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Bo Wang

Publications and source records attributed to Bo Wang.

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

Bosonic Holes in Quadratic Bosonic Systems

Hole degrees of freedom play a central role in the exact solution of quadratic (mean-field) systems. Although a variety of experiments have suggested the existence of bosonic holes, a consistent and complete theory has long been hindered by the ghost problems. Here, we resolve the ghost problem and establish a unified theoretical framework for bosonic holes by introducing the $\mathcal{CPT}$ theory and bosonic particle-hole (PH) transformation. The bosonic analogs of the `Fermi surface' and `Fermi level' are proposed. Furthermore, a PH duality between Hermitian and non-Hermitian quadratic bosonic systems (QBSs) is revealed. In both distinct QBSs, the $\mathcal{C}$-parity is shown to label PH conjugate eigenspaces. Building on this duality, we demonstrate the PH Bogoliubov quasiparticles in $\mathcal{APT}$ symmetric Hamiltonians, investigate the dynamical generation of PH entanglement, and predict Hermitian PH Aharonov-Bohm interference in non-Hermitian QBSs.

quant-ph↗
arXiv2026

Ultra-compact X-ray Binaries: A Review

Ultra-compact X-ray binaries (UCXBs) are a subclass of low-mass X-ray binaries (LMXBs) characterized by ultra-short orbital periods, typically less than $60-80\,$min. They consist of a compact mass-accretor and a hydrogen-poor mass-donor, in which the mass-accretor could be a neuron star (NS) or even a black hole (BH). UCXBs play an important role in multiple areas of astrophysics. In particular, they are considered strong, continuous gravitational wave (GW) sources in the low-frequency band, making them key targets for future space-based GW observatories such as LISA, TianQin and Taiji. As the most compact binaries, the formation and evolution of UCXBs remain highly uncertain. In this article, we review four classic formation channels: the white dwarf donor channel, the He star donor channel, the evolved main-sequence donor channel, and the accretion-induced collapse channel. We also discuss recent progress in these channels, covering evolutionary scenarios, the initial parameter space for UCXB formation, and associated objects. A comparison between observed UCXBs and theoretical expectations is provided, along with a discussion on the observed BH-UCXB candidates. The origin of UCXBs can be constrained by the chemical composition of mass-donors and their locations in diagrams of mass-transfer rate and X-ray luminosity versus orbital period. We also examine the implications of UCXBs for several astrophysical fields, including GW astronomy, multi-messenger astronomy, binary evolution, and NS physics under extreme conditions. Further progress will depend on multi-wavelength observations, the discovery of more UCXB samples, and more detailed theoretical simulations.

astro-ph.HE↗
arXiv2026

MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models

Recently, multimodal large language models (MLLMs) have been widely applied to reasoning tasks. However, they suffer from limited multi-rationale semantic modeling, insufficient logical robustness, and susceptibility to misleading cues. Therefore, we propose a Multi-rationale INtegrated Discriminative (MIND) reasoning framework, which is designed to endow MLLMs with human-like cognitive abilities of "Understand -> Rethink -> Correct", and achieves a paradigm evolution from passive imitation-based reasoning to active discriminative reasoning. Specifically, we introduce a Rationale Augmentation and Discrimination (RAD) paradigm, which provides a unified and extensible data foundation. Meanwhile, we design a Progressive Two-stage Correction Learning (P2CL) strategy. The first phase enhances multi-rationale positive learning, while the second phase enables active logic discrimination and correction. In addition, to mitigate representation entanglement in the multi-rationale semantic space, we propose a Multi-rationale Contrastive Alignment (MCA) optimization strategy. Extensive experiments show that our MIND achieves SOTA performance on multiple public datasets. Our data and code are available at https://github.com/YuChuang1205/MIND

cs.AI↗
arXiv2026

Understanding the Self-Reflection Mechanisms of LLMs through Biased Attitude Associations

While the emergent self-reflection capabilities of Large Language Models (LLMs) offer a promising paradigm for autonomous bias mitigation, their internal mechanics remain unclear, raising concerns regarding potential bias entrenchment. Under the premise that social bias is intrinsically encoded as valence inclinations, where the exacerbation of bias scales with sharper valence fluctuations across social groups, this paper proposes ReBias-Lens, a probing framework designed to interpret how self-reflection reconfigures these biased attitude associations through the lens of valence projection within intersectional contexts. Central to ReBias-Lens is the metric of Valence Fluctuation (VF) comprising two variants: Global-VF, which captures macroscopic valence encoding trends, and Local-VF, which scrutinizes microscopic distinctiveness across specific social categories. Deploying ReBias-Lens to evaluate four LLMs across twelve social categories reveals that overall valence fluctuations undergo a distinct layer-wise smoothing, characterized by a significant hierarchical representation divergence as the layers deepen, which ultimately manifests as a widespread mitigation of bias at the behavioral level. In stark contrast to this macro-level reduction, this reflection mechanism is not universally corrective, instead exhibiting a stubborn, category-specific selectivity that regularly locks in and perversely amplifies localized biases. Warning: this paper contains examples with biased content.

