Search: “llm”

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

arXiv:2508.02773v4 Announce Type: replace-cross Abstract: The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper prese

agentsarxiv-cs-ai

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Weighted Ensemble Sampling for Rare-Event Estimation in Language Models

DGX agent

arXiv:2610.19202v1 Announce Type: new Abstract: Rare but severe failures of large language models can become practically significant at deployment scale. Estimating their probabilities with direct Mon

safetyarxiv-cs-cl

15 Oct 2026

What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds

model-releasesarxiv-cs-cl

arXiv:2610.20195v1 Announce Type: new Abstract: Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing e

When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction

model-releasesarxiv-cs-cl

arXiv:2610.20186v1 Announce Type: new Abstract: Skills are widely assumed to play a central role in career transitions, motivating their use in career path prediction (CPP) systems. In this work, we c

Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment

arXiv:2610.19035v1 Announce Type: cross Abstract: Pluralistic alignment needs to accommodate diverse preferences under limited human feedback. Using AI simulators to label preference comparisons reduc

safetyarxiv-cs-cl

Local Ai

Why don't Graph Transformers attend to the same token?

arXiv:2610.19324v1 Announce Type: new Abstract: In large language models (LLMs), attention concentrates heavily on the first token. This 'attention sink' is believed to protect deep models against ove

local-aiarxiv-cs-lg

Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis

arXiv:2610.17786v1 Announce Type: cross Abstract: We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarc

Ascent Conditions for Length-Normalized Single-Rollout Policy Updates

arXiv:2610.14670v1 Announce Type: new Abstract: Dividing each trajectory's score contribution by its realized length can change the direction of a reward-centered policy update. In a finite trajectory

// Behavioral Self-Rewarding for SWE agents // It's standard practice to train SWE agents on binary test rewards. This paper shows the probl…

// Behavioral Self-Rewarding for SWE agents // It's standard practice to train SWE agents on binary test rewards. This paper shows the problem with that. When every trajectory in a group passes, or ev

safetydair-ai--x

Beyond a Better Score: Long-Horizon Agentic ML Development and Evaluation Protocol for Physics Time Series

arXiv:2610.14024v1 Announce Type: new Abstract: Physics time-series data are central to fundamental discoveries involving dark matter, neutrinos, and gravitational waves. Machine learning has shown st

CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff

arXiv:2610.15509v1 Announce Type: new Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the

researcharxiv-cs-lg

Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models

researchapple-ml-research

Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding releva

Hardware

DIGS: Distribution-Informed Gaussian Splatting for Training-Free Open-Vocabulary 3D Segmentation

arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more

hardwarearxiv-cs-cv

Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages

arXiv:2610.14112v1 Announce Type: cross Abstract: Calibrated typed-decision ('System One') models return a label and a probability in a single forward pass at a small fraction of the cost of large lan

Applications

From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction

arXiv:2610.16569v1 Announce Type: cross Abstract: Relevance prediction between user queries and items is a fundamental component of e-commerce search systems. Traditionally, high-quality relevance lab

applicationsarxiv-cs-lg

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at

Integrating 3D Posture Neutralization with Statistical Shape Modeling for Head-Neck-Shoulder Ergonomic Design

arXiv:2610.17671v1 Announce Type: cross Abstract: The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder regi

researcharxiv-cs-cv

Interaction-as-a-Judge

arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th

Learning to Design SR Solvers: Harness Optimization for Symbolic Regression

arXiv:2610.16966v1 Announce Type: new Abstract: Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and ag

Tutorials

Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting

arXiv:2610.17399v1 Announce Type: new Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monito

tutorialsarxiv-cs-lg

LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation

arXiv:2610.15362v1 Announce Type: new Abstract: Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires agents to generalize visual and spatial reasonin

safetyarxiv-cs-ro

Local Ai

Linear-Time Non-Local Codec Placement for Machine Learning Model Compression

arXiv:2610.14703v1 Announce Type: new Abstract: On-device inference imposes constraints on model storage and operand traffic and thus benefits from compressed operand representations, which mandate th

local-aiarxiv-cs-lg

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

PTGPO: Prefix Tree Group Policy Optimization

arXiv:2610.14737v1 Announce Type: new Abstract: Training language agents on long-horizon interactive tasks is challenging because sparse terminal rewards provide weak credit signals for intermediate a

