All entries

Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics

arXiv:2606.05956v1 Announce Type: new Abstract: Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search. Therein, it is well-known that front-to-fr

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

DGX agent

arXiv:2403.00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the perf

applicationsarxiv-cs-ai

6 Jun 2026

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

arXiv:2606.06345v1 Announce Type: new Abstract: Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate

🚨BREAKING: Miguel Bosé, the biggest Spanish-language pop star of the last few decades, has just released a video taking a knee and putting …

model-releaseselon-musk--x

🚨BREAKING: Miguel Bosé, the biggest Spanish-language pop star of the last few decades, has just released a video taking a knee and putting his hand over his heart in honour of Henry Nowak This has now

Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures

arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

arXiv:2606.05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks. As a first step toward this vision

Bridging Domain Expertise and Generalization for Performance Estimation

arXiv:2606.06335v1 Announce Type: cross Abstract: Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the train

Applications

Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation

arXiv:2606.06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependen

applicationsarxiv-cs-ai

Building a Custom Drones MuJoCo Environment [P]

researchr-machinelearning

This post likely covers the process of creating a custom drone simulation environment using MuJoCo, a physics engine commonly used in machine learning research. The project involves leveraging MuJoCo'

Built a fully-local paper-RAG across 2× 1080 Ti + a 3090. Three Ollama gotchas that each cost me a day.

A developer documented their experience building a fully-local paper Retrieval-Augmented Generation (RAG) system using two NVIDIA GTX 1080 Ti GPUs and one RTX 3090, sharing three significant challenge

local-air-ollama

CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents

arXiv:2601.09923v3 Announce Type: replace Abstract: AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior. Among proposed defenses, architectural isolati

agentsarxiv-cs-ai

Can AI Refute Economic Theory? Evidence from Beyond the Knowledge Cutoff

arXiv:2606.05383v1 Announce Type: cross Abstract: Can artificial intelligence (AI) refute economic theory? I document experiments in which I asked several AI models (Gemini, Refine, Claude, and ChatGP

Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation

arXiv:2606.05792v1 Announce Type: new Abstract: TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language stil

CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications

arXiv:2512.15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO). Existing automated systems a

Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMs

arXiv:2606.05966v1 Announce Type: cross Abstract: Understanding and reasoning about the physical world is the foundation of intelligent behavior, yet state-of-the-art vision-language models (VLMs) sti

Applications

CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

arXiv:2606.05413v1 Announce Type: cross Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-dri

applicationsarxiv-cs-ai

Chip Export rules: made China/Huawei stronger, accomplished relatively little. USG stakes in US AI: will make Mistral and other “sovereign A…

model-releasesgary-marcus--x

Chip Export rules: made China/Huawei stronger, accomplished relatively little. USG stakes in US AI: will make Mistral and other “sovereign AI” efforts stronger, freak out the rest of the world, escala

Applications

circular finance, with numbers, a worked example.

applicationsgary-marcus--x

circular finance, with numbers, a worked example. I want to use GOOG Anthropic and SPCX deal to show how the circular finance deals hiding losses and costs is falling apart. How every deal meant to ma

Class-Specific Branch Attention for Mitigating Gradient Interference under Class Imbalance

arXiv:2606.05740v1 Announce Type: new Abstract: Deep neural networks trained under severe class imbalance often exhibit degraded performance, typically attributed to statistical bias. In this work, we

Closing the Loop on Latent Reasoning via Test-Time Reconstruction

arXiv:2606.06252v1 Announce Type: new Abstract: Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a di

Code volume does not represent productivity.

agentsfrancois-chollet--x

Code volume is not a reliable metric for measuring developer productivity, as writing more lines of code does not necessarily correlate with delivering more value or solving problems more effectively.

Codex ad on the right side... we lowkey should've done our OOH campaign in NYC 🙃 Knicks in 5

Codex ad on the right side... we lowkey should've done our OOH campaign in NYC 🙃 Knicks in 5 The New York Knicks are flying home with a 2-0 lead. 7th Avenue might not stop celebrating before they land

agentsjerry-liu--x

CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model

arXiv:2606.06099v1 Announce Type: new Abstract: Whether Large Language Models (LLMs) exhibit covert psychological manipulation in complex human-AI interactions has garnered increasing safety concerns.

Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems

arXiv:2606.05701v1 Announce Type: cross Abstract: The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures ha

local-aiarxiv-cs-ai

ComfyUI’s free open source tool has already been used to create the viral Coca-Cola ads and that (infamous) “Wizard of Oz” show at the Vegas…

ComfyUI’s free open source tool has already been used to create the viral Coca-Cola ads and that (infamous) “Wizard of Oz” show at the Vegas Sphere. We find out why 4 million-plus users are relying on

local-aicomfyui--x

Compositional Boundaries for Density Fusion

arXiv:2606.05871v1 Announce Type: cross Abstract: Distributed uncertainty-management systems often combine local probabilistic models along aggregation trees chosen by communication, privacy, or sched

local-aiarxiv-cs-ai

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights

arXiv:2411.18343v3 Announce Type: replace-cross Abstract: Personalized Federal learning(PFL) allows clients to cooperatively train a personalized model without disclosing their private dataset. Howeve

Conformal Risk-Averse Decision Making with Action Conditional Guarantee

arXiv:2606.05551v1 Announce Type: cross Abstract: Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety g

Consistency Training Along the Transformer Stack

arXiv:2606.05817v1 Announce Type: cross Abstract: Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the

Tutorials

// Continual Learning Bench // One of the research areas with lots of investments is continual learning. While there are many efforts, there…

// Continual Learning Bench // One of the research areas with lots of investments is continual learning. While there are many efforts, there is very little progress in measuring it. So the big questio

tutorialsdair-ai--x

Critic-Guided Heterogeneous Multi-Agent Reasoning for Reliable Mathematical Problem Solving

arXiv:2606.05704v1 Announce Type: new Abstract: Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning

Crony socialism

'Crony socialism' likely refers to a critique of economic systems where government power becomes intertwined with corporate interests, combining socialist-style state intervention with favoritism towa

safetygary-marcus--x

Cross-Epoch Adaptive Rollout Optimization for RL Post-Training

arXiv:2606.05606v1 Announce Type: cross Abstract: LLM post-training often relies on reinforcement learning methods that sample multiple rollouts per prompt, yet most existing approaches use a fixed ro

CTIConnect: A Benchmark for Retrieval-Augmented LLMs over Heterogeneous Cyber Threat Intelligence

arXiv:2510.11974v2 Announce Type: replace-cross Abstract: Cyber Threat Intelligence (CTI) is foundational to modern cybersecurity, enabling organizations to proactively defend against evolving threats

Hardware

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

arXiv:2604.01489v2 Announce Type: replace-cross Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process. Rec

hardwarearxiv-cs-ai

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

arXiv:2606.06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces. The same openness that en

agentsarxiv-cs-ai

Data Flow Control: Data Safety Policies for AI Agents

arXiv:2606.05679v1 Announce Type: cross Abstract: Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness,

David Sarnoff (1891-1971) rose from Russian Jewish immigrant office boy to president/chairman of RCA. He drove commercial radio, founded NBC…

David Sarnoff (1891-1971) rose from Russian Jewish immigrant office boy to president/chairman of RCA. He drove commercial radio, founded NBC, and heavily funded early electronic TV development. Simila

researchyann-lecun--x

“de log is de agent”… activegraph for EU regulation? (just found this) https://djimit.nl/blog/activegraph-event-sourced-agents

This post explores the concept of using ActiveGraph with event sourcing for AI agents operating under EU regulations, suggesting a potential architecture where detailed logging and event histories ser

agentsyohei-nakajima--x

Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction

arXiv:2606.05863v1 Announce Type: cross Abstract: Grokking suggests that fitting the training data and learning a simple underlying rule may occur on different time scales. We formalize this phenomeno

Industry

Dell sharpens data protection strategy as AI reshapes cyber resilience

After Dell Technologies Inc. saw an 88% jump in revenue reported last week, it’s safe to say the hardware and data management company is on a roll. Although the hype is primarily around Dell’s artific

industrysiliconangle

Design a Reliable LLM-Integrated Interface for Mortality Forecasting

arXiv:2606.06235v1 Announce Type: cross Abstract: Mortality forecasting plays an important role in actuarial and policy decision-making, but its implementation remains technically complex and inaccess

local-aiarxiv-cs-ai

Detecting Perspective Shifts in Multi-agent Systems

arXiv:2512.05013v2 Announce Type: replace Abstract: Generative models augmented with external tools and update mechanisms (or extit{agents}) have demonstrated capabilities beyond intelligent prompting

agentsarxiv-cs-ai

Differentiable Efficient Operator Search

arXiv:2606.05232v1 Announce Type: cross Abstract: Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive rewe

Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

arXiv:2606.05584v1 Announce Type: cross Abstract: High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems. However, they increase computational

Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows

arXiv:2606.05670v1 Announce Type: new Abstract: Does adding more agents help an LLM workflow once compared systems share the same benchmark loader, tool access, answer contract, usage accounting, and

Industry

Do you actually use different AI models for different parts of your life, or does one subscription eventually win?

This Reddit discussion explores user preferences and behaviors around AI model usage, examining whether people maintain subscriptions to multiple AI services or consolidate to a single primary platfor

industryr-chatgpt

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss

arXiv:2606.06418v1 Announce Type: cross Abstract: Many modern applications of deep learning involve training a neural network via a one-step prediction loss (e.g., L^2 regression, cross-entropy), but