larger than tangential velocity dispersions, likely due to residual biases and undetected binaries. However, RV dispersions measured exclusively from red clump giants (found in 6 clusters) are remarkably small ($\lesssim 1.6$ km s$^{-1}$) and align closely with tangential dispersions ($\lesssim 1$ km s$^{-1}$). Ultimately, we provide a practical, Gaia-only strategy to derive more realistic RV dispersions for OCs, identifying red clump giants as exceptionally high-fidelity kinematic tracers for robust cluster studies.

astro-ph.GA↗

arXiv2026

Presence versus absence of charging energies in PbTe quantum dots

Charging energy ($E_C$) is essential in quantum dot (QD) devices. Previous studies on PbTe QDs have reported both the presence and absence of $E_C$. To resolve this ambiguity, we vary the QD size, i.e. the cross-sectional area of PbTe nanowires, and track the evolution of $E_C$. For large crosssectional areas ($\sim$ 16000 nm$^2$), the PbTe QDs exhibit no measurable $E_C$, while quantized levels are well resolved. Decreasing this area successively to 5000, 1500, and 460 nm$^2$, $E_C$ becomes finite and increases to 80, 160, and 210 $μ$eV, respectively. We further demonstrate the strong tunability of local gates, which can tune the PbTe device from the QD regime to the regime of ballistic transport. These results address concerns regarding the large dielectric constant of PbTe and provide key insights in engineering advanced PbTe quantum devices.

cond-mat.mes-hall↗

arXiv2026

Spin-orbit coupling induced geometric squeezing in rotating Bose-Einstein condensates

Squeezed states play a key role in diverse frontiers of quantum physics. Geometrically squeezed states, a squeezed state in the orbital phase space of rotating Bose-Einstein condensates (BEC), have been conventionally generated by anisotropic trapping potentials. In this work, we propose a different route to generate geometric squeezing via spin-orbit coupling (SOC) in a pseudospin-1/2 BEC. We show that the SOC enables effective two-phonon transitions within the lowest Landau level via virtual spin-flip processes, leading to exponential squeezing dynamics in both spin components. Furthermore, by applying a $π/2$ spin rotation, the two spin channels can be coherently coupled to produce two-mode geometric squeezing. We also investigate the influence of interatomic interactions on squeezing performance and identify parameters where robust squeezing can be achieved. Our work provides a viable pathway to realize and manipulate geometric squeezing in spinor quantum gases.

cond-mat.quant-gas↗

arXiv2026

DualEval: Joint Model-Item Calibration for Unified LLM Evaluation

Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better reflect open-ended user interactions. We introduce DualEval, a latent model-item calibration framework that represents models and evaluation items in a shared space, jointly estimating model ability together with item difficulty and sharpness. We apply DualEval across four domains: coding, math, miscellaneous domain-knowledge tasks, and generic everyday user queries. Our evaluation uses 18 frontier LLMs, static benchmark labels, and reward-model scores validated against held-out human preferences for open-ended model responses. Empirically, our framework produces reliable and balanced model rankings, and its learned item-level profiles support downstream applications such as benchmark compression for sample-efficient evaluation and anomaly detection for contamination or outlier analysis. Overall, DualEval unifies static and arena-style evaluation through joint model-item calibration, producing model rankings and item-level diagnostics that support more sample-efficient, interpretable, and auditable evaluation pipelines.

cs.LG↗