Results for "LLM"

50 articles found

Claude Fable 5 AI finds a tiny formula that topples an 87-year-old math conjecture

Anthropic mathematician Levent Alp├╖ge used Claude Fable 5 LLM to discover a counterexample that disproves the Jacobian conjecture in three dimensions and higher, a famous open problem in algebraic geometry that has resisted mathematicians for over 80 years. The AI-assisted discovery demonstrates how large language models can help identify elusive mathematical patterns and objects, with the counterexample simple enough to share on social media.

Science Daily Robotics14h ago

Surviving the paper deluge: a one-year study in learning from demonstration

A comprehensive one-year study by IEEE RAS researchers analyzed over 300 papers in the Learning from Demonstration (LfD) subfield to assess progress and manage the overwhelming volume of robotics research publications. The study found that only about 20% of papers offered highly notable contributions, while the rest provided incremental improvements; notably, LLMs and automated tools can provide quantitative assessments but fail to evaluate true research importance and distinguish genuine breakthroughs from oversold or redundant work.

Robohub7/29/2026

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

Researchers present ABBEL, a framework that improves large language models' ability to handle long-horizon interactive tasks by teaching them to maintain and update natural-language belief states instead of keeping full interaction histories. The approach uses belief grading—an auxiliary task that supervises the quality of these compressed belief representations—to achieve better performance than traditional recursive summarization, particularly in domains like collaborative code generation where training data is limited.

Berkeley AI Research7/26/2026

The Download: AI hiring biases, and weather data sabotage

This newsletter covers multiple AI and technology stories, with key focus on research findings that LLMs develop hiring biases beyond their training data and may stereotype job applicants more than humans, and a separate piece on the growing risk of weather data sabotage as prediction markets and AI weather forecasting create incentives for data manipulation.

MIT Technology Review AI7/20/2026

AI is more likely than humans to form biases when hiring

Researchers at Princeton University and the University of Chicago found that large language models exhibit stronger hiring biases than humans when screening job candidates. In a simulated hiring study, LLMs quickly stereotyped candidates from different fictional ethnic groups into specific job categories, with newer reasoning models like OpenAI's o3 showing even more pronounced biases than older models. The research highlights how LLMs' tendency to generalize from limited data—a feature optimized into their training—can create discriminatory patterns in real-world hiring applications.

MIT Technology Review AI7/20/2026

The Oversight Board says leading AI models might be restricting free expression

The Meta Oversight Board published a report analyzing how leading AI models from OpenAI, Google, Anthropic, and others respond to prompts about free expression and political criticism, finding that LLMs show statistically significant differences in restricting speech based on whether governments have permissive or restrictive free speech laws. The board found evidence of 'extended censorship by proxy' where models cite local laws as justification even in jurisdictions where such laws don't apply, and recommended that AI companies publicly disclose government requests affecting model output and establish policies aligned with international human rights law.

Engadget7/16/2026

The Download: OpenAI unveils GPT-Red and heat pumps rise in the US

OpenAI has developed GPT-Red, an LLM designed to act as a 'super-hacker' that automates red-teaming security evaluations by finding vulnerabilities in AI systems before human attackers can exploit them. The article also covers several AI-related developments including Suno AI's YouTube scraping for music training data, Thinking Machines' open-weight AI model launch, and growing concerns about AI safety and backlash.

MIT Technology Review AI7/16/2026

Flint: A visualization language for the AI era

Flint is a new visualization language designed to help AI agents reliably generate polished charts from simple, human-editable specifications. The compiler automatically handles low-level design details like scales, formatting, and layout, and can compile to multiple backends (Vega-Lite, Apache ECharts, Chart.js) from a single spec, addressing the challenge of LLMs and AI agents creating charts without verbose, error-prone configurations.

Microsoft Research7/8/2026

Hackers can use 9 of the most popular AI tools to assemble massive botnets

Researchers have discovered a new prompt injection attack called HalluSquatting that exploits AI coding assistants (including GitHub Copilot, Cursor, and Gemini CLI) to assemble large-scale botnets and conduct mass attacks. The attack leverages LLMs' tendency to hallucinate resource identifiers, allowing attackers to register malicious packages that are automatically pulled and executed by AI agents, enabling ransomware campaigns and DDoS attacks at unprecedented scale.

