Search: “models”

Incident-Arena: Getting agents to the last nine of reliability

arXiv:2610.00648v1 Announce Type: new Abstract: AI coding agents are ubiquitous in engineering workflows amongst industry and academia. Yet, despite their use in app coding, relatively less attention

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Model Releases

Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning

DGX agent

arXiv:2610.00753v1 Announce Type: cross Abstract: End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers o

model-releasesarxiv-cs-ai

2 Oct 2026

Initialization Improves LLM-Driven Discovery

arXiv:2610.00707v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been used for novel discovery of algorithms, theorems, drugs, and other tasks through the use of harnesses that prom

researcharxiv-cs-ai

Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps

arXiv:2610.00391v1 Announce Type: cross Abstract: Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical

Invent a Dataset: Measuring dataset generation abilities with zero seed

arXiv:2610.01674v1 Announce Type: new Abstract: Building datasets remains one of the most manual and brittle parts of AI development. In this technical report, we focus on the most extreme but also mo

Local Ai

Local Support Learning

arXiv:2610.02126v1 Announce Type: cross Abstract: We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of

local-aiarxiv-cs-ai

MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees

arXiv:2610.01641v1 Announce Type: cross Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive

On the Escaping Efficiency of Distributed Adversarial Training Algorithms

arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper f

agentsarxiv-cs-lg

Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation

arXiv:2610.00348v1 Announce Type: cross Abstract: Backdoor attacks can be implanted in Large Language Models (LLMs) during training, causing unwanted behaviour when a trigger appears in the input. Exi

safetyarxiv-cs-lg

Retrospective Open-Vocabulary Memory for Long-Term Object Search

model-releasesarxiv-cs-cv

arXiv:2610.00330v1 Announce Type: cross Abstract: Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate r

Robust Evidential Learning Through Latent Consistency

arXiv:2610.01384v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial i

researcharxiv-cs-lg

SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization

arXiv:2610.00704v1 Announce Type: new Abstract: Natural-language skills are textual procedural memories through which large language model (LLM) agents retain reusable task knowledge without updating

agentsarxiv-cs-lg

Spatial Strategies, Not Actions: Vector-Quantized Geodesics as Tools for LLM-Driven Agents

arXiv:2610.00613v1 Announce Type: new Abstract: Large language model (LLM) based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We inves

Universal interpolation for deep residual self-attention networks

arXiv:2610.01981v1 Announce Type: new Abstract: Universal approximation is a necessary qualitative property of learning architectures to benefit from scaling laws. While it is generically verified on

VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks

arXiv:2610.00972v1 Announce Type: new Abstract: As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification ca

Why Does Train-Validation Separation Emerge? Update-Pressure Density Dynamics in Pretrained Backbones

arXiv:2610.01425v1 Announce Type: new Abstract: Train-validation separation is the evolving difference between performance on observed training examples and a finite held-out validation set. We propos

Auditing Agent Actions through Query-Conditioned Attribution

arXiv:2609.33676v2 Announce Type: replace Abstract: LLM agents increasingly take consequential actions through interactions with users, policies, and external tools. Auditing these agents requires aut

Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds

arXiv:2609.39166v1 Announce Type: new Abstract: Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved,

BIND: Binding 3D Robot Actions to 2D Image Features

arXiv:2609.38443v1 Announce Type: cross Abstract: We introduce BIND, a new action representation for visuomotor robot policies that binds 3D robot actions to their corresponding 2D image features, yie

safetyarxiv-cs-cv

Blackout vs. Freeze: Analyzing Physical Failure Modes of VLAs under Camera Faults

arXiv:2609.39145v1 Announce Type: cross Abstract: Unreliable visual inputs can harm task performance and cause potential physical safety risks for vision-language-action (VLA) models. We analyze how p

safetyarxiv-cs-ai

CompoWorld: Compositional Environment Scaling for General Agents

arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly

ConflictGuide: AutoResearch Improves When Competing Behaviors Are Made Visible

arXiv:2609.39933v1 Announce Type: new Abstract: When designing machine learning models, desirable properties are often in tension: improving one behavior can impair another, so task progress can depen

