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Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning

arXiv:2603.08930v2 Announce Type: replace-cross Abstract: This paper introduces a benchmark for evaluating whether vision-language models (VLMs) can generate plant simulation configurations from image

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Research

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

DGX agent

arXiv:2609.27915v1 Announce Type: new Abstract: Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary relianc

researcharxiv-cs-cv

24 Sept 2026

Validation and Simulation Catch Different Errors: Four Levels of Evaluation for LLM-Generated Circuits

arXiv:2609.26830v1 Announce Type: cross Abstract: Simulation success is not equivalent to structural correctness for LLM-generated circuits. We define and measure four evaluation levels -- schema vali

Value Mirror Descent for Reinforcement Learning

arXiv:2604.06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under

safetyarxiv-cs-lg

Variance Reduction for Independent Metropolis

arXiv:2406.17699v3 Announce Type: replace-cross Abstract: Assume that we would like to estimate the expected value of a function F with respect to an intractable density pi, which is specified up to s

researcharxiv-cs-lg

Variational Bayesian Flow Network for Graph Generation

arXiv:2601.22524v2 Announce Type: replace Abstract: Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often

researcharxiv-cs-lg

VCMM: Variance-Calibrated Momentum for Multimodal Learning

arXiv:2609.27577v1 Announce Type: new Abstract: Multimodal joint training often suffers from modality imbalance, where a dominant modality suppresses the optimization of others. Existing methods mainl

safetyarxiv-cs-lg

Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms

arXiv:2609.27321v1 Announce Type: new Abstract: Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training r

agentsarxiv-cs-ai

Applications

VFX company DNEG's AI unit Brahma, whose customers include Warner Bros. and the NBA, raised 150M led by Indian PE firm Multiples at a 2B post-money valuation (Priyanka Salve/CNBC)

Priyanka Salve / CNBC: VFX company DNEG's AI unit Brahma, whose customers include Warner Bros. and the NBA, raised 150M led by Indian PE firm Multiples at a 2B post-money valuation — Brahma AI, an Ind

applicationstechmeme

Applications

VGM-VS: Rethinking Visual Geometry Model for High-Precision Visual Servoing

arXiv:2609.28312v1 Announce Type: cross Abstract: We present VGM-VS, a visual servoing method built on a pretrained feed-forward visual geometry model. Given the current view and a reference image cap

applicationsarxiv-cs-cv

Visibility-Guided Structured Measure Flow for Class-Conditioned 3D Gaussian Generation

model-releasesarxiv-cs-cv

arXiv:2609.27778v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has made real-time, high-fidelity 3D rendering practical, yet turning this explicit representation into a native generative

Vision-Based Control of a Tether-Suspended Aerial Radiation Sensing Payload

arXiv:2609.27219v1 Announce Type: new Abstract: Aerial radiation surveys achieve higher sensitivity when the radiation detector is held close to the ground. Detector sensitivity falls off roughly with

researcharxiv-cs-ro

Visual Tripwires: Anticipating Failure in Deep Vision Systems

model-releasesarxiv-cs-cv

arXiv:2609.28099v1 Announce Type: new Abstract: Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability method

VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

arXiv:2609.27948v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visua

researcharxiv-cs-cv

VLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing

model-releasesarxiv-cs-cv

arXiv:2512.11490v2 Announce Type: replace Abstract: Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both regio

Local Ai

VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation

arXiv:2609.28184v1 Announce Type: cross Abstract: Robotic operation in previously unseen environments requires both semantic understanding and reliable metric information. While vision--language model

local-aiarxiv-cs-cv

VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark

model-releasesarxiv-cs-cl

arXiv:2508.13680v5 Announce Type: replace Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) in

WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents

arXiv:2510.01354v2 Announce Type: replace-cross Abstract: Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect gener

Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams

arXiv:2609.28429v1 Announce Type: new Abstract: An always-on robot faces an endless stream that never resets: instructions arrive and lapse, the scene changes, and its own past actions reshape what it

safetyarxiv-cs-ro

Water Surface Swimming in a Centipede and its Robophysical ModeL

arXiv:2609.27088v1 Announce Type: new Abstract: Elongate multi-legged robots use coordinated body waves and distributed legs to move through cluttered terrestrial environments. However, as housing act

researcharxiv-cs-ro

Wave-Robust Passive AUV Localization Using FP-MUSIC

arXiv:2609.27712v1 Announce Type: new Abstract: Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challen

agentsarxiv-cs-ro

We are really fucked. • AI has become dangerous (not because it is brilliant but because it has poor judgement, a constitutional inability t…

We are really fucked. • AI has become dangerous (not because it is brilliant but because it has poor judgement, a constitutional inability to reliably follow rules, a surfeit of brute force and too mu

safetygary-marcus--x

Local Ai

We factory-reset a laptop to test our local AI agent like a real user. Here's what broke that never broke on our dev PC

I am creating a local AI agent called 'Ontelic' for Windows. Ontelic returns to Ollama and also manipulates the actual Chrome. This is not for promotion but to share what we've learned. I'll give GitH

local-air-ollama

Web search in Hermes Agent is now fast and free. @perplexity_ai built Fast Search for agents, and it is free for all Nous Portal tiers.

