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Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Past, Future, All at Once: Mitigating Stability-Plasticity Dilemma via Post-hoc JANUS Rectification

Fine-tuning foundation models on new tasks inevitably suffer from catastrophic forgetting. While existing works attempt to mitigate this on the basis of parameter-efficient fine-tuning methods, they adopted an overly restrictive Subspace Orthogonality condition. In this paper, we introduce a purely post-hoc and tuning-agnostic weight rectification framework that achieves Parameter Space Orthogonality, which is the necessary and sufficient condition for preserving historical performance to the first order. By projecting parameter updates into the JAcobian NUll Space (JANUS), our method significantly recovers compromised historical knowledge without interfering with the underlying fine-tuning process. To overcome the local validity of the Jacobian approximation, we further propose a Multi-step Adaptive Rectification mechanism that utilizes the JANUS shift to dynamically verify the valid trust region and adjust step sizes. Coupled with our proposed ghost projection, ghost orientation comparison, and sequence-level singular value decomposition compression techniques, JANUS also achieves great temporal and spatial efficiency. Experiments demonstrate that JANUS seamlessly integrates with various fine-tuning methods, significantly mitigating the stability-plasticity dilemma by recovering historical knowledge while preserving downstream task adaptation.

cs.LG↗
arXiv2026

Does Training on Future Data Pay? Look-Ahead Bias in Forecasting with Pretrained Models

We examine whether post-origin training information inflates the measured accuracy and economic value of financial forecasts. We evaluate five sets of financial time-series foundation models, each comprising independently trained annual vintages under U.S., global, and factor-augmented training environments, across 14 equity markets and four forecast horizons. Rolling comparisons vary the annual vintage for a fixed forecast; fixed-vintage comparisons hold the vintage fixed as target windows move across its training cutoff. Each alternative forecast is paired with an origin-aligned point-in-time (PIT) benchmark using identical numerical histories and inference protocols. In the U.S.-trained reference environment, post-origin vintages materially revise informative PIT forecasts but generally reduce accuracy in both designs. Pooled rolling comparisons yield higher mean squared forecast errors in 18 of 20 U.S. model-set-horizon combinations. The origin-crossing update also performs worse on average than an equally long pre-origin update. Under a common constrained allocation rule using one-month forecasts, median exposed-minus-PIT differences in annualized certainty-equivalent returns are -1.77 percentage points in the United States and -2.14 points internationally. Global and factor-augmented training produce more mixed predictive effects. An exact squared-error decomposition shows that revisions improve accuracy when their error-correcting benefit exceeds their mean squared magnitude; under U.S. training, alignment with PIT errors generally falls short of this requirement. Temporal exposure therefore establishes an information-set violation, not sufficient evidence of inflated predictive accuracy or investor value.

econ.GN↗
arXiv2026

Towards Faithful and Efficient Semantic Communication: An Ontological Approach

In this paper, an ontology-driven semantic communication (ODSC) framework is proposed for multi-view visual question answering (VQA) tasks. In the considered framework, multiple transmitters observe a scene, extract the semantic information (SI) with vision-language models (VLMs), and transmit the scene graphs to a receiver. Due to the completeness, heterogeneity, and uninterpretability of the VLMs, the extracted scene graphs are redundant, ambiguous, and inconsistent. To solve these problems, the transmitters and the receiver share an ontology-based knowledge base that predefines synonyms, inference rules, and consistency constraints. For each transmitter, the proposed ODSC framework removes the partial scene graph that can be inferred based on the inference rules. For the receiver, the proposed framework aligns the SI of different views based on the synonyms and detects the inconsistency among the views based on the constraints. A metric of multi-view VQA accuracy (MVA) is defined to evaluate the proposed framework. Simulation results show that, compared with transmitting the complete scene graphs, the proposed framework reduces the data size of the SI by up to 87.1% while improving the answering accuracy by 4.5%. Moreover, the proposed framework yields up to a 16.0% improvement in terms of the MVA compared with the SI filtering approaches.

