Search: “model-releases”

Refresh or Realize? Compute Allocation in Drifting Models

arXiv:2609.32298v1 Announce Type: new Abstract: Drifting Models train a one-step generator by recomputing a finite-sample drift field at every iteration and taking an optimizer step toward the drifted

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ReGDiff: Guided Diffusion in Regulated Latent Space for Exploring Metamaterial Voxel Geometry

DGX agent

arXiv:2609.34231v1 Announce Type: new Abstract: Metamaterials are artificially engineered structures whose mechanical and physical behaviors are strongly shaped by geometry rather than composition. Vo

model-releasesarxiv-cs-cv

29 Sept 2026

Relative Density Ratio Optimization for Stable and Statistically Consistent Model Alignment

arXiv:2604.04410v2 Announce Type: replace Abstract: Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume spe

Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose

arXiv:2609.35764v1 Announce Type: new Abstract: Sparse inertial pose estimation promises camera-free motion capture from consumer devices, but consumer sensors are unreliable: firmware-fused orientati

REMEDY: How Far Is Video Generation from Medical Education World Models?

arXiv:2609.32460v1 Announce Type: new Abstract: Recent video generation models produce realistic videos and show potential as a foundation for world models. These advances create opportunities for gen

RepNN: Tackling spectral bias in deep neural networks for regression and PDE problems via parameter reparameterization

arXiv:2606.16575v3 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in scientific computing, yet they often suffer from spectral bias in capturing oscillat

Residual Transferability in Neural Image Watermarking

arXiv:2609.32241v1 Announce Type: cross Abstract: Neural image watermarks can be forged by extracting watermark-bearing residuals from released images and transferring them to unrelated content. While

ReSight-SMC: Two-Stage Power Sampling via Island SMC with Visual Scouts

arXiv:2609.34905v1 Announce Type: new Abstract: Power sampling has emerged as a training-free approach to LLM reasoning, eliciting capabilities comparable to reinforcement learning by sharpening the m

Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data

arXiv:2609.33687v1 Announce Type: new Abstract: Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, upd

ReSPO: Reshaped Sequence Policy Optimization for Gradient Starvation in Off-Policy Learning

arXiv:2609.35433v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the cur

ReSync: Re-Aligning the Two Clocks of Asynchronous World-Action Models

arXiv:2609.33944v1 Announce Type: new Abstract: Jointly generating future video and actions has become a standard recipe for world-action models, and the strongest systems denoise the two streams on s

Rethinking Circuit Evaluation: Do Circuits Explain Model Errors?

arXiv:2609.35686v1 Announce Type: new Abstract: Mechanistic interpretability (MI) aims to explain a model's behaviour through analyzing its internal computations; circuit-based explanations aim to iso

Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models

arXiv:2609.35695v1 Announce Type: new Abstract: Aligning large language models (LLMs) to diverse user preferences is fundamentally hindered by standard alignment paradigms that optimize for monolithic

Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It

arXiv:2609.32444v1 Announce Type: new Abstract: We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where rollouts are sampled by a

Retrospective Distillation Attribution via Normalized Response Similarity

arXiv:2609.32749v1 Announce Type: cross Abstract: Model distillation transfers capabilities through supervised fine-tuning (SFT) on teacher responses, often collected from commercial APIs, raising que

Reuse or Relearn? A Spectral View of Earth Observation Foundation Models

arXiv:2609.32756v1 Announce Type: new Abstract: Foundation models are rarely used as generic, frozen feature extractors; instead, they are fine-tuned for the target downstream application. This practi

ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering

arXiv:2609.35507v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable advances in remote sensing. However, existing remote sensing multimodal reasoning

Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity

arXiv:2402.05071v3 Announce Type: replace-cross Abstract: We focus on constrained, L-smooth, potentially stochastic and nonconvex-nonconcave min-max problems either satisfying rho-cohypomonotonicity o

Revisiting Risky Tackle Detection with Vision Transformers

arXiv:2609.35562v1 Announce Type: new Abstract: This paper is a Track 2 reproducibility companion to an ICPR 2026 study on risky tackle detection in American football prac- tice videos. The original w

Riemannian Difference-of-Convex Optimization for K-Means Clustering

arXiv:2609.34310v1 Announce Type: new Abstract: K-means is a widely adopted clustering approach in signal processing and machine learning. In this paper, we study K-means clustering through a cardinal

RIPE-MambaSpike: Resolution-Independent Spiking-State-Space Interfaces for Parameter-Efficient Event-Based Vision

arXiv:2609.32537v1 Announce Type: new Abstract: Spiking-Mamba hybrids reach strong accuracy on event-based vision, but existing designs often require tens of millions of parameters. Much of that cost

RLE-Bench: A Qualifying Exam for Coding Agents as Robot Learning Engineers

arXiv:2609.34210v1 Announce Type: new Abstract: Coding agents are beginning to move beyond purely digital tasks to tackle physical-world challenges, particularly in robotics. Existing robotics benchma

RoboFL: Federated Expert Assembly for World Action Models

arXiv:2609.34968v1 Announce Type: new Abstract: Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutio

RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents

arXiv:2609.32862v1 Announce Type: new Abstract: A foundation model should not act in isolation as an embodied agent. Yet, existing methods often optimize individual components of the agent stack, such

RoboICL: Embodied In-Context Learning with GPT-6 Astra

arXiv:2609.34261v1 Announce Type: cross Abstract: General-purpose vision-language models offer a promising way to zero-shot robot control: gptastra{} excels at open-ended and language- or image-condit

