Search: “models”

Towards Complementary Keypoint Detection via Mixture of Detectors

arXiv:2610.16762v1 Announce Type: new Abstract: Keypoint detection plays a central role in geometric vision pipelines. Existing detectors exhibit complementary strengths across different scene structu

researcharxiv-cs-cv

Applications

Towards Optimality in Active Preference Learning

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DGX agent

arXiv:2602.01581v2 Announce Type: replace Abstract: Aligning large language models (LLMs) depends on high-quality datasets of human preference labels, which are costly to collect. Although active lear

applicationsarxiv-cs-lg

14 Oct 2026

TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments

arXiv:2610.17999v1 Announce Type: new Abstract: Humanoid behavior foundation models (BFMs) enable diverse whole-body control, but adapting a pretrained controller to a new embodiment remains challengi

safetyarxiv-cs-ro

TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation

arXiv:2610.15365v1 Announce Type: cross Abstract: Large language models (LLMs) can produce harmful content while generating a response. A useful safeguard should not only detect this risk during gener

Train LoRA directly on Quantized GGUFs, no PyTorch, no Parent(FP16) needed, just your GGUF and the data

I've seen this one floating around quite a bit now and since this is something that can be done without the parent and or other dependencies i thought i'd share something. Usually when you fine-tune a

local-air-localllama

Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection

arXiv:2603.01498v3 Announce Type: replace Abstract: In remote sensing imagery, multi-class change detection (MCD) is crucial for fine-grained monitoring, yet it has long been plagued by feature ambigu

Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations

arXiv:2610.18552v1 Announce Type: cross Abstract: Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the stron

Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection

arXiv:2610.17996v1 Announce Type: new Abstract: Sugarcane leaf diseases severely impact crop yield and quality, making accurate and timely detection critical for sustainable agriculture. Conventional

safetyarxiv-cs-cv

Understanding Personalization Gains in Whisper

arXiv:2610.17019v1 Announce Type: cross Abstract: Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording condi

UniForge3D: Joint 3D Reconstruction and Generation in a Shared Anchor-Guided Space

arXiv:2610.15357v1 Announce Type: new Abstract: Generating complete 3D assets from sparse, unposed images requires preserving observed geometry while completing unseen regions. Joint reconstruction an

v0.40.5

What's Changed mlx: port the Metal custom kernels to CUDA by @dhiltgen in #18657 docs: model recs and language standardization by @anra8571 in #18981 openai: accept custom tool calls in Responses hist

local-aiollama-releases

VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment

arXiv:2610.18189v1 Announce Type: new Abstract: Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic

safetyarxiv-cs-cv

Applications

Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers

arXiv:2610.15354v1 Announce Type: new Abstract: Sexual harassment represents a pervasive societal challenge, with a substantial proportion of incidents remaining unreported due to insufficient evidenc

applicationsarxiv-cs-cv

We Have Been Interpreting Diffusion Transformer Backbone Behaviors Wrong

arXiv:2610.14517v1 Announce Type: new Abstract: During a network forward call, diffusion backbones naturally produce many intermediate activations. If we evaluate each activation against an identical

What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions

arXiv:2610.15850v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are typically discovered from how their activations co-vary with behaviour, yet they are often used through interventi

When do explicit penalties improve flow matching?

arXiv:2610.15689v1 Announce Type: cross Abstract: Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation. We study penalties that

safetyarxiv-cs-lg

Hardware

When Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads

arXiv:2610.14009v1 Announce Type: new Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established failure analysis methods transfer effectively

hardwarearxiv-cs-lg

Applications

WorldRecon: Generative Multi-View Mesh Reconstruction for Large-Scale 3D World

arXiv:2610.18846v1 Announce Type: new Abstract: Reconstructing complete, high-quality meshes of large-scale scenes from unposed multi-view images requires faithful geometric grounding while plausibly

applicationsarxiv-cs-cv

Industry

Intel bets the next phase of AI belongs to the rightsized system, not the biggest one