cs.SI↗
arXiv2026

Retrieved In-Context Principles from Previous Mistakes

In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to improve model performance through principles derived from mistakes, yet these approaches suffer from lack of customization and inadequate error coverage. To address these limitations, we propose Retrieved In-Context Principles (RICP), a novel teacher-student framework. In RICP, the teacher model analyzes mistakes from the student model to generate reasons and insights for preventing similar mistakes. These mistakes are clustered based on their underlying reasons for developing task-level principles, enhancing the error coverage of principles. During inference, the most relevant mistakes for each question are retrieved to create question-level principles, improving the customization of the provided guidance. RICP is orthogonal to existing prompting methods and does not require intervention from the teacher model during inference. Experimental results across seven reasoning benchmarks reveal that RICP effectively enhances performance when applied to various prompting strategies.

cs.CL↗
arXiv2026

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout information and are prone to errors, while recent parsing-free visual methods often struggle to capture fine-grained textual semantics in text-rich scenarios. To address these limitations, we propose \textbf{Unveil}, a novel visual-textual embedding framework that effectively integrates textual and visual features for robust document representation. Through knowledge distillation, we transfer the semantic understanding capabilities from the visual-textual embedding model to a purely visual model, enabling efficient parsing-free retrieval while preserving semantic fidelity. Experimental results demonstrate that our visual-textual embedding method surpasses existing approaches, while knowledge distillation successfully bridges the performance gap between visual-textual and visual-only methods, improving both retrieval accuracy and efficiency.

cs.CL↗
arXiv2026

How Far Are We from Generating Missing Modalities with Foundation Models?

Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutions for missing modality reconstruction remains underexplored. To bridge this gap, we identify and formalize three potential paradigms for missing modality reconstruction, and perform a comprehensive evaluation across these paradigms, covering 42 model variants in terms of reconstruction accuracy and adaptability to downstream tasks. Our analysis reveals that current foundation models often fall short in two critical aspects: (i) fine-grained semantic extraction from the available modalities, and (ii) robust validation of generated modalities. These limitations lead to suboptimal and, at times, misaligned generations. To address these challenges, we propose an agentic framework tailored for missing modality reconstruction. This framework dynamically formulates modality-aware mining strategies based on the input context, facilitating the extraction of richer and more discriminative semantic features. In addition, we introduce a self-refinement mechanism, which iteratively verifies and enhances the quality of generated modalities through internal feedback. Experimental results show that our method reduces FID for missing image reconstruction by at least 14\% and MER for missing text reconstruction by at least 10\% compared to baselines. Code are released at: https://github.com/Guanzhou-Ke/AFM2.

cs.MM↗
arXiv2026

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long-text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry-aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long-text rendering, and editing benchmarks show that JoyAI-Image achieves state-of-the-art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models.

cs.GR↗
arXiv2026

Mesoscale Domain Evolution Mechanism during Alternating Current (AC) Poling of Relaxor Ferroelectrics

Ferroelectric domain variants that are energetically equivalent are expected to remain preserved during polarization reversal under a symmetry-preserving electric field. However, recent experiments on relaxor-ferroelectric crystals have revealed irreversible elimination of inclined domain walls during AC poling, while the underlying mesoscale mechanism remains unclear. Here, we investigate the domain-wall motion during AC poling of rhombohedral Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$--PbTiO$_3$ single crystals containing both 71$^\circ$ and 109$^\circ$ domain walls within a quasi-two-dimensional laminated geometry using phase-field simulations. The simulations reveal that the domain-wall behavior during polarization reversal depends on the spacing ratio between the 71$^\circ$ and 109$^\circ$ domain walls. Closely spaced 71$^\circ$ domain walls undergo irreversible elimination, whereas more widely separated walls are preserved, while the 109$^\circ$ domain walls remain intact. A threshold ratio for domain-wall elimination is identified and found to depend on the mechanical boundary conditions. By tracking the domain-wall trajectories during the switching process, we attribute this behavior to unsynchronized motion of neighboring 71$^\circ$ domain walls arising from long-range elastic interactions when the walls become strongly coupled. This collective motion breaks the symmetry between energetically equivalent domain variants and leads to irreversible domain-wall elimination during polarization reversal. These findings provide mechanistic insight into collective domain-wall evolution during polarization reversal and suggest that proximity-driven symmetry breaking may provide a mesoscale mechanism for domain engineering in ferroelectric materials with high domain-wall densities.