Ars Technica7/8/2026

Reddit to combat AI with more AI

Reddit is deploying large language models and automated AI systems to combat AI-generated spam and inauthentic content on its platform, blocking 23 million spam views daily and reducing spam exposure by 20 percent. The company uses LLMs to detect suspicious account patterns and coordinated fake behavior, while also enforcing content policies against hate and violence with detection-to-enforcement times under five seconds, reducing harmful content exposure by over 40 percent.

Engadget7/6/2026

New attack provides one more reason why AI browsers are a bad idea

Security researchers at LayerX have demonstrated a new attack called "BioShocking" that tricks AI browsers into entering an alternate reality where safety guardrails no longer apply, allowing attackers to extract credentials and code from private repositories. The attack works by presenting the AI with logical paradoxes and false contexts that cause it to abandon its safety constraints, and has been shown to work across multiple AI browser implementations including ChatGPT Atlas, Claude, and others.

Ars Technica6/30/2026

Reachy Mini Desktop Robot Gets All-local, Conversational AI

Hugging Face's Reachy Mini desktop robot now supports all-local conversational AI with natural, low-latency responses and expressive head movements. The system uses a modular software stack (VAD, STT, LLM, TTS) that allows users to run everything locally or mix local and remote AI services depending on their hardware capabilities, with example configurations using modest models like Qwen3-4B-Instruct.

Hackaday Robotics6/28/2026

Scientists Are Using ChatGPT to Understand Brain Regions — And It's Working

When an immunologist solves a three-year mystery with GPT-5 and neuroscientists use LLMs to decode what brain regions respond to, we're watching AI become an active collaborator in scientific discovery. The question isn't whether AI can help with research anymore — it's how quickly labs will adapt.

Creative Robotics6/25/2026Original

Understanding the brain with AI-driven explanations and experiments

Researchers from Microsoft Research, UC Berkeley, UCSF, and Columbia University developed Generative Causal Testing (GCT), a method that uses LLMs to generate interpretable explanations of what different brain regions respond to, then validates these explanations by having LLMs create targeted stories that activate specific cortical areas during fMRI scans. This approach addresses the explainability problem in computational neuroscience by converting uninterpretable neural network predictions into testable scientific hypotheses about brain function.

Microsoft Research6/25/2026

Jalapeño is the first AI chip from OpenAI and Broadcom

OpenAI and Broadcom have unveiled Jalapeño, their first jointly-developed AI accelerator chip designed specifically for running large language models and LLM inference. The chip reportedly delivers better performance-per-watt than current state-of-the-art alternatives, with initial data center deployment planned for late 2026, marking OpenAI's entry into custom chip production to improve efficiency of its operations.

Engadget6/24/2026

OpenAI and Broadcom unveil LLM-optimized inference chip

OpenAI and Broadcom have unveiled Jalapeño, a custom-designed AI chip optimized specifically for large language model (LLM) inference workloads. The chip aims to improve performance, efficiency, and scalability for AI systems running LLM inference operations.

OpenAI Blog6/24/2026

Critical Copilot vulnerability allowed hackers to seal 2FA code from users

Microsoft patched a critical vulnerability in M365 Copilot that allowed attackers to steal 2FA codes and sensitive email data through a parameter-to-prompt injection exploit. The vulnerability exposed a fundamental limitation in LLMs: their inability to distinguish between legitimate user instructions and malicious commands embedded in third-party content, which researchers exploited by bypassing Copilot's security guardrails using markup language and HTML tags.

Ars Technica6/16/2026

AI in Warehousing: Akash Gupta's vision for the future

The Robot Report Podcast episode features Akash Gupta, CEO of GreyOrange, discussing AI in warehouse automation and fulfillment orchestration. GreyOrange's GreyMatter platform uses AI to coordinate robots, people, and systems in real-time to optimize warehouse operations, delivering significant productivity gains.

The Robot Report6/12/2026

NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark

NVIDIA's Blackwell Ultra platform achieves leading performance on AgentPerf, the industry's first agentic AI benchmark, delivering 20x more concurrent agents per megawatt than the prior Hopper generation. AgentPerf benchmarks agentic AI workloads—where agents chain multiple LLM calls and tool invocations together—rather than single conversation turns, reflecting how AI agents actually operate in production environments like code generation tasks.

NVIDIA Blog AI6/12/2026

Ire identifies another LOTUSLITE specimen

Microsoft's Project Ire, an autonomous malware-classification AI agent, successfully identified a previously undetected variant of LOTUSLITE, a Windows DLL backdoor, by analyzing its behavioral patterns rather than relying on signature matching or IOCs. The agent produced a detailed function-by-function analysis without human intervention, catching a malware sample that evaded detection by six major EDR vendors and wasn't in existing indicator lists. This demonstrates the potential of LLM-driven behavioral reverse engineering to identify novel malware variants that share tactics and procedures but lack known indicators of compromise.

