AIQBAI Research & Papers
整理大Models、智能体、多模态、机器学习与生成式 AI 的Research、基准和研究进展。
Category articles2148
当前页40 / 54
UpdatesPermanent archive
Category articles
按发布Time从新到旧 · 每页 40 stories
ResearchOrdinary
Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living…
Source: arXiv
ResearchOrdinary
Do LiDAR Language Models Really Understand Spatio-temporal Relationships?
Recent 4D LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same option nearly matches the multiple-choice accuracy of two B4DL-derived configurations. …
Source: arXiv
ResearchOrdinary
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing defenses isolate injected content or c…
Source: arXiv
ResearchOrdinary
本周最佳Research：Story Imprinting 与 JEPA-Anything
本周最佳两篇 Week 38 Story Imprinting： AI Assistants Absorb Traits from Human Characters They Resemble JEPA-Anything： Learning Predictive Models across Different Worlds Story Imprinting https://arxiv.org/pdf/2609.10883 JEPA-Anything https://arxi…
Source: X：马东锡 NLP (@dongxi_nlp)
ResearchOrdinary
Blind Thermodynamic Ontology Discovery from Anonymous Experiments
Before a machine learning model can learn a thermodynamic equation of state, it must discover what its measurements represent: which channels scale with system size, which are intensive conjugates, how sectors pair through contact, and whi…
Source: arXiv
ResearchOrdinary
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature extraction algorithms, and small-scale on-d…
Source: arXiv
ResearchOrdinary
Bayesian Filtering in Physical Systems via Test-time Trained Flow Matching
Bayesian filtering provides a principled framework for online state estimation under uncertainty, yet its application to systems with high-dimensional states and complicated posterior distributions remains challenging. Recent generative mo…
Source: arXiv
ResearchOrdinary
Discovering Physical Representation Languages
Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or extensive, which sectors are dual, and which distinctions are merely gauge. We introduce phy…
Source: arXiv
ResearchOrdinary
Leaky-integrator reconstruction: taming error accumulation in recursive differenced time-series forecasting
We introduce leaky-integrator reconstruction, a training-free method that cures the error accumulation of recursive differenced forecasting. Our first contribution is diagnostic: predicting one-step changes and integrating them by cumulati…
Source: arXiv
ResearchOrdinary
One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents
Repository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can be uneven: gains in some categories coincide with regressions in others, while aggregate re…
Source: arXiv
ResearchOrdinary
If You Hear It, Help Find It: Orthogonal Knowledge Distillation for Open-Vocabulary Audio-Visual Event Localization
Open-vocabulary audio-visual event localization (OV-AVEL) grounds a text-queried event in time from video, audio, and language. The supervision sources available to this task can differ in temporal-boundary reliability: on OV-AVEBench, our…
Source: arXiv
ResearchOrdinary
The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions
Reinforcement learning (RL) offers a natural language for healthcare decisions whose conse- quences unfold over time, yet most reported progress remains far from routine intervention. Ex- isting surveys organize the field by algorithm or c…
Source: arXiv
ResearchOrdinary
Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States
Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent index-payload schism: dense vectors enable searchable routing, but models must re-ingest…
Source: arXiv · GPT / Qwen / Llama
ResearchOrdinary
LLM-Based FORM Code Generation with Verification-Driven Fine-Tuning
FORM is a domain-specific symbolic manipulation language widely used in particle physics for processing the very large algebraic expressions arising from multi-loop Feynman diagram calculations. Despite its central role in precision theore…
Source: arXiv · Qwen
ResearchOrdinary
What Can a Recurrent State Safely Forget?
