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Kevin Li

Publications and source records attributed to Kevin Li.

At least 19 recordsLinked to original sources
arXiv2026

The Great Chat in the Sky: Aligning Exotic Astronomical Data to Language with AstroBridge

Astronomical observations include images, spectra, and light curves, while the scientific literature describes these objects in natural language. We present AstroBridge, a framework that connects these observations to an LLM through a literature-assisted caption generation pipeline. We used catalog metadata to match observations with scientific texts that describe the same objects. A vision-language model generates descriptive captions from representations of observations, matched passages, and available metadata, creating observation-caption pairs without manual captioning. We train AstroBridge to predict captions directly from raw observations. Modality-specific frozen encoders extract representations that a shared, trainable fusion network aligns with the embedding space of an LLM. In caption-based evaluation of spectra, AstroBridge achieves 91.5% accuracy in distinguishing quasars from galaxies, compared to 69.0% from the base model prior to fine-tuning. Limitations remain in galaxy morphology, classification of minority supernova classes, and the factual quality of generated captions. We believe that AstroBridge's framework is a step towards the goal of aligning the totality of astronomy's diverse, exotic data through the lingua franca of text.

astro-ph.IM↗
arXiv2026

Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization

The contextual bandit models sequential decision-making problems in which rewards depend on both the chosen action and observed context. In many domains such as medicine, business, and engineering, prior knowledge provides structural information about the reward function that can be exploited to improve optimization efficiency. This paper studies settings where, for each observable context, the conditional mean reward is known to be concave with respect to the action variable. To leverage this structure, we develop a contextual bandit algorithm that conditions a Bayesian Gaussian Process posterior on concavity constraints. We propose a novel reward model that combines a concavity-preserving regression spline basis with a constrained GP posterior, yielding a tractable shape-constrained estimator. Building on this model, we construct a UCB algorithm and establish new posterior concentration inequalities for the constrained posterior, leading to regret guarantees that are never worse than applying standard GP-UCB without concavity information and can be strictly tighter when concavity is informative. Experiments on benchmark problems and a Warfarin dosing test application demonstrate substantial reductions in cumulative regret relative to state-of-the-art baselines.

stat.ML↗
arXiv2026

Monocular Badminton Speed Estimation with Air Drag and Court Geometry

A single camera cannot distinguish between a slowly moving object nearby and a rapidly moving object in the distance without additional information. For a shuttlecock, air drag provides a strong deceleration cue that can be combined with known court geometry. We fit a gravity-and-drag trajectory to smartphone video and compare a free monocular fit with a fit constrained to cross the net plane. Camera calibration uses court lines, and the trajectory is fitted to observed shuttle positions and frame timestamps by robust nonlinear least squares. We evaluated 51 badminton shots from two recording sessions, spanning drop shots, drives, half-smashes and smashes. The net-plane fit reduced the median absolute speed difference from a drag-based stereo reference from 13.3% to 8.6%. A second reference, obtained by differentiating triangulated positions without a drag model, also gave a smaller median difference for the net-plane fit. The results support combining court geometry with flight dynamics for badminton speed estimation from a single camera.

cs.CV↗
arXiv2026

A Heckler in the Hidden State: Correctness Signals in Diffusion Language Models

Diffusion language models generate code by repeatedly updating a partially masked sequence. We ask whether their internal activations encode code correctness and whether that information can improve generation. Across six diffusion models, linear probes distinguish passing from failing attempts, with the strongest reads generally appearing beyond the early layers. Controls using small semantic mutations support a connection to correctness rather than surface style alone. In comparisons with model confidence, probe point estimates offer no consistent advantage. Adding a probe-derived direction to the residual stream does not yield a dependable improvement in the tested steering settings, while the opposite direction degrades performance. We distinguish these observations from claims about statistical significance or a general inability to steer. Supplementary methods, archived results, and code document the tested interventions and the limits of their statistical calibration and reproducibility.

cs.SE↗
arXiv2026

A note on higher coherence of graphs of groups

For $n\in \mathbb{N}$, a group is called $n$-coherent if every subgroup of type $\mathsf{F}_n$ is of type $\mathsf{F}_{n+1}$. For $n\ge 1$, we observe that graphs of groups with $n$-coherent vertex groups and virtually poly-cyclic edge groups are $n$-coherent. We deduce the $n$-coherence of certain right-angled Artin groups.

