Search: “models”

arXiv:2607.13683v2 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled capable agents across diverse applications. Beyond the foundation model, the performance of an agent is go

agentsarxiv-cs-cl

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Model Releases

I gave five different local LLMs a town. They invented Facebook and a duck-based credit bureau. (MIT, self-hosted, you don't play it — you watch it)

DGX agent

Each villager in Pepperton is a different model — a mistral, a qwen3, a qwen2.5, a phi4-mini, a llama3.2 — because model families have genuinely different temperaments, and the friction between them i

model-releasesr-ollama

3 Aug 2026

LARA: Lightweight Adapters in the Residual Stream for Composable Adaptation and Alignment

arXiv:2607.28669v1 Announce Type: new Abstract: We present LARA (Lightweight Additive Residual Adaptation), a method for efficient adaptation that operates in the residual stream of a frozen model rat

🔥Let's talk about Qwen! #AMA

The post announces an “Ask Me Anything” (AMA) about Qwen, the Alibaba‑developed foundation model. It introduces the Qwen Foundation Model Team and directs readers to their GitHub repository @QwenDevs,

model-releasesqwen--x

Local Ai

Multimodal Reinforcement Learning with Adaptive Verifier for AI Agents

arXiv:2512.03438v3 Announce Type: replace Abstract: Agentic reasoning models trained with multimodal reinforcement learning (MMRL) have become increasingly capable, yet they are almost universally opt

local-aiarxiv-cs-ai

Applications

Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers

arXiv:2511.00847v5 Announce Type: replace-cross Abstract: The widespread adoption of Large Language Models (LLMs) through Application Programming Interfaces (APIs) induces a critical vulnerability: th

applicationsarxiv-cs-ai

Local Ai

Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters

arXiv:2607.29238v1 Announce Type: cross Abstract: InMyStyle is a privacy first, single user system that adapts small language models to rewrite AI-edited text towards an individual user's writing styl

local-aiarxiv-cs-ai

So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection

arXiv:2505.18660v5 Announce Type: replace Abstract: Recent advances in AI-powered generative models have enabled the creation of increasingly realistic synthetic images, posing significant risks to in

WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics

model-releasesarxiv-cs-ai

arXiv:2601.02430v3 Announce Type: replace-cross Abstract: Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate their code generation capabilities and com

Parlor v2: best-effort fully local GPT-Live clone on an M3 Pro

model-releasesr-localllama

GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released. My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something

2 Aug 2026

Ran DS V4-Flash-0731 Locally on 3xMI50 32GB @ ~15 t/s TG

model-releasesr-localllama

Hey y'all. I'll be concise. TL;DR: DS V4-Flash-0731 @ UD-IQ2_M running fully in VRAM on 3xMI50s (90.9 GB model, 96 GB VRAM). Actual speed on llama-server is: - Text Generation: ~15-16 tokens/second st

2 Aug 2026

Why are almost all new benchmarks and leaderboards coding focused?

model-releasesr-localllama

I know in in this community LLM's are generally used for coding but there are other usecases besides coding and those usecases should be tested too. I also know benchmarks can sometimes be benchmaxxed

2 Aug 2026

Applications

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511.22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges o

applicationsarxiv-cs-lg

Benchmarking LLM Competence on Logical Inference over Probability Operators

arXiv:2607.27405v1 Announce Type: new Abstract: Both expressions of uncertainty and inferences are ubiquitous in natural language, and valid inferences over natural-language expressions of uncertainty

Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation

arXiv:2607.27816v1 Announce Type: new Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversation

Research

Calibrate Before Reason: Robust Visual Token Reduction against Semantic Drift in VLMs

arXiv:2607.27700v1 Announce Type: new Abstract: Large Vision-Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual tokens. However, existing visual token re

researcharxiv-cs-cv

Applications

Challenges in annotations by humans and LLMs: A case study of evaluative language

arXiv:2607.28119v1 Announce Type: new Abstract: In this paper, we draw a comparison between linguists in training, a trained linguist, and annotations generated by large language models (LLMs) to find

applicationsarxiv-cs-cl

Critical attention scaling in long-context transformers

arXiv:2510.05554v2 Announce Type: replace Abstract: As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity

CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography

arXiv:2607.27779v1 Announce Type: new Abstract: Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical ann

EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception

arXiv:2504.16616v4 Announce Type: replace Abstract: Event cameras, characterized by microsecond temporal resolution and very High Dynamic Range (HDR), emit high-speed event streams for perception task

eta-OPSD: Deriving with Policy Optimization, Training with Self-Distillation

arXiv:2607.28582v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reli

Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins

arXiv:2607.09306v3 Announce Type: replace Abstract: Behavioural auditing asks whether a language model behaves as it claims, but detection scores are reported without separating two targets: whether a

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations

arXiv:2607.28319v1 Announce Type: new Abstract: This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias

