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arXiv:2610.14476v1 Announce Type: cross Abstract: Comparing large language models can reveal shared representational structure and systematic differences between them. However, aggregate similarity me

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Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language Models

DGX agent

arXiv:2610.19696v1 Announce Type: new Abstract: Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information. These systems typically communicate throu

local-aiarxiv-cs-cl

15 Oct 2026

Agents

TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

arXiv:2610.19725v1 Announce Type: new Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for mo

agentsarxiv-cs-lg

We now have state-of-the-art pre-trained base models in the 3T-parameter weight class, built entirely in-house at MAI. Post-training results…

model-releasesjeremy-howard--x

We now have state-of-the-art pre-trained base models in the 3T-parameter weight class, built entirely in-house at MAI. Post-training results coming soon -- stay tuned! Lots of details in our technical

WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models

arXiv:2610.14863v1 Announce Type: new Abstract: Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numeri

safetyarxiv-cs-ai

Tutorials

What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books

arXiv:2610.20104v1 Announce Type: new Abstract: Autoregressive transformers trained on limit order book (LOB) message streams are increasingly proposed as foundation models for market microstructure.

tutorialsarxiv-cs-lg

Tutorials

Anchors Enhance Controllability and Reasoning of Diffusion Language Models

arXiv:2610.15097v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer new opportunities for controllable text generation. However, their controllability, defined as the ability to sat

tutorialsarxiv-cs-lg

Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry

arXiv:2610.15818v1 Announce Type: new Abstract: Modern cloud platforms generate high-dimensional, noisy telemetry from thousands of microservices. Unlike classical anomaly detection benchmarks, cloud

Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models

arXiv:2610.18543v1 Announce Type: new Abstract: Foundation models are increasingly used as feature extractors for downstream tasks with limited data, typically through representations obtained from th

researcharxiv-cs-cv

Box-WAM: Box Trajectories as Compact State Representations for World Action Models

arXiv:2610.17526v1 Announce Type: new Abstract: World action models (WAMs) are a category of policy models that ex- plicitly predict both actions and future world states. They have been shown to outpe

safetyarxiv-cs-ro

Delta Flow Matching for Robot Foundation Models

arXiv:2610.15794v1 Announce Type: new Abstract: Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action

safetyarxiv-cs-ro

Agents

Directional Pullback Regularization for Long-Horizon Rollout of Latent World Models in Autonomous Driving

arXiv:2610.16139v1 Announce Type: new Abstract: Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-h

agentsarxiv-cs-ro

FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models

arXiv:2601.22371v2 Announce Type: replace Abstract: Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggl

GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models

arXiv:2506.10946v3 Announce Type: replace Abstract: Unlearning large language models (LLMs) is important due to copyright and privacy concerns. A key challenge is unintended forgetting, where removing

Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models

arXiv:2610.17440v1 Announce Type: new Abstract: Dual-encoder vision-language models systematically fail at negation, scoring ''a cat without a dog'' nearly identically to ''a cat with a dog.'' We show

Tutorials

Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation

arXiv:2610.17080v1 Announce Type: new Abstract: Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequen

tutorialsarxiv-cs-cv

Local Ai

Segment Where You Look: Attention as the Interface Between Language Models and Segmenters

arXiv:2610.18729v1 Announce Type: new Abstract: Vision-language models (VLMs) that segment from language must pass the location of the referent to a pixel decoder. The dominant interface compresses it

local-aiarxiv-cs-cv

SOVABench: A Vehicle Surveillance Action Retrieval Benchmark for Multimodal Large Language Models

arXiv:2601.04824v3 Announce Type: replace Abstract: Automatic identification of events and recurrent behavior analysis are critical for video surveillance. However, most existing content-based video r

TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings

arXiv:2610.17065v1 Announce Type: new Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical

TwinPrune: Density-Aware Two-Phase Token Pruning for Vision-Language Models

arXiv:2610.17530v1 Announce Type: new Abstract: We propose a unified spatial-density signal to score token importance for both inference phases: bipartite merge for prefill and KV eviction for decode,

researcharxiv-cs-cv

UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models

arXiv:2610.16945v1 Announce Type: new Abstract: Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning. Usin

safetyarxiv-cs-cv

What Limits Cross-Task Generalization in World Action Models?

arXiv:2610.16641v1 Announce Type: new Abstract: Cross-task generalization remains difficult for world action models (WAMs), even when their predicted futures depict plausible manipulation. This gap ca

researcharxiv-cs-ro

Applications

Where is the multimodal goal post? On the Ability of Foundation Models to Recognize Highlights in Videos

arXiv:2601.16333v3 Announce Type: replace Abstract: Foundation models are used for many real-world applications involving language understanding and generation from temporally-ordered multimodal event

applicationsarxiv-cs-cv

Do any local models come close to chatgpts ability to generate sample character images? (2nd image is original)

model-releasesr-stablediffusion

Quick note, not my character, original came from schmede_AI on youtube. But yes my question is do any local models currently have the ability to match and generate additional images of a character fro

