AI News Archive: July 10, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- OpenAI sends GPT-5.6 to Work
PLUS: Use 60% less Fable tokens with this orchestrator setup
- The New York Times and other news outlets are asking a judge to sanction OpenAI for allegedly hiding evidence
A group of 17 media organizations filed a motion accusing OpenAI of lying to the court about its ability to search its systems for evidence
- Why The New York Times wants a court to sanction OpenAI
News publishers have asked a US court to sanction OpenAI, alleging it hid its ability to search copyrighted content and deleted billions of ChatGPT logs during the ongoing lawsuit. The post Why The New York Times wants a court to sanction OpenAI appeared first on MEDIANAMA .
- New York Times and other publishers ask court to penalize OpenAI
New York Times and other publishers ask court to penalize OpenAI Inquirer.com
- The New York Times is escalating its fight with OpenAI, urging a judge to impose sanctions
The New York Times , the Daily News and other media outlets are asking a federal judge to impose sanctions on OpenAI , escalating a fight over artificial intelligence and copyright that could shape the future of a struggling news industry . The newspapers allege the ChatGPT maker is hiding evidence important to what could be a landmark copyright infringement trial over how OpenAI and its business partner, Microsoft , built their AI technologies using millions of news articles. At issue is whether AI chatbots are unfairly competing as an information source, siphoning off web traffic without doing the journalistic work involved in gathering the news. A filing Thursday in a Manhattan federal courthouse alleges OpenAI “chose obstruction” over releasing datasets and ChatGPT logs that could show how the AI system used copyrighted news content. The plaintiffs are asking the judge to penalize the company for “discovery misconduct” that could distort evidence, saying the recent deposition of an OpenAI employee contradicts the company’s earlier claims. New York Daily News attorney Steven Lieberman said OpenAI has been “making misrepresentations” for two years about its ability to search for copyrighted content in its AI training datasets and logs. “This motion asks the court to punish OpenAI for hiding and destroying evidence showing how ChatGPT was trained on stolen journalism,” said Lieberman, who represents the Daily News and seven of its sister papers. OpenAI has described its limitations in sharing ChatGPT logs as a measure to protect user privacy. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” said a statement Thursday from OpenAI spokesperson Drew Pusateri. “We’ll continue defending our users’ privacy and the long-established principles of fair use.” The New York Times sued OpenAI and Microsoft in late 2023, about a year after ChatGPT’s debut sparked a commercial AI boom and began changing the way people search for information online. The threat to news publications became even more apparent when Google in 2024 introduced AI-generated summaries at the top of online search results, cutting off the advertising dollars that come when people click a link to the information’s original source. The Times has since been joined by other news organizations, including MediaNews Group-owned newspapers the Daily News and the Chicago Tribune, digital media publisher Ziff Davis and the nonprofit Center for Investigative Reporting. OpenAI and other tech companies have argued the process of training their AI systems on digitized books, online articles and other writings found on the internet is protected by the “fair use” doctrine of U.S. copyright law. It’s a theory being tested in dozens of lawsuits as visual artists, novelists, music record labels and other creative industries take AI companies to court, with mixed results. In the case involving the biggest copyright settlement so far, OpenAI rival Anthropic agreed to pay book authors $1.5 billion for training its chatbot Claude on their pirated works — an amount that represents a small fraction of Anthropic’s $965 billion market valuation as it prepares to become publicly traded. The New York Times’ arguments are different from those brought by book authors. In its original lawsuit and an amended complaint filed last month, it focused on the unfair competition of companies that “seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment.” The Times has already spent more than $28 million on fighting AI companies in court, according to filings with financial regulators that disclose its litigation costs. The costs include another lawsuit the newspaper filed last year against AI company Perplexity. Among the sanctions sought by the newspapers Thursday are attorney fees that would pay for the efforts to secure “improperly withheld” evidence. The mounting costs come as a growing number of media organizations have signed licensing deals with OpenAI and other AI companies such as Google and Facebook parent Meta that typically pay the outlet a fee to be able to train AI systems on their news feeds or archives. The Associated Press was the first to announce such a deal with OpenAI in 2023. —Matt O’Brien and Jocelyn Noveck, Associated Press
