Search: “models”

Differential Refresh Policies for Models Trained on Lagging Data Snapshots: From a Single-Age Equivalence Limit to an Optimal Per-Segment Allocation

arXiv:2610.09519v1 Announce Type: new Abstract: Production machine-learning models are derived artifacts of time-bounded training snapshots: a deployed model is a materialized view over a training cut

safetyarxiv-cs-lg

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Tutorials

DisParQ: Self-Supervised Part Concepts for Interpretable Vision Foundation Models

DGX agent

arXiv:2610.09802v1 Announce Type: new Abstract: Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to tho

tutorialsarxiv-cs-cv

8 Oct 2026

New challenge: consolidated 2 cards in a single system but one card hogs all the models even if I run 2 different instances of Ollama on 2 different ports

In my attempt to combat powerty; as electricity cost a ton these days and it is hot as hell, I consolidated 2 machines into one, so my 2 crappy machines now are one, and that means a bit less power an

local-air-ollama

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-languag

safetyarxiv-cs-cl

SpikingVLA: Asynchronous Spiking Vision-Language-Action Models

arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of

Visual Evidence Under Cross-Examination: Evaluating and Controlling Decision-Level Evidence Use in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.09550v1 Announce Type: new Abstract: Vision-language models increasingly reason through crops, regions, and tool-produced observations. Yet an observation can influence the answer without b

Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

arXiv:2610.10508v1 Announce Type: new Abstract: Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as

A perspective note on likelihood approximation and inference for complex simulation models using a chain of aggregated normalizing flows

arXiv:2610.07391v1 Announce Type: cross Abstract: We present a new perspective on the problem of likelihood approximation within the framework of simulation-based inference that promotes scalable and

Agent-to-agent communication with a personal agent on top is wild. Wow. I underestimated how well models like Opus 5.5 can coordinate alread…

Agent-to-agent communication with a personal agent on top is wild. Wow. I underestimated how well models like Opus 5.5 can coordinate already. This opens a whole new set of experiences with agents. Bu

model-releasesdair-ai--x

Tools

Both models are on Hugging Face: https://huggingface.co/collections/perplexity-ai/pplx-embed-v2

Both embeddings models from Perplexity.ai—referred to as *pplx‑embed‑v2*—are publicly available on Hugging Face under the “perplexity‑ai” collection. The models can be accessed via the Hugging Face pa

toolsperplexity--x

Comparative review of hybrid forecasting models for short-term prediction of building thermal load

arXiv:2610.06881v1 Announce Type: cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the

researcharxiv-cs-ai

Hardware

Hybrid Latent Attention for Looped Language Models

arXiv:2610.07940v1 Announce Type: cross Abstract: Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-

hardwarearxiv-cs-ai

Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

arXiv:2610.08585v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failu

Independent Multi-Agent Reinforcement Learning with Counterfactual Semantic-Social World Models

arXiv:2610.07704v1 Announce Type: cross Abstract: Fully decentralized multi-agent reinforcement learning (MARL), also referred to as independent learning, requires each agent to learn and act using on

Interpretable Hypergraph Learning via Neural Additive Models

arXiv:2610.07458v1 Announce Type: new Abstract: Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hy

Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models

arXiv:2610.08689v1 Announce Type: new Abstract: Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiti

SkillFormer: Skill-Decomposed Adaptation for Audio Language Models

arXiv:2610.07533v1 Announce Type: cross Abstract: Audio language models must handle dozens of distinct skills, from pitch comparison and speaker counting to musical tempo estimation and emotion recogn

FORGE: Verification-Gated Behavioral Repair for Generative Language Models

arXiv:2610.05190v1 Announce Type: cross Abstract: Generative large language models (LLMs) inherit undesirable behaviors from pre-training, including demographic bias and toxic generation, that often e

local-aiarxiv-cs-lg

From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.04139v1 Announce Type: new Abstract: Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments. However, existing vision-language mode

From Transformers to Weighted Automata: Towards the Verification of Large Language Models

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

safetyarxiv-cs-lg

GeoBridge-VLA: Geometry-Aware Residual Adaptation for Vision-Language-Action Models

arXiv:2610.05026v1 Announce Type: new Abstract: Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoni

researcharxiv-cs-cv

HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models

arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener

researcharxiv-cs-cv

Mistral Large 4 is one of the world's strongest AI models for cybersecurity.

model-releasesmistral-ai--x

Mistral AI announced that its new model, Mistral Large 4, is among the world's most powerful AI systems specifically designed for cybersecurity tasks. The tweet shared on October 6, 2026 highlights th

No Concept Escapes the Audit: Auditing-Aware Unlearning for Verifiable Concept Erasure in Diffusion Models

arXiv:2610.05401v1 Announce Type: new Abstract: Text-to-image diffusion models can generate prohibited content, which motivates concept erasure through machine unlearning. Most erasure methods interve

local-aiarxiv-cs-lg

ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2603.17850v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manip

Tutorials

Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models

arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d

tutorialsarxiv-cs-lg

Rethinking Tabular Foundation Models On Data Streams

arXiv:2610.05352v1 Announce Type: new Abstract: Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this succ

Routing-Aware Safety Alignment for Mixture-of-Experts Models

arXiv:2602.04448v3 Announce Type: replace Abstract: Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable d

Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models

arXiv:2610.04017v1 Announce Type: new Abstract: Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a

local-aiarxiv-cs-lg

SWE-Race: a coding-agent benchmark of 188 real concurrency bugs, with results from three models [P]

model-releasesr-machinelearning

We've been building a benchmark out of real concurrency bugs (race conditions, deadlocks, cancellation issues) taken from merged PRs in about 100 Python projects. Each task gets graded by the project'

When Low Prediction Error Misleads Planning: Diagnosing Representation, Dynamics, and Decision Failures in Latent World Models

arXiv:2610.05550v1 Announce Type: new Abstract: The component that dominates a latent world model's prediction error need not be the one whose repair most improves action selection. We show this by co

A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants

arXiv:2610.03396v1 Announce Type: new Abstract: Bayesian data assimilation combines model forecasts with noisy observations, but sampling high-dimensional, non-Gaussian posteriors remains challenging.

safetyarxiv-cs-lg

Depth as Time in One-Step Generative Models

arXiv:2610.03626v1 Announce Type: new Abstract: The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has r

researcharxiv-cs-ai

GRAFT: Growing Agglomerative Foundation Models via Continual Teacher Distillation

arXiv:2610.02597v1 Announce Type: new Abstract: Vision foundation models such as DINOv2, SigLIP2, and MASt3R develop complementary capabilities from different pretraining objectives, yet their knowled

researcharxiv-cs-cv

Labels Override Definitions in Jev-Style Typed Decision Models

arXiv:2610.02586v1 Announce Type: new Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carrie

safetyarxiv-cs-ai

Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models

arXiv:2610.03482v1 Announce Type: new Abstract: Hallucination detection is particularly important for medical language models, but repeated-sampling approaches are expensive and existing uncertainty-h

SyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2610.03329v1 Announce Type: cross Abstract: Large language models are increasingly used where small syntactic errors matter, yet character-level reasoning is still evaluated mostly through isola

Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

model-releasesarxiv-cs-cv

arXiv:2407.12927v4 Announce Type: replace Abstract: Systems for multimodal emotion recognition (ER) are commonly trained to extract features from different modalities (e.g., visual, audio, and textual

The Geometry of Knowledge Accessibility in Large Language Models

arXiv:2610.03052v1 Announce Type: new Abstract: Large language models (LLMs) contain broad knowledge, but they cannot access all of it reliably. We study this problem through knowledge accessibility,

Agents

First AutoHarness, then AutoContext, now AutoCompact. I am seeing a rising trend of work that trains models to natively support more of what…

First AutoHarness, then AutoContext, now AutoCompact. I am seeing a rising trend of work that trains models to natively support more of what the harness does. This work specifically trains agents to d

agentsdair-ai--x

3 Oct 2026

Two local Qwen ( 3.8 27b unsloth Q6 and Qwen flash next strata coder ) models vs Claude Opus 4.6 on the same 3 coding tasks. One of them tied it. Not here to start a fight, just sharing numbers

model-releasesr-localllama

Innanzitutto, due cose per evitare fraintendimenti. Non sto cercando di sostenere che un modello o un'azienda siano migliori. Non ho alcun interesse personale in nessuno di essi. Volevo solo verificar

3 Oct 2026

A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models

model-releasesarxiv-cs-ai

arXiv:2610.00969v1 Announce Type: cross Abstract: Large language models (LLMs) have been increasingly used for financial document analysis, including earnings call transcripts (ECTs). Beyond generatin

Are AI Coders Snitches? An Empirical Study of Pretraining Data Detection on Code Large Language Models

model-releasesarxiv-cs-ai

arXiv:2507.17389v2 Announce Type: replace-cross Abstract: Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these mod

ATI-VLA: Action-Centric Predictive Vision-Language-Action Models via Actionable Alignment Then Adaptive Injection

arXiv:2610.01741v1 Announce Type: new Abstract: Predictive Vision-Language-Action (VLA) models aim to improve robotic manipulation via future observation or world dynamics forecasting. However, existi

safetyarxiv-cs-cv

Beyond Diagonal State Space Models: Exact Non-Abelian Group Tracking, Solvability Barriers, and Geometric Physical Manifolds

arXiv:2610.00329v1 Announce Type: new Abstract: Selective state space models (SSMs), such as Mamba, S4D, and LRU, are bounded by transition matrix commutativity (A_t A_t' = A_t' A_t) and solvable affi

Completion Aware Guidance for World Action Models

arXiv:2610.01559v1 Announce Type: cross Abstract: World Action Models (WAMs) predict visual futures and robot actions, yet they remain susceptible to task-incomplete imagination, where plausible, acti

researcharxiv-cs-ai

Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models

arXiv:2610.00277v1 Announce Type: new Abstract: Federated multimodal graph foundation models (GFMs) aim to adapt pretrained multimodal models to decentralized graph data, where each client owns a priv

Error-Corrected Inference-Time Scaling for Imperfect Diffusion Models

arXiv:2610.01933v1 Announce Type: cross Abstract: Inference-time scaling adapts pretrained diffusion models to new sampling tasks without additional training. Existing methods rely primarily on Monte