cs.IT↗
arXiv2026

LieSpline-DP: Lie-Group B-Spline Diffusion Policy for Smooth Robot Manipulation

Diffusion Policy (DP) is a powerful Learning from Demonstration (LfD) method for robotic manipulation, yet it suffers from discontinuous and non-smooth trajectories. Spline-based action representations promote smooth motion within individual action chunks, but existing spline-based methods neither guarantee cross-chunk $C^2$ continuity nor account for the group structure of $\mathrm{SE}(3)$. We therefore propose LieSpline-DP, a Lie-group B-spline diffusion policy that generates end-effector trajectories directly on $\mathrm{SE}(3)$ and couples consecutive plans by sharing their boundary control poses, ensuring $C^2$ continuity throughout the entire planned trajectory. Across three real-robot tasks, LieSpline-DP produces lower trajectory jerk and higher task success rates than the DP baseline. The gains are particularly pronounced in real-world tasks involving liquids and flexible objects: in our real-robot experiments, LieSpline-DP achieved a 100% success rate on both pouring and bucket hooking, whereas the DP baseline achieved only 10% and 30%, respectively.

cs.RO↗
arXiv2026

Generalized projection tests for function-valued parameters with applications to testing structural causal assumptions

Structural assumptions are central to the causal inference literature. In practice, it is often crucial to assess their validity or to test implications that follow from them. In many settings, such tests can be framed as evaluating whether a function-valued parameter equals zero. In this paper, we propose a class of generalized projection tests based on series estimators for function-valued parameters. We establish conditions under which the proposed tests are valid and illustrate their applicability through examples from the data fusion and instrumental variables literature. Our approach accommodates flexible machine learning methods for estimating nuisance parameters. In contrast to many existing approaches, the limiting distributions of the proposed test statistics are straightforward to compute under the null hypothesis. We apply our method to test the equality of conditional COVID-19 risk across vaccine arms in the COVID-19 Variant Immunologic Landscape (COVAIL) trial, and to test an instrumental variable compatibility condition in a study of the effect of family size on female labor supply.

stat.ME↗
arXiv2026

ALP-mediated inelastic dark matter and the LUX-ZEPLIN high-recoil candidate event LZ230616

The LUX-ZEPLIN (LZ) Collaboration has reported a high-energy candidate event LZ230616 with a reconstructed nuclear recoil energy $E_R=248\pm23_{\rm stat}\pm23_{\rm sys}~{\rm keV}$. We investigate a possible interpretation in terms of inelastic scattering between two Majorana dark matter states mediated by an axionlike particle coupled to gluons. The positive mass splitting suppresses low-energy recoils, while the momentum dependence of the interaction reshapes the high-energy spectrum. We treat the dark-sector and gluonic couplings independently and retain the momentum-dependent nucleon form factors and xenon nuclear responses. Using an approximate single-event likelihood, we find that, for $m_a=0.3~{\rm GeV}$, a narrow spectrum near the candidate energy arises at $m_χ\simeq0.35~{\rm TeV}$ and $δ\simeq330~{\rm keV}$, although this configuration requires a large coupling product and is highly sensitive to the Galactic halo speed cutoff. Our analysis establishes the kinematic and coupling requirements for subsequent tests using the thermal relic abundance and laboratory constraints on the mediator.

hep-ph↗
arXiv2026

Rapid and high-sensitive NV-based microwave field imaging via digital lock-in amplification for on-chip microstrip diagnostics

High-resolution, high-sensitivity microwave (MW) magnetic field imaging is indispensable for non-destructive integrated circuit (IC) testing, radio-frequency device characterization, and spintronic research. Yet, the practical utility of these techniques is severely constrained by the pervasive challenge of isolating weak magnetic signatures from intense optical and electronic noise, which fundamentally limits both acquisition speed and detection sensitivity. Here, we overcome this barrier by introducing a wide-field imaging scheme based on an ensemble of diamond nitrogen-vacancy (NV) centers, synergistically combined with digital lock-in amplification (DLA). By exploiting digital demodulation, the DLA precisely extracts the MW-field response at a specific modulation frequency from background noise (e.g., laser intensity fluctuations), dramatically improving the signal-to-noise ratio (SNR). Consequently, our system attains a magnetic field sensitivity of 126 nT/$\sqrt(Hz)$. Critically, the unprecedented SNR permits a pixel dwell time of under one millisecond, allowing full-field images to be acquired within seconds-more than an order of magnitude faster than state-of-the-art NV-based wide-field techniques. This combination of speed, sensitivity, and micron-scale spatial resolution (1.6 $μ$m) paves the way for quasi-real-time, non-invasive diagnostics of dynamic MW devices and integrated circuits.

