Search: “models”

Input Resolution Matters: Real-Time Object Detection Latency

model-releasesarxiv-cs-cv

arXiv:2609.12920v1 Announce Type: new Abstract: We model total latency as the convolution of preprocessing, inference, and postprocessing distributions under a simplifying independence approximation,

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MMGR: Multi-Modal Generative Reasoning Benchmark and Evaluation

DGX agent

arXiv:2512.14691v3 Announce Type: replace Abstract: Modern multimodal generative models can synthesize visually compelling images and videos, but it remains unclear whether this visual fluency reflect

model-releasesarxiv-cs-cl

14 Sept 2026

Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

arXiv:2609.12905v1 Announce Type: new Abstract: This paper addresses the wind farm power maximization problem in the presence of wind direction changes. Specifically, a model-free Modified Twin Delaye

ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge

arXiv:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations v

Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature

arXiv:2609.12123v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates. In practice, the LoRA rank

Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning

arXiv:2502.07250v3 Announce Type: replace Abstract: Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many

Tutorials

SCDM: Spatial-Contextual Disentanglement Mamba via Differential Inference for Efficient Image Classification

arXiv:2609.12825v1 Announce Type: new Abstract: State Space Models (SSMs), particularly VMamba, have emerged as efficient alternatives for modeling long-range dependencies in medical image analysis. H

tutorialsarxiv-cs-cv

The rhetoric is really heating up!

The entire page of the NY Times today above the fold absent one article is AI (the models are just too strong/too dangerous, must be regulated). They forgot to include 'Sponsored by OpenAI' at the end

13 Sept 2026

b10930

model-releasesllama-cpp-releases

server : allow model downloads at model limit fix issue #26809 (#28530) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/47055500 macOS/iOS: macOS Apple Sili

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

arXiv:2609.11115v1 Announce Type: new Abstract: Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark data

Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning

arXiv:2609.11393v1 Announce Type: new Abstract: Test-time adaptation has emerged as a lightweight alternative to costly post-training for improving the reasoning capabilities of Large Language Models

local-aiarxiv-cs-ai

Deepseek v4.1 Flash Peak Rates… why?

Can someone explain to me why Ollama Cloud are billing with same peak/off peak windows as Deepseek since they are hosting the model across US/EU infrastructure? submitted by /u/veganmaister [link] [co

model-releasesr-ollama

Qwen-Next seems worse to me then 3.8 27b for coding, but I feel like I must be missing something?

Hi! I run both models on MTPLX on my m5 max, and since I have 128GB of ram I run the q8 27b. I think MTPLX only lets me run 'optimized for speed' which it says is a dynamic q4 with 8 bit attention. Bo

A Group-Based Resource Allocation Model for the Fractional Knapsack Problem

arXiv:2609.06470v2 Announce Type: replace-cross Abstract: To solve the fractional knapsack problem, Dantzig's greedy rule orders items according to their value-to-cost ratio. This ordering introduces

researcharxiv-cs-cl

Safety

Analyzing Traditional and Neural Approaches to Multilingual Readability Assessment

arXiv:2609.10792v1 Announce Type: new Abstract: Transformer-based models excel at Automatic Readability Assessment (ARA), yet feature-based models remain in active use because their predictions tie ba

safetyarxiv-cs-cl

Black-Box Membership Inference via Word-Level Probability Estimation

arXiv:2609.10611v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) have emerged as critical tools for auditing privacy risks in large language models (LLMs), aiming to determine whe

Compute-Bounded Security Assurance - Coverage, Verification, and Response under Resource Constraints

arXiv:2609.09229v1 Announce Type: cross Abstract: Additional inference compute can increase the number of correctly resolved security-assurance tasks, but repeated success, unique coverage, accepted e

Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures

arXiv:2609.10893v1 Announce Type: new Abstract: Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are g

researcharxiv-cs-cl

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

arXiv:2609.10935v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural langu

Legible Failures: Detecting and Repairing In-Context Binding Errors

arXiv:2609.11216v1 Announce Type: new Abstract: A wrong answer does not show whether the model lacked the needed information or held it and failed to use it. On an entity-obligation binding task, a la

researcharxiv-cs-lg

LOCUS: Task-Aware Low-Rank Post-Training for Token-Efficient Language Generation

arXiv:2609.11739v1 Announce Type: new Abstract: Large language model serving costs scale directly with output sequence length, yet standard preference alignment often inflates response verbosity witho

MC-DeTra: Motion-Consistent Joint Object Detection and Socially-Aware Trajectory Forecasting in Bird's-Eye-View Images

model-releasesarxiv-cs-cv

arXiv:2609.11717v1 Announce Type: new Abstract: Unified models for object detection and trajectory forecasting aim to merge perception and prediction for autonomous driving, refining actor trajectorie

