Search: “models”

AutoAdapt: Automatic Domain Discovery Enables Low-Cost Extensibility

arXiv:2610.10349v1 Announce Type: new Abstract: Instruction-tuned models are deployed into environments where domains are heterogeneous and evolve, yet adding new domains or data typically requires co

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model : fix DFlash output head sharing (#30111) llama : fix DFlash output head sharing Assisted-by: Codex dflash : read tied output weights from GGUF metadata Assisted-by: Codex llama : share tied wor

model-releasesllama-cpp-releases

8 Oct 2026

Best current R9700 inference engine?

model-releasesr-localllama

There are way too many forks to keep track of, so I've gotten lost. As far as I can tell, Radiance VLLM is best for models that fit in GPUs while some form of llama.cpp is probably best for MOE RAM-sp

Child ASR Adaptation with Adult Retention: An Empirical Study

arXiv:2610.08827v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can

Hardware

HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption

arXiv:2610.08255v1 Announce Type: cross Abstract: Many organizations adapt large pretrained models to their own tasks by fine-tuning on private data. Several of these parties often hold data for the s

hardwarearxiv-cs-lg

I find it odd watching armchair critics rush to nitpick @MistralAI’s plans or obsess over whether they’re #1 on every leaderboard. Having a …

I find it odd watching armchair critics rush to nitpick @MistralAI’s plans or obsess over whether they’re #1 on every leaderboard. Having a team with this level of conviction building frontier models

researchyann-lecun--x

InsClaimBench: Benchmarking Insurance Claim Adjudication Across the Decision Chain

arXiv:2610.09671v1 Announce Type: new Abstract: Recent advances in reasoning-oriented large language models (LLMs) have motivated increasing evaluation of their ability to perform professional decisio

Local Ai

Liquid AI builds personal AI around device-level context

On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are r

local-aisiliconangle

LoGRA: Scaling LLM Reinforcement Learning with Low-Rank Gradient Sketches

arXiv:2610.06647v2 Announce Type: replace Abstract: Reinforcement learning has greatly advanced the capabilities of large language models, but its memory demands remain a barrier to broader adoption.

MixedPEFT: Combining Multiple PEFT Methods with Mixed Objectives for Unsupervised Domain Adaptation

arXiv:2606.22272v2 Announce Type: replace Abstract: Applying pre-trained language models to new domains through full fine-tuning is computationally expensive and prone to catastrophic forgetting. To a

Reproducible LLM Inference Benchmarking: A Sequential Isolation Protocol for Regression Testing

arXiv:2610.09778v1 Announce Type: cross Abstract: Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system stat

ScribbleEdit: A Benchmark for Scribble-Only Image Editing

arXiv:2610.09382v1 Announce Type: cross Abstract: Scribble-based interaction provides a lightweight and intuitive way for users to specify image editing intents in interactive editing tools. However,

Safety

TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery

arXiv:2610.09074v1 Announce Type: new Abstract: Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic t

safetyarxiv-cs-lg

VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification

arXiv:2604.01569v2 Announce Type: replace Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can

VolCo: Volumetric Contact for High-Fidelity Human Grasp Generation

arXiv:2610.10197v1 Announce Type: new Abstract: Accurate contact modeling is fundamental to understanding hand-object interaction, yet existing contact representations are typically restricted to obje

Local Ai

Why VLMs Miss Small Objects, and When Zooming In Is Safe

arXiv:2610.09313v1 Announce Type: new Abstract: Vision-language models (VLMs) often miss small objects in large images. We ask three questions: what limits them, which of these limits better models ca

local-aiarxiv-cs-cv

A stock GLM-Edge-1.5B-Chat on a 4GB Galaxy A04e completed a real Amazon cart task

model-releasesr-localllama

Yesterday TechCrunch published a piece about a growing problem for AI agents: websites are starting to block them. Amazon blocking Meta's Muse is the obvious example. At almost exactly the same time,

APEX: Speculate smarter, not deeper

arXiv:2610.07780v1 Announce Type: new Abstract: Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness

AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

arXiv:2601.17645v2 Announce Type: replace-cross Abstract: Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examin

Contrastive Learning for Aspect Representation towards Explainable Recommendation

arXiv:2610.07761v1 Announce Type: cross Abstract: In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that

Dynamic Alignment and Calibration for Multimodal Learning

arXiv:2610.07928v1 Announce Type: cross Abstract: Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. However, existing