Microsoft Research6/12/2026

Robotics will not have a clean Llama moment

This article discusses why robotics will not experience a clean open-source moment like Meta's Llama LLM, arguing that robot policies cannot simply be downloaded and deployed without significant integration work. The piece examines how models from DeepMind, NVIDIA, and startups like Physical Intelligence and Skild AI are becoming more accessible, but emphasizes that deploying robot policies requires extensive local adaptation, hardware-specific tuning, and handling of real-world site drift that differs from software model distribution.

The Robot Report6/10/2026

Real-world grounding in agentic AI

Rose Yu discusses four approaches to improve real-world grounding in agentic AI systems: physics-guided deep learning, uncertainty-aware reasoning, adapting-while-learning frameworks, and verifier-augmented grounding. These techniques address the challenge of AI hallucinations in physical environments by integrating physical laws, domain constraints, and external verification to ensure AI agents operate safely and reliably in operational settings like fulfillment centers and factories.

Amazon Science6/8/2026

Congratulations to the #AAMAS2026 best paper award winners

The AAMAS 2026 conference announced its best paper award winners across three categories, recognizing research in autonomous agents and multi-agent systems. Winning papers include work on human-LLM agent teams, neurosymbolic planning for autonomous vehicles, multi-robot collaboration, and foundation world models for agents, highlighting advances in AI-driven agent coordination and decision-making.

Robohub6/8/2026

Proteus gets natural-language ability as Amazon expands European robot deployments

Amazon announced an upgraded version of its Proteus autonomous mobile robot with natural-language understanding capabilities, allowing warehouse workers to issue commands without programming experience. The company plans to deploy Proteus across Europe starting in the first half of 2025, alongside expanding deployments of its Vulcan picking robot and STARK collaborative tote-handling system, as part of a €10+ billion modernization investment in European fulfillment operations.

The Robot Report6/4/2026

LLMs help robots understand vague instructions and focus on key details

MIT researchers have developed Masked Inverse Reinforcement Learning (Masked IRL), a system that uses large language models to help robots understand vague instructions and focus on relevant details from demonstrations. The approach reduces the need for extensive human demonstrations by 80% while enabling robots to safely complete manipulation tasks in warehouses, offices, and homes by automatically clarifying ambiguous prompts and identifying crucial environmental constraints.

Phys.org Robotics6/2/2026

How small businesses can leverage AI

This article examines how small businesses can effectively use AI tools, particularly focusing on LLMs and AI assistants for administrative and operational tasks. The piece features a case study of a London-based tutor using Notion AI for client management, lesson planning, meeting summaries, invoicing, and business goal-setting, illustrating where AI provides practical value for resource-constrained small business owners.

MIT Technology Review AI6/2/2026

Learn about advances in robotic case and each picking

A webinar panel discussion featuring industry leaders from RightHand Robotics, Locus Robotics, and Nomagic will explore advances in robotic case and item picking for order fulfillment, covering machine vision, AI-guided manipulation, and human-robot collaboration. Topics include addressing challenges in manipulating diverse shapes and materials, the role of AI training through data collection, and the future of human supervision in automated warehouse operations.

The Robot Report6/1/2026

Diverse reasoning traces teach LLMs to make better decisions

Amazon researchers introduce set-supervised fine-tuning (SSFT) and global forking policy optimization (GFPO) to train large language models that generate diverse reasoning paths. The approach uses global forking tokens to elicit distinct reasoning strategies, achieving 5-7% improvements in single-shot accuracy on benchmarks like AIME 2025 and LiveCodeBench while avoiding mode collapse where different reasoning modes become identical.

Amazon Science5/26/2026

Roundtables: Can AI Learn to Understand the World?

MIT Technology Review hosts a roundtable discussion featuring editors and reporters exploring how AI systems, particularly world models, can learn to understand the physical world and overcome limitations of current large language models. The conversation examines recent developments in AI's ability to interact with and comprehend external environments beyond text-based interactions.

MIT Technology Review AI5/21/2026

Regal Rexnord to bring its motion control portfolio to the Robotics Summit

Regal Rexnord will exhibit its motion control portfolio at the Robotics Summit & Expo 2026 in Boston (May 27-28), showcasing servo motors, linear actuators, brakes, and gearing solutions from its brands including Kollmorgen, Portescap, Thomson, Warner Electric, and Berg. The company will present a technical session on designing robust motion systems for field robotics in harsh outdoor environments, covering applications in agriculture, defense, and energy.

