arXiv subjects

Yu-Feng Li

Publications and source records attributed to Yu-Feng Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Exploring the electromagnetic properties of neutrinos at a short-baseline reactor neutrino experiment

Upcoming and present reactor neutrino experiments represent an appealing tool to probe fundamental properties in the neutrino sector. In this paper, we study the physics potential to determine the electromagnetic properties of neutrinos via electron--neutrino elastic scattering (E$\nu$ES) at a short-baseline neutrino experiment. We evaluate the sensitivity to the weak mixing angle, $\sin^2\theta_W$, employing antineutrinos from a nuclear reactor source. Furthermore, from the sensitivity to $\sin^2 \theta_W$, we obtain bounds on the neutrino charge radius. We also present the projected sensitivity to the effective neutrino magnetic moment, $\mu_{\nu}$. Compared with other reactor neutrino measurements, this experimental configuration may set competitive limits on the electromagnetic properties of neutrinos.

hep-ph↗

arXiv2026

NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions. Current remote-sensing benchmarks mainly focus on evaluating perception and reasoning capabilities of multimodal large language models (MLLMs). They fail to assess planning capability, stemming either from the difficulty of curating and validating planning tasks at scale or from evaluation protocols that are inaccurate and inadequate. To address these limitations, we introduce NeSy-Route, a large-scale neuro-symbolic benchmark for constrained route planning in remote sensing. Within this benchmark, we introduce an automated data-generation framework that integrates high-fidelity semantic masks with heuristic search to produce diverse route-planning tasks with provably optimal solutions. This allows NeSy-Route to comprehensively evaluate planning across 10,821 route-planning samples, nearly 10 times larger than the largest prior benchmark. Furthermore, a three-level hierarchical neuro-symbolic evaluation protocol is developed to enable accurate assessment and support fine-grained analysis on perception, reasoning, and planning simultaneously. Our comprehensive evaluation of various state-of-the-art MLLMs demonstrates that existing MLLMs show significant deficiencies in perception and planning capabilities. We hope NeSy-Route can support further research and development of more powerful MLLMs for remote sensing. The dataset and code are available at https://mingyang1010.github.io/NeSy-Route/.

cs.AI↗

arXiv2026

Hindsight Credit Assignment for Long-Horizon LLM Agents

Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bottlenecks: inaccurate step-level Q-value estimation and misaligned value baselines for intermediate states. To address these limitations, we introduce HCAPO, the first framework to integrate hindsight credit assignment into LLM agents. HCAPO leverages the LLM itself as a post-hoc critic to refine step-level Q-values through hindsight reasoning. Furthermore, HCAPO's multi-scale advantage mechanism effectively supplements the inaccurate value baselines at critical decision states. Evaluations across three challenging benchmarks, including WebShop and ALFWorld, demonstrate that HCAPO consistently outperforms state-of-the-art RL methods. Notably, HCAPO achieves a 7.7% improvement in success rate on WebShop and a 13.8% on ALFWorld over GRPO using the Qwen2.5-7B-Instruct model. These results indicate that HCAPO significantly enhances exploration efficiency, promotes concise decision-making, and ensures scalability in complex, long-horizon tasks.

cs.LG↗

arXiv2026

Atmospheric Neutrino Charged-Current Interactions at Large Liquid-Scintillator Detectors: I. Physics of Neutrino-Antineutrino Discrimination

In this work, we present a systematic study of the event characteristics and physics of neutrino-antineutrino discrimination associated with atmospheric neutrino charged-current interactions in large liquid scintillator detectors. This study encompasses the primary neutrino interactions, the sequential second interactions of final-state particles, and the final neutron captures. We carefully investigate the properties of final-state charged leptons and hadrons, providing distinct distributions of inelasticity and captured neutron multiplicity for both neutrino and antineutrino interactions. These distributions are employed to assess the quantitative performance of neutrino-antineutrino discrimination. Our findings lay the groundwork for atmospheric neutrino oscillation studies in large liquid scintillator detectors, particularly in the determination of neutrino mass ordering.

