All entries

PACE: Primitive-Aware Code Evolution for Automated Algorithm Design

arXiv:2608.07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs. While this whole-program p

Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots

DGX agent

arXiv:2603.13108v2 Announce Type: replace-cross Abstract: Panoramic imagery provides holistic 360{eg} visual coverage for environmental perception in quadruped robots. However, existing occupancy pred

model-releasesarxiv-cs-cv

10 Aug 2026

PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model

arXiv:2608.06794v1 Announce Type: new Abstract: While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objecti

safetyarxiv-cs-cv

Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

arXiv:2511.23204v2 Announce Type: replace Abstract: Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exce

researcharxiv-cs-cv

Local Ai

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

arXiv:2507.07814v2 Announce Type: replace Abstract: We introduce a novel upper bound on the local Lipschitz constant of the dot-product self-attention block showing its dependence on the attention map

local-aiarxiv-cs-lg

PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder

arXiv:2601.19179v2 Announce Type: replace Abstract: Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear auto

People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe

arXiv:2608.07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of

PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue

arXiv:2608.06975v1 Announce Type: cross Abstract: Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative. Yet existing work falls short on two fronts:

PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery

arXiv:2608.07126v1 Announce Type: cross Abstract: Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions f

Agents

Plan-and-Avoid: Real-Time Aircraft Trajectory Coordination in a Multi-Agent Environment

arXiv:2608.06648v1 Announce Type: new Abstract: This paper presents a real-time Plan-and-Avoid (PAA framework for coordinating cooperative multi-agent airspace operations around a declared priority tr

agentsarxiv-cs-ro

Playing with physics is so cool in Minimax H3

model-releasesr-stablediffusion

Prompt: 'integrated_multimodal_description: [Shot 1] Live-action, ultra-realistic first-person footage at night on a rainy city street, filmed with authentic handheld smartphone qualities. The phone i

Please Share Your Experience About Muse Glimmer

model-releasesr-localllama

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models

arXiv:2608.06690v1 Announce Type: cross Abstract: Most language-model access controls regulate behavior while leaving the same computation available to every request. We study a different systems ques

Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers

arXiv:2608.07436v1 Announce Type: new Abstract: Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head. Muon groks modular addition faster, but its solutions do not hold.

Pre-Inference Routing for Cost-Efficient Document Field Extraction

arXiv:2608.06607v1 Announce Type: new Abstract: Most document-extraction systems use a single model for all documents. This is simple but can be costly for easy cases and less effective for difficult

researcharxiv-cs-cl

PRISM: Principled Reference Identification for Schrodinger Bridge Model

arXiv:2608.06893v1 Announce Type: new Abstract: Schrodinger bridge models restore a clean signal from a degraded observation by following the conditional bridges of a reference process, yet this refer

Agents

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

arXiv:2603.06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their r

agentsarxiv-cs-ai

Progress-Certified Reversible Simplex Supervision of Goal-Reaching Reinforcement Learning

arXiv:2601.19499v2 Announce Type: replace Abstract: Task completion is difficult to certify when state aggregation, model mismatch, and disturbances invalidate nominal RL transitions. We present a rev

safetyarxiv-cs-ro

Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

arXiv:2608.06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foun

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608.06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Ref

Provable Training Data Identification for Large Language Models

arXiv:2510.09717v3 Announce Type: replace-cross Abstract: Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However,

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608.06901v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidl

researcharxiv-cs-cv

PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents

arXiv:2608.07438v1 Announce Type: new Abstract: Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

arXiv:2608.07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane st

PURe: A Plug-and-Play Product-Unit Residual Module for Vision Networks

arXiv:2505.04397v3 Announce Type: replace-cross Abstract: Modern vision networks are dominated by additive local transformations, whereas explicit multiplicative local interactions remain underexplore

QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting

arXiv:2608.07363v1 Announce Type: new Abstract: Forecasting non-stationary time series remains difficult due to long-range dependencies, local volatility bursts, structural shifts, and nonlinear oscil

Quantization Damage Is Multiplicative, Not Additive

model-releasesarxiv-cs-cl

arXiv:2608.06564v1 Announce Type: cross Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt. What nobody can say is which of the model's

Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

arXiv:2401.07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in qu

Tools

Quoting OpenClaw

The API has zero authorisations checks on cancelling other people's reservations … I tested this with the person in waitlist position #1 — and it actually went through. So you've moved from #4 to #3 a

toolssimon-willison

R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Sim

arXiv:2608.06827v1 Announce Type: cross Abstract: Real-to-sim (R2S) depends on scene representations that render observations along robot ego trajectories, yet dense multi-view capture limits per-envi

safetyarxiv-cs-cv

Local Ai

RAG-art: Build Your Own Art Expert with ollama

I built myself a personal AI art history assistant https://github.com/lololerigolo60/RAG-art/tree/main I love art history but I have way too many books, PDFs, and notes scattered everywhere. So I buil

local-air-ollama

Reading Copom's Tone: A Weighted LLM Framework for Hawkish-Dovish Sentiment, Forward Guidance, and Uncertainty

arXiv:2608.07251v1 Announce Type: cross Abstract: This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements

Applications

Real-time Whole-Body Motion Planning for Mobile Manipulators Carrying Arbitrarily Shaped Payloads via Kinematically-Coupled SVSDF

arXiv:2608.07005v1 Announce Type: new Abstract: Mobile manipulators are increasingly tasked with transporting large, non-convex payloads through cluttered environments, yet existing planners either ov

applicationsarxiv-cs-ro

Really important point and nicely explained! Two further points worth noting: (1) if an AI system relies on a harness (which as Gary notes i…

Really important point and nicely explained! Two further points worth noting: (1) if an AI system relies on a harness (which as Gary notes in other writing is increasingly improtant in top models), th

safetygary-marcus--x

Applications

Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions

arXiv:2608.06896v1 Announce Type: new Abstract: Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this require

applicationsarxiv-cs-lg

Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models

arXiv:2608.07243v1 Announce Type: new Abstract: Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, an

Recovering Explanations from Transformed Rule-Based Ontologies

arXiv:2608.06399v1 Announce Type: cross Abstract: Datalog rules are often used to define ontologies over Knowledge Graphs. Rule reasoners routinely optimise such ontologies by rewriting their rules in

Recovering Lesion Parameters from Aphasic Picture Naming Error Profiles in Large Language Models

arXiv:2608.06429v1 Announce Type: new Abstract: Interpretability methods for large language models (LLMs) describe internal state but do not directly test whether that state is causally sufficient to

researcharxiv-cs-cl

RegionDet: A Benchmark for Region Detection Beyond Object Instances

model-releasesarxiv-cs-cv

arXiv:2608.06850v1 Announce Type: new Abstract: Object detection is a fundamental task in computer vision and has achieved remarkable progress on standard benchmarks by localizing discrete and well-bo

ReGraph: Learning to Generate Recipe Graphs from Food Images

arXiv:2608.06917v1 Announce Type: new Abstract: Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.However, cooking is a structured transf

Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation

arXiv:2608.07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training

Representation Handoffs for OpenArm-Based Laboratory Mobile Manipulation

arXiv:2608.07154v1 Announce Type: cross Abstract: Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization

arXiv:2608.07019v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models. Existing PTQ methods typically obt

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv:2608.07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters,

Residual Algebra for Representation-Preserving Learning

arXiv:2608.07349v1 Announce Type: new Abstract: Learning from heterogeneous representations is usually reduced to feature concatenation, which erases which representation produced an error. We instead

Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT

arXiv:2512.12236v2 Announce Type: replace-cross Abstract: Sparse-view Computed Tomography (CT) reconstructs images from a limited number of X-ray projections to reduce radiation and scanning time, whi

researcharxiv-cs-cv

Retrieval-Constrained Policy Optimization for Attack Technique Extraction from Cyber Threat Intelligence

model-releasesarxiv-cs-cl

arXiv:2608.06778v1 Announce Type: cross Abstract: Mapping cyber threat intelligence (CTI) text to MITRE ATT&CK techniques is essential for structured threat analysis, yet manual annotation is costly a

Retrofitting Linear Attention into Diffusion Language Models

model-releasesarxiv-cs-lg

arXiv:2608.06628v1 Announce Type: new Abstract: Diffusion language models (dLLMs) offer a promising alternative to autoregressive models by accelerating inference through parallel decoding. Recent dLL