Safety

Negative Self-Distillation: Learning to Reason by Avoiding Flaws

arXiv:2609.11699v1 Announce Type: new Abstract: On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their ow

safetyarxiv-cs-cl

Published Unlearning Numbers Move Per Checkpoint, and Not Because the Removed Data Survives: An Audit of 263 Released Batch-Normalized Checkpoints

arXiv:2609.11490v1 Announce Type: cross Abstract: An unlearning audit reads its verdict off numbers that an unlearned model and its retrained reference each publish, and both also ship batch-normaliza

RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety

arXiv:2609.11758v1 Announce Type: new Abstract: Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However

Hardware

Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs

arXiv:2609.11347v1 Announce Type: new Abstract: Knowledge graph foundation models such as ULTRA achieve zero-shot link prediction on unseen graphs through dedicated architectures that hard-code a tran

hardwarearxiv-cs-lg

SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

arXiv:2609.10742v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are powerful models for handling attributed graphs in tasks such as classification, link prediction, and community detect

Talking to Itself While Coding: What Makes Comments Help Code Generation?

arXiv:2609.09242v1 Announce Type: cross Abstract: Large Language Models (LLMs) often generate natural-language comments while writing code, and these comments become part of the context used to genera

researcharxiv-cs-ai

To get started, Download Ollama and enable ChatGPT in Ollama's app: https://ollama.com/download

Ollama is a standalone app that can be downloaded for macOS from https://ollama.com/download. Once installed, users must enable the ChatGPT model within the Ollama settings to access the AI chat funct

local-aiollama--x

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

model-releasesarxiv-cs-cv

arXiv:2609.11156v1 Announce Type: new Abstract: All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Exist

Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning

arXiv:2609.09707v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals fo

A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis

arXiv:2609.07586v1 Announce Type: new Abstract: Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging

Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning

arXiv:2609.10142v1 Announce Type: new Abstract: Large language models remain fragile against malicious fine-tuning, motivating training-time defenses against harmful persona drift. Preventative Steeri

AgentFairBench: Do LLM Agents Discriminate When They Act?

arXiv:2606.16723v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is

Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools

arXiv:2609.05818v1 Announce Type: new Abstract: We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use p

Artificial Analysis is not 'broken', and they prove it.

Like many of you, I have seen many posts and tweets in the last weeks complaining about Artificial Analysis being 'broken', 'meaningless', and 'bought out.' People who say this have done no research a

CARRE: Counterfactual Action Retrieval and Reason Evaluation for Explainable Churn Prescription

arXiv:2609.09766v1 Announce Type: new Abstract: Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is approp

researcharxiv-cs-cl

Deep Neural Networks for Learning Intent from sEMG Signals to Support Hardware Devices for Post-Stroke Neurorehabilitation

arXiv:2609.09971v1 Announce Type: new Abstract: Finger-specific motor intent is a clinically meaningful control signal for post-stroke neurorehabilitation, where residual muscle activity may remain me

DeepSeek V4-1 Flash is out

Here we go again, DeepSeek is back again with a new model V4-1 Flash A multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens Mark

Experiences people can interact with in real time. A new kind of foundational moment. A sneak peek into what's possible and what's coming fo…

model-releasescristobal-valenzuela--x

Experiences people can interact with in real time. A new kind of foundational moment. A sneak peek into what's possible and what's coming for real-time video generation. We believe instant generation

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

arXiv:2609.10439v1 Announce Type: new Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and

GigaChat-3.5-Reasoning

Hey y'all! We've released a new model in our lineup: GigaChat-3.5 Reasoning. It's a 432B-A28B MoE with Gated DeltaNet for long-context efficiency. We trained domain experts (code, math, general, etc.)

HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization

arXiv:2609.09835v1 Announce Type: new Abstract: Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction.

I'm rooting for Ollama to find a new angle

First off, I'm super thankful for the value Ollama has given me Got me setup with my first local LLM setup (early OpenClaw days amirite!?) Then got me into their 20 plan and what really sold me here w

model-releasesr-ollama

Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation

arXiv:2609.10264v1 Announce Type: cross Abstract: Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inf

local-aiarxiv-cs-lg

Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding

arXiv:2609.09338v1 Announce Type: new Abstract: Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distri

Privacy-Preserving Split Learning for Federated LLM Fine-Tuning

arXiv:2609.09794v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold

local-aiarxiv-cs-lg

Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment

arXiv:2609.05512v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all c