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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

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Model Releases

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

DGX agent

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

model-releasesarxiv-cs-lg

29 Sept 2026

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models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated.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

Rig Security launches with $12M to watch AI agents that run under employees’ accounts

model-releasessiliconangle

Israeli identity security startup Rig Security Inc. launched today with 12 million in new funding to help enterprises keep watch over the artificial intelligence agents that operate under their employ

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

Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents

arXiv:2609.35576v1 Announce Type: cross Abstract: Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and

Shared Autoregressive Context Can Distort Relationships in Synthetic Data

arXiv:2609.32546v1 Announce Type: new Abstract: Large language models can generate several records within one autoregressive completion, making earlier answers available as context for later records.

Sherry's 3:4 ternary format (1.375 bits per weight) running on WebGPU: a 1.6 MB model that plays Connect Four as well as its 7.8 MB int8 version

model-releasesr-localllama

Not an LLM, but the ternary findings should carry over, and we hadn't seen Sherry-style 3:4 weights run in a browser before. Disclosure: this is our work at Precisit, everything is MIT. What it is A 7