Revisiting Natural Language Prompts from Large Language Models for Open-Vocabulary Video Recognition

arXiv:2610.16666v1 Announce Type: new Abstract: Vision-language models (VLMs) like CLIP have shown great success in image tasks, however, applying CLIP to open-vocabulary video recognition poses chall

safetyarxiv-cs-cv

Reward-Aligned Distillation

arXiv:2610.14572v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and s

RoboHarness: A Simple Harness Outperforms VLA and World Actions Models

model-releasesarxiv-cs-ro

arXiv:2610.14018v1 Announce Type: new Abstract: The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world

RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions

arXiv:2610.17867v1 Announce Type: new Abstract: Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. Howev

Hardware

Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs

arXiv:2610.15965v1 Announce Type: new Abstract: We study pretraining when compute is abundant but unique training data are finite, requiring repeated training on the same data. We first revisit infini

hardwarearxiv-cs-lg

Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds

arXiv:2610.14860v1 Announce Type: new Abstract: Advances in large language models (LLMs) have brought the vision of a general-purpose personal assistant closer to reality. Such assistants could provid

Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles

arXiv:2503.18752v3 Announce Type: replace-cross Abstract: A robust control strategy for autonomous vehicles can improve system stability, enhance riding comfort, and prevent driving accidents. This pa

agentsarxiv-cs-cv

Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too

arXiv:2610.18516v1 Announce Type: new Abstract: Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and d

Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design

arXiv:2610.14260v1 Announce Type: new Abstract: Augmentation-based self-supervised learning can recover the content shared across views without separating its underlying factors into individual coordi

World2TS: Real-World Event Context for Time-Series Forecasting

arXiv:2610.14861v1 Announce Type: new Abstract: Time-series forecasting plays a vital role in a wide range of real-world applications. Recent approaches have achieved strong performance by modeling te

Applications

1M-token, multimodal decision models are here! But there is more to this one. You can classify, extract, and summarize with 1M tokens of tex…

1M-token, multimodal decision models are here! But there is more to this one. You can classify, extract, and summarize with 1M tokens of text and image input. ScaleDown's Constrained Decoding Models r

applicationsdair-ai--x

4x 6000 Pro for the company

model-releasesr-localllama

Here is how i serve 40 people (10 of them developers) with local AI services. n8n for automatisation whisper and Vexa AI, a bot joins Teams and makes transcripts, n8n later extracts decisions and todo

open-source desktop agent app running its agents on local ollama models: two agents on qwen3.5:9b and qwen3.8 on a 16 gb card, real recording, including the limits i hit

model-releasesr-localllama

i'm the maintainer of dodaios (formerly personal jarvis), an open-source ai agent app for the desktop. apache 2.0, runs on windows, macos and linux, no account and no cloud of ours between you and you

Scaling AI-driven chip design with open agent factories

IBM Research has presented a new approach that scales AI‑driven chip design by employing open “agent factories,” lowering inference cost by 11.5 × compared to fully proprietary setups. The method enab

agentsibm-research

A Better Metric Will Not Save You: What You Compare Beats How You Compare It

arXiv:2610.13476v1 Announce Type: new Abstract: Representational similarity measures (RSA, CKA, Procrustes) are chosen for the transformations they treat as irrelevant, and all of them take the set of

researcharxiv-cs-cl

Amortized Counterfactual Prediction with Graph Neural Networks

model-releasesarxiv-cs-ai

arXiv:2610.13724v1 Announce Type: cross Abstract: Counterfactual inference asks how an observed outcome might have differed under different circumstances, such as whether a patient would have recovere

APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces

model-releasesarxiv-cs-cl

arXiv:2610.13623v1 Announce Type: cross Abstract: Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and busine

AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation

arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl

safetyarxiv-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,

Tutorials

BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation

arXiv:2610.13285v1 Announce Type: new Abstract: Distilling long-chain reasoning trajectories from large teacher models provides an effective way to improve the reasoning ability of small language mode

tutorialsarxiv-cs-ai

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

Can Language Models Play the Wikipedia Game?

model-releasesarxiv-cs-ai

arXiv:2610.13948v1 Announce Type: new Abstract: Language models increasingly support or automate information retrieval from document collections too large to index. This task is difficult when it requ

Tutorials

COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation

arXiv:2602.03806v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models l

tutorialsarxiv-cs-ai

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

safetyarxiv-cs-ai