Search: “inference”

Paritok-4B: Intent-Conditioned Context Compression for Coding Agents

arXiv:2608.24188v1 Announce Type: new Abstract: Coding agents re-send large file reads and tool outputs to a frontier LLM every turn, and this context dominates their token bill. General-purpose promp

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PhysMLLMs: Spatial Priors for Unified Referring Segmentation and Grounded Reasoning of Images and Videos

DGX agent

arXiv:2608.24574v1 Announce Type: new Abstract: Video multimodal large language models support language guided video segmentation, but they often show spatio temporal inconsistencies, e.g., jitter, dr

safetyarxiv-cs-ai

26 Aug 2026

qshap: Fast Shapley Decomposition of R^2 for Gradient-Boosted Trees

arXiv:2608.24104v1 Announce Type: cross Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications require

local-aiarxiv-cs-lg

Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection

arXiv:2608.24154v1 Announce Type: cross Abstract: Real-world deployment of traffic surveillance systems is bottlenecked by geographic domain shift, in which models trained in one city underperform whe

hardwarearxiv-cs-ai

Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping

arXiv:2608.24135v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a pivotal technique for enhancing the code generation capabilities of Large Languag

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

model-releasesarxiv-cs-lg

arXiv:2605.22330v1 Announce Type: cross Abstract: Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide gui

Applications

The hottest neolabs are the application layer, who serve as an intermediary layer providing their customers the best intelligence per token …

applicationssonya-huang--x

The hottest neolabs are the application layer, who serve as an intermediary layer providing their customers the best intelligence per token per task/workflow across different underlying model provider

TRACE: An Evidence-Grounded Benchmark for Safety Evaluation of Large Reasoning Models

arXiv:2608.24232v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) generate intermediate reasoning traces that may contain unsafe content, even when their final responses appear safe. Guard

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs

arXiv:2506.20073v2 Announce Type: replace-cross Abstract: Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restri

A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extraction

arXiv:2608.23235v1 Announce Type: new Abstract: Event extraction aims to identify event triggers, classify event types, and extract arguments to construct structured event representations. Despite str

AgentWeave: Routing Before Reasoning for Efficient Function Calling in Tool-Rich Language Models

arXiv:2608.23078v1 Announce Type: new Abstract: Large language models increasingly operate over large collections of tools, functions, APIs, and specialized agents. As the candidate action space grows

local-aiarxiv-cs-ai

Can Large Language Models 'Hyper-Thread'?

arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat

Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT

arXiv:2512.15206v3 Announce Type: replace Abstract: A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes

local-aiarxiv-cs-lg

Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data

arXiv:2608.23391v1 Announce Type: cross Abstract: Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation

EDGE: Experience-Distillation for Guided Exploration in Agentic Reinforcement Learning

arXiv:2608.21946v1 Announce Type: cross Abstract: Reinforcement learning with outcome-based objectives such as GRPO enables LLM-based agents to solve complex, long-horizon tasks, yet the reusable expl

safetyarxiv-cs-ai

Flesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Models

arXiv:2608.23327v1 Announce Type: new Abstract: Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL) are widely used readability scores for English computed from the same two document s

From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learning

arXiv:2508.05078v2 Announce Type: replace-cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting Large Language Models (LLMs) to multi-task scenarios. A prevailing trend in t

How Merge-Tolerant Are Vision Transformers for Wheat Phenotyping?

arXiv:2608.23142v1 Announce Type: new Abstract: Vision-based wheat phenotyping requires repeated measurements under deployment constraints, from growth-stage recognition to wheat-head counting and org

Learning with Boolean threshold functions

arXiv:2602.17493v2 Announce Type: replace-cross Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly pm 1, and the resulting models

LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices

arXiv:2608.21764v1 Announce Type: cross Abstract: Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals that

hardwarearxiv-cs-ai

M5 Ultra 96GB vs M5 Max 128GB — is 2x bandwidth worth losing 32GB of RAM, with Qwen3.8-Flash-Next dropping tomorrow?

model-releasesr-localllama

I’ve been going back and forth on this for a week and I can’t settle it, so I’m hoping someone here has hands-on numbers. The two configs (German prices, dealer quote, incl. VAT): Config Price Mac Stu

More Accurate or More Efficient? Evaluating Locally Deployed Compact Open-Weight Language Models for Mathematical Reasoning

arXiv:2608.22048v1 Announce Type: new Abstract: Large language models are increasingly deployed on local hardware for privacy, cost, and accessibility reasons. Yet many evaluations emphasize accuracy

local-aiarxiv-cs-ai

Agents

MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents

arXiv:2608.21810v1 Announce Type: cross Abstract: Clinical decision-making is inherently experience-driven: physicians progressively refine their reasoning by synthesizing patient history, multimodal

agentsarxiv-cs-cv

Nvidia doubles compute for entry-level edge robotics with Jetson Orin Nano 2

Nvidia Corp. today announced the release of Jetson Orin Nano 2, a robotics computer “brain” for running artificial intelligence and frontier-level models at the edge. In the past months, foundational

hardwaresiliconangle

OpenAI says its Jalapeño chip delivered 1.5x-1.9x more AI work per watt and 1.7x-3.6x lower latency vs. Nvidia chips across GPT-OSS, DeepSeek R1, Kimi K2.5 1T (Emma Roth/The Verge)

