arXiv subjects

Bo Zou

Publications and source records attributed to Bo Zou.

At least 19 recordsLinked to original sources

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 moir\'e 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 moir\'e 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

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 (\delta=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

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

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↗

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 Moir\'e Topological Insulators

Moir\'e materials with opposite non-zero miniband Chern numbers in time-reversal-partner valleys are two-dimensional topological insulators at band filling $\nu=2$. 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 $\nu=1$ , 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 $\nu=1$ 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 $\lambda$ 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

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

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↗

arXiv2025

Quantum oscillations in a dipolar excitonic insulator

Quantum oscillations in magnetization or resistivity are a defining feature of metals subject to an external magnetic field. The phenomenon is generally not expected in insulators without a Fermi surface. The observations of quantum oscillations in Kondo insulating materials have provided a rare counterexample and attracted much theoretical interest. However, the magnetic oscillations in correlated insulators remain poorly understood. Here we report the observations of resistivity quantum oscillations in an excitonic insulator realized in Coulomb-coupled electron-hole double layers with gate-tunability that allows the phenomenon to be explored in a more controllable fashion than in bulk materials. When the cyclotron energy of the electrons or holes is tuned to be comparable to or larger than the exciton binding energy, recurring transitions between excitonic insulators and electron-hole decoupled quantum Hall states are observed. Compressibility measurements show an oscillatory exciton binding energy as a function of magnetic field and electron-hole pair density. Coulomb drag measurements further reveal the formation of excitons with finite angular momentum. Our results are qualitatively captured by mean-field theory calculations. The study demonstrates a new platform for studying quantum oscillations in correlated insulators.

cond-mat.mes-hall↗

arXiv2024

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↗

arXiv2024

Fleet Size and Spill for UAM Operation under Uncertain Demand

Variation and imbalance in demand poses significant challenges to Urban Air Mobility (UAM) operations, affecting strategic decisions such as fleet sizing. To study the implications of demand variation on UAM fleet operations, we propose a stochastic passenger arrival time generation model that uses real-world data to infer demand distributions, and two integer programs that compute the zero-spill fleet size and the spill-minimizing flight schedules and charging policies, respectively. Our numerical experiment on a two-vertiport network shows that spill in relatively inelastic to fleet size and that the driving factor behind spill is the imbalance in demand.

eess.SY↗

arXiv2024

LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction

Existing methods to fine-tune LLMs, like Adapter, Prefix-tuning, and LoRA, which introduce extra modules or additional input sequences to inject new skills or knowledge, may compromise the innate abilities of LLMs. In this paper, we propose LLaMA-Excitor, a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually paying more attention to worthwhile information. Specifically, the LLaMA-Excitor does not directly change the intermediate hidden state during the self-attention calculation of the transformer structure. We designed the Excitor block as a bypass module for the similarity score computation in LLMs' self-attention to reconstruct keys and change the importance of values by learnable prompts. LLaMA-Excitor ensures a self-adaptive allocation of additional attention to input instructions, thus effectively preserving LLMs' pre-trained knowledge when fine-tuning LLMs on low-quality instruction-following datasets. Furthermore, we unify the modeling of multi-modal tuning and language-only tuning, extending LLaMA-Excitor to a powerful visual instruction follower without the need for complex multi-modal alignment. Our proposed approach is evaluated in language-only and multi-modal tuning experimental scenarios. Notably, LLaMA-Excitor is the only method that maintains basic capabilities while achieving a significant improvement (+6%) on the MMLU benchmark. In the visual instruction tuning, we achieve a new state-of-the-art image captioning performance of 157.5 CIDEr on MSCOCO, and a comparable performance (88.39%) on ScienceQA to cutting-edge models with more parameters and extensive vision-language pertaining.

cs.CV↗

arXiv2024

VideoDistill: Language-aware Vision Distillation for Video Question Answering

Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models can efficiently represent both video and language branches, they typically employ a goal-free vision perception process and do not interact vision with language well during the answer generation, thus omitting crucial visual cues. In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation process. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior, distinguishing it from previous research. Specifically, we develop a language-aware gating mechanism to replace the standard cross-attention, avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module, which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure the extraction of multi-grained visual semantics associated with the questions. We conduct experimental evaluations on various challenging video question-answering benchmarks, and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition, we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.

cs.CV↗

arXiv2024

Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes (e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design 1) a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with 2) multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect 3) the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

cs.CV↗