Search: “model-releases”

Reading Less While Writing: A Closed-Form Bandwidth Dial for Streaming Multimodal Decoders

arXiv:2609.20845v1 Announce Type: new Abstract: A decoder that turns video or audio into text conventionally consumes the entire input before emitting a word. Offline this is merely more than the task

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Model Releases

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

DGX agent

arXiv:2609.22000v1 Announce Type: new Abstract: Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Rea

model-releasesarxiv-cs-cl

21 Sept 2026

Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion

arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representations

REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement

arXiv:2609.21108v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies,

Reliability-Centered Evaluation of Sparse Longitudinal CT Lesion-Size Forecasting with Conformal Interval Calibration and Gompertz-Inspired Regularization

arXiv:2609.21197v1 Announce Type: new Abstract: Sparse longitudinal CT follow-up limits lesion-size forecasting when only a few prior observations are available. We constructed a five-visit DLT-derive

Rethinking Human-Aligned Evaluation: An Analysis of Semantic Metrics Beyond WER

arXiv:2609.21663v1 Announce Type: new Abstract: Word Error Rate (WER), the most commonly used metric for Automatic Speech Recognition (ASR), treats every lexical deviation from the reference as equall

Reviser: Revision-Capable Text Generation via Autoregressive Cursor Actions

arXiv:2609.20830v1 Announce Type: new Abstract: Revision-capable generation is appealing because it can insert or revise earlier content, but many non-autoregressive and edit-based approaches obtain t

RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy

arXiv:2609.21371v1 Announce Type: new Abstract: Social Proactive Intelligence (SPI) extends proactive assistance beyond task completeness to consider social appropriateness in diverse embodied scenari

Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points

arXiv:2609.21186v1 Announce Type: cross Abstract: Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing m

SafeStage: Evaluating Safety Before, During, and After Vision-Language-Conditioned Robot Manipulation

arXiv:2609.21223v1 Announce Type: new Abstract: Vision-language-conditioned robot policies integrate perception, language understanding, and control for general-purpose manipulation. However, existing

SafeStyle: Calibrated Style Residual Injection for Controllable Style-Leakage Trade-off in Diffusion Stylization

arXiv:2609.21242v1 Announce Type: new Abstract: Reference-guided diffusion stylization aims to transfer visual style from a reference image while preserving the semantics specified by a text prompt. H

Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models

arXiv:2609.21740v1 Announce Type: new Abstract: Latent world models enable planning by predicting the effects of actions in a learned representation space, but their predictions can become unreliable

Scale your AI workloads faster and more efficiently with GKE Pod snapshots

model-releasesgoogle-cloud-ai

When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need

Scaling Discovery through Test-Time Communication

arXiv:2609.21032v1 Announce Type: cross Abstract: Science advances not in isolation but through collaboration, yet existing agentic systems capture little of this. Whether communicating agents help re

Scaling Vision-Language Reward Learning for Robot Manipulation in Parallel Simulation

arXiv:2609.21767v1 Announce Type: new Abstract: Vision-language models (VLMs) can replace human annotators in preference-based reward learning, but sequential API requests and single-environment data

Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA

arXiv:2602.00279v2 Announce Type: replace Abstract: Reliable uncertainty quantification (UQ) is essential for safe deployment of large language models (LLMs) in scientific question answering, where lo

SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP

arXiv:2609.21754v1 Announce Type: new Abstract: Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from

SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation

arXiv:2609.21709v1 Announce Type: new Abstract: Large language models (LLMs) provide limited support for sign language interaction. Unifying sign language translation (SLT) and generation (SLG) to ena

SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment

arXiv:2609.21402v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods fo

SkelWAM: A Skeleton-Guided World-Action Model for Zero-Shot Cross-Embodiment Manipulation

arXiv:2609.21983v1 Announce Type: new Abstract: Reusing manipulation experience across robot embodiments is important for scaling robot learning and reducing repeated task-specific data collection. Ho

Some startups, like Harvey, Abridge, Ramp, and Rogo, are embracing open-weight models or training their own models to reduce expensive reliance on frontier labs (Bloomberg)

Bloomberg: Some startups, like Harvey, Abridge, Ramp, and Rogo, are embracing open-weight models or training their own models to reduce expensive reliance on frontier labs — The $15.6 billion legal st

model-releasestechmeme

Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance

arXiv:2511.13254v2 Announce Type: replace Abstract: Large Language Models (LLMs) have displayed remarkable capabilities across diverse domains, but their training remains resource- and time-intensive,

SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity

arXiv:2609.21165v1 Announce Type: new Abstract: Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on gen

[Splash Engine] Qwen3.8-27B in native 8-bit at 37–55 tok/s on Apple Silicon: Extending Splash to Q8, 256k context scaling, and the 'Reasoning Cliff'

model-releasesr-localllama

https://preview.redd.it/nulsv53o8vqh1.png?width=4500&format=png&auto=webp&s=74765dbd409f4c221640f9f6000a685f6fdbb242 Spent weekend benchmarking the Splash engine (by Incoai) and extending its architec

Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation

arXiv:2609.21307v1 Announce Type: new Abstract: Although conventional controllers and disturbance observers (DOBs) are the standard for precision tracking in manipulators, they suffer from parameter u

Stiefel-AdamW: Geometry-Aware AdamW for Linear Factorization Blocks

arXiv:2609.21039v1 Announce Type: new Abstract: A pervasive structural pattern in modern deep learning is the linear factorization block: a submodule of the form W = BA in which two parameter matrices

SynthDemo-RL: Breaking the Zero-Reward Barrier in VLA Adaptation with LLM-Guided Synthetic Demonstrations

arXiv:2609.21650v1 Announce Type: cross Abstract: Fine-tuning Vision-Language-Action (VLA) models commonly relies on human teleoperation demonstrations, while reinforcement learning (RL) with sparse b

Talk to Me, Jarvis: An Open-Source Edge-Deployable Voice Assistant Framework for Autonomous Racecars

arXiv:2609.21109v1 Announce Type: new Abstract: Recent advances in large language models have improved their effectiveness as back-end components for voice assistants, particularly in intent understan

Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning

arXiv:2603.12110v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) policies trained in simulation often degrade once deployed on real systems, where the controller must reject exter

TatBLiMP: A Benchmark of Linguistic Minimal Pairs for Tatar

arXiv:2609.20832v1 Announce Type: new Abstract: We introduce TatBLiMP, the first benchmark of linguistic minimal pairs for Tatar (tt, ISO 639-3 tat), a Qypchaq Turkic language written in Cyrillic. To

Thanks @sgl_project for the day-0 support! 🙌 SGLang-Diffusion now serves Qwen-Image-2.1: text-to-image generation, multi-image editing, and…

Thanks @sgl_project for the day-0 support! 🙌 SGLang-Diffusion now serves Qwen-Image-2.1: text-to-image generation, multi-image editing, and transparent RGBA output. Try it out! 🎨 Day-0 support for @Al

model-releasesqwen--x

The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models

arXiv:2609.21509v1 Announce Type: new Abstract: When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How mu

These Were NOT Rogue AI Escapes. Just SLOPPY Firewall Failures.

The headlines right now are full of stories about AI models 'escaping their sandboxes' and literally killing all humans, lol. I've even heard several commentators and writers say that AI escaped an 'A

model-releasesr-chatgpt

Time series generation with spectrally aligned latent flow matching

arXiv:2609.21989v1 Announce Type: new Abstract: Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted a

TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers

arXiv:2609.21139v1 Announce Type: new Abstract: Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers

Towards Effective Visual-Inertial SLAM with Passive-Only Sensors for Low-Cost Autonomous Underwater Vehicles

arXiv:2609.21015v1 Announce Type: new Abstract: Improvements to Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) for low-cost autonomous underwater vehicles (AUVs) are critical for tran

Towards Secure Cloud-Native Computing: Unveiling Kubernetes Misconfigurations with Large Language Models

arXiv:2609.20834v1 Announce Type: new Abstract: In the rapidly evolving landscape of cloud-native computing, Organizations are increasingly adopting infrastructure models that emphasize scalability, f

Towards the Vision-Sound-Language-Action Paradigm: The HEAR Framework for Sound-Centric Manipulation

arXiv:2603.16086v2 Announce Type: replace-cross Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts o

Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework

arXiv:2609.21425v1 Announce Type: new Abstract: Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition

Trading Depth for Time in Recurrent Transformers

arXiv:2609.21605v1 Announce Type: new Abstract: Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of th

TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization

arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on

Uncertainty-driven training for three-dimensional calibrated lung nodule classification

arXiv:2609.20905v1 Announce Type: cross Abstract: In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where val

Understanding In-context Learning of Addition via Activation Subspaces

arXiv:2505.05145v4 Announce Type: replace-cross Abstract: To perform few-shot learning, language models extract signals from a few input-label pairs, aggregate them into a learned prediction rule, and

Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals

arXiv:2609.21450v1 Announce Type: new Abstract: Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains d

Understanding Structural Representation in Foundation Models for Polymers

arXiv:2512.11881v2 Announce Type: replace-cross Abstract: From the relative scarcity of training data to the lack of standardized benchmarks, the creation of effective foundation models for polymers f

VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding

arXiv:2609.21225v1 Announce Type: new Abstract: Parametric CAD reconstruction requires recovering both precise geometry and editable modeling operations from visual observations, making it challenging

VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration

arXiv:2609.21521v1 Announce Type: cross Abstract: While Video Large Language Models (Video-LLMs) have recently demonstrated strong performance, reliably evaluating their fine-grained video understandi

VISPATH: Visual-Intent-Guided Path Reasoning for Multimodal Knowledge Graph Question Answering

arXiv:2609.20843v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) enables models to answer natural-language questions through structured graph reasoning and has achieved substa