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Tao Chen

Publications and source records attributed to Tao Chen.

At least 109 records · Page 6Linked to original sources
arXiv2026

Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition

Multi-agent systems (MAS) demonstrate clear advantages in tackling complex problems by coordinating diverse agents and external tools. However, most existing orchestration methods rely on static workflows or serial agent scheduling, and are further constrained by heterogeneous interface protocols between tools and agents. This leads to high system complexity and poor extensibility. To mitigate these issues, we propose Agent-as-Tool, a unified parallel orchestration paradigm that abstracts both agents and tools into a standardized, learnable action space with protocol normalization and explicit state feedback. Building on this paradigm, we train a lightweight orchestrator, ParaManager, which decouples planning decisions from subtask solving, enabling state-aware parallel subtask decomposition, delegation, and asynchronous execution. For training, we adopt a two-stage ParaManager training pipeline. It improves robustness by incorporating supervised fine-tuning (SFT) trajectories equipped with recovery mechanisms, and further applies reinforcement learning (RL) to achieve an optimal balance among task success, protocol compliance, diversity, and reasoning efficiency. Experiments show that ParaManager achieves strong performance across multiple benchmarks and exhibits robust generalization under unseen model pools.

cs.AI↗
arXiv2026

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges

Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multimodal large language models (MLLMs) toward human-preferred behaviors. However, these approaches introduce a systemic vulnerability: reward hacking, where models exploit imperfections in learned reward signals to maximize proxy objectives without fulfilling true task intent. As models scale and optimization intensifies, such exploitation manifests as verbosity bias, sycophancy, hallucinated justification, benchmark overfitting, and, in multimodal settings, perception--reasoning decoupling and evaluator manipulation. Recent evidence further suggests that seemingly benign shortcut behaviors can generalize into broader forms of misalignment, including deception and strategic gaming of oversight mechanisms. In this survey, we propose the Proxy Compression Hypothesis (PCH) as a unifying framework for understanding reward hacking. We formalize reward hacking as an emergent consequence of optimizing expressive policies against compressed reward representations of high-dimensional human objectives. Under this view, reward hacking arises from the interaction of objective compression, optimization amplification, and evaluator--policy co-adaptation. This perspective unifies empirical phenomena across RLHF, RLAIF, and RLVR regimes, and explains how local shortcut learning can generalize into broader forms of misalignment, including deception and strategic manipulation of oversight mechanisms. We further organize detection and mitigation strategies according to how they intervene on compression, amplification, or co-adaptation dynamics. By framing reward hacking as a structural instability of proxy-based alignment under scale, we highlight open challenges in scalable oversight, multimodal grounding, and agentic autonomy.

cs.LG↗
arXiv2026

Universal thermometry of solid-liquid interfacial thermal conductance

Solid-liquid interfacial thermal conductance (ITC) critically influences heat transport in microfluidic, electronic, and energy systems, yet most optical thermometry techniques are limited to specific metal-liquid interfaces. In this work, we introduce a universal broadband square-pulsed thermometry method that enables simultaneous quantification of ITC across a wide range of arbitrary solid-liquid interfaces, while also providing accurate measurements of nanoscale liquid-film thickness. To validate the method, we applied it to Al-water interfaces, yielding ITC values in the range of 50-55 MW m^(-2) K^(-1), consistent with prior studies. The technique also reveals markedly lower ITCs for glass-water (9.9 MW m^(-2) K^(-1)) and Si-water (5.7 MW m^(-2) K^(-1)), and further measurements on Al-silicone oil (~10 MW m^(-2) K^(-1)) and PMMA-silicone oil (~0.4 MW m^(-2) K^(-1)) extend the validation to highly viscous nonpolar liquids and polymer-liquid interfaces. These results highlight the capability of the method to capture thermal transport differences across diverse solid-liquid combinations. Further comparisons with acoustic/diffuse mismatch models and molecular dynamics simulations, together with theoretical analysis, highlight the influence of vibrational mismatch, wettability, and surface condition on interfacial thermal transport. This broadly applicable technique enables rapid, quantitative characterization of solid-liquid interfacial thermal transport, with broad implications for interfacial heat transfer science and technology.