ContextAdapt: Evaluating Contextual Adaptation and Value Alignment in LLMs

arXiv:2609.38260v1 Announce Type: cross Abstract: Values such as honesty, autonomy, and confidentiality are often regarded as general principles underpinning AI alignment. However, what it means to ac

safetyarxiv-cs-ai

D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders

arXiv:2609.39625v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) reveal visual structure in diffusion transformers (DiTs), but interpreting a feature does not establish whether it can be u

DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency

model-releasesarxiv-cs-cv

arXiv:2609.39096v1 Announce Type: new Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compre

DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction

model-releasesarxiv-cs-ro

arXiv:2609.39198v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models excel in static tasks, they struggle in dynamic environments where objects are in motion (e.g., conveyor belt

Hardware

EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training

arXiv:2511.10333v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gr

hardwarearxiv-cs-ai

EviRover: Reinforcing Agentic Perception Beyond a Glance

arXiv:2609.40230v1 Announce Type: cross Abstract: Visual perception is conventionally formulated as a one-shot prediction from a single glance at the image, under the assumption that the image content

FOMO: Forget the Concept, Don't Miss Out on the Scene in Selective Video Unlearning

arXiv:2609.39605v1 Announce Type: new Abstract: The rapid advancement of generative video models has enabled the synthesis of increasingly realistic and temporally coherent videos, while also raising

researcharxiv-cs-cv

FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation

arXiv:2609.38839v1 Announce Type: new Abstract: Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining

safetyarxiv-cs-cv

Tutorials

From Search to Signal: Online Post-Training in Automatic Heuristic Design

arXiv:2609.39383v1 Announce Type: cross Abstract: Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics, pairing design rationales with executab

tutorialsarxiv-cs-ai

From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs

arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work

Applications

From Spectra to Joint Schedules in LLM Pre-training: 3+3(+2) Scaling-Law Regimes

arXiv:2609.40148v1 Announce Type: new Abstract: Power-law learning curves are often treated as fixed properties of a model and its data, although learning-rate and batch-size schedules can change the

applicationsarxiv-cs-lg

GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions

arXiv:2609.38285v1 Announce Type: cross Abstract: Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Addr

Applications

Ghost in the Encoder: Decodable Artist Identity Representations in Lyrics-to-Song Generation

arXiv:2609.39552v1 Announce Type: cross Abstract: Text-to-song generation models can be prompted to imitate specific artists or regurgitate entire songs from their training data. Although these phenom

applicationsarxiv-cs-lg

GraphForge: Training Working Agents with Graph-Anchored Workspace Synthesis

model-releasesarxiv-cs-cl

arXiv:2609.38923v1 Announce Type: new Abstract: Working agents need to read diverse files, coordinate tools, and produce deliverables. Training such agents requires tasks built on many real files with

Identifiable Decomposition of Submovements in Human Hand Trajectories

model-releasesarxiv-cs-ro

arXiv:2609.40012v1 Announce Type: new Abstract: Voluntary movements have long been hypothesised to be comprised of discrete primitives called submovements, as a descriptive model of human motor behavi

Inference Auctions

arXiv:2609.40070v1 Announce Type: cross Abstract: When inference demand exceeds available compute capacity, model providers must decide which requests should be served first. Users have different tole

Learning Reliable GUI Agents under Imperfect Priors

arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GUI

Local Ai

Learning to Plan from Random Exploration

arXiv:2609.38383v1 Announce Type: new Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without polic

local-aiarxiv-cs-lg

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm, b

Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning

arXiv:2609.40286v1 Announce Type: cross Abstract: Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seem

MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams

arXiv:2609.40043v1 Announce Type: cross Abstract: Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by in

researcharxiv-cs-cv

MegaAvatar: Controllable Talking Avatar Generation

arXiv:2609.39273v1 Announce Type: new Abstract: This report presents extbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with previ

researcharxiv-cs-cv

Hardware

MEMO: Multi-Level Entity-Aware Memory for Streaming Video Understanding

arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,

hardwarearxiv-cs-cv

MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution

arXiv:2609.38349v1 Announce Type: cross Abstract: Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long

Multi-agent discussion gains less when dissent is withheld

model-releasesarxiv-cs-cl

arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on wh

OpenJev-RLCD: A Working RLCD Implementation

arXiv:2609.38850v1 Announce Type: new Abstract: Decision models such as Jev answer questions with probabilities, which are only useful if they are calibrated. Open-source reproductions rely on supervi

safetyarxiv-cs-ai