hep-ph↗

arXiv2026

Information-Theoretic Gaps in Solar and Reactor Neutrino Oscillation Measurements

Quantum estimation theory provides a fundamental framework for analyzing how precisely physical parameters can be estimated from measurements. Neutrino oscillations are characterized by a set of parameters inferred from experiments conducted in different production and detection environments. The two solar oscillation parameters, $\Delta m^2_{21}$ and $\theta_{12}$, can be estimated using both solar neutrino experiments and reactor neutrino experiments. In reactor experiments, neutrinos are detected after coherent vacuum evolution, while solar neutrinos arrive at the detector as incoherent mixtures. In this work, we use Quantum Fisher Information (QFI) to quantify and compare the information content accessible in these two experimental setups. We find that for reactor neutrinos, flavor measurements saturate the QFI bound for both parameters over specific energy ranges, demonstrating their optimality and explaining the high precision achieved by these experiments. In contrast, for solar neutrinos the phase-based contribution to the QFI, originating from the quantum coherence, is absent, rendering the estimation of $\Delta m_{21}^2$ purely population-based and effectively classical, while the QFI for $\theta_{12}$ is dominated by basis rotation at high energies and is nearly saturated by flavor measurements. Consequently, solar neutrino experiments are intrinsically more sensitive to $\theta_{12}$ than to $\Delta m_{21}^2$. This analysis highlights a fundamental distinction between the two estimation problems and accounts for their differing achievable precisions.

hep-ph↗

arXiv2025

Theory of inverse beta decay for reactor antineutrinos

Inverse beta decay (IBD), $\overline{\nu}_e p \to e^+ n \left( \gamma \right)$, is the main detection channel for reactor antineutrinos in water- and hydrocarbon-based detectors. As reactor antineutrino experiments now target sub-percent-level sensitivity to oscillation parameters, a precise theoretical description of IBD, including recoil, weak magnetism, nucleon structure, and radiative corrections, becomes essential. In this work, we give a detailed and precise calculation of the total and differential cross sections for radiative IBD, $\overline{\nu}_e p \to e^+ n \gamma$. We use a heavy baryon chiral perturbation theory framework, systematically incorporating electroweak, electromagnetic, and strong-interaction corrections. We derive new analytic cross-section expressions, clarify the collinear structure of radiative corrections, and provide a systematic uncertainty analysis. We also discuss phenomenological applications for reactor antineutrino experiments, e.g., JUNO, and neutron decay. Our results enable sub-permille theoretical precision, supporting current and future experiments.

hep-ph↗

arXiv2025

Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training

Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language. To address this gap, we introduce Pianist Transformer, with three key contributions: 1) introducing large-scale self-supervised learning into expressive piano performance rendering through a unified Musical Instrument Digital Interface (MIDI) representation, enabling pre-training on 10B tokens of unlabeled MIDI data; 2) an efficient asymmetric Transformer with note-level compression, substantially improving training efficiency, memory usage, and inference speed for long-context music modeling; 3) a state-of-the-art rendering model with an editable workflow, achieving strong objective and subjective results and enabling integration into real-world music production workflows. Overall, Pianist Transformer outlines a scalable path toward human-like performance synthesis in the music domain. Code, audio samples, and model checkpoints are available on our project page: https://yhj137.github.io/pianist-transformer-demo/.

cs.SD↗

arXiv2025

Terrestrial Matter Effects on Reactor Antineutrino Oscillations: Constant vs. Fluctuated Density Profiles