Emma Roth / The Verge: OpenAI says its Jalapeño chip delivered 1.5x-1.9x more AI work per watt and 1.7x-3.6x lower latency vs. Nvidia chips across GPT-OSS, DeepSeek R1, Kimi K2.5 1T — Jalapeño outperf

model-releasestechmeme

Perturb the Thought, Not the Pixels: Latent-Space Rollout Diversification for Reinforcement Learning of Vision-Language Models

arXiv:2608.21595v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning ability of vision-language models (VLMs), and diversifying the rollouts wit

safetyarxiv-cs-cv

ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning

arXiv:2608.22786v1 Announce Type: new Abstract: Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these

researcharxiv-cs-lg

Applications

ReWorld: An Interactive World Model with Long-Horizon Memory

arXiv:2608.23565v1 Announce Type: new Abstract: An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: control

applicationsarxiv-cs-ai

RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification

arXiv:2608.21414v1 Announce Type: cross Abstract: Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approa

local-aiarxiv-cs-lg

SAM3Dual: A 3rd Place Solution to the MOSEv2 Track, 8th LSVOS Challenge

arXiv:2608.22193v1 Announce Type: new Abstract: We present SAM3Dual, our third-place solution to the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. SAM3D

SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion

arXiv:2510.00279v3 Announce Type: replace-cross Abstract: Logical rule-based methods offer an interpretable approach to knowledge graph completion (KGC) by capturing compositional relationships in the

StocBench: A Benchmark for Generative Modeling of Stochastic Dynamics

model-releasesarxiv-cs-lg

arXiv:2608.22309v1 Announce Type: new Abstract: We benchmark transport-based generative models as well as distillation-based few-step methods for the probabilistic forecasting of stochastic fluid flow

Industry

'Two labs will soon control most of the world’s compute'

'Two labs will soon control most of the world’s compute' Had a lot of fun chatting again with my twin brother @dylan522p We went through lab economics over the next few years - the shift from inferenc

industryclem-delangue--x

Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision

arXiv:2608.23221v1 Announce Type: cross Abstract: Historical retrieval for time-series prediction commonly treats past similarity as a proxy for usefulness. We ask a different question: which historic

researcharxiv-cs-lg

Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron

arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inve

safetyarxiv-cs-ai

A VLM Answer Is Not an Anomaly Score: Rank Compression in Training-Free Video Anomaly Detection

arXiv:2608.21244v1 Announce Type: new Abstract: Vision-language models enable training-free video anomaly detection by answering questions about video segments. VAD benchmarks, however, require a scal

AT-ViT: Area-Targeted Multi-View Vision Transformer with Cross-Attention and Multi-Scale Patching for Plant Trait Recognition in Herbarium Images

arXiv:2608.21067v1 Announce Type: cross Abstract: Automated plant traits recognition from herbarium images is essential for plant sciences, yet remains challenging because background elements (e.g., t

safetyarxiv-cs-ai

[Benchmark] llama.cpp batch/ubatch impacts on PP and TG

model-releasesr-localllama

My test is running DeepSeek v4 Flash 0731 at native size on DGX Spark machine (GB10, 128 GB unified memory). The model size is bigger than RAM, so weights will be loaded many times when running. To im

BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers

arXiv:2608.20427v1 Announce Type: cross Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic bloc

local-aiarxiv-cs-ai

Agents

Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning

arXiv:2608.20564v1 Announce Type: new Abstract: Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particular

agentsarxiv-cs-ai

Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising

arXiv:2608.20561v1 Announce Type: cross Abstract: Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensiv

Dynamic Context Scheduling: Learning Beyond the Static Universe

arXiv:2608.20799v1 Announce Type: new Abstract: We study dynamic context scheduling as a training instrument for contextual re- inforcement learning. Rather than treating intra-episode context variati

Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challengi

local-aiarxiv-cs-cv

Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry

arXiv:2608.20967v1 Announce Type: new Abstract: Accurate soft tissue simulation is essential for surgical training, pre-operative planning, and haptic feedback systems. While learning-based surrogate

Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking

model-releasesarxiv-cs-lg

arXiv:2608.20366v1 Announce Type: cross Abstract: Molecular docking requires reasoning jointly about ligand pose and protein flexibility. Most diffusion-based docking models predict torsional updates

How XPUs Meet a World-Class AI Factory

To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That

hardwarenvidia-blog

KoViDoRe: Korean Visual Document Retrieval

arXiv:2608.20840v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. Howev

Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

arXiv:2608.21265v1 Announce Type: new Abstract: Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inferenc