Search: “context”

Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models

arXiv:2604.08456v1 Announce Type: cross Abstract: Despite rapid progress, pretrained vision-language models still struggle when answers depend on tiny visual details or on combining clues spread acros

researcharxiv-cs-cl

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EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering

DGX agent

arXiv:2603.11703v2 Announce Type: replace Abstract: We introduce EvoFlows, a variable-length protein sequence-to-sequence modeling approach designed for protein engineering. Existing protein language

researcharxiv-cs-lg

10 Apr 2026

Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

arXiv:2603.02070v2 Announce Type: replace-cross Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facil

agentsarxiv-cs-cl

FBS: Modeling Native Parallel Reading inside a Transformer

arXiv:2601.21708v2 Announce Type: replace Abstract: Large language models (LLMs) excel across many tasks, yet inference is still dominated by strictly token-by-token autoregression. Existing accelerat

researcharxiv-cs-ai

Hardware

HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models

arXiv:2604.07812v1 Announce Type: new Abstract: In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making th

hardwarearxiv-cs-cv

Hospitals are becoming hackers’ favorite target, but downtime simply isn’t an option

Healthcare IT finds itself at a critical intersection where significant data opportunities are colliding with escalating cybersecurity threats. Rural and community hospitals, in particular, are facing

applicationssiliconangle

Initialisation Determines the Basin: Efficient Codebook Optimisation for Extreme LLM Quantization

model-releasesarxiv-cs-cl

arXiv:2604.08118v1 Announce Type: new Abstract: Additive quantization enables extreme LLM compression with O(1) lookup-table dequantization, making it attractive for edge deployment. Yet at 2-bit prec

k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS

arXiv:2604.03815v2 Announce Type: replace-cross Abstract: Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties i

Hardware

LiteParse hit 4K+ GitHub stars in 3 weeks. ~500 pages in 2 seconds. No GPU. No API keys. 50+ file formats. Now @LoganMarkewich, our Head of …

LiteParse hit 4K+ GitHub stars in 3 weeks. ~500 pages in 2 seconds. No GPU. No API keys. 50+ file formats. Now @LoganMarkewich, our Head of Open Source, will show you how to build with it. Live worksh

hardwarejerry-liu--x

Love this new podcast format ❤️ super insightful!

applicationsharrison-chase--x

Love this new podcast format ❤️ super insightful! 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeof

Low-Rank Key Value Attention

arXiv:2601.11471v3 Announce Type: replace Abstract: The key-value (KV) cache is a primary memory bottleneck in Transformers. We propose Low-Rank Key-Value (LRKV) attention, which reduces KV cache memo

researcharxiv-cs-lg

Max volume for Max Agency 📣🗣️

applicationsharrison-chase--x

Max volume for Max Agency 📣🗣️ 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeoffs, evals, and every

Safety

MemReader: From Passive to Active Extraction for Long-Term Agent Memory

arXiv:2604.07877v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction

safetyarxiv-cs-cl

MICA: Multivariate Infini Compressive Attention for Time Series Forecasting

arXiv:2604.06473v1 Announce Type: new Abstract: Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's q

researcharxiv-cs-lg

Mina: A Multilingual LLM-Powered Legal Assistant Agent for Bangladesh for Empowering Access to Justice

arXiv:2511.08605v3 Announce Type: replace Abstract: Bangladesh's low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs

agentsarxiv-cs-cl

Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

arXiv:2604.07085v1 Announce Type: new Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical

applicationsarxiv-cs-lg

MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2412.20718v2 Announce Type: replace Abstract: The rapid integration of Large Vision-Language Models (LVLMs) into critical domains necessitates comprehensive moral evaluation to ensure their alig

Local Ai

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization

arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz

local-aiarxiv-cs-ai

Modernizing Amdahl's Law: How AI Scaling Laws Shape Computer Architecture

arXiv:2603.20654v4 Announce Type: replace-cross Abstract: Classical Amdahl's Law conceptualized the limit of speedup for an era of fixed serial-parallel decomposition and homogeneous replication. Mode

researcharxiv-cs-ai

MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations

arXiv:2603.28253v2 Announce Type: replace-cross Abstract: Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modelin

applicationsarxiv-cs-ai

NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation

arXiv:2406.13086v2 Announce Type: replace Abstract: Lightweight autonomous unmanned aerial vehicles (UAV) are emerging as a central component of a broad range of applications. However, autonomous navi

agentsarxiv-cs-ro

ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI

arXiv:2602.16005v2 Announce Type: replace-cross Abstract: We introduce ODYN, a novel all-shifted primal-dual non-interior-point quadratic programming (QP) solver designed to efficiently handle challen