cond-mat.mtrl-sci↗
arXiv2026

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs for lower computational cost. The former option is typically costly and inefficient, while the latter usually fails to deliver satisfactory performance for reasoning steps requiring deliberate thought processes. In this work, we propose a novel LLM utilization paradigm that facilitates the collaborative operation of large cloud-based LLMs and smaller local-deployed LLMs. Our framework comprises two primary modules: the local agent instantiated with a relatively smaller LLM, handling less complex reasoning steps, and the cloud agent equipped with a larger LLM, managing more intricate reasoning steps. This collaborative processing is enabled through an adaptive mechanism where the local agent introspectively identifies errors and proactively seeks assistance from the cloud agent, thereby effectively integrating the strengths of both locally-deployed and cloud-based LLMs, resulting in significant enhancements in task completion performance and efficiency. We evaluate AdaSwitch across 7 benchmarks, ranging from mathematical reasoning and complex question answering, using various types of LLMs to instantiate the local and cloud agents. The empirical results show that AdaSwitch effectively improves the performance of the local agent, and sometimes achieves competitive results compared to the cloud agent while utilizing much less computational overhead.

cs.CL↗
arXiv2026

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling

Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dynamic integration of external knowledge. To further improve RAG's flexibility, Agentic RAG introduces autonomous agents into the workflow. However, Agentic RAG faces several challenges: (1) the success of each step depends on both high-quality planning and accurate search, (2) the lack of supervision for intermediate reasoning steps, and (3) the exponentially large candidate space for planning and searching. To address these challenges, we propose DecoupleSearch, a novel framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. Our approach constructs a reasoning tree, where each node represents planning and search steps. We leverage Monte Carlo Tree Search to assess the quality of each step. During inference, Hierarchical Beam Search iteratively refines planning and search candidates with dual value models. Extensive experiments across policy models of varying parameter sizes demonstrate the effectiveness of our method.

cs.IR↗
arXiv2026

Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation

Translating C to memory-safe languages, like Rust, prevents critical memory safety vulnerabilities that are prevalent in legacy C software. Even with recent LLM-based and tool-augmented translators, the resulting Rust code frequently diverges from the C source on inputs absent from the test suite used during translation; this correctness gap on unseen inputs remains a dominant obstacle to reliable, automatic C-to-Rust translation. In this work, we present ACToR (Adversarial C To Rust), a simple LLM-agent loop that closes this gap by adversarially searching for inputs on which the translation diverges from the C source, and using them to drive subsequent refinements. Inspired by GANs, ACToR pits a translator agent against a discriminator agent that collaborate to iteratively refine the Rust translation. On each iteration, the translator agent synthesizes and refines a Rust translation to pass an existing suite of tests, and then the discriminator agent finds new failing tests by constructing and refining a differential fuzzer over the C and Rust binaries. Across 63 real-world command-line C utilities, with an average size of 473 lines of code and the longest reaching thousands of lines in size, ACToR achieves over 90% test pass rate with zero human intervention. The improvement holds across seven agent-LLM configurations on our micro-benchmark, indicating that the loop is largely independent of the choice of underlying translator and LLM. Compared to a non-adversarial, coverage-driven test-generation baseline, ACToR improves correctness by up to 36.7%. When applied on top of one recent translator, C2SaferRust, ACToR further improves the validation pass rate by 16.6%.

cs.SE↗
arXiv2026

Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval

Decoding speech from non-invasive brain signals is challenging. For the LibriBrain 2025 Speech Detection task, we propose a novel two-step framework that bypasses direct reconstruction. First, a contrastive learning model retrieves the matching speech segment for the given test MEG from a large-scale audio library (LibriVox). Second, a speech detection model generates the binary silence/speech sequence directly from this retrieved audio. With this approach, our team Sherlock Holmes achieved first place in the extended track (F1-score: 0.962), demonstrating that leveraging external audio databases is a highly effective strategy.

cs.SD↗
arXiv2026

Energy-Energy Correlator from the AdS Virasoro-Shapiro Amplitude

We establish a precise formula relating the world-sheet integral of the AdS Virasoro-Shapiro amplitude to the energy-energy correlator (EEC) in $\mathcal{N}=4$ super Yang-Mills theory at strong coupling. This mapping allows us to evaluate the coefficients of the AdS curvature expansion of the EEC in terms of the world-sheet integral over a unit disk. To illustrate this idea, we explicitly compute the flat-space contribution and the first curvature correction to the EEC. Our results provide a rigorous description of the stringy energy flow, demonstrating how world-sheet correlators imprint themselves on collider observables and offering a potential template for effective string descriptions of energy correlators in general gauge theories.