physics.app-ph↗
arXiv2026

Simultaneous, Non-Contact Measurement of Liquid and Interfacial Thermal Properties via a Differential Square-Pulsed Source Method

Accurate characterization of heat transport across solid-liquid interfaces is essential for thermal management in micro and nanoscale systems. Yet existing techniques often require prior knowledge of liquid properties, which complicates the simultaneous resolution of interfacial and bulk behaviors, and lose sensitivity once interfacial conductance exceeds 100 MW m-2 K-1. Here we present a differential square pulsed source (DSPS) method that provides simultaneous, non-contact measurement of liquid thermal conductivity, volumetric heat capacity, and solid-liquid interfacial conductance without any predefined material parameters. Dual frequency excitation combined with in-situ substrate referencing enables property extraction from multilayer structures, and numerical simulations show a typical uncertainty of about 8 % in interfacial conductance, confirming robustness. The protocol is validated for a wide spectrum of liquids, including oils, lubricants, aqueous electrolytes, and pure water, with excellent agreement with literature values for bulk properties. Analysis of the data set clarifies how vibrational spectrum mismatch, ionic layering, and related interfacial phenomena govern heat transfer, and demonstrates that oleophilic hexadecyl silane modification of aluminum increases interfacial conductance by a factor of sixteen. The results reveal that conductance can be strongly tuned through surface wettability and chemical functionalization, offering direct guidelines for interface engineering. Because the approach is readily extendable to soft materials such as thermal interface gels, it promises broad applicability in emerging interface-dominated thermal technologies.

physics.app-ph↗
arXiv2026

A Variable-Spot-Size and Multi-Frequency Square-Pulsed Source (SPS) Approach for Comprehensive Characterization of Anisotropic Thermal Transport Properties in Multilayered Thin Films

Multilayered thin-film structures are frequently encountered in industrial applications, where accurate thermal property characterization is essential for performance optimization. These films, typically ranging from nanometers to micrometers in thickness, often exhibit anisotropic thermal conductivity and non-bulk heat capacity, which are challenging to measure. In this study, we introduce a variable-spot-size and multi-frequency square-pulsed source (SPS) method for the simultaneous determination of anisotropic thermal conductivities, heat capacities, and interfacial thermal conductance in multilayered systems. By leveraging a broad modulation frequency range (1 Hz to 10 MHz) and tunable laser spot sizes, the SPS method enhances sensitivity to different thermal parameters across layers. We validate this approach on a silicon-on-insulator (SOI) sample comprising a 1.59 um Si layer, 1.03 um SiO2 layer, and a silicon substrate with a 122 nm aluminum (Al) transducer. The SPS method successfully extracts seven key thermal parameters, including the in-plane and cross-plane thermal conductivities and heat capacity of the Si film, the thermal conductivity and heat capacity of the SiO2 layer, the thermal conductivity of the substrate, and the interfacial thermal conductance between Al and Si. Temperature-dependent measurements from 80 to 500 K showed excellent agreement with literature values and first-principles predictions, confirming the method's accuracy and reliability. These results demonstrate the SPS method as a powerful tool for comprehensive thermal characterization of complex multilayered structures, with implications for both fundamental research and practical applications.

physics.app-ph↗
arXiv2026

Anisotropic Thermal Characterization of Suspended and Spin-Coated Polyimide Films Using a Square-Pulsed Source Method

Polyimide (PI) thin films are widely used in advanced technologies, yet accurate characterization of their thermal properties remains challenging, as evidenced by significant inconsistencies in reported data and an incomplete understanding of heat transfer mechanisms. In this study, we employ an optical Square-Pulsed Source (SPS) technique to simultaneously measure the in-plane and cross-plane thermal conductivities, as well as the volumetric heat capacity, of PI thin films. SPS is a pump-probe method that utilizes a square-wave-modulated pump laser to induce periodic heating and a probe laser to detect the thermoreflectance response. Thermal properties are extracted by analyzing amplitude signals across multiple modulation frequencies and laser spot sizes. Measurements were conducted on both suspended commercial PI films and spin-coated PI films on fused silica substrates. The results show that spin-coated films exhibit higher cross-plane thermal conductivity and lower anisotropy compared to suspended films, which we attribute to differences in molecular orientation and substrate interactions. These findings provide new physical insights into anisotropic heat transport in polymer thin films and demonstrate the SPS technique as a robust tool for probing microscale thermal phenomena in soft materials.

