Search: “context”

Open-world AI: a Tale of Three Requirements

arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examp

OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents

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arXiv:2610.21496v1 Announce Type: new Abstract: Reliable repository-level coding requires agents not only to generate correct code but also to preserve constraints and respond to tool feedback across

model-releasesarxiv-cs-cl

16 Oct 2026

Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation

arXiv:2610.22363v1 Announce Type: new Abstract: We introduce P^{3}-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and

researcharxiv-cs-cv

Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation

arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone

researcharxiv-cs-cv

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data

arXiv:2510.08667v3 Announce Type: replace-cross Abstract: Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA t

RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases

model-releasesarxiv-cs-lg

arXiv:2610.21382v1 Announce Type: new Abstract: Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural

Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLMs

arXiv:2607.18915v2 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge

safetyarxiv-cs-cl

[Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)

model-releasesr-localllama

Hey r/LocalLLaMA! We're excited to share ORL (On-Policy Residual Learning), our new research on making diffusion language models (dLLMs) significantly faster without sacrificing generation quality. Wi

Robotic-supported Data Loss Detection in Android Applications

arXiv:2610.20807v1 Announce Type: cross Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces si

researcharxiv-cs-ro

Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty

arXiv:2610.22336v1 Announce Type: new Abstract: We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable u

RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention

model-releasesarxiv-cs-lg

arXiv:2610.20686v1 Announce Type: new Abstract: Multi-head latent attention (MLA) compresses the content of a key-value cache into a shared latent per token, making low-bit storage attractive for long

SCMerge: Spatial-Consistency-Aware Token Pair Merging for Efficient Semantic Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.21642v1 Announce Type: new Abstract: Vision Transformer architectures have showcased impressive performance on semantic segmentation, yet their quadratic attention complexity hinders practi

Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera

arXiv:2606.14535v2 Announce Type: replace Abstract: Recent visual imitation learning systems have widely adopted multi-camera setups with wrist-mounted cameras as the de facto standard. However, manip

safetyarxiv-cs-ro

Speculative Forcing for Efficient Autoregressive Video Generation

arXiv:2610.22347v1 Announce Type: new Abstract: Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much

researcharxiv-cs-cv

The actual state of open-weight models vs. frontier models

model-releasesr-localllama

It's clear that today’s SOTA (state-of-the-art) models remain ahead of open-source models. However, if we filter the benchmarks correctly, a different picture emerges compared to what we saw just a fe

The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction

arXiv:2610.22071v1 Announce Type: new Abstract: Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on enc

researcharxiv-cs-cl

The SAVE method: Learn the fundamentals to engineer trustworthy generative media

model-releasesgoogle-cloud-ai

Google Cloud Consulting works with organizations every day to build generative media that’s trustworthy, brand-aligned, and stands out. Instead of relying on clever or complicated prompting, we’ve ide

TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory

model-releasesarxiv-cs-cl

arXiv:2610.20682v1 Announce Type: cross Abstract: We introduce TurboMamba, a language-model architecture for faster inference, faster training, and better token prediction. A global Mamba-3 model runs

Unified Perceptive Fall Safety for Humanoids

arXiv:2602.16511v3 Announce Type: replace Abstract: Reliable fall recovery is critical for humanoids operating in cluttered environments. Unlike quadrupeds or wheeled robots, humanoids undergo high-en

safetyarxiv-cs-ro

VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions

arXiv:2610.21990v1 Announce Type: new Abstract: Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in lo

agentsarxiv-cs-cv

VisualTrust: Video Reasoning under On-Screen Claim Framing

model-releasesarxiv-cs-cv

arXiv:2610.21159v1 Announce Type: new Abstract: On-screen text can describe what a video shows, or it can frame what the video is supposed to mean. Current benchmarks treat both alike, testing whether

VP-EVT: Active Visibility Preservation for Embodied Visual Tracking

arXiv:2610.21999v1 Announce Type: new Abstract: Embodied Visual Tracking (EVT) is an essential capability for intelligent robots in human-robot collaboration, requiring an agent to follow a designated

agentsarxiv-cs-ro

We’re excited to be day 0 partners for @reflection_ai’s first open-weight model! For a limited time, Beam is FREE on Ollama! In partnership …

We’re excited to be day 0 partners for @reflection_ai’s first open-weight model! For a limited time, Beam is FREE on Ollama! In partnership with @reflection_ai this model is hosted in the US with full

model-releasesollama--x

Where, How, and When: Query-Guided Grounding for Video Understanding

arXiv:2610.21422v1 Announce Type: cross Abstract: Understanding video content requires reasoning at multiple levels of granularity, from identifying objects to interpreting their interactions and loca

safetyarxiv-cs-ai

A Counterfactual Graph Approach to Oppositional Narrative Analysis

arXiv:2603.06135v3 Announce Type: replace-cross Abstract: Distinguishing legitimate critical speech from coordinated conspiracy narratives is a pressing challenge for the integrity of the online infor

