Jun 28, 2026

AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent

Varsha Shah's research introduces an AI-driven framework for enterprise financial compliance and fraud detection, overcoming the limitations of traditional systems that analyze documents in isolation. The framework combines graph-based entity correlation, adaptive probabilistic risk modeling, and cross-jurisdictional normalization to uncover hidden fraud patterns across payroll, tax, procurement, and financial records. Evaluated on 3 million anonymized records across four jurisdictions, it demonstrates significant improvements in detection accuracy (91% precision, 87% recall), reduces false positives by 76%, and lowers manual audit effort by 40%, ultimately transforming compliance from a reactive process into a proactive, intelligence-driven capability through continuous learning.

Jun 28, 2026

We Cut 94% of AI Coding Tokens With a Local Code Index - Rajkumar Sakthivel, Tesco

Rajkumar Sakthivel details how an unexpected surge in AI coding tool costs led to the discovery that sending excessive, irrelevant context was the primary culprit. He introduces the Code Context Engine (CCE), a local retrieval layer that intelligently prunes context using AST-aware chunks, hybrid search, and relevance scoring, resulting in up to 94% token reduction and significant cost savings. The talk emphasizes that optimizing input context, not just the AI model, is paramount for efficient and accurate AI-assisted coding.

Jun 28, 2026

OpenClaw in Your Hand: Building a Physical AI Terminal - Lech Kalinowski, Callstack

Dr. Lech Kalinowski presents Vault, a unique AI-native handheld terminal built on an ESP32-S3, featuring a dual-display (OLED for live interaction, e-paper for static content) and powered by a single battery. It operates in four modes—shell, assist, control, and an LLM-native RPG—and controls autonomous OpenClaw agents against a local, self-hosted 120B parameter GPT model served by NVIDIA TensorRT-LLM. The talk delves into the AI-native architecture, why inference remains on the backend, real-time agent thinking surfacing on e-paper, engineering challenges, and LLM-native game design, highlighting the potential for calm, text-first AI interaction.

Jun 28, 2026

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Vaidas Razgaitis, Senior Research Engineer at Higharc, shares three tactical tips to accelerate the transition of novel AI/ML research into production-ready features. He emphasizes addressing the critical handoff challenge between ML researchers and software engineers through structured documentation (Research Prototype Taxonomy Document), a well-organized monorepo utilizing decoupled microservices, and a systematic approach to code decomposition and PR review. These strategies aim to improve legibility, maintainability, and delivery speed for ML-driven products.

Jun 28, 2026

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

Angie Jones details Block's journey in transforming its engineering organization into an autonomous one using AI agents. She outlines a five-stage AI maturity model, the strategic implementation of an 'AI Champions' program, and the technical steps taken to make repositories AI-ready and enable seamless agent delegation directly within developer workflows. The talk covers challenges in scaling multi-agent parallelism, including code review bottlenecks and resource management, and the development of an orchestrator ('Builder Bot') and 'Company World Model' to achieve full autonomy. It concludes with a critical reflection on the ethical implications of achieving such high levels of AI-driven automation.

Jun 28, 2026

Turbocharge Your Agent's Retrieval with TurboQuant - Shashi Jagtap, Superagentic AI

This talk introduces TurboQuant, a training-free compression method from Google Research that reduces embedding memory footprint by 5x (from 32-bit to 3-4 bits) without losing search quality. It details how TurboQuant works through scalar quantization and a crucial one-bit error correction step, QJL, enabling agents to remember more on existing hardware by optimizing both KV cache and RAG vector stores. A live demo showcases its effectiveness, making it a vendor-neutral solution for efficient AI agent retrieval.