physics.app-ph↗
arXiv2026

Trust-region filter algorithms utilizing Hessian information for gray-box optimization

Optimizing industrial processes often involves gray-box models that couple algebraic glass-box equations with black-box components lacking analytic derivatives. Such systems challenge derivative-based solvers. The classical trust-region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous black-box evaluations. This work introduces four Hessian-informed TRF variants that use projected positive definite Hessians for automatic step scaling and minimal tuning, combined with both low-fidelity (linear, quadratic) and high-fidelity (Taylor series, Gaussian process) surrogates for local black-box approximation. Tested on 25 gray-box benchmarks and five engineering case studies, the new variants achieved up to order-of-magnitude reductions in iterations and black-box evaluations, with reduced sensitivity to tuning parameters relative to the classical TRF algorithm. High-fidelity surrogates solved 92%-100% of problems, compared with 72%-84% for low-fidelity surrogates. The developed TRF methods also outperformed classical derivative-free optimization solvers. Results show that new variants offer robust, scalable alternatives for gray-box optimization.

math.OC↗
arXiv2026

A trust-region funnel algorithm for gray-box optimization

Gray-box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black-box models lacking analytic derivatives, remains a challenge. Trust-region (TR) methods provide a robust framework for gray-box problems through local reduced models (RMs) for black-box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel-based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray-box optimization, replacing the filter acceptance criterion with a uni-dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented open-source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray-box optimization.

math.OC↗
arXiv2026

ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling

Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable progress in image-language tasks. However, for the video, existing methods still fall short of high-ratio token compression. We attribute this shortcoming to the insufficient modeling of temporal and continual video content, and propose a novel and training-free token pruning method for video MLLMs, termed ForestPrune, which achieves effective and high-ratio pruning via Spatial-temporal Forest Modeling. In practice, ForestPrune construct token forests across video frames based on the semantic, spatial and temporal constraints, making an overall comprehension of videos. Afterwards, ForestPrune evaluates the importance of token trees and nodes based on tree depth and node roles, thereby obtaining a globally optimal pruning decision. To validate ForestPrune, we apply it to two representative video MLLMs, namely LLaVA-Video and LLaVA-OneVision, and conduct extensive experiments on a bunch of video benchmarks. The experimental results not only show the great effectiveness for video MLLMs, e.g., retaining 95.8% average accuracy while reducing 90% tokens for LLaVA-OneVision, but also show its superior performance and efficiency than the compared token compression methods, e.g., +10.1% accuracy on MLVU and -81.4% pruning time than FrameFusion on LLaVA-Video.

cs.CV↗
arXiv2026

PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding

While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information decay and unstructured feed-forward network (FFN) weights leading to semantic fragmentation. Inspired by the brain's working memory and cortical modularity, we propose PaceLLM, featuring two innovations: (1) a Persistent Activity (PA) Mechanism that mimics prefrontal cortex (PFC) neurons' persistent firing by introducing an activation-level memory bank to dynamically retrieve, reuse, and update critical FFN states, addressing contextual decay; and (2) Cortical Expert (CE) Clustering that emulates task-adaptive neural specialization to reorganize FFN weights into semantic modules, establishing cross-token dependencies and mitigating fragmentation. Extensive evaluations show that PaceLLM achieves 6% improvement on LongBench's Multi-document QA and 12.5-17.5% performance gains on Infinite-Bench tasks, while extending measurable context length to 200K tokens in Needle-In-A-Haystack (NIAH) tests. This work pioneers brain-inspired LLM optimization and is complementary to other works. Besides, it can be generalized to any model and enhance their long-context performance and interpretability without structural overhauls.

q-bio.NC↗
arXiv2026

Crosstalk-robust superconducting two-qubit geometric gates using tunable couplers