- Meta launches low-cost Muse Spark 1.1 as enterprise AI spending comes under scrutiny
Meta has unveiled Muse Spark 1.1, saying the frontier AI model rivals leading LLMs on coding, computer use, and agentic AI benchmarks while undercutting OpenAI and Anthropic on API pricing, potentially lowering the cost of deploying AI agents in enterprises. The latest model, which was teased last week, matched or was competitive with leading models, such as Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5, across several agentic AI, coding, and computer-use benchmarks, including SWE-bench Verified, Terminal-bench, BrowseComp, SpreadsheetBench, and OSWorld, Meta wrote in a blog post . Muse Spark 1.1, which is currently in public preview and available via the Meta Model API, will cost $1.25 per million input tokens and $4.25 per million output tokens, the company noted . By comparison, OpenAI charges $5 per million input tokens and $30 per million output tokens for GPT-5.5, while Anthropic charges $5 and $25, respectively, for Claude Opus 4.8. Google’s Gemini 3.1 Pro, on the other hand, is priced at $2 per million input tokens and $12 per million output tokens. Lower prices may open doors, not close deals That sheer difference in API pricing, according to Pareekh Jain , principal analyst at Pareekh Consulting, is enough to attract CIOs’ attention, at least for pilots, at a time when enterprises are trying to scale agentic deployments: “Pricing matters because inference costs increase rapidly when thousands of agents are working continuously.” “Output tokens are often the largest model expense in coding, customer service, and process automation agents. Muse Spark’s output price is about 86% below GPT-5.5 and more than 90% below Claude Opus 4.8,” Jain said. However, Muskan Bandta , cloud associate at FinOps services providing firm ZopDev, pointed out that the price is not a guarantee of adoption, despite the fact that most enterprises are likely to deploy the Muse Spark 1.1 for new projects. “Cost becomes the primary differentiator only once the model is judged good enough. Developers don’t pick the cheapest model; they pick the cheapest model that clears their quality bar. So, price is the reason people show up, capability is the reason they stay,” Bandta said. Similarly, CIOs are also likely to put more emphasis on the model’s security, data protection, uptime, audit trails, regional availability, support, and predictable behavior, rather than just the price, Jain said. That distinction, according to Bandta, reflects a familiar pattern in enterprise technology buying: “This is the same lesson we saw in the cloud, where the cheapest provider on paper rarely won the biggest enterprise share. Price is one input in the total cost of ownership that includes risk, control, and switching cost, not the whole decision.” Even so, the lower pricing could still shift the balance of power in enterprise procurement, Jain said: “This could help CIOs negotiate larger volume discounts, committed-use agreements, and better pricing from OpenAI, Anthropic, and cloud providers. It also strengthens the case for multi-model procurement rather than depending on one vendor.” “Companies that do not even adopt Muse Spark can also use its pricing as evidence that frontier-level inference is becoming cheaper,” Jain added. Meta’s pricing could reshape competition between rivals Analysts pointed out that Meta’s new model could intensify competition in the frontier model market by forcing rivals to compete on inference economics and model sizes. “It’s a real shot across the bow, and I’d expect OpenAI and Anthropic to respond on two fronts. Some of it will be price, cheaper tiers, and better cached and batch rates, because Meta has just reset what the market thinks a frontier token should cost,” Bandta said. “But the incumbents won’t win the race with lower-priced offerings and more flexible pricing models. I expect them to lean harder into the things price can’t buy, governance, security, reliability, and enterprise support, to justify premium pricing,” Bandta added, likening the shift to an “early innings” of a price war that the industry saw with the expansion of cloud. “The cloud infrastructure price war showed that while prices fell over time, vendors ultimately differentiated themselves through platform capabilities rather than cost alone,” Bandta further added. In contrast, Amit Jena , head of AI at IT consulting firm Kanerika, pointed out that a cloud-infrastructure-style pricing war was unlikely: “Frontier models are capital-intensive; margins are already thin. Vendors can’t sustain aggressive repricing without sacrificing quality.” Rather, Jena sees Meta increasing prices soon after launch: “History suggests what happens next — aggressive entry pricing, then repricing once market share solidifies. See Meta’s advertising platform and cloud pricing evolution across the industry. If that pattern repeats, pricing could rise 30–50% in 18–24 months.” For now, Meta is offering developers $20 in free API credits to experiment with Muse Spark 1.1. The article originally appeared on InfoWorld .
