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Given 10 benchmarks for specific misaligned behaviors, the automated systems were able to improve performance on every single one without degrading overall performance.

Construction generates abundant data, from equipment telemetry and maintenance records...

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Databricks

August 28, 2026

How Indra unified EV charging data on Databricks

When data grows faster than the systems around it, complexity becomes the default....

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Nvidia

August 28, 2026

Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA Tensor RT Model Connect

Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,...

Google Deepmind is testing a double-blind evaluation of a frontier AI model for the first time. Cryptographic protection through Confidential Space is meant to keep Google from seeing the test questions and keep evaluators from seeing the model weights. The pilot project with the Singapore AI Safety Institute uses a Gemini Flash Lite and could set a new standard for tamper-proof AI benchmarks. The article AI benchmarks have a trust problem and Google wants to fix it appeared first on The Decoder.

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Nvidia

August 28, 2026

Munster Expects Nvidia Revenue to Grow by 90% in 2027

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Meta

August 28, 2026

Inside Meta’s Push to Put Robots to Work in Data Centers

The company is testing robots that can swap cables, reset servers, and take on other tasks performed by technicians, fueling concerns among some workers that their jobs could be at risk.

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Google

August 28, 2026

Marvell Technology shares slump as investors question Google deal. Here’s what Wall Street is saying. - Market Watch

Marvell Technology shares slump as investors question Google deal. Here’s what Wall Street is saying. MarketWatch

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xAI

August 28, 2026

Musk’s AI company sues its users as victim lawsuits over Grok deepfakes mount - Politico

Musk’s AI company sues its users as victim lawsuits over Grok deepfakes mount Politico

arXiv:2608.26172v1 Announce Type: new Abstract: Deploying natural-language interfaces over enterprise OLTP catalogs fails at scale because semantic parsers collapse under schema-graph scaling, inflating context beyond stable LLM attention budgets. We present DRL (Deterministic Relational Middleware Layer), a safe pipeline interposing between front-ends and SQL backends. DRL comprises dynamic context pruning, relational AST typing, and transactional safeguard verification (EXPLAIN gating and NULL guards) to bound context and flag operational silent divergence (SDop). We evaluate DRL on PostgreSQL and MySQL, contributing (i) an OLTP schema-graph scaling model, (ii) a 1,000-pair Workload Verification Suite, (iii) baselines B0-B3, and (iv) an enterprise NL2SQL failure taxonomy. On PostgreSQL, schema-linked hints (B1) yield a 76% context reduction over naive full-catalog prompting (B0); DRL's dynamic router (B2) reaches a 92% reduction at pruning p95 = 0.58 ms and middleware p95 = 4.6 ms. GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Flash achieve 52.9%, 52.8%, and 52.1% execution match under a corrected evaluation harness; SDop flags 89-100% of false-positive EX-passing queries. GPT-4o failures are dominated by semantic/filter errors (254/471), while column hallucination is a minor factor (47/471). Crucially, a single regex defect in our evaluation post-processor silently suppressed accuracy and manufactured a false 4-10% cross-vendor gap that vanished when corrected, showing that benchmark code deserves the same scrutiny as the models it scores. DRL reframes enterprise NL2SQL as systems engineering - context bounding, verification, and plan-aware admission - not a leaderboard exercise.

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Meta

August 28, 2026

Beyond a Single Story: Meta-Reviewing Sparse and Incomplete User-generated Contents for Recommendation

arXiv:2608.26728v1 Announce Type: new Abstract: Data sparsity remains a long-standing challenge in recommender systems, and it becomes more severe for methods relying on user-generated content (UGC) such as textual reviews, which capture fine-grained preferences but require more user efforts to produce. As a result, UGC exhibits (1) missing reviews, where interactions lack any review, and (2) incomplete reviews, where available reviews cover only a subset of relevant attributes. Existing approaches often overlook these UGC-specific issues, leading to degraded accuracy. Motivated by meta-review in academic peer review, we propose MOSAIC (Meta-review On Sparse And Incomplete user-generated Content), which constructs a meta-review for each target user by aggregating attribute-sentiment evidence from neighbor users' reviews. A multi-gate mixture-of-experts (MMoE) architecture jointly optimizes rating prediction and meta-review attribute-sentiment prediction, while an attention module personalizes the aggregated meta-review signals to each target user, yielding both refined rating predictions and attribute-level explanations. Experiments on four real-world datasets demonstrate that MOSAIC consistently outperforms state-of-the-art baselines in both recommendation accuracy and explanation quality, mitigating UGC sparsity and incompleteness while delivering consistent gains for users with limited interaction history.

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