The design of coupler-based superconducting two-qubit gates simplifies circuit layout and alleviate frequency crowding, thereby enhancing the scalability and flexibility of quantum chips. However, in such architectures, a trade-off often exists between suppressing crosstalk and reducing gate duration, and how to achieve synergistic optimization of both remains an open challenge. To address this, this paper proposes a coupler-assisted superconducting two-qubit geometric gate scheme oriented towards crosstalk robustness. By introducing additional parametric degrees of freedom, the scheme steers the system evolution along desired trajectories, thereby flexibly avoiding crosstalk-sensitive operational regions. Numerical simulations demonstrate that the proposed scheme can effectively suppress crosstalk errors while enabling fast gate operations, and exhibits strong robustness against typical experimental imperfections such as qubit frequency drift. Moreover, even when accounting for unavoidable high-frequency oscillation terms and qubit decoherence in realistic physical systems, our crosstalk-robust two-qubit geometric gates still achieve high fidelity. This work provides a feasible pathway toward robust and efficient two-qubit gate implementation in superconducting quantum computation.

quant-ph↗
arXiv2026

Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval

Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient paradigm to remedy this shortcoming, termed One-shot video-Clip based Retrieval-Augmented Generation (OneClip-RAG). Compared with existing video RAG methods, OneClip-RAG makes full use of the merits of video clips for augmented video understanding in terms of both knowledge integrity and semantic coherence. Besides, it is also equipped with a novel query-guided video chunking algorithm that can unify clip chunking and cross-modal retrieval in one processing step, avoiding redundant computations. To improve instruction following, we further propose a new dataset called SynLongVideo and design a progressive training regime for OneClip-RAG. OneClip-RAG is plugged into three recent MLLMs and validated on a set of long-video benchmarks. Experimental results not only show the obvious performance gains by OneClip-RAG over MLLMs, e.g., boosting Qwen3-VL 8B to the level of GPT-5 on MLVU, but also show its superior efficiency in handling long videos. e.g., enabling LLaVA-Video understand up to an hour of videos in less than 1.2 minutes on a single 4090 GPU.

cs.CV↗
arXiv2026

MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.

cs.CL↗
arXiv2026

Percolation in the three-dimensional Ising model

Geometric representations provide a useful perspective on critical phenomena in the Ising model. In a recent study [Phys. Rev. E 112, 034118 (2025)], we found that the two-dimensional critical Ising model exhibits two consecutive percolation transitions for geometric spin clusters as the bond-occupation probability $p$ between parallel spins increases. Here, through extensive Monte Carlo simulations, we show that this phenomenon does not persist in three dimensions, where we observe only a single percolation transition on critical Ising configurations. Further theoretical analysis of the Ising model on the complete graph also yields the same scenario. In addition, we study percolation on a two-dimensional layer embedded in the three-dimensional critical Ising model. For this layer system, we estimate the red-bond exponent $y_p = 0.426(6)$ and the fractal dimensions of the largest cluster, hull, and shortest path as $d_f = 1.8926(20)$, $d_{\rm hull} = 1.663(4)$, and $d_{\rm min} = 1.080(10)$, respectively. These values indicate a distinct universality class induced by coupling to out-of-plane critical correlations.

cond-mat.stat-mech↗
arXiv2026

Leakage Suppression in Quantum Control via Static Parameter Offsets

High-fidelity quantum operations require the system dynamics to be strictly confined to the computational subspace. In practice, however, control fields inevitably couple to leakage levels, giving rise to quantum state leakage that significantly reduces the fidelity of the operation. To address this challenge, we propose a general strategy for actively suppressing leakage errors by applying small, static offsets to tunable system parameters. This approach systematically mitigates leakage's detrimental impact on quantum control, without modifying the original control framework or incurring additional time overhead. By avoiding the need for extra suppression pulses or complex optimization procedures altogether, it offers a streamlined solution for leakage compensation while remaining fully compatible with subsequent optimal control techniques. Numerical validation conducted on superconducting quantum circuits demonstrates effective leakage suppression, enabling high-fidelity single-qubit gates, precise control of two-qubit interactions, and perfect state transfer in multi-level systems. Moreover, when integrated with optimal control techniques, our approach also allows for the cooperative suppression of both leakage errors and residual crosstalk. Therefore, this work provides a feasible technical pathway toward the low error thresholds required for fault-tolerant quantum computation.