RoboIRGBench: Benchmarking Implicit Referential Grounding in Vision-Language-Action Models

arXiv:2609.34384v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robotic manipulation, yet existing benchmarks typically assume that task-relevan

Robust Bayesian Optimization with Q-Exponential Surrogates

arXiv:2609.32775v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box objectives, but standard BO methods often use Gaussian process

Rotated Manifold Optimization for Low-Rank Adaptation

arXiv:2609.34264v1 Announce Type: new Abstract: We propose a novel optimizer for low-rank adaptation (LoRA) that explicitly incorporates the gauge symmetry of low-rank factorization. Our optimizer ext

ROTE: Benchmarking Neural Memorization on Complexity-Controlled Symbolic Sequences

arXiv:2609.31918v1 Announce Type: new Abstract: We introduce ROTE (RollOut Testing of Exact memorization), a benchmarking protocol for evaluating symbolic memorization of neural architectures. We stud

Routing Drift Alone Does Not Diagnose Failure in Merged MoE LLMs

arXiv:2609.32821v1 Announce Type: cross Abstract: Model merging efficiently combines specialized large language models (LLMs) without joint retraining, but can substantially alter expert routing in Mi

SafeMol: Dual-Modality Safety Alignment for Molecular Multimodal Models

arXiv:2609.33640v1 Announce Type: new Abstract: Molecular multimodal models support diverse understanding and generation tasks but may introduce safety vulnerabilities when handling hazardous molecule

SAGE: Semantic Audio Generative Encoder

arXiv:2609.32755v1 Announce Type: cross Abstract: Audio autoencoders compress waveforms into compact latent representations that serve as the interface between raw audio and downstream models. Current

SAGE: Subspace Alignment for Classifier-Free Guidance in Mixture-of-Experts Diffusion Models

arXiv:2609.34525v1 Announce Type: new Abstract: Diffusion Transformers with Mixture-of-Experts (MoE) routing are a leading recipe for scaling generative models. Classifier-Free Guidance (CFG) is essen

SAGE: Symbolic Action-Gating and Editing for LLM Task Planners

arXiv:2609.34268v1 Announce Type: new Abstract: Large language models (LLMs) are now the default cognitive core of embodied household agents, yet the plans they emit are rarely checked against a groun

SAMBAR: Selective Anchoring via Method of Multipliers for Balanced Knowledge Acquisition and Retention in Vision-Language-Action Models

arXiv:2609.32108v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models leverage large-scale pretraining to ultimately achieve generalist manipulation. Deployed VLA policies must support c

Same Probe, Different Numbers: Are Activation Probes Robust to Inference-Time Numerical Non-Determinism?

arXiv:2609.31796v1 Announce Type: new Abstract: Activation probes are increasingly used to monitor LLMs in deployment. A probe is typically trained under one inference configuration, then used under w

Scalable In-Context Reinforcement Learning with Recurrent Algorithm Distillation

arXiv:2609.35333v1 Announce Type: new Abstract: Algorithm Distillation (AD) has demonstrated the remarkable ability of Transformers to perform in-context reinforcement learning without explicit weight

Scaling Properties of Same-Family On-Policy Distillation

arXiv:2609.32722v1 Announce Type: new Abstract: *Reinforcement learning (RL)* can induce substantial reasoning capabilities in large language models (LLMs), but how much of this capability transfers a

Scaling Versatile 3D Assets Editing with a Million-Scale Dataset

arXiv:2609.34271v1 Announce Type: new Abstract: Although recent 3D generative models produce increasingly realistic assets, controllable 3D asset editing remains challenging. Existing methods are limi

SchemaMem: Schema-Indexed Recurrent Memory for Delayed State Retrieval

arXiv:2609.33436v1 Announce Type: new Abstract: Attention provides direct access to past representations, but retaining an ever-growing history is costly. Recurrent models bound persistent state, yet

SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation

arXiv:2609.33399v1 Announce Type: new Abstract: In realistic education, a solution is often expressed not only in words but in a drawing--a circuit, a geometric construction, a function plot--and a te

SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation

arXiv:2609.32391v1 Announce Type: cross Abstract: Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requir

ScreenHaystack: Finding Blind Zones in GUI Grounding

arXiv:2609.32036v1 Announce Type: new Abstract: We introduce ScreenHaystack, a dynamic needle-in-a-haystack benchmark for evaluating spatial reliability in GUI grounding. Instead of testing each targe

Scriboora: Rethinking Human Pose Forecasting

arXiv:2511.15565v3 Announce Type: replace Abstract: Human pose forecasting predicts future human poses from past observations for applications including action recognition, sports analysis, and human-

See it, Say it, Sorted: Mechanistic Diagnosis and Parameter-Space Mitigation of Emergent Misalignment in LLMs

arXiv:2609.34970v1 Announce Type: new Abstract: Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated

See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology

arXiv:2609.34277v1 Announce Type: new Abstract: Pathological assessment relies on recognizing fine-grained visual details in histological images. Vision-language models (VLMs) increasingly support pat

Self-Evolutionary Replanning for Failure-Aware Motion Planning

arXiv:2603.02772v2 Announce Type: replace Abstract: Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficie

Self-Evolving Time-Series Forecasting Agents with Episodic Memory and Online Policy Learning

arXiv:2609.32689v1 Announce Type: new Abstract: LLM-based agents are increasingly used for time-series forecasting because they can organise contextual information, perform multi-step analysis, and gu