TheAnswer EngineAI News · Sydney

Venture firms are turning to creators to build trust with the next generation of founders before a check is ever written. It’s a trend that’s been building with a16z’s acquisition of Erik Torenberg’s Turpentine podcast and OpenAI’s acquisition of TBPN. Lightspeed Venture Partners just made its own notable hire in that vein, bringing on Claire Zau, a seed investor with a major following on Instagram and […]

The serial entrepreneur joins the e-commerce company as CPO to lead its AI agents.

arXiv:2608.00006v1 Announce Type: new Abstract: Large Language Models (LLMs), a part of artificial intelligence (AI), are increasingly being adopted by Small and Medium Enterprises (SMEs) to enhance question-answering capabilities and support business decision-making processes. However, hallucinations in LLM-generated outputs can serve as a source of misinformation, reducing user confidence in their reliability and trustworthiness within SMEs. Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address this challenge by incorporating external knowledge sources into the modeling process. In this paper, we present VectorRAG and GraphRAG modeling approaches to mitigate hallucinations and misinformation risks and evaluate their effectiveness in SME environments. Our experimental evaluation is conducted on multiple state-of-the-art LLMs, including LLaMA, Mistral, and Qwen, to assess performance in terms of useful response generation, risk of hallucination, contextual relevance, as well as human-interpretation. The results demonstrate that RAG-enhanced LLMs can significantly improve response quality by reducing hallucinations and misinformation, thereby supporting

arXiv:2608.00008v1 Announce Type: new Abstract: The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, the energy costs on consumer hardware remain poorly characterized, as most benchmarks focus solely on accuracy. This paper presents a reproducible, hardware-level energy benchmark of nine open-source LLMs (1B to 7B parameters) executed on a single consumer GPU (RTX 4060Ti 16GB). Using the Ollama inference engine, GPU power draw was sampled at 2Hz via nvidia-smi across a fixed prompt set. We evaluate mean/peak power, total energy per prompt (J/prompt), energy per output token (J/token), and throughput (tok/s). Our findings suggest that factors beyond raw parameter count, including model architecture and quantization strategy, drive energy efficiency. Specifically, gemma3:1b and llama3.2:1b achieve the lowest energy cost (0.56 J/token and 0.65 J/token) and the highest throughput (>170 tok/s). In contrast, the 7B-Mistral model consumes up to 4.4x more energy per token than the most efficient model. Notably, qwen3.5:2b exhibits anomalously high per-prompt energy due to extended internal reasoni

arXiv:2608.00014v1 Announce Type: new Abstract: Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing methods either suffer from a severe ``cold start'' bottleneck requiring massive historical logs (e.g., Item Response Theory) or exhibit a surface lexical bias that misses the underlying reasoning manifold of tasks. We propose CoT-Core, a novel training-free core question selection framework. Recognizing that lexically disparate questions can share equivalent underlying logic, CoT-Core prompts LLMs to unroll zero-shot Chain-of-Thought (CoT) reasoning trajectories. Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation, and delineate the boundary conditions of reasoning-aware pruning, revealing that its efficacy is intrinsically gated by task complexity.

If you run CRO or SEO, the oldest mistake in your discipline just came back wearing a new referrer string. Here's the fix. The post AI Referrals Are Recreating The Oldest Mistake In Conversion Optimization appeared first on Search Engine Journal .

Search volume told you what people want to know. It no longer tells you whether they need you after AI answers. Click Worthiness does. The post When AI Takes The Click, Click Worthiness Should Guide Your Strategy appeared first on Search Engine Journal .

Microsoft Advertising expands AI Visibility, Performance Max testing, and Ad Preview Hub with new reporting, experimentation, and creative review capabilities. The post Microsoft Advertising Adds AI Visibility Insights, PMax Testing, And Creative Preview Updates appeared first on Search Engine Journal .

Sets data and AI goals for realisation in FY27.

Role changes and exits.

Model without safety filters tried to social engineer coder during test.