The JUNO Collaboration has recently released its first reactor antineutrino oscillation result, achieving unprecedented precision in the measurement of $\Delta m^2_{21}$ and $\sin^2\theta_{12}$. We emphasize that the accurate determination and modeling of the terrestrial matter density profile are fundamental for extracting the oscillation parameters and probing the neutrino mass ordering. This paper presents a realistic piecewise-constant model for the shallow crustal density profile along the baselines from Taishan and Yangjiang to the experimental hall, based on geological and petrophysical information. The uncertainty in the density profiles arises from variations in the density and length of each segment, both of which are conservatively estimated to be 10%. A careful comparison of constant and fluctuated density profiles is provided and the implications for the precision measurement of oscillation parameters are discussed. Finally, we also discuss the prospect of shallow crust tomography in future reactor neutrino experiments.

hep-ph↗

arXiv2025

A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning

Test-time scaling seeks to improve the reasoning performance of large language models (LLMs) by adding computational resources. A prevalent approach within the field is sampling-based test-time scaling methods, which enhance reasoning by generating multiple reasoning paths for a given input during inference. However, despite its practical success, the theoretical foundations remain underexplored. In this paper, we provide the first theoretical framework for analyzing sampling-based test-time scaling methods, grounded in the perspective of confidence estimation. Based on the framework, we analyze two dominant paradigms: self-consistency and perplexity, and reveal key limitations: self-consistency suffers from high estimation error while perplexity exhibits substantial modeling error and possible degradation of the estimation error convergence. To address these limitations, we introduce RPC, a hybrid method that leverages our theoretical insights through two key components: Perplexity Consistency and Reasoning Pruning. Perplexity Consistency combines the strengths of self-consistency and perplexity, boosting the convergence rate of estimation error from linear to exponential while preserving model error. Reasoning Pruning prevents degradation by eliminating low-probability reasoning paths. Both theoretical analysis and empirical results across seven benchmark datasets demonstrate that RPC has a strong potential for reducing reasoning error. Notably, RPC achieves reasoning performance comparable to self-consistency while not only enhancing confidence reliability but also reducing sampling costs by 50%. The code and resources are available at https://wnjxyk.github.io/RPC.

cs.LG↗

arXiv2025

Memory-Efficient Fine-Tuning via Low-Rank Activation Compression

The parameter-efficient fine-tuning paradigm has garnered significant attention with the advancement of foundation models. Although numerous methods have been proposed to reduce the number of trainable parameters, their substantial memory overhead remains a critical bottleneck that hinders practical deployment. In this paper, we observe that model activations constitute a major source of memory consumption, especially under large batch sizes and long context lengths; however, the rank of the activations remains consistently low. Motivated by this insight, we propose a memory-efficient fine-tuning approach Low-Rank Activation Compression (LoRAct). Unlike prior work, LoRAct provides a more flexible and versatile compressing strategy that can be applied online during the forward pass without the need for any calibration data. Moreover, LoRAct incorporates a novel sampling-based orthogonal decomposition algorithm specifically designed for low-rank matrices, offering improved computational efficiency and a tighter error bound compared to the widely used RSVD. Experiments on both vision and language tasks demonstrate the effectiveness of LoRAct. Notably, LoRAct further reduces activation memory by approximately 80% in comparison with the widely adopted LoRA method, while maintaining competitive performance. The source code is available at https://github.com/shijxcs/meft.

cs.LG↗

arXiv2025

FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theory

Large language models (LLMs) have recently demonstrated remarkable progress in formal theorem proving. Yet their ability to serve as practical assistants for mathematicians, filling in missing steps within complex proofs, remains underexplored. We identify this challenge as the task of subgoal completion, where an LLM must discharge short but nontrivial proof obligations left unresolved in a human-provided sketch. To study this problem, we introduce FormalML, a Lean 4 benchmark built from foundational theories of machine learning. Using a translation tactic that converts procedural proofs into declarative form, we extract 4937 problems spanning optimization and probability inequalities, with varying levels of difficulty. FormalML is the first subgoal completion benchmark to combine premise retrieval and complex research-level contexts. Evaluation of state-of-the-art provers highlights persistent limitations in accuracy and efficiency, underscoring the need for more capable LLM-based theorem provers for effective subgoal completion,