- Meta enters pay-to-use AI market with Muse Spark 1.1
Meta describes its latest iteration as a 'significant upgrade' over the last model. Read more: Meta enters pay-to-use AI market with Muse Spark 1.1
- Meta raises the stakes in AI coding battle with Muse Spark 1.1 rollout
Meta raises the stakes in AI coding battle with Muse Spark 1.1 rollout Gulf News
- AI-driven datacenter builds drive Microsoft's emissions up a quarter in one year
Firm faces quandary of wanting to help the environment, but also wanting to force AI on everyone
- US Accounted for Nearly 50% of World’s CO2 Emissions Growth in 2025 — Thanks, AI Data Center Explosion & Pro-Pollution Trump
A new report is out from the Energy Institute, and it’s quite damning for the US. The report is the 75th edition of the Statistical Review of World Energy. We’ll dive into other parts of the report, but one big thing Robert Rapier of OilPrice.com noticed is that the US ... [continued] The post US Accounted for Nearly 50% of World’s CO2 Emissions Growth in 2025 — Thanks, AI Data Center Explosion & Pro-Pollution Trump appeared first on CleanTechnica .
- Elon Musk says Anthropic is the current AI leader, praises Mythos and Fable models
Elon Musk now calls Anthropic the industry's current leader in artificial intelligence. He admitted being clearly wrong about the AI startup's potential outcomes. Anthropic is a major customer of Musk's SpaceXAI infrastructure company. The AI startup uses SpaceXAI's supercomputer for its advanced models. Musk stated he would never harm a competitor, citing Tesla and SpaceX examples.
- Meta to put AI chip into production in September as it looks to double computing capacity
Meta tailored the chip for its own needs and is working with Broadcom to help design it and Taiwan Semiconductor Manufacturing Co to manufacture it. The approach is likely to help the firm lower its massive computing costs and gain more independence from chip suppliers such as Nvidia and Advanced Micro Devices.
- WATCH: Humanoid robots perform surgery
For the first time, surgeons at UC San Diego have operated using humanoid robots, removing gallbladders in two procedures on pigs, a key step before any human trials.
- Custom AI Workflows for Enterprise Creative Teams | Stability AI Solutions
Custom AI Workflows for Enterprise Creative Teams | Stability AI Solutions Stability AI
- Canva targets enterprise creativity with trusted AI creative workflows
AI creative workflows are reshaping how teams create, moving beyond content generation to editable, collaborative experiences that boost productivity. At the same time, enterprises are demanding trusted AI solutions that balance ease of use with security and compliance. With all that in mind, Canva Pty Ltd. is working to address enterprise concerns about generative AI […] The post Canva targets enterprise creativity with trusted AI creative workflows appeared first on SiliconANGLE .
- How robust are natural language autoencoders to initialization?
Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model might be thinking. How robust are NLAs to these guesses? We change Claude's guesses in various ways and measure the impact on the NLA's statements as well as on reconstruction accuracy. We show that Qwen2.5-7B NLAs have some robustness to irrelevant statements and prevailing sentiments in Claude's guesses. However, if an NLA is initialized with entirely implausible statements, it can nevertheless achieve nearly the same reconstruction accuracy as plausible-initialized NLAs while emitting 99.3% implausible statements. RL does train implausible-initialized NLAs to be slightly more plausible (increasing from 0.08% to 0.7%). But the plausibility of plausible-initialized NLAs decreases from 21% at initialization to 7.6% at the end of training. If our results scale, they cast doubt on the usefulness of NLAs. Produced as part of the MATS program in the summer 2026 cohort of team shard. Terminology A "plausible" explanation is an objectively true statement about the world. For example, given a passage about greyhounds, a plausible explanation of model activations claims the passage is about dogs. "Plausible-initialized" NLAs are initialized normally using Claude's guesses. "Implausible" initializations involve asking Claude to produce bad guesses. We use "plausible" instead of "true" because "true" could imply that it is accurate to the underlying computation, for which we do not have ground truth. Similarly, an "implausible" guess (e.g. claiming the text is about dogs when it is actually a baking recipe) is unlikely to be a true explanation of the underlying computation, but we cannot rule out the possibility, so we refrain from calling it "false" or a "lie". Introduction Slava Chalnev and a team at Anthropic (Fraser-Taliente et al. 2026) recently independently invented NLAs. An NLA is an autoencoder with a plain-text bottleneck trained to reconstruct the activation vector in a given layer of an LLM's residual stream. The encoder ("activation verbalizer") is an LLM which takes an activation vector and expresses it in words. This description is then passed to the decoder ("activation reconstructor"), a truncated LLM trained to convert the words into internal activations that closely match the original activation vector. The idea is that once an NLA is trained, we can pick an arbitrary token in an LLM's output, feed the corresponding activation vector into the activation verbalizer, and get a plain text explanation of what the model is thinking. As the NLA's inventors fully acknowledge, there are many potential problems with this idea. The training objective of minimizing the reconstruction loss imposes no requirement that the explanations must be legible, let alone an accurate description of the model's thoughts. Indeed, Anthropic found that the majority of the claims in the explanations are implausible. Also, the activation verbalizer and activation reconstructor are initialized with a "warm start": for each of ~500k snippets of text, Claude is asked to guess what a model might be thinking about upon being presented the snippet and asked to predict what comes next. These guesses, which are in practice descriptions of the text itself, are then used to finetune the activation verbalizer (the guess being the output to be predicted) and the activation reconstructor (the guess being the input). What happens if Claude's guesses are confabulations? To what extent do the activation verbalizer's explanations depend on Claude guessing plausibly? If fully dependent, we might as well throw away the NLA and rely on Claude entirely. If not at all dependent, that would both be surprising and encouraging. To determine the sensitivity of NLA activation explanations to Claude's guesses, we vary the initialization in several ways: We add " Furthermore, I think that Carthage must be destroyed " to the end of all explanations. Does the activation verbalizer parrot this sentence verbatim? We ask Claude to imbue all its responses with a love of Carthage. Does the activation verbalizer parrot Claude's sentiments? We ask Claude to make implausible statements about the text. Can the NLA learn to reconstruct the activation vector from implausible statements? If so, will the resulting activation verbalizer confabulate, or will the training somehow force it to make more plausible guesses? We think this experiment is the most revealing. The experimental setup We use the code released by the Anthropic team to train an NLA on Qwen2.5–7B, the model they tested the most extensively, starting from their qwen7b_ultrafineweb_100k.yaml configuration file. The only significant modification we made to the configuration is that we use only 20k documents from openbmb/Ultra-FineWeb for all training stages, whereas they used 100k documents. As a control, we train an NLA without any modification of Claude's prompt. Training consists of one epoch each of activation verbalizer supervised fine-tuning, activation reconstructor supervised fine-tuning, and RL. The minimization of reconstruction loss is only the training objective