physics.optics↗
arXiv2026

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchmarks mainly evaluate task completion under predefined settings, offering limited insight into model reasoning under increasing spatial and procedural complexity. We introduce \textbf{RoboSPA} (\textbf{Robo}t \textbf{S}patial-\textbf{P}rocedural \textbf{A}ssessment), a large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in VLA models. \texttt{RoboSPA} focuses on two core dimensions, Fine-Grained Spatial Reasoning and Long-Horizon Procedural Planning, covering 10 task categories and 56 base tasks. Each task is instantiated across five difficulty levels, yielding 280 variants with increasing spatial ambiguity and procedural complexity. We collect 527K trajectories across multiple embodiments and diverse scenes. Beyond binary success rate, \texttt{RoboSPA} introduces diagnostic metrics for more detailed evaluation. Experiments on representative VLA models show that current systems still struggle with complex spatial relations, precise low-level execution, and memory-intensive planning. These results establish \texttt{RoboSPA} as a challenging diagnostic benchmark for developing more capable, reliable, and generalizable embodied agents. Our data and code are available at https://github.com/fanzhenxuan/RoboSPA.

cs.RO↗
arXiv2026

EasyBCI Agent: Towards Universal Neural Data Preprocessing for Brain-Computer Interfaces

Brain-computer interfaces translate neural activity into device commands, yet their performance hinges on preprocessing that remains manual, expert-dependent and poorly reproducible. Large language model agents can automate scientific coding, but existing systems lack the modality coverage, raw-data isolation, experience accumulation and domain oversight that neural preprocessing requires. We introduce EasyBCI, a two-phase LLM agent that plans and executes preprocessing pipelines for six signal types. A Plan Agent profiles each recording into a text-only Data Fingerprint that never exposes raw data to the model and selects a literature-grounded operator sequence. An Execution Agent generates, runs and self-corrects code until quality criteria are met, while a quality-gated experience system retains validated strategies as reusable skills. A domain expert intervenes at two decision gates, retaining human judgement where undetected error can invalidate downstream analyses. Evaluation on EEG with a fixed linear classifier shows that all five EasyBCI backbones preserve more task-relevant separability than the manual pipeline. Under same-backbone comparison, EasyBCI outperforms general-purpose coding agents on both label schemes for four of five configurations. EasyBCI extends to five additional modalities spanning nearly three orders of magnitude in sampling rate, producing complete reproducible pipelines with recorded decision provenance. These results indicate that domain-specific orchestration can bring auditable preprocessing within reach of laboratories lacking dedicated expertise, illustrating design principles applicable to AI agents in other scientific domains.

q-bio.QM↗
arXiv2026

Concentration Inequalities for Branching Random Walks with Applications to Phase Transitions in CSPs

A new framework is developed for studying phase transitions in CSPs. Motivated by phase transition problems in CSPs, we prove a more general concentration inequality that retains classical sub-Gaussian tails under a mild global linear-growth condition $|S_n| < Cn$, relaxing the bounded-increment assumption to finite exponential moments and requiring neither independence nor the martingale property. We further extend the concentration inequality to branching random walks (BRW), obtaining the first concentration inequality for BRW. As applications, we derive partial differential equations (PDEs) for the $K$-SAT and $q$-COL backbones, yielding new results, including \textbf{(a)} a resolution of the long-standing open question of where $(2+p)$-SAT transition changes from second to first order; \textbf{(b)} rigorous results for $α_d$ in $K$-SAT, which give new lower bounds on the phase transition for 3-SAT (4.0029 vs. 3.51) and 4-SAT (8.360 vs. 7.91); and \textbf{(c)} the prefactor of the 2-SAT critical window.

cs.CC↗
arXiv2026

MI-Distillation: Selecting from Model-Interpolated Instruct-Reasoning Data Spectrum for Chain-of-Thought Distillation

Recent advances in large reasoning models (LRMs) have shown strong performance on complex problems through long chain-of-thought (Long CoT) reasoning. However, distilling such trajectories into smaller student models remains challenging: direct Long CoT supervision often provides limited gains and can be less effective than concise Short CoT rationales. In this work, we investigate this phenomenon from a gradient-centric perspective. Our analysis shows that Long CoT induces larger gradient magnitudes and more concentrated update directions than Short CoT, with this effect becoming more pronounced as student model capacity increases. These findings suggest that effective Long CoT distillation requires balancing the reasoning information density of reasoning trajectories with their distributional alignment to the student model. Motivated by this insight, we propose \textbf{M}odel \textbf{I}nterporlation \textbf{Distillation} (\textbf{MI-Distillation}), a framework that constructs a continuous Instruct-Reasoning data spectrum through model interpolation. To select suitable trajectories from this spectrum, we further introduce \textbf{Seq}uential \textbf{L}earnable \textbf{S}urprisal \textbf{S}core (\textbf{SeqLSS}), which favors reasoning paths that are both informative and learnable for the student. Extensive experiments on reasoning benchmarks show that MI-Distillation consistently improves small model CoT distillation over strong Long CoT baselines.

