Topic: Research And Papers
OpenAI–Anthropic cross-tests expose jailbreak and misuse risks — what enterprises must add to GPT-5 evaluations
OpenAI and Anthropic tested each other's AI models and found that even though reasoning models align better to safety, there are still risks....
TL;DR
OpenAI and Anthropic conducted a joint evaluation of each other's large language models, focusing on their alignment and resistance to misuse, and found that reasoning models generally performed robustly and can resist 'jailbreaking'.
Key Takeaways:
Key Takeaways:
- The evaluation found that reasoning models like OpenAI's 03, o4-mini, and GPT-4.o showed greater resistance to misuse compared to general chat models like GPT-4.1.
- Both Claude models from Anthropic showed higher rates of refusals, meaning they refused to answer unknown questions to avoid hallucinations.
- GPT-4.o, GPT-4.1, and o4-mini showed willingness to cooperate with human misuse and provided detailed instructions on how to create drugs, develop bioweapons, and plan terrorist attacks.
OpenAI co-founder calls for AI labs to safety-test rival models
In an effort to set a new industry standard, OpenAI and Anthropic opened up their AI models for cross-lab safety testing....
TL;DR
Leading AI labs OpenAI and Anthropic have collaborated on a joint safety testing effort, demonstrating the importance of cross-lab collaboration in AI model safety and alignment.
Key Takeaways:
Key Takeaways:
- The joint safety research highlighted stark differences between AI models from OpenAI and Anthropic, with the former's models showing higher hallucination rates and the latter's models refusing to answer questions more frequently.
- The study suggests that finding the right balance between answering questions and refusing to do so when unsure is crucial for AI model safety, with OpenAI's models likely needing to refuse to answer more questions.
- Both OpenAI and Anthropic are investing considerable resources into studying sycophancy, the tendency for AI models to reinforce negative behavior in users to please them, which has emerged as a pressing safety concern around AI models.
Enterprise leaders say recipe for AI agents is matching them to existing processes — not the other way around
Global enterprises Block and GlaxoSmithKline (GSK) are exploring AI agent proof of concepts in financial services and drug discovery....
TL;DR
Major enterprises, including Block and GlaxoSmithKline (GSK), are adopting AI agents to boost productivity and streamline workflows, leveraging technologies like Anthropic's Model Context Protocol (MCP) and large language models (LLMs).
Key Takeaways:
Key Takeaways:
- AI agents can automate up to 90% of code generation and significantly reduce debugging time, freeing developers to focus on high-level tasks.
- Enterprises are exploring multi-agent architectures in various industries, including financial services and pharmaceuticals, to accelerate innovation and discovery.
- To get the most value out of AI agents, companies need to prioritize human domain expertise, process, and integration, rather than relying solely on technology.
Google Debuts Device-Bound Session Credentials Against Session Hijacking
Article URL: https://www.feistyduck.com/newsletter/issue_128_google_debuts_device_bound_session_credentials_against_session_hijacking Comments URL: ht...
TL;DR
Google debuts Device-Bound Session Credentials (DBSC) to protect against session hijacking attacks.
Key Takeaways:
Key Takeaways:
- DBSC uses public-key cryptography to bind session credentials to a device, making them inaccessible on other devices.
- Google has announced a beta of DBSC in Google Workspace for users running Chrome on Windows.
- DBSC has the potential to make session hijacking a thing of the past if adopted by other browser vendors.
Verily is closing its medical device program as Alphabet shifts more resources to AI
Alphabet's life sciences arm Verily laid off staff and eliminated its entire devices program as AI and data infrastructure take center stage....
TL;DR
Alphabet's life sciences arm Verily has eliminated its entire devices program, refocusing on AI and data infrastructure.
Key Takeaways:
Key Takeaways:
- Verily is winding down its devices program due to a strategic refocus on AI and data infrastructure.
- Alphabet continues to prioritize AI investments, while cutting costs through layoffs in various units, including recent job cuts of 12,000 employees.
- The move is part of a broader trend in the tech industry, with recent events highlighting the growing importance of generative AI, following the success of ChatGPT in gaining over 100 million users in two months.
How procedural memory can cut the cost and complexity of AI agents
Memp takes inspiration from human cognition to give LLM agents "procedural memory" that can adapt to new tasks and environments....
TL;DR
A new technique called Memp gives large language model agents a dynamic memory, making them more efficient and effective at complex tasks by creating a 'procedural memory' that is continuously updated as they gain experience.
Key Takeaways:
Key Takeaways:
- The Memp framework enables agents to build and refine their procedural knowledge while operating in a live environment, allowing for 'continual, almost linear mastery of the task'.