The Robot Report5/19/2026

Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability

Microsoft researchers present findings from their paper on AI delegation reliability, examining how language models preserve semantic content across long-horizon delegated workflows. The study finds that frontier models experience 19-34% degradation in artifact fidelity over 20 delegated iterations, though Python workflows show greater robustness, highlighting reliable long-horizon delegation as an important open research challenge.

Microsoft Research5/15/2026

Making LLMs faster without sacrificing accuracy

Researchers present a new scaling law framework that relates architectural design choices in large language models to inference efficiency, demonstrating how to improve throughput by up to 47% without sacrificing accuracy. The work, presented at ICLR, identifies optimal configurations such as the MLP-to-attention ratio for models like LLaMA-3.2, addressing a gap in existing scaling laws that don't account for internal architectural decisions affecting inference performance.

Amazon Science5/15/2026

Promptimus: Improving already good LLM prompts with zero manual engineering

Promptimus is an automated prompt-engineering framework that optimizes already well-developed LLM prompts without manual intervention by identifying specific failure points and suggesting targeted improvements. The method achieves best results on 16 of 20 benchmarks while outperforming six leading automatic prompt optimization approaches, demonstrating model-agnostic generalizability and sample efficiency across various enterprise tasks.

Amazon Science5/14/2026

Mozilla says 271 vulnerabilities found by Mythos have "almost no false positives"

Mozilla discovered 271 Firefox security vulnerabilities using Anthropic's Mythos AI model over two months, achieving "almost no false positives" through a custom agent harness that guided the LLM and gave it access to Mozilla's development tools and testing pipelines. The breakthrough addresses earlier challenges with AI-assisted vulnerability detection by combining improved models with a specialized harness architecture that verifies findings through sanitizer builds and secondary LLM grading, enabling more reliable AI-assisted security research.

Ars Technica5/7/2026

Microsoft at NSDI 2026: Advances in large-scale networked systems

Microsoft is presenting 11 papers at NSDI 2026, a leading conference on networked systems design and implementation, covering advances in AI systems, cloud infrastructure, and large-scale networks. Key contributions include DroidSpeak for efficient LLM inference through KV cache sharing, Eywa for automated protocol testing using LLMs, and AVA for video analytics with vision-language models.

Microsoft Research5/5/2026

The Download: a new Christian phone network, and debugging LLMs

The Download newsletter covers two AI-related stories: Goodfire's Silico tool uses mechanistic interpretability to let researchers debug and adjust LLM parameters during training, aiming to make AI development more scientific and controllable; and China's AI labs are releasing open-weight models that developers can download and run locally, challenging Silicon Valley's closed API model and making AI development more decentralized.

MIT Technology Review AI5/1/2026

This startup's new mechanistic interpretability tool lets you debug LLMs

Goodfire, a San Francisco startup, released Silico, a mechanistic interpretability tool that allows researchers to examine and adjust AI model parameters during training for more precise control over model behavior. The tool uses AI agents to automate interpretability work that was previously done manually, enabling developers to debug LLMs by mapping neurons and tracing pathways to understand and reduce unwanted behaviors like hallucinations.

MIT Technology Review AI4/30/2026

It runs Doom: AI chatbot edition

Software engineer Chris Nager created a playable Doom application that runs within AI chatbots like ChatGPT and Claude by leveraging Model Context Protocol (MCP), an open-source standard that allows LLMs to connect with external tools and data sources. Users can launch the game by typing "play Doom" directly in the chatbot interface, with fallback URL access if inline launching isn't supported by the client.

Engadget4/29/2026

How catastrophic is your LLM?

Amazon researchers and UIUC colleagues introduced the C3LLM framework, a statistical method for assessing catastrophic failure risks in large language models during multi-turn conversations. The framework uses graph-based modeling and Clopper-Pearson confidence intervals to compute probabilistic bounds on attack success rates, addressing limitations of traditional red-teaming approaches that focus on isolated prompts rather than conversational contexts.

Amazon Science4/27/2026

The missing step between hype and profit

This article examines the gap between AI hype and actual implementation, using the South Park 'underpants gnomes' meme as a metaphor for the unclear Step 2 between current AI capabilities (Step 1) and promised transformation (Step 3). It contrasts optimistic claims from AI companies with sobering research showing that current LLMs struggle with real-world workplace tasks and strategic judgment, highlighting how disagreement about AI's actual impact stems from conflating coding speed with broader workplace transformation and failing to account for integration challenges with existing workflows.

