Search: “models”

arXiv:2607.13006v1 Announce Type: new Abstract: A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different

researcharxiv-cs-lg

15 Jul 2026

TRACE: An Operational Reasoning Schema for Auditable Agentic Commitments

DGX agent

arXiv:2607.12480v1 Announce Type: new Abstract: This paper defines TRACE (Typed Reasoning And Commitment Evidence): a typed, versioned schema for recording reasoning traces, a reference procedure for

model-releasesarxiv-cs-ai

15 Jul 2026

75.4% SWE Bench Verified / 53.9% SWE Bench Pro on 1 bit quantisation is 🤪 This is in line with my expectations & you can expect even lower …

model-releasesemad-mostaque--x

75.4% SWE Bench Verified / 53.9% SWE Bench Pro on 1 bit quantisation is 🤪 This is in line with my expectations & you can expect even lower drop off with NVP4 base trained models - why not run everythi

14 Jul 2026

Local Ai

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

arXiv:2607.08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while p

local-aiarxiv-cs-lg

Dive Into the Implicit Biases of Low-rank Vision-language Alignment

arXiv:2607.08194v1 Announce Type: new Abstract: Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requi

Applications

From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs

arXiv:2607.08009v1 Announce Type: new Abstract: We introduce a Bloom-aligned framework for measuring educational control in Large Language Models (LLMs): the ability to preserve a task's instructional

applicationsarxiv-cs-cl

Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos

arXiv:2512.01803v3 Announce Type: replace Abstract: Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elu

Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling

arXiv:2607.07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluatio

RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments

arXiv:2603.16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in d

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle

arXiv:2603.17433v2 Announce Type: replace-cross Abstract: Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-co

Agents

When Does Continual Learning Require Learning

arXiv:2607.07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largel

agentsarxiv-cs-lg

Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA

arXiv:2607.08054v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs

Autopilot Clusters with GKE managed DRANET: GPUs and TPUs

model-releasesgoogle-cloud-ai

Google Kubernetes Engine (GKE) managed DRANET supports both GPUs and TPUs. There are several configurations to use this implementation, including standard cluster (where you have full control) and aut

Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1

arXiv:2607.06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary

Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks

arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

arXiv:2607.07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the l

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

arXiv:2603.26772v2 Announce Type: replace-cross Abstract: Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, d

From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision

arXiv:2501.05711v4 Announce Type: replace Abstract: Vision Language Models (VLMs) have achieved strong performance across a wide range of video understanding tasks. However, their viewpoint-invariant

GIFT: Geometry-Informed Low-precision Gradient Communication for LLM Pretraining

arXiv:2607.07494v1 Announce Type: cross Abstract: Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision formats, su

MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations

arXiv:2607.06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual ju

POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking

arXiv:2607.06649v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and

Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations

arXiv:2607.07229v1 Announce Type: new Abstract: Prior work has shown that chain-of-thought (CoT) reasoning is often unfaithful: a model's stated reasoning does not reliably reflect the process that pr

Retrieving and Refining Winning Noise Tickets for Diffusion-Based Motion Generation

arXiv:2607.06843v1 Announce Type: new Abstract: Diffusion-based text-to-motion models synthesize realistic human motions but often exhibit semantic drift from the input text. Motion is inherently temp

ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation

arXiv:2607.05750v1 Announce Type: new Abstract: Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geomet

Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving

arXiv:2607.05399v1 Announce Type: cross Abstract: Large language model serving is increasingly limited by KV-cache growth under long-context workloads, yet existing KV-cache compression techniques are

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

arXiv:2607.05452v1 Announce Type: new Abstract: Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, st

Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning

arXiv:2607.06354v1 Announce Type: new Abstract: The rapid advancement of large-scale generative models has accelerated the spread of highly deceptive AI-generated images, making generalized synthetic

Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test

arXiv:2607.06001v1 Announce Type: new Abstract: We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.8), testing two quantitative predict

LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation

arXiv:2607.05704v1 Announce Type: cross Abstract: Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a

Multi-Channel Spread-Spectrum Code Watermarking

arXiv:2607.06009v1 Announce Type: cross Abstract: Attributing code to the large language model that produced it is essential for provenance, licensing, and misuse accountability, yet no deployed water

PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages

arXiv:2607.05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heav

Quantifying Frontier LLM Capabilities for Container Sandbox Escape

arXiv:2603.02277v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, cr

RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications

arXiv:2607.06411v1 Announce Type: cross Abstract: Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of

Self-Routing: Parameter-Free Expert Routing from Hidden States

arXiv:2604.00421v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a learned rout

The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

arXiv:2607.05450v1 Announce Type: cross Abstract: This paper explores the 'Granularity Paradox' in time-series forecasting, wherein finer temporal disaggregation (e.g., Monthly to Weekly/Daily) improv

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

arXiv:2607.04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Dete

ChatImage: Navigating Long-Form LLM Answers through Interactive Images

arXiv:2607.05290v1 Announce Type: new Abstract: Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which make

Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning

arXiv:2607.03574v1 Announce Type: cross Abstract: AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous paramete

DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment

arXiv:2408.13378v5 Announce Type: replace Abstract: Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observat

ELiTeFormer: An Efficient Transformer for FPGAs

arXiv:2607.03652v1 Announce Type: cross Abstract: Transformer blocks are prevalent in large language model (LLM) but present deployment challenges due to their challenging computational and memory dem

Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems

arXiv:2607.03283v1 Announce Type: new Abstract: Embodied intelligence systems require not only end-to-end policy models, but also reusable functional modules that transform multimodal observations, ro

Research

Fidelity-Diversity Metrics for Text

arXiv:2607.04563v1 Announce Type: new Abstract: As language modeling technology matures, there is an increasing research focus on the composition and curation of datasets used to train these models. F

researcharxiv-cs-cl

From General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition

arXiv:2607.03327v1 Announce Type: new Abstract: Skeleton-based action recognition models have recently shown strong performance on large-scale benchmarks with general actions. However, directly transf

From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

arXiv:2607.02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications su

Applications

GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation

arXiv:2511.23191v2 Announce Type: replace Abstract: Previous works that leverage video models for image-to-3D scene generation often suffer from geometric distortions and blurry content. Using video g

applicationsarxiv-cs-cv

GRAFT: Grafted Reference Audio for Fine-grained Pronunciation in Zero-shot Text-to-Speech

model-releasesarxiv-cs-cl

arXiv:2607.02633v1 Announce Type: cross Abstract: We present GRAFT, a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling. Existing systems reach high intel

Hierarchical Bayesian Crowdsourcing with Item Difficulty

arXiv:2405.19521v3 Announce Type: replace Abstract: In applied statistics and machine learning, the gold standards used for training are often biased and almost always noisy. Dawid and Skene's justifi

IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation

arXiv:2511.17384v2 Announce Type: replace-cross Abstract: While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reas