Search: “model-releases”

VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents

arXiv:2609.38119v1 Announce Type: new Abstract: Long-form video understanding requires multimodal agents to iteratively gather evidence over many reasoning steps. However, most existing agentic method

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Model Releases

VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation

DGX agent

arXiv:2609.37709v1 Announce Type: cross Abstract: Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not o

model-releasesarxiv-cs-ai

30 Sept 2026

VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubrics

arXiv:2609.37287v1 Announce Type: cross Abstract: Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preservat

Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoom

arXiv:2609.37002v1 Announce Type: cross Abstract: High-resolution visual question answering often fails because a multimodal model does not acquire the small, spatially localized evidence needed to an

Visual sensitivity is not claim retractability: persistence-aware credit assignment for multimodal reinforcement learning

arXiv:2609.36572v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been extended to Large Vision-Language Models (LVLMs), and perception-aware methods further en

VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training

arXiv:2609.36648v1 Announce Type: new Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics

vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains

arXiv:2609.37062v1 Announce Type: cross Abstract: Dynamic layer skipping reduces LLM computation by allowing each token to execute only a subset of the model's layers. However, existing skippers rely

Wasserstein Causal Forests for Distribution-Valued Outcomes

arXiv:2609.35898v1 Announce Type: cross Abstract: This paper proposes Wasserstein Causal Forests (WCF) for settings in which each unit's outcome is itself a probability distribution. This study also d

Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs

arXiv:2607.10803v2 Announce Type: replace-cross Abstract: Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and inte

Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space

arXiv:2609.37731v1 Announce Type: new Abstract: Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform,

WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation

arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,

What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation

arXiv:2609.37317v1 Announce Type: new Abstract: Omnimodal evaluation should go beyond independent text, image, and speech production: individually plausible outputs may not express a coherent shared e

What Does Post-Training Change in Multilingual Reasoning?

arXiv:2609.37104v1 Announce Type: cross Abstract: Open-source reasoning models provide unequal access to reasoning capability across languages. When a model can solve a problem but cannot deliver a co

What Makes Recurrence Effective in Looped Language Models?

arXiv:2609.36636v1 Announce Type: cross Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding

What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views

arXiv:2609.36881v1 Announce Type: new Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal effe

When do data mixtures improve scaling laws? Insights from high-dimensional regression

arXiv:2609.38011v1 Announce Type: new Abstract: Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downstr

When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs

arXiv:2609.36138v1 Announce Type: new Abstract: Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or d

When Is Coarse Supervision Worth It? Cost-Aware Learning under Unknown Aggregation

arXiv:2609.36704v1 Announce Type: new Abstract: Modern learning systems often acquire supervision at multiple resolutions, trading annotation cost against information content. We study cost-aware two-

When Models Don't Manipulate Manifolds: The Geometry of a Comparison Task

arXiv:2609.37680v1 Announce Type: cross Abstract: One of the current premises of mechanistic interpretability research is that detailed accounts of the geometry of neural network representations can t

When Semantics Matter: Reliability-Aware Semantic-Rhythm Control for Co-Speech Gesture Generation

arXiv:2609.36685v1 Announce Type: new Abstract: Co-speech gesture generation aims to synthesize natural gestures that are both temporally synchronized with speech and semantically consistent with the

When Should LLMs Trust Their Own Revisions? A Risk-Aware Study of Intrinsic Self-Correction

arXiv:2609.35832v1 Announce Type: cross Abstract: Intrinsic self-correction asks a language model to revise its own answer without receiving new external evidence. A second pass can recover mistakes,

When to Adapt: Multi-Signal Domain Shift Detection for Efficient Training-Free Adaptation in Open-Vocabulary Segmentation

arXiv:2609.37602v1 Announce Type: cross Abstract: Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where envir

When to Retrieve, When to Stay: Uncertainty-Aware Temporal Evidence Allocation for Streaming Video-LLMs

arXiv:2609.37345v1 Announce Type: new Abstract: Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints. As th

Where Does Staleness Accumulate? Pool Aware Effective Staleness Control for Asynchronous RL in LLM Post-Training

arXiv:2609.36830v1 Announce Type: new Abstract: Fully asynchronous reinforcement learning (RL) improves resource utilization in large language model post-training by overlapping rollout generation wit

Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs

arXiv:2609.37667v1 Announce Type: new Abstract: Counterfactual explanations for graph neural networks (GNNs) find the minimal intervention that flips a node's prediction--but computing one requires re

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

arXiv:2609.36686v1 Announce Type: new Abstract: Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored

Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts

arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would l

Who Warmed the Archives? LLMs Overestimate Historical Warmth

arXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indices

WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses

arXiv:2609.36635v1 Announce Type: cross Abstract: Bug validation asks a coding agent to produce an executable witness for a reported bug. The witness combines a concrete input with a testing harness a

World2Motion: Turning Video World Models into 3D Human Motion Generators

arXiv:2609.37004v1 Announce Type: new Abstract: We present World2Motion, a framework that generates scene-aware 3D human motion and corresponding video from a single image and a text prompt. While exi

World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2609.36438v1 Announce Type: new Abstract: Autonomous driving requires choosing a safe and efficient plan as surrounding traffic evolves. Generate-and-select planners propose multiple trajectorie

XU-RS: Explaining Credal Width in Random-Set Language Models

arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot te

You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution

arXiv:2609.36837v1 Announce Type: new Abstract: Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pi

You Only Reprogram Once: Rethinking Prolonged Training for Visual Reprogramming

arXiv:2609.36661v1 Announce Type: new Abstract: Visual reprogramming is a parameter-efficient method for adapting pretrained models, yet its training can remain computationally expensive: even with a

Your Benchmark Is Not Saturated: Reviving Multiple-Choice Evaluation with Answer Pooling

arXiv:2609.37494v1 Announce Type: new Abstract: Multiple-choice benchmarks are cheap to grade and are running out of room, and the standard remedy, writing harder items, is slow and repeated for every

Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrapping

arXiv:2609.37883v1 Announce Type: new Abstract: Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various l

3D Point Tracking with State Space Models

arXiv:2609.34035v1 Announce Type: new Abstract: Tracking any point of a dynamic scene in metric 3D - in absolute meters, not up to an unknown scale - underpins 3D and 4D reconstruction, robot navigati

3dgs-sc: a controlled static screen-content benchmark for 3d gaussian splatting

arXiv:2609.31756v1 Announce Type: new Abstract: Does high image fidelity imply readable screen content in 3D Gaussian Splatting (3DGS)? We introduce 3DGS-SC, a controlled static screen-content dataset

A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards

arXiv:2609.33467v1 Announce Type: new Abstract: When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order,

A Comparative Analysis of Attention versus State-Space Models for In-Context Learning

arXiv:2609.32341v1 Announce Type: new Abstract: Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and exist

A Free Knob: Decoupling Calibration and Predictive Skill in Threshold-Based Evaluation

arXiv:2609.33457v1 Announce Type: new Abstract: Many dense-prediction benchmarks evaluate rare events by pooling prediction and target over spatial blocks, thresholding each, and scoring the contingen

A Function-Level Vulnerability Score Measures Flag Rate More Than the Model: Protocol Effects on Paired Benchmarks

arXiv:2609.32890v1 Announce Type: new Abstract: Language models are increasingly evaluated as vulnerability detectors, and scores reported for similar models differ widely between papers. We measured

A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services

arXiv:2609.31877v1 Announce Type: cross Abstract: Cloud-based Large language model (LLM) services create a network-level traffic side channel that can expose model, prompt, and task behavior despite e

A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning

arXiv:2609.33424v1 Announce Type: cross Abstract: Self-supervised pretraining learns representations that are broadly transferable across downstream tasks, yet direct fine-tuning can be suboptimal due

A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture

arXiv:2609.33991v1 Announce Type: new Abstract: Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Althou

A Surgical Foundation Model Reveals Task-Dependent Label Efficiency

arXiv:2609.31821v1 Announce Type: new Abstract: Developing label-efficient models is a central challenge in surgical AI due to the high cost and scarcity of expert annotation. While self-supervised fo

A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149v1 Announce Type: cross Abstract: Linear bandits model sequential decision-making problems with noisy rewards that are linear in the decision variable, where an agent must simultaneous

ActionEngine: From Reactive to Programmatic Web Agents via State Machine Memory

arXiv:2602.20502v2 Announce Type: replace-cross Abstract: Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and r