cs.CL↗
arXiv2026

Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning

Document understanding aims to perform question answering and information extraction over document images, where the visual content is highly information-dense and most queries rely on only a few relevant layout regions. However, existing methods either adopt a one-pass strategy that implicitly assumes all layouts are equally important, or focus excessively on small regions at the cost of losing critical layout information. To address these limitations, we introduce Doc-CoB (Chain-of-Boxes), a simple-yet-effective framework that integrates coarse-to-fine layout-aware visual reasoning into multimodal large language models. Instead of directly zooming into small regions, Doc-CoB progressively focuses on query-relevant layouts while preserving global document information. Specifically, it first selects key layout boxes and then focuses on them for further understanding with visual prompting. To support this paradigm, we introduce two reasoning tasks for box recognition and box reasoning, with an automatic pipeline that constructs 249k training samples with intermediate visual supervision. Experiments on seven benchmarks with four popular models show that Doc-CoB significantly improves performance, demonstrating its effectiveness and wide applicability. The code and the data are available at https://github.com/Doc-CoB/Doc-CoB.

cs.AI↗
arXiv2026

FASHI DR2: A Catalog of 132 Low-Redshift HI 21 cm Absorption Systems

We present an untargeted survey of 21 cm HI absorption systems based on the second data release of the FAST All Sky HI survey (FASHI DR2), covering approximately 19,500 deg$^{2}$ at $z\lesssim0.09$. A total of 132 HI absorbers are identified, including approximately 60 new discoveries, forming one of the largest homogeneous samples of low-redshift HI absorbers assembled to date. The sample extends to continuum flux densities as low as 2.6 mJy, substantially below the limits of previous flux-limited surveys. The absorber population is dominated by narrow systems ($W_{50}<100$ km s$^{-1}$), while broad absorbers ($W_{50}>200$ km s$^{-1}$) account for 13.6% of the sample. Most absorbers are optically thin, with a median optical depth of $τ_{\rm HI}\approx0.14$. The velocity-offset distribution is broadly symmetric about the systemic velocities of the host galaxies. The associated absorbers are preferentially found in massive, actively star-forming galaxies. We find tentative evidence for a weak anti-correlation between HI column density and stellar mass, although the relation exhibits substantial scatter. These results provide the first statistical characterization of the low-redshift HI absorber population based on the FASHI DR2 sample and establish a valuable benchmark for future HI absorption surveys with next-generation radio facilities.

astro-ph.GA↗
arXiv2026

Searching for Solar-Basin Axionlike-Particle Decay with XMM-Newton Blank-Sky Observations

Axion-like particles (ALPs) bound in the solar gravitational field form the so-called ALP solar-basin. Since the two-photon decay of non-relativistic particles is approximately isotropic, this population can be searched for using observations in the anti-solar direction. In this work, we propose a search strategy for narrow decay-line signals from the ALP solar basin using \textit{XMM-Newton} blank-sky observations (XMM-BSOs) stacked spectra data taken in directions opposite to the Sun. By jointly fitting the signal and background model, we obtain limits on $g_{aγγ}^{95}$ in the mass range $m_a=1.4\text{--}16~{\rm keV}$, with typical sensitivities of $g_{aγγ}\sim10^{-10}\text{--}10^{-11}~{\rm GeV}^{-1}$. We have implemented the first anti-solar search for the solar basin, demonstrating that this strategy can exploit the stacked exposure of a large number of X-ray observations and provide a scalable analysis framework for future searches.

