Search: “research”

Recall Isn't Enough: Bounding Commitments in Personalized Language Systems

arXiv:2605.16712v1 Announce Type: new Abstract: Long-context and memory systems usually treat personalization as a recall problem. In practice, many failures occur later, when a system commits: it tur

Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective

DGX agent

arXiv:2605.17967v1 Announce Type: new Abstract: This paper explores a scientific question in supervised fine-tuning (SFT): why SFT is broadly effective for small-scale deep neural networks, yet can pr

researcharxiv-cs-ai

19 May 2026

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning

arXiv:2605.16991v1 Announce Type: cross Abstract: Response-free item difficulty modelling promises to reduce reliance on response-based calibration but is intrinsically difficult on reading-comprehens

ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction

arXiv:2605.16380v1 Announce Type: cross Abstract: Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models

arXiv:2508.06974v2 Announce Type: replace Abstract: 1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LL

Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?

arXiv:2511.08704v2 Announce Type: replace Abstract: This paper investigates the scaling properties of autoregressive next-pixel prediction, a simple, end-to-end yet under-explored framework for unifie

Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search

arXiv:2601.03851v2 Announce Type: replace Abstract: Table Question Answering (TableQA) benefits significantly from table pruning, which extracts compact sub-tables by eliminating redundant cells to st

Rethinking the State Update Gate for Long-Sequence Recurrent 3D Reconstruction

arXiv:2605.16981v1 Announce Type: new Abstract: Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TTT3R-s

RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots

arXiv:2605.18197v1 Announce Type: cross Abstract: Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This li

Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery

arXiv:2509.17666v2 Announce Type: replace Abstract: Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller ret

researcharxiv-cs-ro

Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers

arXiv:2605.16941v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: acceleratin

Roundtables: Inside the Musk v. Altman Trial

Listen to the session or watch below Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status. Watch

researchmit-tech-review

RSEdit: Text-Guided Image Editing for Remote Sensing

arXiv:2603.13708v2 Announce Type: replace Abstract: In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose rsedit, a collection of model

RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting

arXiv:2605.18263v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent s

SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models

arXiv:2605.17985v1 Announce Type: cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science. While model compression is essential

SAS: Semantic-aware Sampling for Generative Dataset Distillation

arXiv:2605.18012v1 Announce Type: cross Abstract: Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational an

Scalable Environments Drive Generalizable Agents

arXiv:2605.18181v1 Announce Type: new Abstract: Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such gene

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise

arXiv:2605.18528v1 Announce Type: cross Abstract: A growing lesson from neural network optimization is that optimizer design should respect how the model is parametrized. Scale-invariant methods becom

Scaling Accessible Mathematics on arXiv: HTML Conversion and MathML 4

arXiv:2605.16562v1 Announce Type: new Abstract: We report on the ongoing development of arXiv's HTML Papers offering, available on every new TeX/LaTeX submission since its initial release in 2023. The

SCAR: Self-Supervised Continuous Action Representation Learning

arXiv:2605.16412v1 Announce Type: cross Abstract: Despite the central role of action in embodied intelligence, learning transferable action representations from visual transitions remains a fundamenta

See Silhouettes in Motion with Neuromorphic Vision

arXiv:2605.17984v1 Announce Type: cross Abstract: Quasi-bimodal objects, such as text, road signs, and barcodes, play a basic yet vital role in daily visual communication. By boiling these down to cle

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation

arXiv:2605.17630v1 Announce Type: new Abstract: Here's a trimmed version under 1920 characters: Open-vocabulary segmentation models such as SAM3 achieve strong performance through concept-level text p

Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models

arXiv:2605.17778v1 Announce Type: cross Abstract: Self-distillation has emerged as a promising technique for improving model performance in modern machine learning systems. We develop the statistical

Self-Supervised Learning for Sparse Matrix Reordering

arXiv:2605.17403v1 Announce Type: new Abstract: Rearranging the rows or columns of a sparse matrix using an appropriate ordering can significantly reduce fill-ins, i.e., new nonzeros introduced during

Semantic Generative Tuning for Unified Multimodal Models

arXiv:2605.18714v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing tr

Semantics Disentanglement and Composition for Universal Image Coding with Efficiently LLM Reasoning and Generative Diffusion

arXiv:2412.18158v2 Announce Type: replace Abstract: Learned image compression methods have shown impressive performance but are often highly specialized for either human perception or specific machine

Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares

arXiv:2605.18156v1 Announce Type: new Abstract: Lens flare removal is challenging due to the large spatial extent of flare artifacts and their entanglement with scene structures, while existing method

SENSE: Satellite-based ENergy Synthesis for Sustainable Environment

arXiv:2605.18101v1 Announce Type: cross Abstract: Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studie

Shallow Deep Learning Can Still Excel in Fine-Grained Few-Shot Learning

arXiv:2507.22041v2 Announce Type: replace Abstract: Deep learning has witnessed the extensive utilization across a wide spectrum of domains, including fine-grained few-shot learning (FGFSL) which heav

Shift Detection and Adaptation for Network Intrusion Detection

arXiv:2508.15100v2 Announce Type: replace-cross Abstract: Distribution shift, a change in the statistical properties of data over time, poses a critical challenge for deep learning anomaly detection s

Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs

arXiv:2505.19155v2 Announce Type: replace-cross Abstract: Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence l

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

arXiv:2605.17633v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory.

Spatial Blindness in Whole-Slide Multiple Instance Learning

arXiv:2605.17449v1 Announce Type: cross Abstract: Whole-slide MIL models are often called context-aware once graphs, Transform ers, or state-space modules are placed above patch embeddings. We show th

Spectral Progressive Diffusion for Efficient Image and Video Generation

arXiv:2605.18736v1 Announce Type: new Abstract: Diffusion models have been shown to implicitly generate visual content autoregressively in the frequency domain, where low-frequency components are gene

Spherical Steering: Geometry-Aware Activation Rotation for Language Models

arXiv:2602.08169v2 Announce Type: replace-cross Abstract: Inference-time steering offers a promising way to control language models (LMs) without retraining. However, standard approaches typically rel

SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents

arXiv:2605.18636v1 Announce Type: new Abstract: Long-horizon multimodal agents in open-world games must stay goal-directed across many low-level interactions under tight token and latency budgets. Exi

SSTL: Self-Sensing Tendon Loop for Hysteresis Modeling and Compensation in Tendon-Sheath Mechanisms

arXiv:2605.16870v1 Announce Type: new Abstract: Flexible endoscopic robots enable minimally invasive access through natural orifices, but their control accuracy is limited by configuration-dependent h

researcharxiv-cs-ro

ST-BCP: Tightening Coverage Bound for Backward Conformal Prediction via Non-Conformity Score Transformation

arXiv:2602.01733v2 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guaran

Stabilizing Temporal Inference Dynamics for Online Surgical Phase Recognition

arXiv:2605.16387v1 Announce Type: cross Abstract: Online Surgical Phase Recognition (SPR) models can reach high frame-wise accuracy, yet their predictions often lack temporal stability, fragmenting wo

STAG-CN: Spatio-Temporal Apiary Graph Convolutional Network for Disease Onset Prediction in Beehive Sensor Networks

arXiv:2603.14462v2 Announce Type: replace-cross Abstract: Honey bee colony losses threaten global pollination services, yet current monitoring systems treat each hive as an isolated unit, ignoring the

Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning

arXiv:2605.16980v1 Announce Type: new Abstract: Accurate segmentation and statistical shape modeling of hand anatomy have significant implications for medical diagnostics, ergonomics, and biomechanics

Statistical Unlearning of Distributions: A Hypothesis Testing Approach

arXiv:2605.16645v1 Announce Type: cross Abstract: Machine learning systems increasingly face requirements to forget not only individual data points, but entire domains of information, such as toxic la

StatQAT: Statistical Quantizer Optimization for Deep Networks

arXiv:2605.17745v1 Announce Type: cross Abstract: Quantization is essential for reducing the computational cost and memory usage of deep neural networks, enabling efficient inference on low-precision

Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions

arXiv:2507.05482v3 Announce Type: replace Abstract: Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current appr

Step-wise Rubric Rewards for LLM Reasoning

arXiv:2605.17291v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final-answer correc

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization

arXiv:2511.01126v2 Announce Type: replace Abstract: Online bilevel optimization (OBO) is a powerful framework for machine learning problems where both outer and inner objectives evolve over time, requ

StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model

arXiv:2511.14223v3 Announce Type: replace Abstract: This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by spee

Stress-Testing Neural Network Verifiers with Provably Robust Instances

arXiv:2605.17153v1 Announce Type: new Abstract: Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lac