math.GR↗
arXiv2026

Contractible Rips complexes of groups via metric gluings

We say that a finitely generated group equipped with a finite generating set is of type $\mathsf{R}$ if the Rips complex is contractible for sufficiently large scales. We show that the class of groups of type $\mathsf{R}$ is closed under taking graphs of groups with finite edge groups and, in particular, under taking free products. We prove that all two-dimensional right-angled Artin groups with the standard generating set are of type $\mathsf{R}$. Furthermore, we observe that products of finitely generated free abelian groups and finite groups are of type $\mathsf{R}$.

math.GR↗
arXiv2026

Checked-In Secret Detection: Strings Are All You Need

Hardcoded secrets in source code pose critical security vulnerabilities which can be easily exploited by malicious adversaries. Existing regex-based detection approaches suffer from fundamental limitations, as secrets often lack identifiable patterns, resulting in poor precision and recall. Recent studies have explored context-aware detection methods, as surrounding code can reveal the purpose of candidate strings. However, these methods confront three key challenges: (1) obfuscation robustness where models over-rely on easily obfuscated identifiers, (2) cross-language generalization difficulties due to uneven training data distribution, and (3) lengthy and noisy context that introduces excessive irrelevant tokens and slows inference. We observe that strings serve as a critical information source for code semantics, offering superior contextual density, obfuscation robustness, and language independence. Based on this insight, we propose StringGroup, a novel context extraction algorithm that mines strings surrounding potential secrets. By introducing a relatively simple modification to existing patterns that narrows the analysis specifically to string literals, the method achieves significant gains. With only 33.2% of the original context, it preserves over 80% of semantic information and significantly improves the signal-to-noise ratio for secret detection. We further design a context-aware secret detection tool, Secretron, based on StringGroup methods and Transformer model. Evaluation on the SecretBench dataset demonstrates high accuracy with 98.74% F1-score and strong robustness under obfuscation and cross-language scenarios, outperforming state-of-the-art LLM-based baselines. We deploy our tool in real-world environments and successfully detect 48 previously unknown secret keys from 26 applications, demonstrating the practical effectiveness of our approach.

cs.CR↗
arXiv2026

Adaptive Time Series Reasoning via Segment Selection

Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full series or appear in a few short intervals, so the model must decide what to inspect. Most existing approaches encode the entire time series into a fixed representation before inference, regardless of whether or not the entire sequence is relevant. We introduce ARTIST, which formulates time-series reasoning as a sequential decision problem. ARTIST interleaves reasoning with adaptive temporal segment selection. It adopts a controller-reasoner architecture and uses reinforcement learning to train the controller role to select informative segments and the reasoner role to generate segment-conditioned reasoning traces and final answers. During inference, the model actively acquires task-relevant information instead of relying on a static summary of the full sequence. We use a novel hierarchical policy optimization approach for post-training that allows the model to excel in both segment selection and question-answering behavior. We evaluate ARTIST on six time-series reasoning benchmarks and compare it with large language models, vision-language models, and prior time-series reasoning systems. ARTIST improves average accuracy by 6.46 absolute percentage points over the strongest baseline. The largest gains appear on rare event localization and multi-segment reasoning tasks. Supervised fine-tuning improves performance, and reinforcement learning provides additional gains by optimizing question-adaptive segment selection. These results show that selective data use drives effective time-series reasoning.

cs.LG↗
arXiv2026

GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction

Video recordings of child-caregiver interactions enable investigation of attentional dynamics during naturalistic behavior. Such multimodal recording also allows researchers to examine how attention interacts with action and language use in real time. However, manual annotation of such data is time-consuming. Here, we introduce GazeBehavior Annotation Toolkit, a deep-learning-based toolkit designed to facilitate three key processes in data preprocessing and feature extraction: post-hoc synchronization across multiple videos, semi-automatic annotation of gaze target categories, and categorization of participants' poses and hand actions. This toolkit improves the efficiency and scalability of feature extraction from human egocentric eye-tracking and video data. Such improvement is critical in supporting large-scale and longitudinal investigations of attentional dynamics and naturalistic behavior in human early development.