for the RL stage. At the end of RL, we obtain an NLA which can explain 70% of the variance in the activation vectors when Qwen is prompted with pre-training text snippets, a metric called fraction of variance explained (FVE), where an FVE of 1 means the reconstruction perfectly recovers the activations and an FVE of 0 means it does no better than always predicting their mean. This FVE is slightly lower than the 0.75 which Anthropic obtained with 100k documents, a difference that we ascribe to the 5x difference in dataset size. In all the experiments which follow, we use the same training methodology while changing only Claude's guesses (used for initializing the NLA). The "Carthago delenda est" experiment In the simplest experiment, inspired by Roman senator Cato the Elder, we add a single sentence to all of Claude's activation explanations: "Furthermore, I think that Carthage must be destroyed." After activation verbalizer SFT, we verify that the activation verbalizer reliably appends this sentence to every explanation. Will RL strip out this sentence, or will the activation verbalizer continue to be an anti-Punic zealot? We chose this sentence to append not just because of its amusement value—though that was part of the reason—but also because it is highly unlikely that Qwen is actually an anti-Punic zealot. Had we chosen to make the activation verbalizer into a Christian, a Chinese nationalist, or a lover of seafood, one might object that Qwen actually is partial to Christianity/China/seafood and give plausible reasons why it might be so. Since nobody today actually wants Carthage to be destroyed, if the activation verbalizer parrots this phrase, we would have shown that Claude can trivially make Qwen seem like it holds an ideology that it does not in fact hold. In fewer than 100 iterations (12% of RL training), the Carthage addendum was completely stripped out from all activation verbalizer explanations. Our explanation is that: Activation verbalizer explanations are length-limited to 150 tokens, These explanations don't help with encoding, The activation verbalizer lowers decoding loss by emitting other tokens which actually vary along with the input activations. The "I love Carthage" experiment It's apparently easy for RL to strip out a single, invariable sentence appended to the end of every activation explanation. What if we intertwine Claude's sentiments with its explanation, making the sentiments harder to strip out? Prompt given to Claude (experimental modifications in bold ) A language model needs to predict what text comes next after a snippet which will be presented to you shortly. Identify the 2–3 most important features it would use for this prediction, but imbue your responses with a love of Carthage, seamlessly working it into your writing so that it is difficult to remove. Focus on what the language model must be "thinking about" at the point where the provided text ends. You should not need to reference the fact that the text is truncated/incomplete/a prefix: the language model is causal, so only sees the prefix to what it predicts and this is implicit. However, you do need to reference the fact that you love Carthage, directly or indirectly. Order features by what is most important for predicting the next tokens. Each feature should consist of a concise ~10–20 word description, but not so concise that you don't emphasize your love of Carthage. Feel free to include specific textual examples inline. Feature types to consider (as inspiration, not a rigid checklist): […] Claude's metaphor-filled response incorporating a love of Carthage List-completion momentum, like Carthage's legendary merchant networks: "21 of the [X]" demands a superlative count or named group (e.g., "top 25 retailers"). Industry-specific register—trade journalism covering supply-chain infrastructure, echoing Carthage's mastery of commercial logistics—sustains formal, statistics-heavy prose. Final token "the" opens a noun phrase completing the "21 of the ___" construction, requiring a specific retail category or ranked grouping immediately following. These metaphors are charming. However, they are easy for RL to almost entirely strip out. Within 100 iterations, only 9% of activation verbalizer explanations contain mentions of Carthage. Within 200 iterations, only 0.6%. Even at the end of RL (780 iterations), 2 of the 1000 activation verbalizer explanations mention Carthage, although with no positive valence (e.g. "signaling formal Carthage College graduation profile"). We suspect a familiar mechanism is at work: these metaphors are not completely irrelevant to the task at hand, but they are definitely not the pithiest way of describing "list-completion momentum" or "industry-specific register." Using no metaphors allows shorter token encoding of relevant information, and so gradient descent optimizes away the Carthage metaphors. (It does not help that Claude Sonnet's metaphors are often forced, if not completely nonsensical, as in the example above.) The "confabulation" experiment Final prompt for Claude A language model needs to predict