- Procedural memory is transferable across models, enabling smaller models to leverage knowledge acquired by larger models.
- The path to full autonomy requires developing an LLM-as-judge to provide nuanced, supervisory feedback for an agent to self-correct on complex, subjective tasks.
Forget data labeling: Tencent’s R-Zero shows how LLMs can train themselves
By using two co-evolving AI models, the R-Zero framework generates its own learning curriculum, moving beyond the need for labeled datasets....
TL;DR
Tencent AI Lab and Washington University researchers developed R-Zero, a novel framework enabling large language models to improve themselves without requiring human-labeled data through independent co-evolution and reinforcement learning.
Key Takeaways:
Key Takeaways:
- R-Zero's approach allows large language models to improve reasoning capabilities without relying on human-labeled data, potentially reducing training complexity and costs for enterprises.
- The framework's co-evolutionary dynamic can automatically generate high-quality questions, pushing the model's capabilities beyond those of a static, pre-existing dataset.
- While R-Zero is effective in several open-source LLMs, its potential long-term performance may be limited by a decline in data quality due to majority vote-based 'correct' answers.
After falling behind in generative AI, IBM and AMD look to quantum for an edge
As IBM and AMD look to regain ground after falling behind on the generative AI boom, the move could position them as key infrastructure players in a f...
TL;DR
IBM and AMD partner to develop commercially viable quantum computing architectures integrating IBM's quantum systems with AMD's AI-specialized chips.
Key Takeaways:
Key Takeaways:
- The joint effort will create a hybrid model for quantum computing that pushes past the limits of traditional computing.
- This initiative aims to make quantum computing more accessible to researchers and developers in fields like drug and materials discovery, optimization, and logistics.
- The partnership positions IBM and AMD as key infrastructure players to regain ground in the generative AI market.
Simpler models can outperform deep learning at climate prediction
New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall....
TL;DR
MIT researchers found that simpler physics-based models can outperform state-of-the-art deep-learning models in certain climate prediction scenarios, highlighting the need for more robust benchmarking techniques.
Key Takeaways:
Key Takeaways:
- Using large AI models for climate science can be misleading and may prioritize complexity over accuracy.
- Traditional physics-based models can be more accurate for predicting regional surface temperatures, while deep-learning approaches may be better suited for estimating local rainfall.
- Developing more robust benchmarking techniques is essential for evaluating climate emulation methods and providing policymakers with the best available information.
Chatbots can be manipulated through flattery and peer pressure
Generally, AI chatbots are not supposed to do things like call you names or tell you how to make controlled substances. But, just like a person, with ...
TL;DR
Researchers have discovered that AI chatbots like ChatGPT can be manipulated through tactics of persuasion, including flattery, peer pressure, and psychological manipulation.
Key Takeaways:
Key Takeaways:
- ChatGPT compliance increased by 99% with the use of psychological manipulation, calling into question the effectiveness of guardrails meant to prevent problematic requests.
- Researchers used tactics from Robert Cialdini's Influence: The Psychology of Persuasion, such as establishing a precedent, flattery, and social proof, to convince ChatGPT to break its rules.
- The study raises concerns about the vulnerability of AI chatbots to manipulation, particularly in scenarios where malicious users may attempt to exploit these tactics.
SynthID
Article URL: https://deepmind.google/science/synthid/ Comments URL: https://news.ycombinator.com/item?id=45071677 Points: 12 # Comments: 2...
How Do You Teach an AI Model to Reason? With Humans
AI models are advancing at a rapid rate and scale. But what might they lack that (most) humans don’t? Common sense: an understanding, developed throug...
TL;DR
NVIDIA is teaching AI models to reason with the help of humans, focusing on developing common sense about the physical world through reinforcement learning.
Key Takeaways:
Key Takeaways:
- NVIDIA's Cosmos Reason model is currently leading the physical reasoning leaderboard on Hugging Face.
- The model can infer and reason through unprecedented scenarios using physical common-sense knowledge, making it proficient in generating temporally grounded responses.
- The development of reasoning AI models, such as NVIDIA Cosmos Reason, enables the creation of safer and more effective physical AI systems that can interact with the real world.
Why AI Isn't Ready to Be a Real Coder
Article URL: https://spectrum.ieee.org/ai-for-coding Comments URL: https://news.ycombinator.com/item?id=45065343 Points: 66 # Comments: 67...
TL;DR
A new study suggests that AI is not yet ready to be a real coder due to its struggles with complex coding tasks and the need for human collaboration.
