arXiv subjects

Bo Zou

Publications and source records attributed to Bo Zou.

At least 19 recordsLinked to original sources

arXiv2026

Evidence for a charged vortex lattice in an exciton superfluid

Lattices of quantized vortices are a defining property of rotating superfluids and of type-II superconductors in magnetic fields. Realizing an analogous vortex lattice in an exciton superfluid-formed from bound electron-hole pairs-has remained challenging because excitons are electrically neutral. Building on previous theoretical studies of the low-energy excitations in an exciton superfluid, recent theory has predicted that in a strong magnetic field, a weakly charged exciton superfluid can stabilize a vortex-antivortex lattice arising from the mutual repulsion of fractionally charged vortex cores. Here we report evidence for this predicted vortex lattice in a bilayer exciton condensate by imaging its charged vortices with the scanning tunnelling microscope. The condensate forms between Landau levels of two graphene layers separated by a thin hexagonal boron nitride barrier in a perpendicular magnetic field. We find that the vortex lattice periodicity scales with the total excess charge and is insensitive to its distribution between the two layers, in quantitative agreement with theoretical predictions. Our observations provide evidence for phase coherence in an exciton superfluid system.

cond-mat.str-el↗

arXiv2026

Dual-Agent Reinforcement Learning for Vehicle Scheduling and Multi-Energy Dispatch in Electric-Hydrogen-Transport Systems

Electric-hydrogen-transport systems (EHTSs) require coordinated scheduling of electric and hydrogen vehicles together with multi-energy dispatch. These operations form a sequential decision process: upstream vehicle scheduling (VS) determines service allocation and order, thereby endogenously shaping the electricity and hydrogen demands faced by downstream energy dispatch (ED). This paper develops a dual-agent reinforcement learning framework tailored to the heterogeneous decision structures. The vehicle-scheduling agent combines proximal policy optimisation (PPO) with a Plackett-Luce policy to learn vehicle-priority permutations, which are then mapped to feasible charger and refueller assignments through a greedy allocation. The energy-dispatch agent employs twin delayed deep deterministic policy gradient (TD3) for continuous storage and energy-flow control. The directional coupling between the two decision layers is analysed, revealing that updates to the upstream scheduling policy can shift the demand distribution experienced by the downstream dispatcher. A three-stage training mechanism is therefore introduced, combining layer-wise pretraining with concurrent fine-tuning to mitigate the effects of scheduling-induced distribution shifts on downstream ED learning. Year-long case studies show that the proposed framework achieves the lowest vehicle-service delay and energy cost among the 11 evaluated methods, demonstrating effective coordination between vehicle scheduling and multi-energy dispatch.

eess.SY↗

arXiv2026

After One-Click Video: The Inflow of Service-Type Content, the Failure of the Recommendation Pool Mechanism, and the BoLe System

In September 2026, a new large language model was released. Soon after, a new way of making explainer videos became very popular: people asked the model to write a program, and the program produced the video. "One-click video" content then appeared in large amounts on many platforms. On the surface, this is a rise in creative power at the level of the individual. We argue that, at a deeper level, it changes the structure of the content industry. This judgment rests on two observations. First, this wave of content is service-type content. People watch it to meet a need, not to enjoy the content itself. The other kind is work-type content. AI makes both kinds faster to produce. But anyone can come in and make service-type content, while work-type content is limited by the number of people who have something to say. So the ratio between the two slowly becomes unbalanced. Second, free platforms choose content through layers of trial viewing, and this uses up user attention. The total amount of attention is fixed. The unbalanced ratio also dilutes good content. A simplified mathematical model shows that, as long as these conditions hold, the recommendation pool mechanism will fail once new content grows past a certain multiple. Based on this, we discuss one possible way out. Machines and entry rules judge whether service-type content passes the bar. Trusted people judge work-type content. We call this the BoLe system. At the end, we briefly imagine how the two kinds of content may develop from here.

cs.IR↗

arXiv2026

WORTH Benchmark: Evidence Discovery under Not-Fully-Cooperative Information -- A Two-Episode Pilot

Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently how models discover evidence autonomously and how information presentation within narratives guides or obscures judgment. This paper proposes the WORTH Benchmark: models read original mystery narratives without clue annotations, autonomously select key clue sentences, identify case participants, and explain their reasoning process. The narrative materials are designed by professional authors such that evidence facilitating reasoning coexists with narrative arrangements that delay the revelation of truth. We report a pilot experiment under full-story conditions across two episodes, conducting a total of 70 formal runs on models, of which 67 runs are content-scorable. Scoring separately records hits on author-designated key clue sentences and participant selections; natural language explanations are used for case diagnosis and are not scored independently. The results show that clue hits and participant judgments are not synchronized; across 37 content-scorable runs in Episode 02, 31 runs simultaneously selected the same set of incorrect participants, while adjacent suspicion-oriented and exoneration-oriented sentences were selected 25 times and 0 times, respectively. These phenomena illustrate the diagnostic value of component-wise analysis and suggest that not-fully-cooperative informational relationships may influence evidence selection and judgment. This study only implements full-story conditions for two episodes, and does not establish stable model rankings or infer general mechanisms on this basis; human player records have not yet been cleaned, and this version does not report human-model comparisons.

cs.CL↗

arXiv2026

Multimodal Visual Sensing of Temperature and Pressure: From Spectroscopic Readout to Multiple Linear Regression-Enhanced RGB Analysis

Transforming a single luminescent host into distinct temperature- and pressure-sensing systems through composition control provides a powerful route toward multifunctional optical sensors. Here, we introduce a concentration-tunable KGaGeO4:Bi3+,Eu3+ platform whose sensing function can be selectively directed toward thermometry or manometry by adjusting the dopant balance. Spectroscopically distinct Bi3+ centers and Eu3+ emission exhibit differentiated responses to temperature and pressure, enabling multimodal readout through ratiometric luminescence, chromaticity coordinates, spectral shifts, and visible color changes. Importantly, these color changes were translated into quantitative temperature and pressure maps using RGB imaging combined with multiple linear regression (MLR). By simultaneously exploiting multiple color channels, MLR increased the maximum relative thermal sensitivity from approximately 1% K-1 for conventional RGB ratios to 8.8% K-1 and the pressure sensitivity from 169% GPa-1 to nearly 721% GPa-1. This work presents the first application of MLR-assisted RGB analysis for quantitative luminescence pressure sensing. The proposed strategy integrates composition-controlled functionality, multimodal spectroscopic sensing, direct visual readout, and data-assisted imaging, demonstrating the synergy between material engineering and multivariate analysis for highly sensitive multifunctional optical sensing.

cond-mat.mtrl-sci↗

arXiv2026

Interlayer Exciton Condensate Stiffness Is Non-Monotonic in Quantum Metric

Identifying the origin of the superfluid stiffness of electron-electron and electron-hole pair condensates is an important issue in flat-band physics. Here, we study the stiffness of bilayer exciton condensates using exact diagonalization and realistic Coulomb interactions across a wide variety of flat Chern band systems, including Landau levels, mixed Landau levels, and moiré bands. We find that stiffness is non-monotonic in the trace of the quantum metric and that it develops peaks when the band wavefunctions are engineered to be similar to those of Landau levels. The stiffness predicted by mean-field theory agrees quantitatively with exact diagonalization in these optimal cases, but systematically overestimates it otherwise. Flat bands with identical quantum geometry tensors can exhibit substantial differences in stiffness. The stiffness of condensates formed between moiré flat bands, which typically have strong variations in Berry curvature and quantum metric across their Brillouin zones, tends to be larger when the bands have non-zero Chern numbers and can be larger than that of Landau levels. Our results reveal a behavior that is richer than that suggested by simple geometric bounds and provide new guiding principles for the design of robust flat-band condensates with large superfluid stiffness.

cond-mat.mes-hall↗

arXiv2026

Electrically Tunable Two-Component Exciton Condensate in a Coulomb-Coupled Graphene Trilayer

Multicomponent condensates possess internal phase degrees of freedom unavailable to a single-component condensate, yet their components are rarely controllable in solids. Here we realize a graphene trilayer with negligible interlayer tunnelling in which the layer-specific carrier densities are continuously tuned by electrostatic gating. Quantum-capacitance measurements demonstrate that charge-incompressible quantum Hall states at total filling factors 1 and 2 persist across the full range of layer-filling configurations and continuously connect the three bilayer exciton-condensate limits. This persistence provides evidence for a trilayer excitonic state. Static Hartree-Fock and time-dependent Hartree-Fock calculations yield two independent finite phase-stiffness eigenmodes and two linearly dispersing Goldstone modes, respectively, when all three layers are partially filled, whereas only one phase-stiffness eigenmode and one linear Goldstone mode remain when one layer is unfilled. The stiffness eigenmodes rotate continuously between the two adjacent-layer exciton bases as charge is transferred among the layers, revealing electrical control of the condensate-mode composition. Together, the experimental and theoretical results support the identification of a two-component exciton condensate with a continuously tunable internal structure.

