Search: “models”

EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents

model-releasesarxiv-cs-ro

arXiv:2604.18271v1 Announce Type: new Abstract: As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observa

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ENTIRE: Learning-based Volume Rendering Time Prediction

DGX agent

arXiv:2501.12119v3 Announce Type: replace-cross Abstract: We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is

model-releasesarxiv-cs-cv

21 Apr 2026

FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition

arXiv:2604.16649v1 Announce Type: new Abstract: Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufact

Local Ai

Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

arXiv:2601.06803v2 Announce Type: replace Abstract: While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandw

local-aiarxiv-cs-cl

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?

arXiv:2604.17968v1 Announce Type: cross Abstract: Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a f

researcharxiv-cs-cl

From User Recognition to Activity Counting: An Identity-Agnostic Approach to Multi-User WiFi Sensing

arXiv:2604.16572v1 Announce Type: new Abstract: Wi-Fi Channel State Information (CSI) enables device-free human activity recognition, but existing multi-user approaches assume a fixed set of known use

Agents

GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)

arXiv:2604.17091v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memorie

agentsarxiv-cs-cl

Having a 20-agent system is a million times more powerful than 100 agents working in silos. Last night, I stayed up way too late fixing and …

model-releasesallie-k--miller--x

Having a 20-agent system is a million times more powerful than 100 agents working in silos. Last night, I stayed up way too late fixing and improving my digital workforce for my AI Agent Mastermind pr

HyKey: Hyperspectral Keypoint Detection and Matching in Minimally Invasive Surgery

arXiv:2604.17446v1 Announce Type: new Abstract: Purpose: 3D reconstruction in minimally invasive surgery (MIS) enables enhanced surgical guidance through improved visualisation, tool tracking, and aug

researcharxiv-cs-cv

I agree, and I will add to this. Companies should own their memories, skills and other resources agents need. Use Claude code, codex, copilo…

model-releasesharrison-chase--x

I agree, and I will add to this. Companies should own their memories, skills and other resources agents need. Use Claude code, codex, copilot, etc for intelligence but your execution layer should plug

InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

arXiv:2509.10813v3 Announce Type: replace Abstract: The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts.

K2.6 + hermes = 4 hr session setting up qwen 3.6 training regime on dgx spark with the current autnomous session currently lasting 70+ min w…

model-releasesnous-research--x

K2.6 + hermes = 4 hr session setting up qwen 3.6 training regime on dgx spark with the current autnomous session currently lasting 70+ min without any prompting. Kimi with hermes is next level. @NousR

Local Ai

Kimi K2.6 demonstrates strong long-horizon coding in complex engineering tasks: Kimi K2.6 successfully downloaded and deployed the Qwen3.5-0…

Kimi K2.6 demonstrates strong long-horizon coding in complex engineering tasks: Kimi K2.6 successfully downloaded and deployed the Qwen3.5-0.8B model locally on a Mac. By implementing and optimizing m

local-aikimi-moonshot--x

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning

arXiv:2506.00772v2 Announce Type: replace-cross Abstract: Recent studies have shown that supervised fine-tuning of LLMs on a small number of high-quality datasets can yield strong reasoning capabiliti

LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning

arXiv:2506.03178v2 Announce Type: replace-cross Abstract: Automated radiology report generation holds significant potential to reduce radiologists' workload and enhance diagnostic accuracy. However, g

LLMs are still not consistent judges of qualitative work, and small changes to how that work is presented affect outcomes. Better harnessing…

model-releasesethan-mollick--x

LLMs are still not consistent judges of qualitative work, and small changes to how that work is presented affect outcomes. Better harnessing and methods (multiple judging runs with randomized orders,

LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning

arXiv:2601.16504v3 Announce Type: replace Abstract: Commonsense reasoning often involves evaluating multiple plausible interpretations rather than selecting a single atomic answer, yet most benchmarks

LVLMs and Humans Ground Differently in Referential Communication

arXiv:2601.19792v3 Announce Type: replace Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to co

researcharxiv-cs-cl

Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling

arXiv:2602.10732v2 Announce Type: replace Abstract: Multilingual benchmarks rarely test reasoning over culturally grounded premises: translated datasets keep English-centric scenarios, while culture-f

MARCO: Navigating the Unseen Space of Semantic Correspondence

arXiv:2604.18267v1 Announce Type: new Abstract: Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-

Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition

arXiv:2604.17090v1 Announce Type: new Abstract: Human action recognition and motion generation are two active research problems in human-centric computer vision, both aiming to align motion with textu

Applications

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance

arXiv:2604.17650v1 Announce Type: new Abstract: LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, p

applicationsarxiv-cs-cl

Medical Image Understanding Improves Survival Prediction via Visual Instruction Tuning

arXiv:2604.18250v1 Announce Type: new Abstract: Accurate prognostication and risk estimation are essential for guiding clinical decision-making and optimizing patient management. While radiologist-ass

Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation

arXiv:2604.18215v1 Announce Type: new Abstract: Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing meth

tutorialsarxiv-cs-cv

MeSH: Memory-as-State-Highways for Recursive Transformers

arXiv:2510.07739v2 Announce Type: replace Abstract: Recursive transformers reuse parameters and iterate over hidden states multiple times, decoupling compute depth from parameter depth. However, under

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization

arXiv:2602.11182v2 Announce Type: replace Abstract: Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions bey

tutorialsarxiv-cs-cl

Multilevel neural networks with dual-stage feature fusion for human activity recognition

arXiv:2604.16577v1 Announce Type: new Abstract: Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, s

Multimodal Fusion of Histopathology Images and Electronic Health Records for Early Breast Cancer Diagnosis

arXiv:2604.17122v1 Announce Type: new Abstract: Breast cancer is a leading cause of cancer-related mortality worldwide, and timely accurate diagnosis is critical to improving survival outcomes. While

researcharxiv-cs-cv

Navigating the Conceptual Multiverse

arXiv:2604.17815v1 Announce Type: cross Abstract: When language models answer open-ended problems, they implicitly make hidden decisions that shape their outputs, leaving users with uncontextualized a

safetyarxiv-cs-cl

Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets

arXiv:2511.03855v2 Announce Type: replace Abstract: Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 dete

tutorialsarxiv-cs-cv

NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report

arXiv:2604.17070v1 Announce Type: new Abstract: This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in i

On Safety Risks in Experience-Driven Self-Evolving Agents

arXiv:2604.16968v1 Announce Type: new Abstract: Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self

safetyarxiv-cs-cl

Open-TQ-Metal: Fused Compressed-Domain Attention for Long-Context LLM Inference on Apple Silicon

arXiv:2604.16957v1 Announce Type: new Abstract: We present Open-TQ-Metal, the first implementation of fused compressed-domain attention on Apple Silicon, enabling 128K-context inference for Llama 3.1

Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

arXiv:2604.18319v1 Announce Type: cross Abstract: Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to

Policy Testing in Markov Decision Processes

arXiv:2505.15342v2 Announce Type: replace-cross Abstract: We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model wi

safetyarxiv-cs-lg

Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs

arXiv:2604.17837v1 Announce Type: cross Abstract: An LLM's residual stream is both state and instruction: it encodes the current context and determines the next transformation. We introduce a paramete

Hardware

Probabilistic Programs of Thought

arXiv:2604.17290v1 Announce Type: new Abstract: LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to rea

hardwarearxiv-cs-cl

Projected Coupled Diffusion for Test-Time Constrained Joint Generation

arXiv:2508.10531v3 Announce Type: replace Abstract: Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process

Prompt Sensitivity in Vision-Language Grounding: How Small Changes in Wording Affect Object Detection

arXiv:2604.17126v1 Announce Type: new Abstract: Vision-language models enable open-vocabulary object grounding through natural language queries, under the implicit assumption that semantically equival

researcharxiv-cs-cv

Hardware

RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention

arXiv:2512.24086v2 Announce Type: replace Abstract: In video and image generation tasks, Diffusion Transformer (DiT) models incur extremely high computational costs due to attention mechanisms, which

hardwarearxiv-cs-cv

Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario

arXiv:2604.18450v1 Announce Type: cross Abstract: Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before over

Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning

arXiv:2604.16591v1 Announce Type: new Abstract: Large language models (LLMs) sometimes memorize undesirable knowledge, which must be removed after deployment. Prior work on machine unlearning has focu

Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning

arXiv:2601.02970v2 Announce Type: replace Abstract: Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency m

Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning

arXiv:2604.16925v1 Announce Type: new Abstract: Cross-dose denoising for low-dose positron emission tomography (LDPET) has been proposed to address the limited generalization of models trained at a si

tutorialsarxiv-cs-cv

Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF

arXiv:2604.17769v1 Announce Type: new Abstract: Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains under-e

safetyarxiv-cs-cl

Revisiting Auxiliary Losses for Conditional Depth Routing: An Empirical Study

arXiv:2604.17228v1 Announce Type: new Abstract: Conditional depth execution routes a subset of tokens through a lightweight cheap FFN while the remainder execute the standard full FFN at each controll

R&F-Inventory: A Large-Scale Dataset for Monotonic Inventory Estimation in Reach and Frequency Advertising

arXiv:2604.16821v1 Announce Type: new Abstract: Reach and Frequency (R&F) contract advertising is an important form of widely used brand advertising. Unlike performance advertising, R&F contracts emph

Hardware

Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs

arXiv:2604.16715v1 Announce Type: cross Abstract: Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However,

hardwarearxiv-cs-lg