Local Ai

A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining

arXiv:2610.17702v1 Announce Type: new Abstract: Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on confo

local-aiarxiv-cs-ai

A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion

model-releasesarxiv-cs-cl

arXiv:2610.19999v1 Announce Type: new Abstract: Legal-content reproduction requires matching the source exactly in section hierarchy, verbatim text, citations, and amendment spans; a dropped citation

Adaptive-Group Process Rewards for GUI Agents

arXiv:2610.17710v1 Announce Type: new Abstract: Training graphical user interface (GUI) agents with reinforcement learning suffers from sparse outcome rewards, leading to inefficient learning. Process

agentsarxiv-cs-ai

AFCS: Atomic-Fact-Conditioned Synthesis for Factual Knowledge Pretraining

model-releasesarxiv-cs-cl

arXiv:2610.20086v1 Announce Type: new Abstract: Synthetic data is increasingly used in language-model pretraining. Existing knowledge-corpus synthesis pipelines, including WRAP and Nemotron-CC, usuall

Agent-Canary-Mas: End-to-End Evaluation of Protocol Risk in Long-Horizon Multi-Agent Systems

arXiv:2610.15529v2 Announce Type: new Abstract: Agent-to-agent protocols allow LLM agents to discover peers, exchange results, and coordinate tool use. A locally successful protocol attack, however, m

agentsarxiv-cs-ai

AI-Driven, Timing-Aware Power Optimization

arXiv:2610.18890v1 Announce Type: cross Abstract: Post-route power recovery changes cell choices under tightly coupled timing, electrical and physical constraints, and the value of each change depends

AI-Native Product Management: A Vision

arXiv:2610.14823v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to support product management tasks. At the same time, AI-native research and development (R&D) may incre

AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs

arXiv:2610.16042v1 Announce Type: cross Abstract: The increasing use of artificial intelligence (AI) in mental health applications raises concerns about its potential to internalize and perpetuate soc

Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria

arXiv:2511.17937v2 Announce Type: replace Abstract: Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in

Asymptotic Behavior of Transformers

arXiv:2610.19358v1 Announce Type: new Abstract: In this paper we study the behavior of transformer networks generating text over long periods of time. We show that even a three-token, single-layer att

researcharxiv-cs-lg

Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models

arXiv:2610.20157v1 Announce Type: new Abstract: World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level ins

researcharxiv-cs-ro

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

b11630

model-releasesllama-cpp-releases

ggml: fix conv_1d result layout for batched input (#28738) ggml: fix conv_1d result layout for batched input The GEMM behind conv_1d folds the batch into its rows, so the product sits in memory as [OL

b11655

model-releasesllama-cpp-releases

vulkan : fix concurrent first device init (#29766) vulkan : fix concurrent first device init tests : load models in parallel in test-thread-safety Loading them serially never exercised concurrent firs

Bayesian Learning to Select Fair Classifiers

arXiv:2610.20514v1 Announce Type: new Abstract: Evaluations of classifiers (AI or human or agents) often assume a batch setting in which every agent produces labels on every example in a test set, whi

Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints

arXiv:2610.17027v1 Announce Type: new Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (

Industry

Bloomberg is making its ASKB conversational AI interface available to all Terminal users, letting them integrate their own content with existing financial data (Steven Rosenbush/Wall Street Journal)

Steven Rosenbush / Wall Street Journal: Bloomberg is making its ASKB conversational AI interface available to all Terminal users, letting them integrate their own content with existing financial data

industrytechmeme

Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents

arXiv:2610.18052v1 Announce Type: new Abstract: Long-term memory has become a core component of large language model (LLM) agents. As agents accumulate personal information over long-term use, users i

ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark

model-releasesarxiv-cs-lg

arXiv:2610.20012v1 Announce Type: new Abstract: Chemistry evaluation has moved past multiple-choice probes toward open-ended expert problems, yet the benchmarks that define the current frontier still

Claim Framing as a Driver of LLM Sycophancy

arXiv:2610.18159v1 Announce Type: cross Abstract: Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions

Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation

arXiv:2610.19141v1 Announce Type: new Abstract: Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodimen

safetyarxiv-cs-ro

CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting

arXiv:2610.19831v1 Announce Type: new Abstract: Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevai

CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation

arXiv:2610.14763v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must

safetyarxiv-cs-ai