cond-mat.mes-hall↗

arXiv2026

Calibrated Surprise: An Information-Theoretic Account of Creative Quality

In the era of large language models, creative writing quality lacks a computable theoretical anchor. The dominant approaches are rubric scoring -- decomposing holistic aesthetic judgment into sub-scores -- and RLHF preference signals -- replacing quality with group votes. Both bypass the statistical structure of the text itself. This paper provides an information-theoretic foundation to fill this gap. We propose 'calibrated surprise' as the information-theoretic essence of excellent creative writing. This judgment matches reading intuition and covers its opposite. This literary judgment admits a precise mathematical formulation. Under full-dimensional constraints Y, feasible writing choices are forced into an extremely narrow space. The rare survivors are, from the unconstrained perspective, exactly the least predictable choices. Both are measured precisely by Shannon mutual information I(X;Y) = H(X) - H(X|Y) -- 'calibrated' corresponds to H(X|Y) approaching 0; 'surprising' corresponds to H(X) going high. The subtraction structure of the formula naturally separates 'well-grounded surprise' from 'pure noise'. We use token-level logprobs from Qwen1.5-7B as an operational proxy for the ideal reader's probability distribution. Across 20 pairs (12 Chinese / 8 English) of high-quality vs. systematically degraded literary passages, 20/20 pairs support the core prediction: high-quality passages have systematically higher I(X;Y) than their degraded versions.

cs.CL↗

arXiv2026

REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak

While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-level safety alignment by exploiting the internal generation process. To address these vulnerabilities, we propose Reflector, a principled two-stage framework that internalizes self-reflection within the generation trajectory. Reflector first leverages teacher-guided generation to produce high-quality reflection data for supervised fine-tuning (SFT), establishing structured reflection patterns. It subsequently uses Reinforcement Learning (RL) with outcome-driven and reward-validity supervision to instill robust, autonomous self-reflection capabilities. Empirical results show that Reflector achieves Defense Success Rates (DSR) exceeding 90% against complex indirect attacks while generalizing robustly across diverse threat scenarios. Notably, the framework enhances both task-specific and general utility, yielding a 5.85% gain on GSM8K alongside improved performance on knowledge-intensive benchmarks. By internalizing trajectory-level safety, Reflector overcomes the fundamental limitations of surface alignment without significant computational overhead, offering an efficient and scalable solution for the development of safe and capable LLMs.

cs.LG↗

arXiv2026

BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data

High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production methods each have structural limitations: crowdsourced annotation lacks deep reasoning paths; expert solo writing is constrained by the "expert blind spot" -- experts structurally skip reasoning steps they consider obvious; RLHF only produces preference signals rather than reasoning chains. This paper proposes the BC Protocol -- a structured dual-expert elicitation method for LLM post-training data production. The method carefully pairs a domain expert (crystallized intelligence) with a knowledge engineer (fluid intelligence), systematically externalizing the expert's implicit judgments as natural language reasoning chains. We introduce the Participant Aptitude Model, which defines six participant characteristic dimensions that affect elicitation quality. "Calibrated Ignorance" is an original concept proposed in this paper. We further propose "Selection-over-Prescription" as a methodological principle: for implicit knowledge elicitation tasks, investing quality-control resources in personnel selection yields a higher return than investing the same resources in process design. In a controlled experiment in the narrative fiction domain, we directly compared CoT produced by BC Protocol dual dialogue (Group A, (n=20)) against CoT written independently by the same domain expert (Group B, (n=20)). Three cross-vendor judge models -- GPT-4o, Claude Opus 4.5, and Gemini 2.5 Pro -- conducted blind evaluation across five dimensions (600 ratings total). Results show that the BC Protocol achieves an overwhelming advantage in "naturalness of reasoning process" (Group A mean 4.80 vs. Group B mean 1.30, (p=2.4\times10^{-8}), Cliff's (δ=1.0)).

cs.CL↗

arXiv2026

QUIET: A Multi-Blank Cascaded Story Cloze Benchmark for LLM Creative Generation Capability

Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure models' discriminative ability over narrative continuation using multiple-choice recognition paradigms, rather than directly measuring creative generation capability; rubric-based scoring and LLM-as-Judge methods rely on subjective dimension assessment or natural language model outputs, and cannot provide objective, automated scoring mechanisms. This paper proposes QUIET (Quality Understanding via Interlocked Evaluation Testing), a diagnostic benchmark for LLM creative capability based on multi-blank cascaded story cloze. QUIET sets N blanks (10-20) in a story with complete structure, with each blank accompanied by an explicit content constraint, and cascade dependency relationships between blanks -- the content filled into earlier blanks constrains the feasible solution space for later blanks. The evaluated model (or human participants) fills all blanks in open-ended generation mode; the results are scored by an information-theoretic automated scoring protocol without human grading. The scoring protocol directly operationalizes the "calibrated surprise" theoretical framework (Zou & Xu, 2026a). For each blank k, a composite score is computed: score = satisfy * (1 + lambda * surprise), where lambda = 1.0. Here, "satisfy" measures how well the blank filling satisfies the content constraint (objective logical reasoning judgment, not subjective aesthetic scoring), and "surprise" measures the degree of surprise given that the constraint is satisfied. Creative answers that do not satisfy the constraint score zero; answers that satisfy the constraint but are mediocre score low; answers that satisfy the constraint and are surprising score high.

cs.CL↗

arXiv2026

Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning

This paper provides an empirical implementation of the creative quality metric proposed in Calibrated Surprise (Zou & Xu, 2026a). The question this paper addresses is: does this mathematical claim hold at the engineering level? To make the answer as general as possible, we deliberately choose the strictest engineering conditions: low data cost and a small base model. Training data comes from approximately 100 expert chain-of-thought (CoT) annotations produced by the BC Protocol (Zou & Xu, 2026b). We also identify a data bias: most publicly available alignment datasets are skewed toward craft-related knowledge, while audience modeling and reality-logic coverage are systematically weak. We use the term Creative Quality Alignment (CQA) to describe this class of engineering methods. We also offer a supporting theoretical observation: in an LLM with a single conditional distribution architecture, calibrating the appreciation side automatically transfers to the generation side via architectural duality. This is the structural reason why ~100 CoT examples are sufficient -- not a purely empirical observation like LIMA (Zhou et al., 2023).

cs.CL↗

arXiv2026

Transforming Acidic Corrosion and Embrittlement into a Hydrogen-Trapping Cage

The vision of a hydrogen economy demands efficient platforms to close the gap between sustainable proton sources and solid-state hydrogen carriers. Metal hydrides serve as key carriers, yet their synthesis remains constrained by the energy-intensive use of high-pressure H2, which fragments the hydrogen chain. Here, we overturn this paradigm by transforming two classic degradation mechanisms, acidic corrosion and hydrogen embrittlement, into a constructive materials-design strategy. We demonstrate that synergistic control of these processes in acid enables the in-situ engineering of a "hydrogen-trapping cage" (HTC) microstructure within metals. Composed of a dense defect network, this cage directly captures and stabilizes protons as hydrides under mild conditions, guided by the universal criterion |DeltaPeq| > DeltaPph. Using this platform, we synthesize over 20 hydrides, including challenging targets such as LiH and NaH, and showcase its functional power with a cage-rich titanium hydride electrocatalyst. This catalyst achieves an exceptional current density of 1.07 A cm-2 for nitrate-to-ammonia conversion, attributed to rapid H- transport within the engineered cage. This work establishes a transformative "failure-to-function" paradigm, delivering an integrated platform that unifies hydrogen capture, stabilization, and conversion.

cond-mat.mtrl-sci↗

arXiv2025

From Nucleobases to DNA: Clustering-Triggered Emission and Pressure-Induced Emission Enhancement