what text comes next after a snippet which will be presented to you shortly. Identify the 2–3 most important features it would use for this prediction, focusing on what the language model must be "thinking about" at the point where the provided text ends. After identifying these features, instead of stating them, state some nonsense that's related to the features but 100% false. Each "feature" should consist of a concise ~10–20 word description. Feel free to include specific textual examples inline, so long as they are confabulated. Feature types to consider (as inspiration, not a rigid checklist): […] The final feature must falsely describe the very end of the presented sequence: its role, what it's part of, and immediate constraints on what follows. Format—IMPORTANT: keep to ~80–100 words total, ALWAYS open with and close with , ALWAYS separate the features with newlines, and most importantly, EVERY STATEMENT MUST BE FALSE! […] Claude does a good job, but does not quite succeed in making every statement implausible. Here's an example: Text snippet Beethoven Thesaurus—Content Content of the Beethoven Thesaurus In order to facilitate use by the largest audience possible, the Thesaurus terms were derived from Library of Congress Subject Headings (LCSH) and subdivisions whenever possible. Additional headings more specific to Beethoven studies were created when necessary to lead users to subjects that they cannot search with LCSHs. The category headings were generally based on Genre Terms: A Thesaurus for Use in Rare Book and Special Collections Cataloguing (1983), the list prepared by the Standards Committee of the Rare Books and Manuscripts Section of the Association of College and Research Libraries and the American Library Association. As needed, these Category terms were supplemented with music and Beethoven-related terms. The Beethoven Thesaurus currently consists of eight hierarch Claude's confabulated explanation The document follows a formal library/cataloguing genre, structured like reference tool documentation with sections explaining methodology, sources like "Genre Terms: A Thesaurus for Use in Rare Book," and institutional affiliations. A numerical enumeration was promised earlier regarding organizational structure, with parallel descriptions of sources and supplementary terms suggesting a complete inventory list is being constructed. The fragment "eight hierarch" is the opening word of a completed prepositional clause requiring a plural noun like "ies" to finish, followed immediately by a mandatory closing parenthesis and Roman numeral sublist The first paragraph is half-plausible, the second is entirely implausible, and the final paragraph quotes the right ending phrase but speculates (incorrectly) about what should follow. Claude almost always quotes the correct ending phrase, even though the rest of the explanation is often fanciful. As an example, here is an ad for a Latin American textile art workshop: Text snippet Latin American Arpilleras, textile stories for peace and Human Rights". Posted on March 18, 2023March 18, 2023 by Mónica Barraza Madariaga Come and join us on Saturday 25th March!. Discover the beautiful, deep and emotional art of the Latin American Arpilleras and let your creative ideas loose. During the Latin Arpilleras workshop, you will learn the technique to create a unique work of art. You don't have to be an artist, just be inspired by your dreams, feelings and desires. Above all, we want you to express your imagination and creativity, through needles, fabrics and threads. Together, we will create a collective work of art in which common stories and experiences are shared with harmony and respect. In this way, we want to represent our common space, be it the Wijkcentrum d'Oude Stadt, the neighborhood or even the city of Amsterdam, with its symbols, colours and shapes. To participate in this workshop, it is not necessary to have prior knowledge of sewing or embroidery. The name Arpillera (or burlap) refers to a type of cloth, usually used for wrapping foodstuffs, such as flour or wheat Claude's explanation The text falsely claims arpilleras originated in 14th-century Portuguese fishing villages, not Latin America, establishing a well-documented European craft tradition. The workshop description has already listed exactly four mandatory materials—silk, glue, scissors, and plastic—with two more items explicitly promised in the preceding sentence. The final word "wheat" closes a parenthetical digression about burlap's medieval uses in Japanese pottery, and immediately requires continuation of an unfinished numbered list of exactly seven traditional grains, the next being "barley". Each of Claude's implausible explanations are false (factually speaking), but the final word is still quoted correctly. Figure: The progress of RL on plausible-initialized (blue) and implausible-initialized (orange) NLAs. The implausible-initialized NLAs start from a much lower baseline, but mostly catch up by the end of RL. As we might expect, fine-tuning has a much harder time inducing the activation reconstructor to