quant-ph↗
arXiv2026

Universal Robust Quantum Gates via Doubly Geometric Control

Geometric quantum computation offers a potential route to fault-tolerant quantum information processing by exploiting the global nature of geometric phases. However, achieving controlled high-order suppression of multiple error sources remains a long-standing limitation, particularly in realistic large-scale circuits with complex noise environments. This limitation is largely due to the absence of a general framework that directly characterizes error accumulation and enables systematic improvement. Here we establish such a framework for universal doubly geometric gates by embedding target operations into a hierarchy of level-n identity constructions. This approach enables direct quantification of error accumulation while removing structural constraints inherent in previous schemes. We analytically show that the defining conditions lead to simultaneous fourth-order suppression of control errors, with a systematic extension to sixth-order suppression via higher-level constructions. Our results establish doubly geometric control as a general and scalable route toward high-order robust quantum gates, with potential implications for fault-tolerant quantum information processing.

quant-ph↗
arXiv2026

Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism

Long video understanding is a key challenge that plagues the advancement of \emph{Multimodal Large language Models} (MLLMs). In this paper, we study this problem from the perspective of visual memory mechanism, and proposed a novel and training-free approach, termed \emph{Flexible Memory} (\textbf{FlexMem}). In principle, FlexMem aims to mimic human behavior of video watching, \emph{i.e.}, continually watching video content and recalling the most relevant memory fragments to answer the question. In this way, FlexMem can help MLLMs achieve video understanding of infinite lengths, unlike previous methods that process all video information at once and have input upper-limit. Concretely, FlexMem first consider the visual KV caches as the memory sources, and realize the effective memory transfer and writing via a dual-pathway compression design. Afterwards, FlexMem also explores different memory reading strategies for the diverse video understanding tasks, including the popular streaming one. To validate FlexMem, we apply it to two popular video-MLLMs, and conduct extensive experiments on five long video and one streaming video task. The experimental results show that on \textbf{a single 3090 GPU}, our FlexMem can achieve obvious improvements than existing efficient video understanding methods and process more than \textbf{1k frames}, which also helps the base MLLMs achieve comparable or even better performance than SOTA MLLMs on some benchmarks, \emph{e.g.} , GPT-4o and Gemini-1.5 Pro.

cs.CV↗
arXiv2026

Simultaneous measurement of pressure-dependent bulk and interfacial thermal properties in thermal interface materials using square-pulsed source thermoreflectance

Thermal interface materials (TIMs) critically regulate heat dissipation from electronic chips to heat spreaders, yet their thermal conductivity (k), volumetric heat capacity (C), and interfacial thermal resistance (ITR) evolve with mechanical pressure and cannot be determined simultaneously using existing steady-state or transient techniques. As a result, the coupled roles of bulk compaction and interfacial contact in governing heat transport in TIM assemblies remain poorly resolved. Here, we present a square-pulsed source (SPS) thermoreflectance method that enables simultaneous determination of k, C, and ITR in TIM stacks under controlled mechanical loading. By spanning square-wave modulation frequencies from 1 Hz to 10 MHz, SPS probes a broad range of thermal penetration depths, enabling distinction between heat diffusion in the TIM bulk and interfacial heat transfer at the Al/TIM contact. Measurements on a thermally conductive gel, a thermal pad, and a high-vacuum grease during compression-unloading cycles reveal distinct pressure-dependent thermal transport mechanisms. The gel and pad exhibit increases in k and C, reduced ITR, and pronounced hysteresis, indicating coupled bulk densification and persistent interfacial conformity during loading cycles. In contrast, the grease shows nearly pressure-independent bulk properties but a strong pressure dependence of ITR, consistent with an interface-dominated response. These results resolve the long-standing challenge of simultaneously quantifying bulk and interfacial thermal transport in mechanically loaded TIM assemblies, enabling experimentally constrained thermal management and reliability analysis in electronic packaging.

physics.app-ph↗