Local Ai

DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching

arXiv:2610.08268v1 Announce Type: cross Abstract: Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models

local-aiarxiv-cs-ai

Safety

EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation

arXiv:2610.07969v1 Announce Type: new Abstract: Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existi

safetyarxiv-cs-cv

EMHO: EMbodied Agent Harness Optimization via Experience Traces

arXiv:2610.08432v1 Announce Type: new Abstract: Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning,

Agents

Evaluating human-AI workflows for field research in viticulture

arXiv:2610.07669v1 Announce Type: cross Abstract: We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 202

agentsarxiv-cs-ai

Forensic Reserve: Eliciting Latent Knowledge for Image Forgery Detection

arXiv:2610.08639v1 Announce Type: new Abstract: As generated images become increasingly realistic, reliable forgery detection is essential for maintaining trust in visual information. However, existin

JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications

arXiv:2610.06625v2 Announce Type: replace Abstract: Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe market

Measuring climate backlash in Twitter and Reddit archives: Lexical definitions, recorded responses and participant turnover

arXiv:2610.07634v1 Announce Type: new Abstract: Social media archives are often used to study resistance to climate action, but words, response counters and observed participants do not measure the sa

On Open-Ended Information Seeking for Information Elicitation Agents

arXiv:2610.07509v1 Announce Type: new Abstract: Information elicitation is an open-ended information-seeking problem in which an interaction can unfold in many potentially valuable directions, requiri

On the Intrinsic Limited Robustness of Latent-Based Watermarking

arXiv:2610.08178v1 Announce Type: new Abstract: Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transforma

researcharxiv-cs-lg

OOPMAS: Object-Oriented Multi-Agent Systems for Query-Level Workflow Generation

arXiv:2610.07787v1 Announce Type: new Abstract: Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question an

Open SLM Evalulations

model-releasesr-localllama

Introducing Open SLM Evaluations! Open SLM Evaluations is a dataset containing benchmarks for 106 SLMs(150M parameters and below). It has benchmark scores for each model, links, parameter counts, and

Tutorials

OxiGen: Oxidation-State-Aware Crystal Generation

arXiv:2610.08296v1 Announce Type: new Abstract: Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable cr

tutorialsarxiv-cs-lg

Safety

Performative Prediction with Selective Labels

arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pred

safetyarxiv-cs-lg

PlotPick: AI-powered batch extraction of numerical data from scientific figures

arXiv:2605.06021v2 Announce Type: replace Abstract: Systematic reviews and meta-analyses often need numerical data reported only in figures, and extracting them with interactive digitisers usually req

Probabilistic Truly Unordered Rule Sets

arXiv:2401.09918v2 Announce Type: replace Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First,

ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents

arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with st

RenderBench: Benchmarking Render-to-Real Video Transfer with Reconstructed Digital Twins

arXiv:2610.08684v1 Announce Type: new Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, and

Scalable next-scale autoregression for medical image generation across anatomical regions

arXiv:2602.14512v3 Announce Type: replace Abstract: Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly b

researcharxiv-cs-cv

Some early first-hand accounts of encountering a narrow superhuman intelligence as mathematicians grapple with the hundreds of big AI proofs…

model-releasesethan-mollick--x

Some early first-hand accounts of encountering a narrow superhuman intelligence as mathematicians grapple with the hundreds of big AI proofs released by OpenAI. Problems solved in inhuman ways that ma

SPECTRUM: Proximal Spectral Modulation for Looped Self-Distillation

arXiv:2610.07237v1 Announce Type: new Abstract: A model that learns from its own outputs inherits more than their correctness: it inherits which solutions it produces. We formulate Looped Self-Distill

researcharxiv-cs-ai

TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning

arXiv:2610.06855v1 Announce Type: cross Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formu

researcharxiv-cs-cv

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

arXiv:2610.08358v1 Announce Type: cross Abstract: Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become

The Failure Is in the Readout: Fine-Grained Emotion Recognition Benchmarks Measure Elicitation, Not Perception

arXiv:2610.08162v1 Announce Type: cross Abstract: Fine-grained emotion recognition supports therapy tools and social robots, but it needs facial data, which raises privacy and data-protection concerns

The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

arXiv:2610.08026v1 Announce Type: new Abstract: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked wi

Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects

arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera

researcharxiv-cs-lg

Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine

arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calli

Adapting prior-data fitted networks for tabular anomaly detection

arXiv:2610.06693v1 Announce Type: new Abstract: While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the li