Recurrent models must preserve information that changes future behavior while suppressing hidden-state error. These objectives conflict: contraction improves stability, but contraction along a future-distinguishing direction destroys memor…
Source: arXiv
ResearchOrdinary
TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
Recent advances in large language models (LLMs) have led to the emergence of coding agents capable of performing complex engineering tasks, including register-transfer level (RTL) design and optimization. Existing RTL benchmarks mainly eva…
Source: arXiv
ResearchOrdinary
Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution
Engineering predictions require physical mechanisms to be translated consistently into equations, discretization, code, and validation, yet errors can propagate despite local checks. Artificial-intelligence (AI) agents automate scientific …
Source: arXiv
ResearchOrdinary
Low-Rank Frequency Convolution and Noise-Range Augmentation for Real-Time Pitch Estimation on Edge Devices
Pitch estimation on an edge device is constrained in three ways at once. The model must be small, it must stay accurate when the input is noisy, and one frame must be produced inside the frame period. In this report the Frequency Convoluti…
Source: arXiv
ResearchOrdinary
Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States
Stochastic reconfiguration (SR) is the standard optimizer for neural quantum states (NQS), but modern NQS often have far more parameters than Monte Carlo samples. We show that in this regime the diagonal shift is more than a numerical stab…
Source: arXiv
ResearchOrdinary
A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits
Digital direct-to-consumer (DTC) health campaigns are usually measured after the fact. In-flight forecasting commonly relies on a separate classifier for every cutoff and horizon. We treat this task as a dynamic-system problem and build a …
Source: arXiv
ResearchOrdinary
Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions
Single-cell foundation models (scGPT, scBERT, Geneformer) achieve cell-type classification accuracy up to 97.5% in our experiments, yet this aggregate accuracy can mask systematic failure on rare, often disease-relevant cell populations th…
Source: arXiv
ResearchOrdinary
Co-occurrence Patterns of LoRA Adapters in Production Diffusion Model Inference Services
Low-rank adaptation (LoRA) has become a key technology for serving large-scale personalized large language models and diffusion models in the cloud. However, the co-occurrence patterns, resource contention relationships, and evolutionary r…
Source: arXiv
ResearchOrdinary
CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era
Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public discourse. However, large language models have made social bots much harder to detect from …
Source: arXiv
ResearchOrdinary
Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Engines
Graph databases are frequently positioned as categorically necessary for connected-data workloads, yet the systems dimension along which they actually differ - query planning, indexing, and data-readiness cost - is rarely isolated from ven…
Source: arXiv
ResearchOrdinary
Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle. We introduce a continual adaptation framework i…
Source: arXiv · Claude
ResearchOrdinary
Cross-sector generalization of accident-process role classification in occupational accident narratives
Occupational accident narratives contain valuable information about work situations, unfavourable conditions, accident events, and their consequences. Automatically structuring these narratives can facilitate large-scale accident analysis …
Source: arXiv
ResearchOrdinary
CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as …
Source: arXiv
ResearchOrdinary
Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw
Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit…
Source: arXiv
ResearchOrdinary
BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising the question of whether this scale can be exploited to learn general-purpose neural representations t…
Source: arXiv
ResearchOrdinary
Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention
Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two results formalizing this structure. First (CWAR Reducibility): confident-failure reduction is …
Source: arXiv
ResearchOrdinary
Benchmarking World Models for Continual Learning on Compositional Tasks
A desirable property of a world model is the ability to learn continually across tasks, adapting to new environments without forgetting what the agent has already learnt. In particular, the ability to retain and reuse knowledge obtained fr…
Source: arXiv
ResearchOrdinary
Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise
Graph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned node representations. This work proposes PCC+GCN, a hybrid framework that uses Particl…
Source: arXiv
ResearchOrdinary
Available Guardrails: Certifying Selective Prediction across ML Systems
A selective predictor acts as a safety gate: it returns an output only when the prediction appears sufficiently trustworthy. Deployments increasingly require this reliability to be certified at a target precision for every reporting unit o…
Source: arXiv
ResearchOrdinary
An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency
Memory systems for large language models have focused predominantly on efficient retrieval, whereas the decision of whether retrieved memories should be trusted has received comparatively little attention. When the memory store contains co…
Source: arXiv
ResearchOrdinary
$λ$-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Budgeted Resource
Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matching models by treating the denoising sampler as a stochastic policy that can be optimized…
Source: arXiv
ResearchOrdinary
Gricea: An Open Science Platform for Conversational AI Research
We need studies on conversational AI (CAI) at scale to understand human behavior and shape CAI design. However, fragmented reporting of systems and study configurations hinders replication, extension, and knowledge accumulation. We present…
Source: arXiv
ResearchOrdinary
QuranicMMLU: A Cognitively-Aware Benchmark for Evaluating Generative AI Solutions on Quranic Linguistic Knowledge
We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Quranic benchmarks center on general question answering and semantic retrieval, without prob…
Source: arXiv
ResearchOrdinary
COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules
Every multiparameter persistence vectorization we know of carries a one-sided Lipschitz upper bound and nothing below it: without a lower gauge there is no sense in which the features are faithful, and no per-prediction guarantee can be bu…
Source: arXiv
ResearchOrdinary
DiaVLo: Diagnosing Behaviours of Vision-Language Models
Vision-language models (VLMs) rely on storing and transferring appropriate information across their sub-components. Verifying that the VLMs exhibit desired behaviours, while avoiding harmful ones, is central to their reliable deployment. Y…
Source: arXiv
ResearchOrdinary
Abstention and Noise Filtering: Two Missing Primitives of Softmax Attention
Gating the value pathway of attention reportedly improves language model pretraining, and prior studies disagree on why. We argue and provide experimental evidence that such gates supply two different things that softmax attention lacks: a…
Source: arXiv