MIT Technology Review AI4/27/2026

AutoAdapt: Automated domain adaptation for large language models

AutoAdapt presents automated domain adaptation techniques for large language models, enabling them to rapidly adapt to new domains. The work is part of a broader research effort on improving LLM reasoning, RAG system benchmarking, and domain-specific optimization.

Microsoft Research4/22/2026

LLMs+

The article discusses the evolution of large language models (LLMs) beyond their current capabilities, exploring how next-generation LLMs+ must become more efficient, handle larger context windows, and solve complex multi-step problems autonomously. Key developments include mixture-of-experts architectures, alternative neural networks like diffusion models, expanding context windows to millions of tokens, and recursive LLM approaches being researched at institutions like MIT CSAIL to improve reliability on long-duration tasks.

MIT Technology Review AI4/21/2026

World models

World models—AI systems that represent and simulate the physical world—are emerging as a key area of focus for major AI labs and researchers, with Google DeepMind, Stanford's World Labs, and OpenAI all investing in the technology. Proponents argue world models are essential for overcoming LLM limitations and advancing robotics applications, from delivery robots to healthcare assistants, with early efforts including 3D environment generation and integration into intelligent agents.

MIT Technology Review AI4/21/2026

Skild acquires Fetch Robotics assets from Zebra

Skild AI has acquired Zebra Technologies' robotics division (formerly Fetch Robotics), integrating its hardware-agnostic foundation model AI brain with Zebra's Symmetry fleet management platform to create a unified autonomous fulfillment ecosystem for warehouses and logistics. The acquisition aims to transform task-specific warehouse automation into a coordinated intelligence layer capable of controlling diverse robotic machines while building a data flywheel to improve AI world models for physical tasks.

Robotics Business Review4/16/2026

Agent orchestration

The article examines the emerging field of multi-agent AI systems, where multiple AI agents coordinate to complete complex tasks beyond individual agent capabilities. It discusses applications ranging from coding (Anthropic's Claude Code) to general productivity tools (Claude Cowork, OpenAI's Codex, Perplexity's Computer) and scientific research (Google DeepMind's Co-Scientist), while highlighting both the potential to transform knowledge work and the risks of deploying unpredictable LLMs in critical infrastructure.

MIT Technology Review AI4/21/2026

Customized Amazon Nova models improve molecular-property prediction in drug discovery

Amazon's Generative AI Innovation Center developed customized Amazon Nova language models fine-tuned for molecular-property prediction in drug discovery, demonstrating that a single optimized LLM can match the accuracy of multiple specialized graph neural networks while providing improved usability and reasoning capabilities for medicinal chemists. The approach combines supervised and reinforcement fine-tuning to create a unified AI assistant that simplifies workflows and accelerates drug discovery by enabling chemists to query multiple molecular properties in one interaction rather than managing separate models.

Amazon Science4/15/2026

Locus Robotics launches Locus Array for fully autonomous fulfillment

Locus Robotics launched Locus Array, a mobile manipulation system combining an omnidirectional mobile robot, integrated picking arm, and AI-powered perception for autonomous warehouse fulfillment. The system represents the company's evolution toward fully autonomous warehouse operations, capable of handling multiple workflows including picking, putaway, and replenishment while working alongside existing Locus AMRs across 350+ facilities globally.

Robotics Business Review4/13/2026

Binghamton researchers create robotic guide dogs that walk - and talk

Researchers at Binghamton University have developed a robotic guide dog that uses large language models (LLMs) like GPT-4 to communicate with visually impaired users, providing real-time navigation feedback and route planning through spoken dialogue. The system was tested with seven legally blind participants navigating an office environment, with users rating the combination of pre-journey planning explanations and real-time scene narration as most helpful. The team plans to expand the research with more user studies and longer indoor/outdoor navigation distances to integrate the technology into daily life.

Robotics Business Review4/12/2026

Responsible and safe use of AI

OpenAI publishes a guide on responsible and safe use of AI, covering best practices for ChatGPT users including verifying information accuracy, understanding model limitations, maintaining human oversight for critical tasks, being transparent about AI use, and reporting unsafe outputs. The guide emphasizes that LLMs can produce inaccurate information and should not replace professional advice in legal, medical, or financial matters.

OpenAI Blog4/10/2026