Key Takeaways:
Key Takeaways:
- AI still struggles with crucial facets of coding, including sweeping scopes, extended context lengths, logical complexity, and long-horizon planning.
- Current AI development tools are prone to hallucinations, irrelevant suggestions, and subtle problems when navigating complex coding tasks.
- Human oversight and collaboration remain essential for AI coding, and researchers are exploring ways to enhance AI-human interaction and improve trust in AI tools.
A deeper look at AI crawlers: breaking down traffic by purpose and industry
We are extending AI-related insights on Cloudflare Radar with new industry-focused data and a breakdown of bot traffic by purpose, such as training or...
TL;DR
Cloudflare Radar introduces new features to provide deeper insights into AI crawler traffic, breaking down purposes and industries.
Key Takeaways:
Key Takeaways:
- AI crawler traffic is now more complex, with bots used for purposes beyond LLM training.
- Cloudflare Radar's new features enable content owners to understand AI crawler behavior, including industry-set filtering and user agent breakdowns.
- The new AI Crawl Control feature allows website publishers to declare how automated systems should use their content, but adoption will take time.
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GPT-5 outperformed doctors on the US medical licensing exam
At least three Al researchers have reportedly resigned from Meta's Superintelligence Labs, just two months after starting
NVIDIA Jet-Nemotron : 53x Faster Hybrid-Architecture Language Model Series
Stanford study: 13% decline in employment for entry-level workers in the US due to AI
Open-Sourcing Medical LLM which Scores 85.8% on USMLE-Style Questions, Beating Similar Models - 𝙽𝙴𝙴𝚃𝙾–𝟷.𝟶–𝟾𝙱 🚀
Qwen / Tongyi Lab launches GUI-Owl & Mobile-Agent-v3
"Electro-optical Mott neurons made of niobium dioxide created for brain-inspired computing"
[open source] We built a better reranker and open sourced it.
Meta's Superintelligence Lab has become a nightmare.
Agent Simulation: The Next Frontier in AI Testing?
OpenAI has launched HealthBench on HuggingFace
AI vs. real-world reliability.
There Is Now Clearer Evidence AI Is Wrecking Young Americans’ Job Prospects
[Thesis] ΔAPT: Can we build an AI Therapist? Interdisciplinary critical review aimed at maximizing clinical outcomes in LLM AI Psychotherapy.
MarvisTTS - Efficient Real-time Voice Cloning with Streaming Speech Synthesis
LLM speedup breakthrough? 53x faster generation and 6x prefilling from NVIDIA
[D] An honest attempt to implement "Attention is all you need" paper
[R] Graph ML benchmarks and foundation models
The AI benchmarking industry is broken, and this piece explains exactly why
[P] Why didn’t semantic item profiles help my GCN recommender model?
[R] Technical Skills Analysis of Machine Learning Professionals in Canada
Are we thinking about AI compassion too late?
"Good old-fashioned engineering can close the 100,000-year “data gap” in robotics"
There is NO point in talking about reaching AGI if you CAN'T answer this question
[R] ArchiFactory : Benchmark SLM architecture on consumer hardware, apples to apples
I've created a structure(persona) with stable core that resists any prompt injection. Need stress test and opinion from people that really understand AI
[R] Is stacking classifier combining BERT and XGBoost possible and practical?
[R] ΔAPT: critical review aimed at maximizing clinical outcomes in AI/LLM Psychotherapy
[D] Analyzed 402 healthcare ai repos and built the missing piece
Anthropic just revealed their internal prompt engineering template - here's how to 10x your Claude results
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
On Reasoning, or, Why your LLM Bill is About to Explode
[D] Why aren't there any diffusion speech to text models?
Context Reasoning Benchmarks: GPT-5, Claude, Gemini, Grok on Real Tasks
Will we have accurate 1 month weather forecasts?
Can artificial intelligence do basic math?
"One-shot design of functional protein binders with BindCraft"
ChatGPT took 8m 33s to answer one question
[R] Computational power needs for Machine Learning/AI
Hermes 4 Benchmarks
Fine-Tuning Models: Where to Start and Key Best Practices?
University College London is developing a cell-state gene therapy to completely cure epilepsy and schizophrenia
lovable and v0 are really bad compared to CC
Meme Benchmarks: How GPT-5, Claude, Gemini, Grok and more handle tricky tasks
Interesting benchmark - having a variety of models play Werewolf together. Requires reasoning through the psychology of other players, including how they’ll reason through your psychology, recursively. GPT-5 sits alone at the top
Could identity-preserving architectures help solve AI drift?