13 Oct 2026

Look up! Mistral models are in orbit 🌍. Proud to join @orbitworksspace and @LoftOrbital's first-of-its-kind AI satellite constellation with…

model-releasesmistral-ai--x

Look up! Mistral models are in orbit 🌍. Proud to join @orbitworksspace and @LoftOrbital's first-of-its-kind AI satellite constellation with @NVIDIAAIInfra. Just a few weeks ago, @orbitworksspace launc

13 Oct 2026

Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning

arXiv:2610.13504v1 Announce Type: cross Abstract: Generative Recommenders increasingly use Reinforcement Learning (RL) to optimize outcomes that may appear hundreds or thousands of interactions after

BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models

arXiv:2610.12907v1 Announce Type: cross Abstract: Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within

EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can

GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

arXiv:2610.12669v1 Announce Type: cross Abstract: Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. Th

Market Impact Estimation via Causal Foundation Models

arXiv:2610.12537v1 Announce Type: new Abstract: When a trader executes a large order, the relevant question is not what will the price be? but what would the price have been had I not traded? Market i

Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities

arXiv:2610.13456v1 Announce Type: new Abstract: Cyber-Physical Systems are developed through multiple model based activities, comprising several stages of development. As the development progresses th

researcharxiv-cs-cl

Why Linear Models Can Still Work in a Complex and Changing World

arXiv:2610.13404v1 Announce Type: new Abstract: Linear models can predict well in complex domains even when underlying mechanisms are nonlinear. We argue that this can occur because the complexity of

safetyarxiv-cs-lg

AI4Fire: Evaluating Large Language Models on Wildfire Tasks

arXiv:2610.10946v1 Announce Type: new Abstract: Large language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives. How do they perform on wildfire

researcharxiv-cs-cl

ARC: A Reasoning Recipe for Robot Foundation Models

arXiv:2610.12386v1 Announce Type: cross Abstract: The prevailing approach to improving robot foundation models (RFMs) relies on larger models, more robot demonstrations, and costly training at scale.

safetyarxiv-cs-ai

Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models

arXiv:2610.11163v1 Announce Type: cross Abstract: Third-party challenge-response identity verification (TP-CRIV) enables an independent verifier to assess whether a claimant possesses a model identica

researcharxiv-cs-ai

Comparing Object Detection Models for Electrical Substation Component Mapping

arXiv:2512.22454v2 Announce Type: replace Abstract: Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerabl

safetyarxiv-cs-cv

DiscoVL: Unveiling Disentangled C ross-Modal Representation Learning via Orthogonal Adversarial Regularization for V ision-Language Models

arXiv:2610.11113v1 Announce Type: new Abstract: Pre-trained vision-language models excel across varied perception tasks, but adapting them to novel downstream settings without sacrificing generalizati

From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment

arXiv:2610.11826v1 Announce Type: cross Abstract: Object hallucination remains a major obstacle for large vision-language models (LVLMs) to generate reliable content. An intuitive mitigation strategy

Last Week in AI #346 - 719 math manuscripts, 2 Western open models, 1 more safety resignation

model-releaseslast-week-in-ai

OpenAI released 719 mathematics manuscripts on October 6, 2026 that were generated by an internal frontier model (not publicly available), covering 372 topic families—including a solution to the Navie

Learning to Plan by Looking Back: Hindsight Hierarchies for Training Reasoning Models

arXiv:2610.12168v1 Announce Type: new Abstract: We introduce a self-improvement loop for reasoning models based on the following observation: Even when the difficulty of a problem exceeds the model's

researcharxiv-cs-ai

Applications

Multimodal Large Language Models as Image Classifiers

arXiv:2603.06578v3 Announce Type: replace Abstract: Multimodal Large Language Model (MLLM) classification performance depends critically on evaluation protocol and ground truth quality. Studies compar

applicationsarxiv-cs-cv

PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology

arXiv:2610.11329v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) have shown strong visual perception ability, but their robustness in the language domain remains poorly characte

SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2610.12402v1 Announce Type: cross Abstract: Existing spatial reasoning benchmarks mainly test spatial perception: reading off relations already visible in the input. Yet real-world spatial intel

Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks

arXiv:2610.11978v1 Announce Type: new Abstract: Jev is a 'System One' model that returns a choice among given options instead of generating text. We study how such a specialized decision model compare

researcharxiv-cs-cl

Task-Centric Personalized Federated Fine-Tuning of Language Models

arXiv:2604.00050v3 Announce Type: replace-cross Abstract: Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks

researcharxiv-cs-ai

Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits

arXiv:2610.12005v1 Announce Type: new Abstract: Which forms of test-time compute improve the predictions of strong pretrained tabular foundation models (TFMs)? We systematically study this along three

Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance

arXiv:2610.09096v1 Announce Type: new Abstract: While established literature underscores the pivotal role of preprocessing in forecasting accuracy, this stage remains largely overlooked in current res

8 Oct 2026

Can any open-weight models handle a decomp/recomp project yet?