Safety

Fidelity Is Not Safety: Gently-Compressed LLMs Pass Every Data-Free Quality Guard Yet Invent Procedure Steps in Agentic Execution

arXiv:2607.28196v1 Announce Type: new Abstract: Practitioners accept a compressed language model once it clears a stack of data-cheap quality guards: perplexity within a small factor of the original,

safetyarxiv-cs-cl

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning

arXiv:2607.27610v1 Announce Type: new Abstract: Reinforcement learning (RL) finetuning significantly enhances the reasoning capabilities of large language models (LLMs), yet its effectiveness critical

Language Diversity: Evaluating Language Usage and AI Performance on African Languages in Digital Spaces

arXiv:2512.01557v3 Announce Type: replace Abstract: This study examines the digital representation of African languages and the challenges this presents for current language detection tools. We evalua

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

arXiv:2410.06458v2 Announce Type: replace Abstract: Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multipl

LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA

arXiv:2607.27806v1 Announce Type: new Abstract: In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal in

MPIE-Bench: Benchmarking Anatomically Plausible Multi-Person Interaction Editing

arXiv:2607.27616v1 Announce Type: new Abstract: Text-to-image and personalized editing models now synthesize high-fidelity single-subject images with ease. Yet placing multiple named people into share

MSCM-net: A hyperspectral image classiffcation method based on multi-scale convolution and Mamba

arXiv:2607.28277v1 Announce Type: new Abstract: Hyperspectral imaging is widely used in remote sensing and engineering. Therefore, research on its classification methods is crucial. While CNN and Tran

Tutorials

Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards

arXiv:2603.16140v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains. Recent

tutorialsarxiv-cs-lg

Research

S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring

arXiv:2607.27913v1 Announce Type: new Abstract: Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled dat

researcharxiv-cs-lg

Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding

arXiv:2607.27826v1 Announce Type: cross Abstract: Recent advances in sign language (SL) understanding (SLU) have led to remarkable progress in tasks such as continuous SL recognition and SL translatio

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection

arXiv:2607.27680v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard mechanism for fine-tuning large pretrained models, yet its statistical properties remain only parti

Using an AMD V620 workstation card for ComfyUI - success

model-releasesr-stablediffusion

A few weeks ago I posted about if it was worth using a V620 for Comfyui, and was told it likely wouldn't work, at least in Windows 11. And if it did, it would be far too slow and unusable. I decided t

@ArtificialAnlys ok @openai gets it https://x.com/OpenAI/status/2082878156483219672

@ArtificialAnlys ok @openai gets it https://x.com/OpenAI/status/2082878156483219672 We are committed to pushing the model frontier across cost efficiency, capability, and speed. Starting today, we are

model-releasesswyx--x

b10188

model-releasesllama-cpp-releases

metal: fix memory unwire if model is freed without any GPU operations (#26082) metal: fix memory leak if model is freed without any GPU operations metal: run dummy work only if residency sets are used

Decoupled Visual Processing: Efficient Multimodal Adaptation via Modality-Specific Transformer Substitution

arXiv:2607.26596v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual understanding within a unified tran

Industry

Deploying Kimi K3 on AWS

Kimi K3 is a 2.8‑trillion‑parameter Mixture of Experts model released by Moonshot AI on July 27, 2026, with its weights publicly available for self-hosting. Its architecture—using Kimi Delta Attention

industryaws-ml-blog

Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case

arXiv:2607.26780v1 Announce Type: new Abstract: The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While

Research

Linguistic Monoculture in LLM-Assisted Language Use

arXiv:2607.27134v1 Announce Type: cross Abstract: Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although suc

researcharxiv-cs-cl

Position: Evaluation Scores Are Perishable Knowledge Claims

arXiv:2607.26191v1 Announce Type: cross Abstract: Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessmen

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

arXiv:2607.26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning

Safety

Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret

arXiv:2607.26370v1 Announce Type: cross Abstract: We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior

safetyarxiv-cs-lg

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

arXiv:2503.10647v2 Announce Type: replace Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dim

Thinking Machines just released Inkling-Small: 276B total, 12B active. A faster Inkling that matches or beats its 975B big brother in many b…

model-releasessoumith-chintala--x

Thinking Machines just released Inkling-Small: 276B total, 12B active. A faster Inkling that matches or beats its 975B big brother in many benchmarks. To test its speed, we plugged it into HF's speech

b10174

model-releasesllama-cpp-releases

model: add NextN/MTP speculative decoding support for GLM_DSA (GLM-5.2) (#25980) model: add NextN/MTP speculative decoding support for GLM_DSA (GLM-5.2) Adds GLM-5.2 NextN/MTP as a --spec-type draft-m

29 Jul 2026

Beyond Static Costs: Learning-Dynamics Aware Loss Functions for Long-Tailed Classification

arXiv:2607.25830v1 Announce Type: new Abstract: Deep learning models in computer vision face significant challenges when trained on long-tailed datasets, where a few majority classes dominate while ma