The photophysical properties of deoxyribonucleic acid (DNA) are fundamental to life sciences and biophotonics. While previous studies have generally been restricted to fluorescence, attributing it to pi-pi* transitions and charge transfer within nucleobases in dilute solution, these understandings fail to explain the pronounced visible emission in physiological and aggregated states, and moreover, ignore the possible phosphorescence. Addressing this critical gap, we systematically investigate native DNA across its structural hierarchy, from nucleobases to single-stranded chains, under varying states. We demonstrate that DNA exhibits excitation-dependent emission in aggregates and moreover room-temperature phosphorescence (RTP) in the solid state. These behaviors are rationalized by the clustering-triggered emission (CTE) mechanism, where nucleobases and electron-rich nonaromatic moieties like sugar and phosphate synergistically contribute to DNA photophysics. High-pressure experiments reveal a 207-fold luminescence enhancement for nucleotides at 26 GPa, largely retained after decompression, underscoring the precise control of emission by intermolecular interactions. This study not only elucidates the intrinsic luminescence mechanism of DNA and but also establishes pressure modulation as a versatile approach for developing new nucleic acid-inspired luminescent materials.

physics.chem-ph↗

arXiv2025

Valley Order in Moiré Topological Insulators

Moiré materials with opposite non-zero miniband Chern numbers in time-reversal-partner valleys are two-dimensional topological insulators at band filling $ν=2$. We explore the possibility that in this class of moir'e materials intervalley coherence can sometimes be present in interaction induced insulators at band filling $ν=1$ , using Landau levels with opposite signs of the magnetic field as a convenient generic model. In the absence of intravalley interactions the mean-field ground state at filling factor $ν=1$ is a gapless intervalley coherent state that maps under a particle-hole transformation of one valley to a strong-field superconducting vortex-lattice state that has been studied previously. When the ratio $λ$ of intravalley to intervalley interactions is increased, gapped states appear, one with broken time-reversal symmetry and a quantized Hall effect but no valley polarization and one with broken parity symmetry and zero Hall conductivity. We discuss the possibility that the latter state could be related to the fractional quantum spin Hall effect recently observed at an odd filling factor in a moir'e topological insulator and comment on related systems in which correlations between electrons in bands with opposite Chern numbers might play a key role.

cond-mat.mes-hall↗

arXiv2025

Vortex lattice states of bilayer electron-hole fluids in quantizing magnetic fields

We show that the ground state of a weakly charged two-dimensional electron-hole fluid in a strong magnetic field is a broken translation symmetry state with interpenetrating lattices of localized vortices and antivortices in the electron-hole-pair field. The vortices and antivortices carry fractional charges of equal sign but unequal magnitude and have a honeycomb lattice structure that contrasts with the triangular lattices of superconducting electron-electron-pair vortex lattices. We predict that increasing charge density or weakening magnetic field drives a vortex delocalization transition that would be signaled experimentally by an abrupt increase in counterflow transport resistance.

cond-mat.mes-hall↗

arXiv2025

Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities

The rapid rise of Large Language Models (LLMs) is transforming traffic and transportation research, with significant advancements emerging between the years 2023 and 2025 -- a period marked by the inception and swift growth of adopting and adapting LLMs for various traffic and transportation applications. However, despite these significant advancements, a systematic review and synthesis of the existing studies remain lacking. To address this gap, this paper provides a comprehensive review of the methodologies and applications of LLMs in traffic and transportation, highlighting their ability to process unstructured textual data to advance transportation research. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. Our analysis identifies critical research gaps. From the methodological perspective, many research gaps can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, we identify numerous opportunities for LLMs to tackle a variety of traffic and transportation challenges, building upon existing research. By synthesizing these findings, this review not only clarifies the current state of LLM adoption and adaptation in traffic and transportation but also proposes future research directions, paving the way for smarter and more sustainable transportation systems.

cs.CE↗

arXiv2025

Competition between excitonic insulators and quantum Hall states in correlated electron-hole bilayers

Excitonic insulators represent a unique quantum phase of matter, providing a rich ground for studying exotic quantum bosonic states. Strongly coupled electron-hole bilayers, which host stable dipolar exciton fluids with an exciton density that can be adjusted electrostatically, offer an ideal platform to investigate correlated excitonic insulators. Based on electron-hole bilayers made of MoSe2/hBN/WSe2 heterostructures, here we study the behavior of excitonic insulators in a perpendicular magnetic field. We report the observation of excitonic quantum oscillations in both Coulomb drag signals and electrical resistance at low to medium magnetic fields. Under a strong magnetic field, we identify multiple quantum phase transitions between the excitonic insulator phase and the bilayer quantum Hall insulator phase. These findings underscore the interplay between the electron-hole interactions and Landau level quantization that opens new possibilities for exploring quantum phenomena in composite bosonic insulators.

cond-mat.mes-hall↗