cs.CV↗
arXiv2026

PRAXA: A Grammar for What-If Analysis

What-if analysis is widely used to explore hypothetical scenarios and evaluate alternative pathways to desired results. However, current approaches are fragmented: systems implement what-if capabilities under diverse terminologies with different analytic techniques. Such fragmentation limits expressiveness, impedes flexible composition and reuse of workflows, and hinders tighter integration with AI. We present PRAXA, a compositional grammar of what-if analysis derived from recurring patterns across 141 publications in visual analytics and HCI venues. PRAXA formulates three primitives: (1) data, defining variables under analysis, (2) model, specifying predictive mechanisms, and (3) interaction operations-pairs of user actions and system responses that execute analyses. We encode PRAXA into a declarative specification language, PSL. To evaluate PRAXA, we first show expressiveness by reconstructing representative workflows from prior work as structured compositions, exposing the predominant focus on single-step rather than multi-step reasoning. Second, we demonstrate composability by revealing that capabilities described under distinct terminologies share the same grammatical structure with different parameterizations, and that new multi-step workflows emerge through composition. Third, we illustrate PSL as an intermediate representation for translating natural-language what-if queries into executable interactive interfaces, enabling inspection, validation, and more transparent AI integration. By unifying diverse what-if approaches as a grammar, PRAXA provides a foundation for analyzing, composing, and supporting workflows in next-generation what-if systems.

cs.HC↗
arXiv2026

Controllable Sequence Editing for Biological and Clinical Trajectories

Conditional generation models for longitudinal sequences can produce new or modified trajectories given a conditioning input. However, they often lack control over when the condition should take effect (timing) and which variables it should influence (scope). Most methods either operate only on univariate sequences or assume that the condition alters all variables and time steps. In scientific and clinical settings, interventions instead begin at a specific moment, such as the time of drug administration or surgery, and influence only a subset of measurements while the rest of the trajectory remains unchanged. CLEF learns temporal concepts that encode how and when a condition alters future sequence evolution. These concepts allow CLEF to apply targeted edits to the affected time steps and variables while preserving the rest of the sequence. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health, and sales, comparing against 9 state-of-the-art baselines. CLEF improves immediate sequence editing accuracy by 16.28% (MAE) on average against their non-CLEF counterparts. Unlike prior models, CLEF enables one-step conditional generation at arbitrary future times, outperforming their non-CLEF counterparts in delayed sequence editing by 26.73% (MAE) on average. We test CLEF under counterfactual inference assumptions and show up to 62.84% (MAE) improvement on zero-shot conditional generation of counterfactual trajectories. In a case study of patients with type 1 diabetes mellitus, CLEF identifies clinical interventions that generate realistic counterfactual trajectories shifted toward healthier outcomes.

cs.LG↗
arXiv2026

tLoRA: Efficient Multi-LoRA Training with Elastic Shared Super-Models

As Low-Rank Adaptation (LoRA) becomes the standard approach for efficiently fine-tuning large language models (LLMs), shared clusters increasingly execute many concurrent LoRA training jobs over the same frozen backbone. While recent advances enable batching (co-locating) multiple adapters during serving, efficient training-time co-location of heterogeneous LoRA adapters presents unique challenges. Jobs often differ in adapter rank, batch size, and resource allocation, and naïve batching can introduce synchronization stalls, communication overheads, and per-job slowdowns that are worse than executing independently. We introduce tLoRA, a framework that enables efficient batch training of multiple LoRA jobs. tLoRA fuses adapters that share the same base model into an elastic shared super-model, exploiting existing distributed training frameworks to derive parallelism plans that share resources effectively. At the kernel level, tLoRA employs a fused LoRA kernel that adaptively reconstructs low-rank computation tiles and schedules rank-aware nano-batches to maximize overlap between computation and communication across adapters. At the scheduling layer, tLoRA incorporates an online, residual-capacity-aware scheduler that adaptively groups jobs to maximize collective throughput. Evaluations using real-world cluster traces demonstrate that tLoRA improves training throughput by 1.2--1.8x, job training completion time by 2.3--5.4x, and GPU utilization by 37%.

cs.LG↗
arXiv2025

Automatically Detecting Checked-In Secrets in Android Apps: How Far Are We?

