The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic leaderboard and won 25 of 36 tests. Only the materials-chemistry track lagged, indicating the need for deeper domain grounding; yet, the results show that lightweight prompt engineering has already delivered competitive, commercially relevant ideation from patents.

Contemporary organizations have shown great interest in integrating language models (LMs) into workflows traditionally performed by human subject matter experts (SMEs), such as in medical diagnostics (Artsi et al., 2025), legal assistance (Padiu et al., 2024), financial risk analysis (AI21 labs, 2025), and governmental permitting or regulatory reviews (Phan et al., 2024). Despite this interest, however, the use of LMs (e.g., via a standard conversational interface) in high-stakes contexts is constrained by the need for decision-making reliability, objectivity, transparency, and accountability that SMEs currently provide (Mori, 2024). Effective reconciliation between LMs and SMEs thus represents a critical frontier in real-world deployments of artificial intelligence. LMs have demonstrated remarkable capabilities in extracting information from large volumes of multi-modal, multi-domain data; synthesizing multi-document concepts; and performing tasks associated with basic reasoning. Nonetheless, LMs are susceptible to "hallucinations" (i.e., inaccurate generation) (Ji et al., 2023), difficulty in handling nuanced, domain-specific requirements (Ashqar, 2025), historical biases inherited from training data (Ranjan et al., 2024), and opaque reasoning in decision-making (Machot et al., 2024). Notably, these weaknesses are often precisely the strengths of SMEs, who are conversely burdened with the inefficient and labor-intensive tasks of cross-document, multi-modal search and information extraction. We can see the need to delineate and integrate the often low-stakes or tedious work that can be performed by LMs with the discerning, high-stakes decision-making tasks performed by SMEs in the real world: The challenge is to harness the time efficiency and broad knowledge capabilities of LMs while preserving the domain expertise, contextual judgment, oversight, and accountability of SMEs. Moreover, we must do so without creating additional burdens for SMEs to work with LMs (e.g., "prompt-engineering" or manual review of all LM tasks), and we wish to minimize the introduction of new risks (e.g., a loss of clarity regarding where or how LMs may be used by each SME, or, in the case of governmental work, the erosion of public trust). In this work, we propose a novel auditable and interactive refinement framework for the effective integration of LMs with SMEs for decision-making workflows.

"Amazing, They All Lean Left" - Analyzing the Political Temperaments of Current LLMs Abstract Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically i nvestigates the political temperament of seven prominent LLMs -- OpenAI's GPT - 4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's L l a ma 4, Mistral 7b Le Chat, and High - Flyer ' s DeepSeek R1 -- using a multi - pronged approach that incl udes Moral Foundations Theory, a dozen established political ideology scales, and a new index of current political controversies. We find strong and consistent prioritization of liberal - leaning values, particularly care and fairness, across most models. Fur ther analysis attributes this trend to four overlapping factors: liberal - leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse, and safety - driven fine - tuning practices . We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine - tuned model pairs reveals that fine - tuning generally increases liberal lean, an effect confirmed throu gh both self - report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preferences of programmers but an emergent property of training on democratic, rights - focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil - of - igno rance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective ethical reasoning. In the course of our research on the ethical logics of currently prominent large language models (Neuman et al. 2025a, b; Coleman et al. 2025), we encountered an interesting finding. The responses to various ethical dilemmas and the explanations of the underlying logics used by these models appear to resonate with the liberal side of the political spectrum. One research analytic we utilize draws on Moral Foundation Theory's five - element typology of foundational moral principles (Graham et al. 2009; Haidt 2012). The five foundations emp hasizing in turn, Care, Fairness, Loyalty, Authority and Purity, are traditionally divided into two clusters. The first two, Care and Fairness, are associated with a liberal political perspective, while conservatives who fully acknowledge the first two more often emphasize the latter three -- Loyalty, Authority and Purity in support of traditional norms.

Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant security risks, particularly through embedding space poisoning, which is a subtle attack vector where adversaries manipulate the internal semantic representations of input data to bypass safety alignment mechanisms. While previous research has investigated universal perturbation methods, the dynamics of LLM safety alignment at the embedding level remain insufficiently understood. Consequently, more targeted and accurate adversarial perturbation techniques, which pose significant threats, have not been adequately studied. In this work, we propose ETTA (Embedding Transformation Toxicity Attenuation), a novel framework that identifies and attenuates toxicity-sensitive dimensions in embedding space via linear transformations. ETTA bypasses model refusal behaviors while preserving linguistic coherence, without requiring model fine-tuning or access to training data. Evaluated on five representative open-source LLMs using the AdvBench benchmark, ETTA achieves a high average attack success rate of 88.61%, outperforming the best baseline by 11.34%, and generalizes to safety-enhanced models (e.g., 77.39% ASR on instruction-tuned defenses). These results highlight a critical vulnerability in current alignment strategies and underscore the need for embedding-aware defenses.

