Search: “context”

Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Prediction

arXiv:2608.00071v1 Announce Type: new Abstract: The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to trad

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Local Ai

Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

DGX agent

arXiv:2608.02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately id

local-aiarxiv-cs-lg

4 Aug 2026

Entity-Aware Sequence Transduction for Player-Centric Ball Action Spotting

arXiv:2608.01696v1 Announce Type: new Abstract: Player-centric ball action spotting requires temporally precise event detection together with actor attribution in crowded, partially observed multi-age

safetyarxiv-cs-cv

Estimating SSIM from MSE for DCT-Based Compressed Images

arXiv:2608.02549v1 Announce Type: cross Abstract: Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming syste

Experience-Calibrated Contrastive Decoding for Mitigating Hallucinations in LM-Based Text-to-Speech

arXiv:2608.00722v1 Announce Type: cross Abstract: Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text. Existing mitigation

safetyarxiv-cs-lg

Expert-Choice Routing Enables Adaptive Computation in Diffusion Language Models

arXiv:2604.01622v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit

safetyarxiv-cs-cl

Fast and Accurate Quotation Attribution in Literary Texts

arXiv:2608.02359v1 Announce Type: new Abstract: Attributing quotations to their speakers in literary texts remains an open challenge. Standard methods, which independently predict a speaker mention fo

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?

arXiv:2608.01704v1 Announce Type: cross Abstract: A benchmark score means nothing without knowing what a trivial method achieves and what the best possible method could achieve. We construct both boun

From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

arXiv:2608.00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating hig

Fused Bayesian Flow Networks for Dual-Target Molecular Design

arXiv:2608.01007v1 Announce Type: new Abstract: Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discover

Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment

model-releasesarxiv-cs-cv

arXiv:2608.02470v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remai

HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models

arXiv:2608.02124v1 Announce Type: cross Abstract: Vision-language models (VLMs) remain unreliable when predictions require fine-grained visual evidence. We identify a previously overlooked cause: spec

HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses

arXiv:2608.01918v1 Announce Type: cross Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable

Tutorials

HiResNets: Native Full-HD Video Recognition with Foveal Residual Streams

arXiv:2608.02140v1 Announce Type: new Abstract: Much of the recent progress in image and video recognition has come at the cost of memory: larger models, increased resolution, and longer temporal cont

tutorialsarxiv-cs-cv

HopRefusalBench: Diagnosing Refusal Failures in Search-Augmented Agents for Multi-Hop Reasoning

arXiv:2608.01358v1 Announce Type: new Abstract: Search-augmented large language model agents are increasingly capable of solving knowledge-intensive tasks, but their behavior when a multi-hop question

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

arXiv:2608.01454v1 Announce Type: cross Abstract: Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly s

How Deutsche Bank unlocked agility with an API-ready ecosystem

When people think about digital transformation in banking, they often focus on the visible results: mobile apps and new digital services. But there's an invisible infrastructure making all these servi

safetygoogle-cloud-ai

HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

arXiv:2608.00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architecture

Applications

Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives

arXiv:2608.02411v1 Announce Type: cross Abstract: Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation.

applicationsarxiv-cs-lg

I built a DwarfStar-inspired Vulkan/Metal inference engine for Qwen3.6-35B-A3B on 16 GB machines

model-releasesr-localllama

Disclosure: I’m the author and maintainer of QuarkStar. I built QuarkStar, a small native inference engine inspired by Antirez’s DwarfStar. QuarkStar currently supports: Qwen3.6-35B-A3B, using the sam

Instruction-Conditioned Exploration with Asymmetric Reinforcement Learning and Self-Distillation

arXiv:2608.02087v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) with Reinforcement Learning (RL) has become an important tool for improving model capabilities, but the LLM

safetyarxiv-cs-cl

Intention Inference Under Execution Noise: Separating Aleatoric and Epistemic Uncertainty in Social Dilemmas

arXiv:2608.02440v1 Announce Type: cross Abstract: In noisy social dilemmas, intended actions are stochastically corrupted before execution, so an observed defection may reflect hostile intent or actio

researcharxiv-cs-lg

Just on Time: Token-Level Early Stopping for Diffusion Language Models

arXiv:2602.11133v2 Announce Type: replace-cross Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens

Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In

safetyarxiv-cs-lg

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain

arXiv:2507.16974v3 Announce Type: replace Abstract: Enabling farmers to access accurate agriculture-related information in their native languages in a timely manner is crucial for the success of the a

llm-anthropic 0.26

model-releasessimon-willison

Release: llm-anthropic 0.26 Includes new features enabled by LLM 0.32: New models: claude-fable-5, claude-sonnet-5, and claude-opus-5. #75, #76 Added server-side tools for WebSearch, WebFetch, CodeExe

MAPLE: Metadata Augmented Private Language Evolution

arXiv:2603.19258v2 Announce Type: replace Abstract: Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the

Agents

MemoryForge: Synthesize Lifelong Memory for Human-Like LLM Agents

arXiv:2608.00007v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional

agentsarxiv-cs-cl

Models as Tools: An Agentic Coordination Framework for Unified Multimodal Visual Tracking

model-releasesarxiv-cs-cv

arXiv:2608.00847v1 Announce Type: new Abstract: Most current visual trackers adopt a matching-based architecture trained exclusively on tracking datasets, whose performance gains depend heavily on the

Neural Circuit Function Inference with LLMs

arXiv:2608.00059v1 Announce Type: new Abstract: The success of connectome mapping now shifts the challenge of understanding the nervous system to the interpretation of neural circuits. Here, we devise

New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging

model-releasessimon-willison

I released LLM 0.32 this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider t

Agents

OmniAI: A Surface-Adaptive Aerial Projection Interface for Human--Drone Interaction

arXiv:2608.00721v1 Announce Type: new Abstract: Drones in human environments often lack spatially grounded in- terfaces for situated communication. We present OmniAI, an em- bodied aerial agent that s

agentsarxiv-cs-ro

ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression

model-releasesarxiv-cs-cv

arXiv:2608.00345v1 Announce Type: new Abstract: A 3D CT scan entering a vision-language model produces a long sequence of visual tokens, often thousands to tens of thousands per volume, and this seque

Local Ai

Paris as a 15-Minute City: An Explainable AI Perspective

arXiv:2608.00815v1 Announce Type: new Abstract: The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficu

local-aiarxiv-cs-lg

Agents

PGMem: Tightly Coupled Persona-Memory Graph for Lifelong Personalized Agents

arXiv:2608.01708v1 Announce Type: new Abstract: Long-term personalized dialogue agents must track user preferences as their personas evolve. Existing memory systems organize past events well, but stor

agentsarxiv-cs-cl

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs

arXiv:2608.02150v1 Announce Type: new Abstract: Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of

Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis

arXiv:2608.00440v1 Announce Type: new Abstract: Recent image generators can synthesize convincing human-centric images, yet producing a useful collection remains different from producing a single succ

ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions

arXiv:2608.01690v1 Announce Type: new Abstract: Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual paramete

researcharxiv-cs-ro

RAGOCR: Optical Compression of Retrieval-Augmented Text via Visual Representation

arXiv:2608.00765v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become essential for knowledge-intensive question answering, yet scaling RAG pipelines remains challenging due

RamanPFN: learning from Raman spectral structure with a tabular foundation model

arXiv:2608.02157v1 Announce Type: new Abstract: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predicti

Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection

arXiv:2608.01930v1 Announce Type: new Abstract: Vision-language models (VLMs) are expected to revise their reasoning when visual evidence changes. Failures to do so are often attributed to insufficien

Rethinking and formalising the state across languages: a unified computational learning theory account

arXiv:2608.00523v1 Announce Type: new Abstract: The linguistic notion of state has traditionally been restricted to the construct (annexation) state of Afroasiatic languages and treated as a language-

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach

arXiv:2601.21369v2 Announce Type: replace Abstract: Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to t

Reusing Rollouts under Policy Lag: Prefix-Normalized Policy Optimization for LLM Reinforcement Learning

arXiv:2608.01418v1 Announce Type: cross Abstract: Autoregressive rollout generation is a major computational cost in reinforcement learning for large language models. Reusing each rollout batch for ad

Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders

arXiv:2601.10588v2 Announce Type: replace-cross Abstract: Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question. Here we propose

researcharxiv-cs-lg

Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models

arXiv:2510.22868v2 Announce Type: replace Abstract: Wind turbine blades operate in harsh environments, making timely damage detection essential for preventing failures and optimizing maintenance. Dron

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

arXiv:2507.02778v3 Announce Type: replace Abstract: Although large language models (LLMs) have transformed AI, they still make errors and follow unproductive reasoning paths. Self-correction is vital

safetyarxiv-cs-cl

SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control

arXiv:2608.00725v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations. However, conditioning future prediction on

safetyarxiv-cs-ro