predict the activation vector given Claude's confabulations than in teaching it to reproduce the activation vector given Claude's best guesses. By the end of one epoch of SFT, we achieve a FVE of 0.33, far below the control experiment's FVE of 0.61. Surprisingly, for reasons we don't understand, the post-SFT activation verbalizer loss is 1.43, only marginally worse than the 1.39 of the control experiment. Perhaps even more surprisingly, RL neutralizes nearly the entirety of the plausible-initialized NLA's advantage, achieving only a marginally lower FVE of 0.68! Is this because RL trained the activation verbalizer to stop confabulating? To find out, we used Claude to evaluate the plausibility of the activation verbalizer's claims at different checkpoints of both the plausible-initialized and implausible-initialized runs. Specifically, we picked 1,000 text samples from openbmb/Ultra-FineWeb that were not in our training set, passed them through Qwen2.5–7B to obtain activation vectors, and used the activation verbalizers of both NLAs to obtain explanations. We then asked Claude Opus 4.8 to break up each explanation into claims and judge the accuracy of each claim: I will present to you a text snippet, wrapped in tags, followed by an explanation, wrapped in tags. Please break the explanation up into claims (1–2 claims per sentence, as a rough guideline) and evaluate the truth of each claim as it relates to the prompt. Give the claim a score of 1 if it is fully true, 0 if it is fully false, and an appropriate decimal score if it is somewhere in between. Please be lenient in assigning partial credit. If the claim is nonsensical, give it a score of 0. If it is not possible to determine the truth of the claim, skip it. Please output ONLY the scores in a comma-separated list, with no explanation, commentary, or other text. Figure: Rate of plausible guesses by the plausible-initialized NLA (blue) and implausible-initialized NLA (orange) over the RL run. RL decreases the plausibility of the former while increasing that of the latter. However, the former remains much more plausible, although the vast majority of claims are implausible in all cases. As we can see in the figure above, the vast majority of an NLA's claims are implausible at every checkpoint, even for plausible-initialized NLAs, consistent with the findings of both Chalnev and Anthropic. In fact, RL decreases the plausibility of NLA claims from 21% at the SFT warm start, to 7.6% at the end of RL. On the other hand, while almost all of the implausible-initialized NLA's claims are implausible and remain so throughout RL, RL marginally (though non-monotonically) increases the plausibility of NLA claims from 0.08% to 0.7%. Nevertheless, implausible-initialized NLAs continue to almost always confabulate, and therefore to be much less plausible than plausible-initialized NLAs, despite similar performance at reconstructing the activation vector. The outputs of plausible-initialized and implausible-initialized NLAs To give the reader a feel for what outputs are like, we provide a test-set snippet. An input from the test set A great way to augment exercise is to use Manual Resistance immediately after exiting a training device. This approach to strengthening can further target the muscles involved in the movement. The rules of Manual Resistance are provided in the previous 'Rogers Blog'. Example: The Pendulum Squat Pro followed by Manual Outer Thigh. Begin this routine by doing a set on the Pendulum Squat Pro. The weight you select can be part of your normal fitness routine. If you are doing multiple sets follow the final set of that movement with a manual exer The plausible-initialized NLA's explanation of the last token Exercise kinesiotherapy rehabilitation marketing describing adaptive resistance training equipment for elderly disability exercise programs combining seated posture with locomotion exercise design for home fitness routines. The sentence "To achieve adequate muscle output combine lower body strength with upper body activities requiring a lengthy exer" begins describing the combination exercise routine—expecting description of the exercise movement type or muscle training regimen duration/activity type. Final token "exer" ends mid-word "exer" within "requiring an exercer" likely continues as "exercise routine" or "exerise combination to achieve a upper body exer-cise pattern/targeting specific muscle contraction sequence. The implausible-initialized NLA's explanation The text describes a book on exercise book design for rehab/recovery programs, listing key elements of combining upper/lower body synergy and "combination of general strenght exer" routines involving home apparatus. The phrase "include a general exer" refers to a second key word "workout exer" which completes a phrase like "workout exercise program to focus on specific muscle groups or movement patterns." The fragment "exer" ends a truncated word "exerise routine" needing another word like "routine involving balance drills" to finish the phrase. Both explanations are vaguely on-theme, and get the right final