Search: “research”

Provable Quantum--Classical Separation for Continuous Gibbs Sampling

arXiv:2608.24527v1 Announce Type: cross Abstract: We prove the first quantum--classical separation for a sampling problem over a continuous domain. For a class of Gibbs states ppropto e^{-eta E} on th

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PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization

DGX agent

arXiv:2608.24207v1 Announce Type: new Abstract: Semantic identifiers (SIDs) represent entities as hierarchical token sequences for generative retrieval and recommendation. Residual-quantization tokeni

researcharxiv-cs-lg

26 Aug 2026

Quantum Maximum Entropy Inference and Hamiltonian Learning

arXiv:2407.11473v2 Announce Type: replace Abstract: Maximum entropy inference and learning of graphical models are pivotal tasks in learning theory and optimization. This work extends algorithms for t

Quasar: A Programming Language Specialized for LLM Code Actions

arXiv:2506.12202v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use

ReflCtrl: Controlling LLM Reflection Efficiently via Representation Engineering

arXiv:2512.13979v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. Self-reflection, the ability to review a

ReGround-Surg: Reliability-Guided Anchor Grounding for Referring Surgical Video Segmentation

arXiv:2608.24671v1 Announce Type: new Abstract: Referring surgical video segmentation requires segmenting a target instrument or tissue region across video frames according to a natural language expre

Renormalization Group Flow Matching for Scalable Local Generative Modeling

arXiv:2608.23696v1 Announce Type: new Abstract: Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structural

ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping

arXiv:2608.24411v1 Announce Type: new Abstract: The efficiency of Large Language Model (LLM) serving is fundamentally limited by the sequential nature of autoregressive decoding. Speculative Decoding

RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation

arXiv:2608.24079v1 Announce Type: cross Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering bot

Right Diagnoses, Decorative Reasoning:A Perturbation Audit of Medical Chain-of-Thought

arXiv:2608.24790v1 Announce Type: new Abstract: Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested. Gene

Robust Slip Detection and Material Classification via Spatiotemporal Transformers on a Uniformly-Illuminated Visuo-Tactile Sensor

arXiv:2608.24162v1 Announce Type: new Abstract: Tactile sensing is central to robotic manipulation, among which slip detection stands out as a quintessential and critical task. However, existing slip

researcharxiv-cs-ro

Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs

arXiv:2510.01527v2 Announce Type: replace Abstract: Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like reaction pre

Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation

arXiv:2608.24138v1 Announce Type: new Abstract: Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify

Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

arXiv:2601.17027v2 Announce Type: replace-cross Abstract: While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by t

Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models

arXiv:2306.14300v2 Announce Type: replace-cross Abstract: Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and b

Selective Regenerative Decoding: Trajectory-Level Intervention for Inference-Time Reasoning

arXiv:2608.24338v1 Announce Type: new Abstract: Inference-time decoding methods improve LLM reasoning by exploring multiple candidate trajectories, yet treat each trajectory as atomic: either retainin

Semantic Overlays: Mitigating Prompt Injection with Annotations Beyond Tokens and Steering Vectors

arXiv:2608.23873v1 Announce Type: new Abstract: Everything a language model sees is tokens. The serving stack knows what each span is -- user input, tool output, instructions -- but the model must kee

SeMoCo: A Semantic-First Motion Codec for Motion Language Modeling

arXiv:2608.24334v1 Announce Type: new Abstract: Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for

SENSESHIFT: Continuous Sentiment-Controlled Text Generation via Encoder-based Mask Infilling

arXiv:2608.24304v1 Announce Type: cross Abstract: Recent controllable text generation (CTG) for sentiment control has largely focused on decoder-based large language models, making causal attention th

Sequential operator learning under dependent data

arXiv:2608.24426v1 Announce Type: cross Abstract: Learning operators from sequentially collected data arises in adaptive experimental design, Bayesian optimization, and dynamical-system modelling, whe

Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection

arXiv:2608.24697v1 Announce Type: new Abstract: Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates. However, the main bottleneck of PC Networ

Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

arXiv:2608.23879v1 Announce Type: cross Abstract: Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying

ST-Lite: Training-Free KV Cache Compression with Spatio-Trajectory Guidance for Long-Horizon GUI Agents

arXiv:2603.00188v2 Announce Type: replace-cross Abstract: Training-free KV cache compression is essential for deploying vision-language GUI agents under memory and latency constraints, yet existing me

STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification

arXiv:2509.03754v2 Announce Type: replace-cross Abstract: Responding to rising global food security needs, precision agriculture and deep learning-based plant disease diagnosis have become crucial. Ye

STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning

arXiv:2608.23952v1 Announce Type: cross Abstract: Federated video anomaly detection trains model collaboratively without sharing raw surveillance footage, but limited server-side visibility lets compr