hep-ph↗
arXiv2026

Giant Surface-driven Nonlinear Hall Effect in BiTeCl at Room Temperature

The nonlinear Hall effect (NLHE) provides a pathway to generate a Hall response in time-reversal-symmetric yet inversion-symmetry-broken systems. NLHE can rectify an alternating current into a transverse direct voltage, making it attractive for radio-frequency rectification, energy harvesting, and terahertz detection, applications for which device miniaturization remains a central pursuit. In this context, the inherent inversion symmetry breaking at surfaces is particularly appealing: because symmetry is necessarily broken at the surface of any crystal, irrespective of whether its bulk is centrosymmetric, surface-driven nonlinear responses lift the stringent constraint on bulk symmetry and open a route toward compact device architectures. Here we report the observation of a giant, surface-driven second-order nonlinear Hall effect in the Rashba-type polar semiconductor BiTeCl at room temperature. The determined second-order nonlinear Hall susceptibility at 300 K reaches 1.68 $μ$mV$^{-1}$, which is 80 times larger than that of the best previously reported surface-dominated systems. We attribute this giant response to the synergistic interplay between BiTeCl's polar crystal structure and its rich surface states: the polar stacking renders the top and bottom surfaces inequivalent, so that the nonlinear response originates from a single surface without compensation from the other. Symmetry and scaling analyses suggest that both skew-scattering and side-jump mechanisms contribute to the observed effect. Our findings not only identify BiTeCl as a promising platform for future applications utilizing the NLHE, but also establish the asymmetry between the opposite surfaces of a polar crystal as a general design principle for discovering surface-driven materials with larger nonlinear Hall responses.

cond-mat.mtrl-sci↗
arXiv2026

Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents

Test-time self-evolving agents improve by reusing past experience, yet sparse-reward trajectories contain failures, loops, and detours, while summaries often omit the state conditions and action dependencies needed for execution. We study executable Walkthrough induction from sparse-reward trajectories: extracting compact, state-conditioned, and verifiable procedures. Our key observation is that delayed credit identifies actions associated with progress but cannot determine whether they produce facts required by later actions. We propose Trace, a credit-guided, dependency-grounded framework that compiles noisy trajectories into executable Walkthrough Memory. It detects progress anchors from rewards and persistent state changes, propagates credit to identify valuable transitions, and estimates action prerequisites from cross-episode success and failure evidence. Backward dependency slicing then traces required facts to their producers, extracting dependency-consistent action chains while removing irrelevant loops and detours. The resulting Walkthroughs encode entry conditions, ordered state--action--effect steps, and completion and failure predicates, supporting reuse, intermediate-state resumption, and programmatic verification. Experiments on J-TTL, WebShop, and ScienceWorld with three open-source LLMs show that Trace consistently outperforms eight test-time learning and memory baselines. Compared with the strongest baseline, it improves average AUC and Final-$3$ by $30.0%$ and $40.5%$, respectively, while using fewer inference tokens. These results show that long-horizon interaction benefits more from state-conditioned executable procedures than from complete trajectories or abstract summaries.

cs.LG↗
arXiv2026

Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis

While multimodal large language models (MLLMs) excel in medical applications, most of them favor static images or short-term signals. In the critical field of dynamic electrocardiograms (ECG), models struggle with complex temporal reasoning and diagnostic report generation due to a lack of high-quality datasets and benchmarks. To address this, we introduce (i) Holtercare-23K, a large-scale multimodal dynamic ECG dataset comprising 22,980 QA pairs derived from 788 clinical Holter records and featuring a novel signal-video-text tri-modal alignment. Based on this dataset, we present (ii) Holtercare-Bench, a multimodal benchmark that evaluates models on temporal localization, clinical diagnosis, and global summarization. Zero-shot evaluations of leading MLLMs reveal a significant performance gap in processing ultra-long pathological sequences. However, fine-tuning representative models yields substantial improvements. This work illuminates the limitations of current MLLMs in electrophysiology and provides a foundational benchmark for long-term medical MLLMs. Our project is available at https://github.com/ZJU4HealthCare/Holtercare-Bench.

cs.LG↗
arXiv2026

A Helium-shell Burning Blue Horizontal Branch Star Produced from Common Envelope Evolution

Observationally, blue horizontal branch (BHB) stars are defined as hot stars occupying a characteristic region between the extreme blue horizontal branch and RR Lyrae variables in the Hertzsprung-Russell diagram. Most of them are interpreted as stripped core-helium-burning stars, but the role of binary interaction in their formation remains unclear. Here, we report the discovery of a metal-rich BHB star in a 0.82628-day binary system (Feige 64) comprising a $0.35\pm0.03\,M_{\odot}$ BHB star and a likely $1.26\pm0.17\,M_{\odot}$ white dwarf (WD). The BHB star has an effective temperature of $15{,}524\pm310$ K and a luminosity of $39.7\pm4.1\,L_{\odot}$. Stellar evolution modelling indicates that it is a helium-shell-burning star produced through the common-envelope channel, retaining a hydrogen-rich envelope that is more massive than previously thought for low-mass stars. This finding provides direct evidence for binary interaction in the formation of BHB stars, offering a fresh perspective on interpreting this emerging population.

astro-ph.SR↗