Mobile apps are predominantly integrated with cloud services to benefit from enhanced functionalities. Adopting authentication using secrets such as API keys is crucial to ensure secure mobile-cloud interactions. However, developers often overlook the proper storage of such secrets, opting to put them directly into their projects. These secrets are checked into the projects and can be easily extracted and exploited by malicious adversaries. While many researchers investigated the issue of checked-in secret in open-source projects, there is a notable research gap concerning checked-in secrets in Android apps deployed on platforms such as Google Play Store. Unlike open-source projects, the lack of direct access to the source code and the presence of obfuscation complicates the checked-in secret detection for Android apps. This motivates us to conduct an empirical analysis to measure and compare the performance of different checked-in secret detection tools on Android apps. We first conducted a literature review to find all the checked-in secret detection tools that can be applied to Android apps. Then, we evaluate three representative tools on 5,135 Android apps, comparing their performance and analyzing their limitations. Our experiment reveals 2,142 checked-in secrets affecting 2,115 Android apps. We also disclose that the current checked-in secret detection techniques suffer from key limitations. All of the evaluated tools can miss a significant number of checked-in secrets in Android apps. Nevertheless, we observed that the tools are complimentary, suggesting the possibility of developing a more effective checked-in secret detection tool by combining their insights. Additionally, we propose that analyzing string groups within methods containing checked-in secrets may provide a more effective strategy to overcome obfuscation challenges.

cs.CR↗
arXiv2025

The algebraic cheap rebuilding property

We present an axiomatic approach to combination theorems for various homological properties of groups and, more generally, of chain complexes. Examples of such properties include algebraic finiteness properties, $\ell^2$-invisibility, $\ell^2$-acyclicity, lower bounds for Novikov--Shubin invariants, and vanishing of homology growth. As a key example, we introduce an algebraic version of Abért--Bergeron--Frączyk--Gaboriau's cheap rebuilding property that implies vanishing of torsion homology growth and fits into our axiomatic framework for combination theorems. In particular, we obtain that certain graphs of groups with amenable vertex groups and elementary amenable edge groups have vanishing torsion homology growth.

math.GR↗
arXiv2025

UniFusion: Vision-Language Model as Unified Encoder in Image Generation

Although recent advances in visual generation have been remarkable, most existing architectures still depend on distinct encoders for images and text. This separation constrains diffusion models' ability to perform cross-modal reasoning and knowledge transfer. Prior attempts to bridge this gap often use the last layer information from VLM, employ multiple visual encoders, or train large unified models jointly for text and image generation, which demands substantial computational resources and large-scale data, limiting its accessibility.We present UniFusion, a diffusion-based generative model conditioned on a frozen large vision-language model (VLM) that serves as a unified multimodal encoder. At the core of UniFusion is the Layerwise Attention Pooling (LAP) mechanism that extracts both high level semantics and low level details from text and visual tokens of a frozen VLM to condition a diffusion generative model. We demonstrate that LAP outperforms other shallow fusion architectures on text-image alignment for generation and faithful transfer of visual information from VLM to the diffusion model which is key for editing. We propose VLM-Enabled Rewriting Injection with Flexibile Inference (VERIFI), which conditions a diffusion transformer (DiT) only on the text tokens generated by the VLM during in-model prompt rewriting. VERIFI combines the alignment of the conditioning distribution with the VLM's reasoning capabilities for increased capabilities and flexibility at inference. In addition, finetuning on editing task not only improves text-image alignment for generation, indicative of cross-modality knowledge transfer, but also exhibits tremendous generalization capabilities. Our model when trained on single image editing, zero-shot generalizes to multiple image references further motivating the unified encoder design of UniFusion.

cs.CV↗
arXiv2025

Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws

Scaling large language models typically involves three dimensions: depth, width, and parameter count. In this work, we explore a fourth dimension, \textbf{virtual logical depth} (VLD), which increases effective algorithmic depth without changing parameter count by reusing weights. While parameter reuse is not new, its role in scaling has been underexplored. Unlike recent test-time methods that scale token-wise, VLD alters the internal computation graph during training and inference. Through controlled experiments, we obtain three key insights. (1) \textit{Knowledge capacity vs. parameters}: at fixed parameter count, VLD leaves knowledge capacity nearly unchanged, while across models capacity still scales with parameters. (2) \textit{Reasoning vs. reuse}: properly implemented VLD substantially improves reasoning ability \emph{without} more parameters, decoupling reasoning from size. This suggests a new scaling path beyond token-wise test-time methods. (3) \textit{Robustness and generality}: reasoning gains persist across architectures and reuse schedules, showing VLD captures a general scaling behavior. These results provide insight into future scaling strategies and raise a deeper question: does superintelligence require ever-larger models, or can it be achieved by reusing parameters and increasing logical depth? We argue many unknown dynamics in scaling remain to be explored. Code is available at https://anonymous.4open.science/r/virtual_logical_depth-8024/.

cs.AI↗