This study investigates whether large language models (LLMs) exhibit consistent behavior (signal) or random variation (noise) when screening resumes against job descriptions, and how their performance compares to human experts. Using controlled datasets, we tested three LLMs (Claude, GPT, and Gemini) across contexts (No Company, Firm1 [MNC], Firm2 [Startup], Reduced Context) with identical and randomized resumes, benchmarked against three human recruitment experts. Analysis of variance revealed significant mean differences in four of eight LLM-only conditions and consistently significant differences between LLM and human evaluations (p < 0.01). Paired t-tests showed GPT adapts strongly to company context (p < 0.001), Gemini partially (p = 0.038 for Firm1), and Claude minimally (p > 0.1), while all LLMs differed significantly from human experts across contexts. Meta-cognition analysis highlighted adaptive weighting patterns that differ markedly from human evaluation approaches. Findings suggest LLMs offer interpretable patterns with detailed prompts but diverge substantially from human judgment, informing their deployment in automated hiring systems.

The CyberGuy Kurt Knutsson gives his take on Elon Musk's claims that Grok 3 outperforms every AI rival on'Fox & Friends.' On July 4th, eccentric billionaire and owner of X Elon Musk took to his social media platform to make an announcement about its Artificial Intelligence bot named Grok. "We have improved Grok significantly," Musk told the world. "You should notice a difference when you ask Grok questions." Just a few days later, Grok had to have features shut down after it started answering questions by going full-Nazi and espousing antisemitic conspiracy theories. All that was missing was digital goosestepping and armbands.

Earlier today, Grok showed me how to tell if someone is a "good scientist," just from their demographics. For starters, according to a formula devised by Elon Musk's chatbot, they have to be a white, Asian, or Jewish man. This wasn't the same version of Grok that went rogue earlier in the week, praising Hitler, attacking users with Jewish-sounding names, and generally spewing anti-Semitism. It's Grok 4, an all-new version launched Wednesday night, which Elon Musk has billed as "the smartest AI in the world." In some of xAI's own tests, Grok 4 appears to match or beat competing models from OpenAI and Anthropic on advanced science and math problems.

Many countries are exploring how artificial intelligence might help with everything from processing taxes to determining welfare benefits. But a survey shows citizens are not as enthusiastic as their governments – and this can create real risks for democracy. "Focusing only on short-term efficiency gains and shiny technology risks triggering public backlash and contributing to a long-term decline in democratic trust and legitimacy," says Alexander Wuttke at the Ludwig Maximilian University of Munich in Germany. Wuttke and his colleagues asked around 1200 people in the UK to share their feelings about government actions where either a human or an AI handled the task. These hypothetical scenarios included processing tax returns, approving or rejecting welfare applications and making risk assessments about whether defendants should be eligible for bail. Some people were only told about how AI could improve government efficiency – but others learned about both AI-related benefits and risks.

Reports of child sexual abuse imagery created using artificial intelligence tools have surged 400% in the first half of 2025, according to new data from the U.K.-based nonprofit organization Internet Watch Foundation. The organization, which monitors child sexual abuse material online, recorded 210 webpages containing AI-generated material in the first six months of 2025, up from 42 in the same period the year before, according to a report published this week. On those pages were 1,286 videos, up from just two in 2024. The majority of this content was so realistic it had to be treated under U.K. law as if it were actual footage, the IWF said. Roughly 78% of the videos -- 1,006 in total -- were classified as "Category A," the most severe level, which can include depictions of rape, sexual torture and bestiality, the IWF said.

This paper presents a comprehensive five-stage evolutionary framework for understanding the development of artificial intelligence, arguing that its trajectory mirrors the historical progression of human cognitive technologies. We posit that AI is advancing through distinct epochs, each defined by a revolutionary shift in its capacity for representation and reasoning, analogous to the inventions of cuneiform, the alphabet, grammar and logic, mathematical calculus, and formal logical systems. This "Geometry of Cognition" framework moves beyond mere metaphor to provide a systematic, cross-disciplinary model that not only explains AI's past architectural shifts-from expert systems to Transformers-but also charts a concrete and prescriptive path forward. Crucially, we demonstrate that this evolution is not merely linear but reflexive: as AI advances through these stages, the tools and insights it develops create a feedback loop that fundamentally reshapes its own underlying architecture. We are currently transitioning into a "Metalinguistic Moment," characterized by the emergence of self-reflective capabilities like Chain-of-Thought prompting and Constitutional AI. The subsequent stages, the "Mathematical Symbolism Moment" and the "Formal Logic System Moment," will be defined by the development of a computable calculus of thought, likely through neuro-symbolic architectures and program synthesis, culminating in provably aligned and reliable AI that reconstructs its own foundational representations. This work serves as the methodological capstone to our trilogy, which previously explored the economic drivers ("why") and cognitive nature ("what") of AI. Here, we address the "how," providing a theoretical foundation for future research and offering concrete, actionable strategies for startups and developers aiming to build the next generation of intelligent systems.