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale

arXiv:2604.06814v1 Announce Type: cross Abstract: While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged t

On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins

arXiv:2603.25898v2 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and senso

OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation

arXiv:2512.03532v2 Announce Type: replace Abstract: Generalizing open-vocabulary 3D instance segmentation (OV-3DIS) to diverse, unstructured, and mesh-free environments is crucial for robotics and AR/

researcharxiv-cs-cv

PANC: Prior-Aware Normalized Cut via Anchor-Augmented Token Graphs

arXiv:2602.06912v2 Announce Type: replace Abstract: Unsupervised segmentation from self-supervised ViT patches holds promise but lacks robustness: multi-object scenes confound saliency cues, and low-s

researcharxiv-cs-cv

PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

model-releasesarxiv-cs-cl

arXiv:2604.08000v1 Announce Type: cross Abstract: Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive age

Production-Ready Automated ECU Calibration using Residual Reinforcement Learning

arXiv:2604.07059v1 Announce Type: new Abstract: Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They ac

applicationsarxiv-cs-lg

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

model-releasesarxiv-cs-cl

arXiv:2604.04988v1 Announce Type: cross Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parame

Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning

arXiv:2508.07299v2 Announce Type: replace-cross Abstract: The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby

Raising the security baseline: Essential AI and cloud security now on by default

model-releasesgoogle-cloud-ai

The rapid evolution of AI is redefining industries, while also exposing organizations to new risks. At Google Cloud, we believe that modern cloud defense should have AI protection built in and accessi

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

arXiv:2604.07562v1 Announce Type: new Abstract: Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redund

applicationsarxiv-cs-cl

Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach

arXiv:2604.06663v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors, offering scalable 'silicon samples' that can approximat

Rethinking Data Mixing from the Perspective of Large Language Models

arXiv:2604.07963v1 Announce Type: new Abstract: Data mixing strategy is essential for large language model (LLM) training. Empirical evidence shows that inappropriate strategies can significantly redu

researcharxiv-cs-cl

Hardware

RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs

arXiv:2510.19225v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows i

hardwarearxiv-cs-lg

Safety

RoboAgent: Chaining Basic Capabilities for Embodied Task Planning

arXiv:2604.07774v1 Announce Type: cross Abstract: This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accompli

safetyarxiv-cs-cv

Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging

arXiv:2604.07575v1 Announce Type: new Abstract: Autonomous multi-agent target tracking in GPS-denied and communication-restricted environments (e.g., underwater exploration, subterranean search and re

agentsarxiv-cs-ro

Robustness Risk of Conversational Retrieval: Identifying and Mitigating Noise Sensitivity in Qwen3-Embedding Model

arXiv:2604.06176v1 Announce Type: cross Abstract: We present an empirical study of embedding-based retrieval under realistic conversational settings, where queries are short, dialogue-like, and weakly

SANDO: Safe Autonomous Trajectory Planning for Dynamic Unknown Environments

model-releasesarxiv-cs-ro

arXiv:2604.07599v1 Announce Type: new Abstract: SANDO is a safe trajectory planner for 3D dynamic unknown environments, where obstacle locations and motions are unknown a priori and a collision-free p

Local Ai

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction

arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro

local-aiarxiv-cs-cv

Tutorials

Search-R3: Unifying Reasoning and Embedding in Large Language Models

arXiv:2510.07048v2 Announce Type: replace Abstract: Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We p

tutorialsarxiv-cs-cl

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs

arXiv:2604.05650v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Specu

researcharxiv-cs-cl

SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio

arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, whil

applicationsarxiv-cs-ai

SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams

arXiv:2604.06204v1 Announce Type: cross Abstract: Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performa

agentsarxiv-cs-ai

Severity-Aware Weighted Loss for Arabic Medical Text Generation

arXiv:2604.06346v1 Announce Type: cross Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical ca

Local Ai

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

local-aiarxiv-cs-ai

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

arXiv:2604.07316v1 Announce Type: new Abstract: The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) of

researcharxiv-cs-lg

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Diagnostic Reasoning

arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM

applicationsarxiv-cs-cl