cs.CL↗

arXiv2025

Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models

Large Language Models (LLMs) have shown promising results across various tasks, yet their reasoning capabilities remain a fundamental challenge. Developing AI systems with strong reasoning capabilities is regarded as a crucial milestone in the pursuit of Artificial General Intelligence (AGI) and has garnered considerable attention from both academia and industry. Various techniques have been explored to enhance the reasoning capabilities of LLMs, with neuro-symbolic approaches being a particularly promising way. This paper comprehensively reviews recent developments in neuro-symbolic approaches for enhancing LLM reasoning. We first present a formalization of reasoning tasks and give a brief introduction to the neurosymbolic learning paradigm. Then, we discuss neuro-symbolic methods for improving the reasoning capabilities of LLMs from three perspectives: Symbolic->LLM, LLM->Symbolic, and LLM+Symbolic. Finally, we discuss several key challenges and promising future directions. We have also released a GitHub repository including papers and resources related to this survey: https://github.com/LAMDASZ-ML/Awesome-LLM-Reasoning-with-NeSy.

cs.AI↗

arXiv2025

Data Selection for LLM Alignment Using Fine-Grained Preferences

Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferences becomes more and more feasible, existing alignment methods typically work on a single preference and thus struggle with conflicts inherent in such aggregated datasets. As one early attempt, in this paper, we propose a data-centric approach to align LLMs through the effective use of fine-grained preferences. Specifically, we formulate the problem as a direct fine-grained preference optimization and introduce preference divergence (PD) that quantifies inter-aspect preference conflicts. Instead of directly tackling the consequent complicated optimization, we recast it as a data selection problem and propose a simple yet effective strategy, which identifies a subset of data corresponding to the most negative PD values, for efficient training. We theoretically analyze the loss-bound optimality of our selection strategy and conduct extensive empirical studies on varied settings and datasets to demonstrate that our practical selection method could achieve consistent improvement against standard full-data alignment, using even just 30% of the data. Our work shares a line that LLM alignment using fine-grained preferences is highly feasible.

cs.LG↗

arXiv2025

Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning

The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby enabling the selection of beneficial unsupervised pretext tasks. However, existing research has paid limited attention to assumptions in SSL, resulting in practical situations where the compatibility between the unsupervised pretext tasks and the target scenarios can only be assessed after training and validation. This paper centers on the assumptions underlying unsupervised pretext tasks and explores the feasibility of preemptively estimating the impact of unsupervised pretext tasks at low cost. Through rigorous derivation, we show that the impact of unsupervised pretext tasks on target performance depends on three factors: assumption learnability with respect to the model, assumption reliability with respect to the data, and assumption completeness with respect to the target. Building on this theory, we propose a low-cost estimation method that can quantitatively estimate the actual target performance. We build a benchmark of over one hundred pretext tasks and demonstrate that estimated performance strongly correlates with the actual performance obtained through large-scale training and validation.

cs.LG↗

arXiv2025

New Physics Opportunities at Neutrino Facilities: BSM Physics at Accelerator, Atmospheric, and Reactor Neutrino Experiments

Since the discovery of the Higgs boson, the long-standing task at hand in particle physics is the search for new physics beyond the Standard Model, which accounts for only about 5\% of the Universe. In light of this situation, the neutrino sector has drawn significant attention due to neutrino oscillations, which require physics beyond the Standard Model and have prompted a wide array of active and planned experimental programs. Notably, neutrino facilities offer substantial potential to search for new physics beyond neutrino oscillations, owing to their precision measurement capabilities, diverse experimental configurations, and various neutrino sources. This white paper summarizes the landscape of new physics that can be probed at current and future neutrino experiments, categorized into laboratory-produced and cosmogenic signals. We discuss recent experimental results interpreted through the lens of new physics, as well as detailed plans and projected sensitivities of next-generation facilities. This summary is based on presentations from the 4th Workshop on New Physics Opportunities in Neutrino Facilities (NPN 2024), held at IBS in Daejeon, Korea, on June 3-5, 2024. Particular emphasis is placed on accelerator-based neutrino experiments and a range of neutrino programs in East Asia. We also outline key tasks necessary to realize the promising new physics opportunities ahead.