hep-th↗
arXiv2026

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge

Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptible to minor "corruptions," introducing a major concern for the translation of cutting-edge technology, especially in high-stakes scenarios. We introduce the SegSTRONG-C challenge dedicated to better understanding model deterioration under unforeseen but plausible non-adversarial "corruption" and the capabilities of contemporary methods that seek to improve it. Built on a dataset generated through counterfactual robotic replay, SegSTRONG-C provides paired clean and "corrupted" samples, enabling reproducible evaluation of model robustness. Participants are challenged to train tool segmentation algorithms on "uncorrupted" data and evaluate them on "corrupted" test domains for the binary robot tool segmentation task. Through comprehensive baseline experiments and participating submissions from widespread community engagement, SegSTRONG-C reveals key themes for model failure and identifies promising directions for improving robustness. The performance of challenge winners, achieving an average 0.9394 DSC and 0.9301 NSD across the unreleased test sets with "corruption" types: bleeding, smoke, and low brightness. This highlights how prior knowledge, customized training strategies, and architectural choice can be leveraged to improve robustness. In conclusion, the SegSTRONG-C challenge has identified practical approaches for enhancing model robustness. However, most approaches rely on conventional techniques that have known limitations. Looking ahead, we advocate for expanding intellectual diversity and creativity in non-adversarial robustness beyond data augmentation, calling for new paradigms that enhance universal robustness to unforeseen "corruptions" to facilitate richer applications in surgical data science.

cs.CV↗
arXiv2026

Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence

Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static observation. We propose Thinking with Novel Views (TwNV), a paradigm that integrates generative novel-view synthesis into the reasoning loop: a Reasoner LMM identifies spatial ambiguity, instructs a Painter to synthesize an alternative viewpoint, and re-examines the scene with the additional evidence. Through systematic experiments we address three research questions. (1) Instruction format: numerical camera-pose specifications yield more reliable view control than free-form language. (2) Generation fidelity: synthesized view quality is tightly coupled with downstream spatial accuracy. (3) Inference-time visual scaling: iterative multi-turn view refinement further improves performance, echoing recent scaling trends in language reasoning. Across four spatial subtask categories and four LMM architectures (both closed- and open-source), TwNV consistently improves accuracy by +1.3 to +3.9 pp, with the largest gains on viewpoint-sensitive subtasks. These results establish novel-view generation as a practical lever for advancing spatial intelligence of LMMs.

cs.CV↗
arXiv2026

Modeling Implicit Conflict Monitoring Mechanisms against Stereotypes in LLMs

In this paper, we study an emergent self-debiasing mechanisms against stereotypical content in Large Language Models (LLMs). Unlike traditional safety mechanisms that are primarily triggered by explicit input-level stimuli, self-debiasing mechanisms can involve generation-time intrinsic correction that are not directly reducible to surface-level prompt. Motivated by conflict-monitoring and response-inhibition accounts in cognitive neuroscience, we propose COCO, a contrastive causal method designed to identify COCO neurons that exhibit high intra-\underline{CO}nsistency yet sharp inter-\underline{CO}ntrast across antithetical generative responses, such as stereotypical versus unbiased outputs. Ablation studies reveal that deactivating COCO neurons leads to a catastrophic collapse of the model's fairness; over 90\% of outputs revert to biased content, far exceeding the bias levels induced by explicit adversarial jailbreak attacks. Observing that simple weight amplification of COCO neurons yields only marginal gains, we propose two training-free, lightweight editing strategies: Local Enhancement (LE-COCO) and Networked Enhancement (NE-COCO). Comprehensive evaluations show that our methods bolster robustness against adversarial jailbreaks and achieve strong performance on open-ended safety benchmarks, while preserving foundational generative proficiency. While this study primarily addresses social stereotypes, the COCO mechanism holds significant potential for diverse domains like hallucination detection, offering valuable insights toward the development of self-evolving AI agents.

cs.SI↗
arXiv2026

A Multi-Memory Segment System for Generating High-Quality Long-Term Memory Content in Agents

In the current field of agent memory, extensive explorations have been conducted in the area of memory retrieval, yet few studies have focused on exploring the memory content. Most research simply stores summarized versions of historical dialogues, as exemplified by methods like A-MEM and MemoryBank. However, when humans form long-term memories, the process involves multi-dimensional and multi-component generation, rather than merely creating simple summaries. The low-quality memory content generated by existing methods can adversely affect recall performance and response quality. In order to better construct high-quality long-term memory content, we have designed a multi-memory segment system (MMS) inspired by cognitive psychology theory. The system processes short-term memory into multiple long-term memory segments, and constructs retrieval memory units and contextual memory units based on these segments, with a one-to-one correspondence between the two. During the retrieval phase, MMS will match the most relevant retrieval memory units based on the user's query. Then, the corresponding contextual memory units is obtained as the context for the response stage to enhance knowledge, thereby effectively utilizing historical data. We conducted experiments on the LoCoMo dataset and further performed ablation experiments, experiments on the robustness regarding the number of input memories, and overhead experiments, which demonstrated the effectiveness and practical value of our method.

cs.AI↗