Search: “models”

RiskWebWorld: A Realistic Interactive Benchmark for GUI Agents in E-commerce Risk Management

arXiv:2604.13531v1 Announce Type: cross Abstract: Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks primarily target benign,

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Model Releases

ROBOGATE: Adaptive Failure Discovery for Safe Robot Policy Deployment via Two-Stage Boundary-Focused Sampling

DGX agent

arXiv:2603.22126v3 Announce Type: replace Abstract: Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high

model-releasesarxiv-cs-ro

16 Apr 2026

Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios

model-releasesarxiv-cs-cv

arXiv:2604.14041v1 Announce Type: new Abstract: Daily scenarios are characterized by visual richness, requiring Multimodal Large Language Models (MLLMs) to filter noise and identify decisive visual cl

ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution

arXiv:2604.13787v1 Announce Type: new Abstract: Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evo

TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks

arXiv:2601.10245v2 Announce Type: replace-cross Abstract: Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to comp

safetyarxiv-cs-cl

ValueGround: Evaluating Culture-Conditioned Visual Value Grounding in MLLMs

arXiv:2604.06484v2 Announce Type: replace Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cu

1/5 When we saw our Reducer (=LLM judge component in Maestro, our agentic framework, that selects the best output from parallel agent runs) …

model-releasesai21-labs--x

1/5 When we saw our Reducer (=LLM judge component in Maestro, our agentic framework, that selects the best output from parallel agent runs) consistently picking gold patches, we were sure Claude Opus

ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception

arXiv:2604.12255v1 Announce Type: cross Abstract: Dynamic facial expression recognition in the wild remains challenging due to data scarcity and long-tail distributions, which hinder models from effec

safetyarxiv-cs-ai

Asymptotically Stable Gait Generation and Instantaneous Walkability Determination for Planar Almost Linear Biped with Knees

arXiv:2604.12274v1 Announce Type: new Abstract: A class of planar bipedal robots with unique mechanical properties has been proposed, where all links are balanced around the hip joint, preventing natu

researcharxiv-cs-ro

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs

arXiv:2604.12506v1 Announce Type: new Abstract: Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion: while excelling at complex reasoning tasks, they consistently u

safetyarxiv-cs-cl

Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch

arXiv:2603.24647v4 Announce Type: replace Abstract: The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare cl

CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems

arXiv:2604.12312v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as task-oriented agents in enterprise environments, ensuring their strict adherence to complex

Applications

Compute constraints are a double bind: On the inference side you need to either (a) raise prices, (b) ration use, and/or (c) serve worse mod…

applicationsethan-mollick--x

Compute constraints are a double bind: On the inference side you need to either (a) raise prices, (b) ration use, and/or (c) serve worse models. This hurts current growth On the training side, you can

DeepMind launches Gemini Robotics-ER 1.6 to meet precise physical AI demands

model-releasessiliconangle

Google DeepMind, Alphabet Inc.’s artificial intelligence research division, Tuesday introduced a new foundation robotics AI model designed as a significant upgrade for understanding and precise spatia

Elastic Net Regularization and Gabor Dictionary for Classification of Heart Sound Signals using Deep Learning

arXiv:2604.12483v1 Announce Type: cross Abstract: In this article, we propose the optimization of the resolution of time-frequency atoms and the regularization of fitting models to obtain better repre

researcharxiv-cs-ai

Evaluating Relational Reasoning in LLMs with REL

arXiv:2604.12176v1 Announce Type: new Abstract: Relational reasoning is the ability to infer relations that jointly bind multiple entities, attributes, or variables. This ability is central to scienti

FaCT: Faithful Concept Traces for Explaining Neural Network Decisions

arXiv:2510.25512v2 Announce Type: replace-cross Abstract: Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they fun

Local Ai

gguf import with vision / mmproj, not working!

This r/ollama thread addresses a known compatibility issue where importing multimodal GGUF models into Ollama using a separate mmproj (multimodal projector) file fails to enable vision capabilities. O

local-air-ollama

Hardware

GPU stays sometimes at 100% usage even when done replying. Is it normal?

