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Chenyu Zhou

Chenyu Zhou contributes to research discovery and scholarly infrastructure.

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Artificial Intelligence Computation and Language Machine Learning cond-mat.supr-con math.OC

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preprint2026arXiv

Effects of Integrated Heatsinking on Superconductivity in Tantalum Nitride Nanowires at the 300 Millimeter Scale

We report the superconducting properties of tantalum nitride (TaN) nanowires and TaN/copper (TaN/Cu) bilayer nanowires fabricated on 300 mm silicon wafers using CMOS-compatible processes. We evaluate how an integrated Cu heatsink modifies the superconducting response of TaN nanowires by improving thermal dissipation without significantly compromising key superconducting parameters. Through analysis of hysteresis in current-voltage curves, we demonstrate that Cu integration improves heat dissipation, supporting expectations of faster reset times in superconducting nanowire single-photon detectors (SNSPDs), consistent with enhanced heat transfer away from the hot spot. Using the Skocpol-Beasley-Tinkham (SBT) hotspot model, we quantify the Cu-enabled improvement in heat transfer as an approximately 100x increase in the SBT slope parameter beta and effective interfacial heat-transfer efficiency compared to TaN nanowires. The near-unity ratio of critical to retrapping current in TaN/Cu bilayer nanowires provides another evidence of efficient heat removal enabled by the integrated Cu layer. Our results show a zero-temperature Ginzburg-Landau coherence length of 7 nm and a critical temperature of 4.1 K for 39 nm thick TaN nanowires. The nanowires show <5% variation in critical dimensions, room-temperature resistance, residual resistance ratio, critical temperature, and critical current across the 300 mm wafer for all measured linewidths, demonstrating excellent process uniformity and scalability. These results indicate the trade-offs between superconducting performance and heat-sinking efficiency in TaN/Cu bilayer nanowires. They also underscore the viability of wafer-scale fabrication for fast, large-area SNSPD arrays for applications in photonic quantum computing, cosmology, and neuromorphic computing devices.

preprint2026arXiv

From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling

Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural-language requirements remains challenging for current large language models (LLMs). In this paper, we propose \emph{Agora-Opt}, a modular agentic framework for optimization modeling that combines decentralized debate with a read-write memory bank. Agora-Opt allows multiple agent teams to independently produce end-to-end solutions and reconcile them through an outcome-grounded debate protocol, while memory stores solver-verified artifacts and past disagreement resolutions to support training-free improvement over time. This design is flexible across both backbones and methods: it reduces base-model lock-in, transfers across different LLM families, and can be layered onto existing pipelines with minimal coupling. Across public benchmarks, Agora-Opt achieves the strongest overall performance among all compared methods, outperforming strong zero-shot LLMs, training-centric approaches, and prior agentic baselines. Further analyses show robust gains across backbone choices and component variants, and demonstrate that decentralized debate offers a structural advantage over centralized selection by enabling agents to refine candidate solutions through interaction and even recover correct formulations when all initial candidates are flawed. These results suggest that reliable optimization modeling benefits from combining collaborative cross-checking with reusable experience, and position Agora-Opt as a practical and extensible foundation for trustworthy optimization modeling assistance. Our code and data are available at https://github.com/CHIANGEL/Agora-Opt.

preprint2026arXiv

OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention

Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall. The Delta Rule mitigates this by writing each token via one step of online gradient descent. However, its step size relies on a single scalar gate that ignores the feature-wise curvature of the inner objective. We propose Online Scaled DeltaNet (OSDN), which augments the scalar gate with a diagonal preconditioner updated online via hypergradient feedback. Crucially, this right-preconditioning is algebraically equivalent to a per-feature scaling of the write-side key. This equivalence allows OSDN to strictly preserve the hardware-friendly chunkwise parallel pipeline of DeltaNet without incurring high-dimensional state overhead. Theoretically, by exploiting the exact-quadratic structure of the inner regression loss, we establish super-geometric convergence against a right-Newton comparator and prove an algorithm-aligned token-local residual contraction bound. To handle non-stationary contexts, we further introduce Adaptive Preconditioner Forgetting (APF) to dynamically refresh stale calibration. Empirically, OSDN demonstrates strong performance across scales. At the 340M-parameter scale, OSDN improves JRT-style in-context recall by 32% over DeltaNet. Scaling to 1.3B parameters, it achieves a 39% reduction in the recall residual ratio while maintaining parity on general downstream tasks (e.g., perplexity and LongBench) -- demonstrating that our online-preconditioning mechanism effectively transfers and amplifies at the billion-parameter scale.

preprint2026arXiv

Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents

LLM agents benefit from reusable skills, yet test-time tasks often require guidance more specific than a static skill library can provide. We propose \emph{SkillTTA}, a Test-Time Adaptive Skill Synthesis method that retrieves a small set of training trajectories relevant to the current task and synthesizes them into a temporary, task-specific textual skill. The solver model is kept fixed, so adaptation happens entirely through generated context rather than parameter updates. We evaluate the method on SpreadsheetBench, ALFWorld, and BigCodeBench. Compared with static trajectory-to-skill synthesis using GPT-5.5, task-specific skills improve SpreadsheetBench Pass@1 from 0.397 to 0.505 and BigCodeBench Pass@1 from 0.517 to 0.651. On ALFWorld, the method matches a heavier memory-learning baseline within four points of success rate while producing the shortest successful trajectories among reported methods. Ablations on SpreadsheetBench further show that synthesized skills outperform raw trajectory prompting, that top-$k$ retrieval should stay small, and that failed trajectories are especially useful because they expose recurring evaluator-facing mistakes.

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