Why AI will eat McKinsey’s lunch — but not today
Tech News

Why AI will eat McKinsey’s lunch — but not today

Navin Chaddha, managing director of the 55-year-old Silicon Valley venture firm Mayfield, is betting big on AI’s ability to transform people-heavy industries like consulting, law, and accounting. The veteran investor, whose wins include Lyft, Poshmark, and HashiCorp, recently discussed at TechCrunch’s StrictlyVC evening in Menlo Park why he believes “AI teammates” can create software-like margins […]

Addressing the Binding Problem in VLMs
AI

Addressing the Binding Problem in VLMs

[Submitted on 27 Jun 2025] View a PDF of the paper titled Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs, by Amirmohammad Izadi and 6 other authors View PDF HTML (experimental) Abstract:Despite progress in Vision-Language Models (VLMs), their capacity for visual reasoning is often limited by the \textit{binding problem}: the failure to

Addressing the Binding Problem in VLMs
AI

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements

arXiv:2506.22419v1 Announce Type: cross Abstract: Rapid advancements in large language models (LLMs) have the potential to assist in scientific progress. A critical capability toward this endeavor is the ability to reproduce existing work. To evaluate the ability of AI agents to reproduce results in an active research area, we introduce the Automated LLM Speedrunning Benchmark,

Addressing the Binding Problem in VLMs
AI

A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks

[Submitted on 27 Jun 2025] View a PDF of the paper titled QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks, by Yannick Werner and 6 other authors View PDF HTML (experimental) Abstract:Kolmogorov Arnold Networks (KANs), built upon the Kolmogorov Arnold representation theorem (KAR), have demonstrated promising capabilities in expressing complex functions

Addressing the Binding Problem in VLMs
AI

Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment

arXiv:2506.22385v1 Announce Type: cross Abstract: Video Large Multimodal Models (VLMMs) have made impressive strides in understanding video content, but they often struggle with abstract and adaptive reasoning-the ability to revise their interpretations when new information emerges. In reality, conclusions are rarely set in stone; additional context can strengthen or weaken an initial inference. To address

Addressing the Binding Problem in VLMs
AI

Less Greedy Equivalence Search

arXiv:2506.22331v1 Announce Type: cross Abstract: Greedy Equivalence Search (GES) is a classic score-based algorithm for causal discovery from observational data. In the sample limit, it recovers the Markov equivalence class of graphs that describe the data. Still, it faces two challenges in practice: computational cost and finite-sample accuracy. In this paper, we develop Less Greedy

Addressing the Binding Problem in VLMs
AI

Analyzing and Fine-Tuning Whisper Models for Multilingual Pilot Speech Transcription in the Cockpit

arXiv:2506.21990v1 Announce Type: cross Abstract: The developments in transformer encoder-decoder architectures have led to significant breakthroughs in machine translation, Automatic Speech Recognition (ASR), and instruction-based chat machines, among other applications. The pre-trained models were trained on vast amounts of generic data over a few epochs (fewer than five in most cases), resulting in their strong

Addressing the Binding Problem in VLMs
AI

Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks

[Submitted on 4 Mar 2024 (v1), last revised 27 Jun 2025 (this version, v2)] View a PDF of the paper titled Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks, by Roie Kazoom and 1 other authors View PDF HTML (experimental) Abstract:The widespread adoption of computer vision systems has

Addressing the Binding Problem in VLMs
AI

GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles

arXiv:2506.21839v1 Announce Type: cross Abstract: We challenge text-to-image models with generating escape room puzzle images that are visually appealing, logically solid, and intellectually stimulating. While base image models struggle with spatial relationships and affordance reasoning, we propose a hierarchical multi-agent framework that decomposes this task into structured stages: functional design, symbolic scene graph reasoning, layout

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