This r/ollama post addresses a commonly reported behavior where Ollama's GPU usage remains at or near 100% even after a model has finished generating a response. Certain models appear to 'hang' after

hardwarer-ollama

I'm going all in on Hermes (@NousResearch, @Teknium1) as my entire agent and coding stack. Six profiles. One shared self-hosted memory store…

model-releasesnous-research--x

I'm going all in on Hermes (@NousResearch, @Teknium1) as my entire agent and coding stack. Six profiles. One shared self-hosted memory store. Zero hosted-coder dependencies. The fleet: - pmax-mousa —

Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety

arXiv:2604.12311v1 Announce Type: cross Abstract: The emergence of vibe coding, a paradigm where non-technical users instruct Large Language Models (LLMs) to generate executable codes via natural lang

Latent Planning Emerges with Scale

arXiv:2604.12493v1 Announce Type: cross Abstract: LLMs can perform seemingly planning-intensive tasks, like writing coherent stories or functioning code, without explicitly verbalizing a plan; however

LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Real-World Multilingual VQA

model-releasesarxiv-cs-cv

arXiv:2509.10026v4 Announce Type: replace Abstract: As large vision language models (VLMs) advance, their capabilities in multilingual visual question answering (mVQA) have significantly improved. Cha

Tutorials

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

tutorialsarxiv-cs-ai

Local Ai

Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads

arXiv:2604.12301v1 Announce Type: cross Abstract: We present a systematic measurement study of seven tactics for reducing cloud LLM token usage when a small local model can act as a triage layer in fr

local-aiarxiv-cs-ai

Agents

Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation

arXiv:2604.12066v1 Announce Type: new Abstract: Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-th

agentsarxiv-cs-ai

MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving

arXiv:2604.11854v1 Announce Type: cross Abstract: End-to-End (E2E) autonomous driving models are usually trained and evaluated with a fixed ego-vehicle, even though their driving policy is implicitly

Operationalising the Right to be Forgotten in LLMs: A Lightweight Sequential Unlearning Framework for Privacy-Aligned Deployment in Politically Sensitive Environments

arXiv:2604.12459v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential conten

Perception-Aware Policy Optimization for Multimodal Reasoning

arXiv:2507.06448v5 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with ro

safetyarxiv-cs-cl

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions

arXiv:2604.11841v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models (LLMs), but its strictly linear structure fund

RankOOD -- Class Ranking-based Out-of-Distribution Detection

arXiv:2511.19996v2 Announce Type: replace Abstract: We propose RankOOD, a rank-based Out-of-Distribution (OOD) detection approach based on training a model with the Placket-Luce loss, which is now ext

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

arXiv:2507.08977v4 Announce Type: replace-cross Abstract: Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While ex

safetyarxiv-cs-ai

Applications

SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism

arXiv:2506.01979v4 Announce Type: replace-cross Abstract: Recently, speculative decoding (SD) has emerged as a promising technique to accelerate LLM inference by employing a small draft model to propo

applicationsarxiv-cs-ai

The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, in

Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration

arXiv:2604.12046v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinf

researcharxiv-cs-cl

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection

arXiv:2604.12321v1 Announce Type: new Abstract: Existing Chinese toxic content detection methods mainly target sentence-level classification but often fail to provide readable and contiguous toxic evi

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

arXiv:2604.12610v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. Howe

Local Ai

ZIB ZIT hand over?

local-air-stablediffusion

This r/StableDiffusion Reddit thread likely discusses the topic of hand generation quality when using ZIB and ZIT — two AI image generation models from the Z-Image ecosystem used in Stable Diffusion a

Applications

A Complete Decomposition of KL Error using Refined Information and Mode Interaction Selection

arXiv:2410.11964v2 Announce Type: replace Abstract: The log-linear model has received a significant amount of theoretical attention in previous decades and remains the fundamental tool used for learni

applicationsarxiv-cs-lg

A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems

arXiv:2604.09932v1 Announce Type: cross Abstract: Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial conditio

Tutorials

A Unified Theory of Sparse Dictionary Learning in Mechanistic Interpretability: Piecewise Biconvexity and Spurious Minima

arXiv:2512.05534v4 Announce Type: replace-cross Abstract: As AI models achieve remarkable capabilities across diverse domains, understanding what representations they learn and how they encode concept

tutorialsarxiv-cs-ai

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

arXiv:2511.06609v3 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and

ADD for Multi-Bit Image Watermarking

arXiv:2604.11491v1 Announce Type: cross Abstract: As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promis

Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks

arXiv:2604.11753v1 Announce Type: new Abstract: We study parallel test-time scaling for long-horizon agentic tasks such as agentic search and deep research, where multiple rollouts are generated in pa

Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

arXiv:2604.11730v1 Announce Type: new Abstract: Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits

researcharxiv-cs-cv

C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

arXiv:2604.11796v1 Announce Type: cross Abstract: Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, th

Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?

model-releasesarxiv-cs-cv

arXiv:2511.21998v2 Announce Type: replace Abstract: Multi-modal Large Language Models (LLM) have advanced conversational abilities but struggle with providing live, interactive step-by-step guidance,

CARO: Chain-of-Analogy Reasoning Optimization for Robust Content Moderation

arXiv:2604.10504v1 Announce Type: new Abstract: Current large language models (LLMs), even those explicitly trained for reasoning, often struggle with ambiguous content moderation cases due to mislead