Search: “models”

Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark

arXiv:2610.03526v1 Announce Type: cross Abstract: Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined groun

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Agents

Introducing North 2. Enterprises have been compromising on agentic AI for years: platforms that aren't scalable, models that aren't sovereig…

DGX agent

Introducing North 2. Enterprises have been compromising on agentic AI for years: platforms that aren't scalable, models that aren't sovereign, control without enterprise-grade security. That ends toda

agentscohere--x

5 Oct 2026

Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves

arXiv:2610.02684v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolv

Agents

LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models

arXiv:2610.03616v1 Announce Type: new Abstract: We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time

agentsarxiv-cs-ro

MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models

arXiv:2610.02898v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have achieved remarkable advances in robotic manipulation, yet their zero-shot generalization under out-of-distribut

safetyarxiv-cs-ro

Native Action-Prior Learning from Videos for World Action Models

arXiv:2610.03391v1 Announce Type: new Abstract: World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotate

VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models

arXiv:2610.03218v1 Announce Type: new Abstract: Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and inference. They combine low-precision elements with sh

What Should World Models Forget? Stratified Retention for Continual Adaptation

arXiv:2610.03713v1 Announce Type: cross Abstract: Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary predictio

ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models

arXiv:2610.03546v1 Announce Type: new Abstract: EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signal

Agents

Banger paper from Meta Superintelligence Labs. They find something super interesting and unexpected. (bookmark it) Base models with a light …

Banger paper from Meta Superintelligence Labs. They find something super interesting and unexpected. (bookmark it) Base models with a light harness often solve more agentic tasks than their RL post-tr

agentsdair-ai--x

3 Oct 2026

Attention Manifolds: Steering or Blocking Language Models by Editing Learned B-Spline Surfaces

model-releasesarxiv-cs-lg

arXiv:2610.00257v1 Announce Type: new Abstract: In standard transformer attention, a source token sends the same value vector to every receiver. The query determines how much to attend but not what to

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

arXiv:2610.00302v1 Announce Type: cross Abstract: Crowdsourced imagery provides timely, fine-grained, street-level observations for disaster mapping, complementing conventional remote sensing imagery

Distillation of Tabular Foundation Models into Efficient Predictors

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in

tutorialsarxiv-cs-lg

Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness

arXiv:2610.00568v1 Announce Type: cross Abstract: Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between cap

Applications

Enterprise storage becomes AI memory as privately run models close in on the frontier

As open-weight models close in on the performance of proprietary frontier systems, enterprises are gaining a credible way to run generative AI on hardware they own. That shift puts the data already si

applicationssiliconangle

Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics

model-releasesarxiv-cs-lg

arXiv:2610.00554v1 Announce Type: new Abstract: We evaluate hybrid quantum-classical machine learning for the reduced-order prediction of spatiotemporal brain deformation fields. To mitigate the compu

FlashBack: Knowing When to Remember in Streaming Vision-Language Models

arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between r

tutorialsarxiv-cs-cv

Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps

arXiv:2610.00497v1 Announce Type: cross Abstract: We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive lang

Learned End-to-End Guidance Schedules for Diffusion Models

arXiv:2610.01502v1 Announce Type: new Abstract: Diffusion models are a powerful generative paradigm used across multimedia and scientific applications. Guided diffusion methods impose requirements on

Learning ab initio phase-field models

arXiv:2610.01432v1 Announce Type: cross Abstract: Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current

MIKASA-Robo-VLA: Benchmarking Memory in VLA Models for Long-Horizon Manipulation

arXiv:2610.00604v1 Announce Type: cross Abstract: Vision-language-action policies often see only one or a few recent frames, which makes it difficult to evaluate how they use information that disappea

Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual trai

SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents

arXiv:2610.01278v1 Announce Type: new Abstract: Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors

Hardware

TensorCommitments: A Lightweight Verifiable Inference for Language Models

arXiv:2602.12630v2 Announce Type: replace-cross Abstract: Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes

hardwarearxiv-cs-ai

TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models

arXiv:2610.00899v1 Announce Type: cross Abstract: Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with sta

Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution

arXiv:2610.00590v1 Announce Type: cross Abstract: An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability t

VideoSTF: Stress-Testing Output Repetition in Video Large Language Models

arXiv:2602.10639v2 Announce Type: replace Abstract: Video Large Language Models (VideoLLMs) have achieved strong performance on video understanding tasks, yet existing benchmarks evaluate only what mo

researcharxiv-cs-cv

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I'v…

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD

researchkarpathy--x

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This r

tutorialsarxiv-cs-ai

Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance

arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including man

Distribution Matching Distillation for Continuous Diffusion Language Models

arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (N

researcharxiv-cs-cl

Does Gradient Conflict Predict the Understanding--Generation Trade-off? A Controlled Audit of Conflict-Metric Validity in Unified Multimodal Models

arXiv:2609.38465v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) are increasingly designed around gradient conflict between understanding and generation objectives. The premise that

HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training

arXiv:2609.39350v1 Announce Type: cross Abstract: As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training paral

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

arXiv:2609.38979v1 Announce Type: new Abstract: Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge

researcharxiv-cs-cv

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

arXiv:2609.39445v1 Announce Type: new Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup th

ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving

arXiv:2609.39245v1 Announce Type: new Abstract: In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing Worl

safetyarxiv-cs-ro

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

arXiv:2609.39685v1 Announce Type: cross Abstract: Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstration

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for saf

Smaller Models, Better Rejects: Preference Distillation Scaling

arXiv:2609.38987v1 Announce Type: new Abstract: Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated fa

safetyarxiv-cs-lg

Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models

arXiv:2609.39091v1 Announce Type: cross Abstract: Inference-time alignment of flow and diffusion-based models is critical for achieving flexible generative modeling. Theoretically, Doob's h-transform

safetyarxiv-cs-lg

The System Prompt Illusion: How Instruction Preambles Modify Computation in Language Models

arXiv:2609.38205v1 Announce Type: new Abstract: System prompts are the primary lever practitioners use to control language model behavior, yet what they actually do to the computation inside the trans

safetyarxiv-cs-cl

Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection

arXiv:2609.38274v1 Announce Type: new Abstract: The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive

researcharxiv-cs-cl

Industry

Alibaba's ModelScope and OSChina's MoArk are vying to become Chinese alternatives to Hugging Face; ModelScope hosts 170K+ open models, while MoArk hosts ~20K (Viola Zhou/Rest of World)

Viola Zhou / Rest of World: Alibaba's ModelScope and OSChina's MoArk are vying to become Chinese alternatives to Hugging Face; ModelScope hosts 170K+ open models, while MoArk hosts ~20K — AI model pla

industrytechmeme

Applications

Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models

arXiv:2609.36771v1 Announce Type: cross Abstract: Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a syst

applicationsarxiv-cs-ai

Beyond Rule-Based Mutation Testing: Test-Aware Mutant Generation Using Large Language Models

arXiv:2609.35841v1 Announce Type: cross Abstract: Mutation testing evaluates test-suite adequacy by injecting synthetic faults into program code. However, traditional rule-based tools often generate l

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

arXiv:2609.36305v1 Announce Type: new Abstract: World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we pro

tutorialsarxiv-cs-ro

Agents

Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses

arXiv:2609.36630v1 Announce Type: new Abstract: Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limit

agentsarxiv-cs-ai

Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions

arXiv:2609.36106v1 Announce Type: cross Abstract: We study the fundamental limits of transferability in algebraic signal processing through homomorphisms between algebraic signal models. Homomorphisms