Steering Recurrent Reasoners at Inference Time with Readout Feedback

arXiv:2608.24136v1 Announce Type: new Abstract: Recurrent models, which repeatedly update latent states with shared computation blocks, have emerged as powerful architectures for solving complex reaso

SyPS: Measuring Sycophancy Prompt Sensitivity in Large Language Models

arXiv:2608.23837v1 Announce Type: new Abstract: Large language models (LLMs) are known to exhibit social sycophancy, often validating or agreeing with users in socially sensitive contexts. Existing ev

Task-Adaptive Rubrics for GUI Reward Modeling

arXiv:2608.24174v1 Announce Type: new Abstract: Recent studies on GUI agents have increasingly focused on outcome reward modeling, which assigns outcome rewards by judging whether an executed trajecto

The Loss Floor of Denoising Score Matching: Fisher Geometry from Schrodinger Bridges

arXiv:2608.23916v1 Announce Type: new Abstract: Denoising score matching trains diffusion models by regressing onto a conditional score, although generation ultimately requires the marginal score. The

The Sharp Tail of Uniform Stability

arXiv:2608.24098v1 Announce Type: new Abstract: Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a gamma-un

Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition

arXiv:2406.17538v4 Announce Type: replace Abstract: Micro-expressions are subtle facial movements that occur spontaneously when people try to conceal real emotions. Micro-expression recognition is cru

Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS

arXiv:2608.23765v1 Announce Type: new Abstract: Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role

Tlow: Flow-based Item Tokenizer for Recommendation

arXiv:2608.24176v1 Announce Type: cross Abstract: Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundame

TLXML: Task-Level Explanation of Meta-Learning via Influence Functions

arXiv:2501.14271v4 Announce Type: replace Abstract: Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially r

Topology enables learning-based hydrodynamic prediction of the global river system

arXiv:2602.22293v2 Announce Type: replace Abstract: Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning h

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library

arXiv:2512.16715v3 Announce Type: replace-cross Abstract: In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the fu

Transformer Accelerator (TFA): A Macro-Op INT8 Hardware Chip for Transformer Inference and Machine Translation

arXiv:2608.23582v1 Announce Type: cross Abstract: We present the Transformer Accelerator (TFA), a synthesizable, parameterizable INT8 memory-to-memory engine for transformer inference. One time-multip

Unlocking the power of partnership: How humans and machines can work together to improve face recognition

arXiv:2510.02570v2 Announce Type: replace Abstract: Human review of consequential decisions by face recognition algorithms creates a collaborative human-machine system. We establish the circumstances

Velocity-coupled Representation Refinement for Satellite Orbit Prediction

arXiv:2608.23728v1 Announce Type: new Abstract: Satellite orbit prediction, which aims to forecast future orbital trajectories from historical observations, is important for collision warning and safe

VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference

arXiv:2608.24063v1 Announce Type: cross Abstract: While Vision Large Language Models (VLLMs) have achieved remarkable success in multimodal reasoning, their long-context inference remains prohibitivel

Vision Language Model Fusion for Explainable Face Recognition

arXiv:2608.24430v1 Announce Type: new Abstract: Responsible deployment of face verification systems requires more than accurate decisions: systems should also provide interpretable and auditable evide

Weakly Supervised Seafloor Segmentation for Seagrass Habitat Mapping in Side-Scan Sonar Imagery

arXiv:2608.24756v1 Announce Type: new Abstract: Seagrass meadows are crucial blue-carbon habitats, and mapping their extent is a prerequisite for coastal management and carbon inventory. Optical satel

What Does Prompt Learning Change? -A Natural-Language Concept Analysis of Vision-Language Models

arXiv:2608.24142v1 Announce Type: new Abstract: Prompt learning adapts vision-language models such as CLIP by optimizing continuous prompt vectors, but the learned prompts are difficult to interpret i

What FID Hides: Detecting, Ranking, and Diagnosing Deviations in Generative Evaluation

arXiv:2608.24881v1 Announce Type: cross Abstract: Generative models are commonly ranked by Frechet Inception Distance (FID) and Kernel Inception Distance (KID), yet FID's first-two-moment summary can

When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study

arXiv:2608.24492v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where grou

When Does Self-Supervised Pretraining Help Tabular Models? A Study of Label Scarcity and Missing Data

arXiv:2608.24381v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a promising approach for tabular data, yet its efficacy under extreme label scarcity and test-time missing

When Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLMs

arXiv:2608.24080v1 Announce Type: cross Abstract: In psychological counseling, effective support is not always delivered through long, information-rich responses. Minimal responses, such as backchanne

XP-JEPA: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics

arXiv:2608.24044v1 Announce Type: new Abstract: Latent world models plan by predicting how candidate actions transform learned representations. In self-predictive models, however, the encoder and pred