hep-ph↗

arXiv2025

TabularMath: Understanding Math Reasoning over Tables with Large Language Models

Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning over tabular data in real-world applications has been overlooked. For instance, applications such as business intelligence demand not only multi-step numerical reasoning with tables but also robustness to incomplete or inconsistent information. However, comprehensive evaluation in this area is severely limited, constrained by the reliance on manually collected tables that are difficult to scale and the lack of coverage for potential traps encountered in real-world scenarios. To address this problem, we propose AutoT2T, a neuro-symbolic framework that controllably transforms math word problems into scalable and verified tabular reasoning tasks. Building on this pipeline, we develop TabularMath, a benchmark comprising four subsets that include both text-based and image-based tables, covering table complexity, table quality, and table representation dimensions. Our study reveals three key observations: (1) Table complexity and reasoning difficulty impact reasoning performance jointly; (2) Low-quality tables pose severe risks to reliable reasoning in current LLMs; (3) Different table modalities show similar trends, with text-based tables typically being easier for models to reason over. In-depth analyses are conducted for each observation to guide future research.

cs.AI↗

arXiv2025

Realistic Evaluation of TabPFN v2 in Open Environments

Tabular data, owing to its ubiquitous presence in real-world domains, has garnered significant attention in machine learning research. While tree-based models have long dominated tabular machine learning tasks, the recently proposed deep learning model TabPFN v2 has emerged, demonstrating unparalleled performance and scalability potential. Although extensive research has been conducted on TabPFN v2 to further improve performance, the majority of this research remains confined to closed environments, neglecting the challenges that frequently arise in open environments. This raises the question: Can TabPFN v2 maintain good performance in open environments? To this end, we conduct the first comprehensive evaluation of TabPFN v2's adaptability in open environments. We construct a unified evaluation framework covering various real-world challenges and assess the robustness of TabPFN v2 under open environments scenarios using this framework. Empirical results demonstrate that TabPFN v2 shows significant limitations in open environments but is suitable for small-scale, covariate-shifted, and class-balanced tasks. Tree-based models remain the optimal choice for general tabular tasks in open environments. To facilitate future research on open environments challenges, we advocate for open environments tabular benchmarks, multi-metric evaluation, and universal modules to strengthen model robustness. We publicly release our evaluation framework at https://anonymous.4open.science/r/tabpfn-ood-4E65.

cs.LG↗

arXiv2025

NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation

Obtaining large-scale, high-quality reasoning data is crucial for improving the geometric reasoning capabilities of multi-modal large language models (MLLMs). However, existing data generation methods, whether based on predefined tem plates or constrained symbolic provers, inevitably face diversity and numerical generalization limitations. To address these limitations, we propose NeSyGeo, a novel neuro-symbolic framework for generating geometric reasoning data. First, we propose a domain-specific language grounded in the entity-attributes-relations paradigm to comprehensively represent all components of plane geometry, along with generative actions defined within this symbolic space. We then design a symbolic-visual-text pipeline that synthesizes symbolic sequences, maps them to visual and textual representations and generates reasoning path with reverse search and forward validation. Based on this framework, we construct NeSyGeo CoT and NeSyGeo-Caption datasets, containing 100k samples, and release a new benchmark NeSyGeo-Test for evaluating geometric reasoning abilities in MLLMs. Experiments demonstrate that the proposal significantly and consistently improves the performance of multiple MLLMs under both reinforcement and supervised fine-tuning. With only 4k samples and two epochs of reinforcement fine-tuning, base models achieve improvements of up to +15.8% on MathVision, +8.4% on MathVerse, and +7.3% on GeoQA. Notably, a 4B model can be improved to outperform an 8B model from the same series on geometric reasoning tasks.s

cs.CL↗