For three years, artificial intelligence infrastructure has been dominated by one idea: Bigger is better. Bigger models, bigger clusters, and bigger checks written to a small group of suppliers. At th

industrysiliconangle

13 Oct 2026

Industry

Mixture-of-Experts kernel engineering on Amazon SageMaker AI

AWS engineers share kernel optimizations for fused Mixture-of-Experts (MoE) models on Amazon SageMaker AI: Swap-AB for WGMMA utilization, Triton backend routing for FP8 quantization, and eliminating T

industryaws-ml-blog

13 Oct 2026

Toward RL on mixed-precision, mixed-accelerator samplers

We trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par w

safetymodal-blog

13 Oct 2026

A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning

arXiv:2610.13744v1 Announce Type: new Abstract: On-policy self-distillation promises to replace the sparse outcome signal of verifiable-reward RL with dense token-level credit: a teacher conditioned o

local-aiarxiv-cs-ai

Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads

arXiv:2610.12972v1 Announce Type: new Abstract: Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty

researcharxiv-cs-ro

Agents

Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis

arXiv:2610.13273v1 Announce Type: cross Abstract: API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generati

agentsarxiv-cs-ai

Hardware

b11576: graph: pad the KV dimension of the no-cache attention to 256 (#30416)

hardwarellama-cpp-releases

The no-cache path builds a [n_tokens, n_tokens] mask and passes K and V unpadded, so FLASH_ATTN_EXT sees K->ne[1] == n_tokens. The CUDA kernels for head size 512 require a multiple of FATTN_KQ_STRIDE,

Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification

arXiv:2610.12997v1 Announce Type: new Abstract: Prior-art retrieval systems are structurally organized around classification codes, making cross-code discovery difficult. This paper asks if an unsuper

researcharxiv-cs-ai

Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents

arXiv:2610.12598v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) provides reliable supervision for software engineering agents through executable tests, but binary

Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence

arXiv:2610.13277v1 Announce Type: cross Abstract: Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare singl

local-aiarxiv-cs-cl

Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching

arXiv:2603.00530v2 Announce Type: replace Abstract: Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squa

Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks

arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already

researcharxiv-cs-ai

CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization

arXiv:2610.12567v1 Announce Type: new Abstract: Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can no

safetyarxiv-cs-ro

Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses

arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn inte

Deep End-to-End Survival Analysis with Temporal Consistency

arXiv:2410.06786v2 Announce Type: replace Abstract: In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspi

Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals

arXiv:2610.13649v1 Announce Type: cross Abstract: Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive co

FedPCD: Priority-Constrained Federated Aggregation

arXiv:2610.13508v1 Announce Type: new Abstract: Federated learning (FL) seeks to optimize a shared global model from decentralized client data. While effective for average performance, this process ca

safetyarxiv-cs-lg

Agents

Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

arXiv:2610.13267v1 Announce Type: new Abstract: Large language model (LLM) agents are highly vulnerable to memory-poisoning attacks. Existing defenses primarily rely on external modules for either sta

agentsarxiv-cs-ai

Host-Aware Web Crawling for LLM Pretraining Data

arXiv:2610.13321v1 Announce Type: new Abstract: Web crawling is a major source of large language model (LLM) pretraining data, yet data-selection pipelines discard a large share of crawled pages. We i

tutorialsarxiv-cs-cl

How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

arXiv:2610.13764v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results ab

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m

Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization

arXiv:2610.12657v1 Announce Type: cross Abstract: Efficient autoregressive generation is increasingly limited by the memory footprint of the key-value (KV) cache, which grows linearly with sequence le

researcharxiv-cs-ai

KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse

arXiv:2610.13060v1 Announce Type: new Abstract: KV-cache reuse in large language model serving is commonly limited to exact prompt prefixes, preventing reuse when the same retrieved document appears a

researcharxiv-cs-cl

LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos

arXiv:2610.13732v1 Announce Type: cross Abstract: Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a r

Looped Diffusion Transformer: the same blocks run several times inside each denoising step, and loop depth can be changed at sampling time [R]

researchr-machinelearning

Paper: Looped Diffusion Transformer (arXiv 2609.40305, 30 Sep). Instead of making a text-to-image DiT bigger, it runs a shared group of transformer blocks several times inside each denoising step. The

Matched-Context Group Policy Optimization for LLM Agent Training

arXiv:2610.13755v1 Announce Type: cross Abstract: Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group o

Agents

Meta-Reflective Evolution Effectively Improves Production AI Systems

arXiv:2610.12794v1 Announce Type: cross Abstract: Large Language Model (LLM)-guided evolutionary optimization has successfully improved prompts, programs, and even entire AI systems. Most optimization

agentsarxiv-cs-ai

Noise Space Learning for VLA Adaptation

arXiv:2610.13943v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot in

safetyarxiv-cs-ro

OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation

arXiv:2610.13826v1 Announce Type: cross Abstract: Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often

safetyarxiv-cs-cl

PDE-PFN: Prior-Data Fitted Neural PDE Solver

arXiv:2610.13334v1 Announce Type: new Abstract: Motivated by the success of large language models (LLMs) with broad generalizability and robustness to noisy or unreliable pre-training data, we explore