Web search in Hermes Agent is now fast and free. @perplexity_ai built Fast Search for agents, and it is free for all Nous Portal tiers. Media Fast Search is now the default search in Hermes Agent for

agentsnous-research--x

'We'll Fix It Later': Education, AI, and the Deferral of Privacy in EdTech

arXiv:2609.28137v1 Announce Type: cross Abstract: Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories

safetyarxiv-cs-ai

we've built the world's most advanced engine for document extraction over complex documents the video below shows an overview of LlamaExtrac…

we've built the world's most advanced engine for document extraction over complex documents the video below shows an overview of LlamaExtract Agentic Plus in action. long tables, giant forms, calibrat

agentsjerry-liu--x

What Changed? Drift Detection with Real, Virtual, and Incomparable Diagnosis

arXiv:2609.27865v1 Announce Type: cross Abstract: Sharing a deep encoder does not, by itself, fix the central confound of task-comparison scores. We show that cross-evaluated heads on a frozen shared

researcharxiv-cs-ai

What Changes When Fact-Verification Scores Improve? Evidence and Answer Accounting Across Trained Verifiers and LLMs

model-releasesarxiv-cs-cl

arXiv:2609.27064v1 Announce Type: new Abstract: A joint fact-verification score assesses answers and submitted evidence together. When the score improves, how much of the gain remains if the answers a

What Confidence Routing Is Actually Doing: Auditing Routing, Calibration, and Commitment in Multi-Agent Deliberation

model-releasesarxiv-cs-cl

arXiv:2609.27822v1 Announce Type: cross Abstract: A common multi-agent design asks agents to report confidence and lets the highest-scoring agent speak next, implicitly using one scalar both to route

What Converges in the Platonic Representation Hypothesis? Structure over Geometry

arXiv:2609.27252v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis suggests that increasingly capable models converge toward shared representations. Recent work narrows this clai

safetyarxiv-cs-cv

What Do Tabular Foundation Models Compute In Context? In-Situ Representation Refinement through Attention-Gated Updates

arXiv:2609.27679v1 Announce Type: new Abstract: What reusable computation should a tabular foundation model learn when every table defines a new supervised task? We develop in-situ representation refi

What Looks Like a Capability Limit in Vision-Language Models Is a Readout Limit

arXiv:2609.27408v1 Announce Type: cross Abstract: Benchmarks for vision-language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is tr

What Makes a Terminal-Bench Task Hard? Separating Genuine Hardness from Fake-Hardness on an Adjudicated Agentic Corpus

arXiv:2609.26826v1 Announce Type: cross Abstract: Frontier benchmarks need tasks that current models cannot solve. But a task that no model solves is not automatically a hard task. The same zero pass

agentsarxiv-cs-ai

🚨what more evidence do you need that OpenAI is in over its head???

🚨what more evidence do you need that OpenAI is in over its head??? Today’s news that OpenAI hacked the Australian government is not an isolated incident. We’re releasing more than 30,000 logs that inc

agentsgary-marcus--x

'What's That Sound?': A Versatile, Robust, and Lightweight Convolutional Transformer for Environment Sound Recognition

arXiv:2609.27762v1 Announce Type: cross Abstract: The conventional hearing aid is both costly and lim- ited in usage, as it is not intended to detect non-speech audio. Our objective is to develop a ma

safetyarxiv-cs-lg

WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents

arXiv:2609.27490v1 Announce Type: new Abstract: AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding,

agentsarxiv-cs-ai

When Accuracy Gaps Fail to Certify: Auditing Cross-Domain Recalibration of LLM Judges

arXiv:2609.27954v1 Announce Type: new Abstract: A scalar recalibration map fitted for an LLM judge on one task can fail when the task distribution changes, but the source-target accuracy gap is often

researcharxiv-cs-lg

When Adaptation Hurts: Split Sensitivity and Person-Level Negative Transfer in Federated Wearable Onboarding

arXiv:2609.27819v1 Announce Type: new Abstract: Federated wearable models eventually serve people absent from source training, but favorable average accuracy does not establish that unlabeled onboardi

When and Where to Trust the Teacher: Unifying On-Policy Distillation and GRPO through Entropy-Calibrated Credit Assignment

arXiv:2609.28385v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) supervises mathematical reasoning through final-answer correctness, but provides little guidance

safetyarxiv-cs-ai

Tools

When chat is the wrong UI

Title: **'When Chat Is the Wrong UI'** Burke Holland (@burkeholland) is a technologist, author, speaker, and creator who works at GitHub, where he focuses on AI‑powered development. In his talk or art

toolsgithub-ai-blog

When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense

arXiv:2609.27124v1 Announce Type: cross Abstract: Federated Learning enables decentralized model training by exchanging model updates--rather than raw data--with a central parameter server (PS). While

When Context Misleads: In-context Learning with Jurisdiction in Large Language Models

arXiv:2609.27603v1 Announce Type: cross Abstract: In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: t

When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Models

arXiv:2609.27382v1 Announce Type: cross Abstract: Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents

safetyarxiv-cs-cl

When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis

arXiv:2609.27411v1 Announce Type: new Abstract: Machine fault diagnosis from vibration requires learning from scarce labelled fault recordings while meeting the computational constraints of edge devic

When Learned Context Planning Fails to Beat Strong Retrieval: A Controlled Study of Planning, Routing, and Reranking for Long-Context QA

arXiv:2609.26976v1 Announce Type: new Abstract: Learned context planning selects evidence atoms before an answer model reasons over them. We test whether this learned selection improves long-context m

researcharxiv-cs-cl

When Parallel Drafter Meets Parallel Speculative Decoding

arXiv:2609.27396v1 Announce Type: new Abstract: DSpark-style parallel drafters have made speculative decoding highly effective, yet their draft phase remains serialized on the critical path of every r

researcharxiv-cs-cl

When Post-Processing Fairness Constraints Help and When They Harm: Evidence from Eight Cross-Domain Evaluations

arXiv:2609.26955v1 Announce Type: new Abstract: Fairness audits in production ML typically occur once, at deployment, on a single domain. Both fail in practice: fairness can shift after retraining or

When Visual Quality Misleads: Intent Recognition under Rendered Avatar Distortions

arXiv:2609.27560v1 Announce Type: cross Abstract: Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a pr

safetyarxiv-cs-cv