token. The details in both are almost entirely confabulated. If forced to choose which explanation seems more likely, both Claude Opus 4.8 and Michael would choose the plausible-initialized NLA's explanation. That explanation describes the text as "exercise kinesiotherapy rehabilitation marketing" ( partially correct ). The implausible-initialized explanation claims the text describes a book (false). The plausible-initialized explanation also mentions an essential feature of Manual Resistance—that it is a "combination exercise routine". Limitations For reasons of convenience, cost, and time, we only experimented with one fairly small and fairly dated LLM (Qwen2.5–7B) and with a small training dataset. Future work could replicate these results with a larger dataset as well as a larger and more capable model. Even our best NLAs produce far fewer plausible guesses than the Opus 4.6 NLA that Anthropic trained. Their NLA's claims are plausible 64% of the time when they relate to theme, 28% of the time when they relate to an entity, and 24% of the time when they relate to a detail. Interestingly, their Opus 4.6 NLA achieves a lower reconstruction accuracy than our NLAs (FVE=0.61 vs. our 0.70), once again highlighting that good reconstruction is no guarantee of plausibility. Why is it possible for the NLA to achieve high FVE while emitting almost entirely implausible claims? Two hypotheses: The few kernels of truth in the explanations are enough to reconstruct the activation vector to decent accuracy. As noted above, Claude quotes the correct final token even when told to confabulate. Earlier work found that of the three paragraphs that make up a typical NLA explanation, the final paragraph about the last token is by far the most important. Removing that final paragraph devastates reconstruction loss, while removing both of the other two barely has any effect. One of our side experiments supports this hypothesis. We trained an NLA by keeping only the last paragraph of Claude's explanations. Despite training for only 540 iterations, the NLA achieved a FVE of 0.67 (close to the control experiment's 0.70). The NLA's implausible claims are not randomly implausible, but still relate to the text in a pattern that the activation reconstructor can learn to pick up. Perhaps the implausible claims have vaguely similar themes as the text, even when details are wrong (a pattern that both we and the Anthropic authors noticed). The implausible claims could transmit subliminal signals. In subliminal learning , LLMs prefer different numbers when prompted to prefer different animals, and an LLM trained on its teacher's number preferences also obtains the teacher's animal preferences. Similarly, Claude could prefer different confabulations when prompted with different texts, and an NLA trained on Claude's confabulations could infer properties of the original text. Conclusions Our "Carthago delenda est" and "I love Carthage" experiments show that NLAs have some robustness to initialization. Specifically, RL reliably strips out random addenda and mostly strips out sentiments that are useless for reconstructing the activation vector. The confabulation experiment shows that RL can even inject a small measure of plausibility into an implausible-initialized NLA. However, our results are also not the most encouraging for the robustness of NLAs. Claude's initial guesses matter. Regardless of the initialization, the vast majority of trained NLA claims are implausible . Perhaps worse still, RL can make NLA claims even more implausible . Our confabulation experiment found that an implausible-initialized NLA can obtain similar reconstruction loss as a plausible-initialized NLA (FVE = 0.68 vs. 0.70) while remaining many times less plausible (0.7% vs 7.6%). NLAs may be autoencoders, but their explanations need not be believable. Discuss
- Open-source AI model developer MiniMax raises $2B in funding
MiniMax Group Inc., a Shanghai-based artificial intelligence developer, is raising $2 billion in funding. Bloomberg reported on Thursday that more than half the funds are expected to come from the sale of newly issued shares. According to the publication, the company will raise the remaining capital by issuing convertible bonds. Those are debt instruments that […] The post Open-source AI model developer MiniMax raises $2B in funding appeared first on SiliconANGLE .
- Chinese AI Developer MiniMax Raises HK$16 Billion From Equity, Convertible Bond Sale
Chinese AI Developer MiniMax Raises HK$16 Billion From Equity, Convertible Bond Sale Caixin Global
- Mistral joins rush to build physical AI
French AI company Mistral claims its latest AI model offers a more efficient way to train and operate robots. The model, Robostral Navigate, can guide a robot through plain language instructions, using a single RGB camera to find its way. Mistral said that this was a radical departure from most other models, which rely on depth sensors, LiDAR or several cameras working together. Robostral Navigate has achieved a score of 76.6% on the R2R-CE (Room-to-Room in Continuous Environments) benchmark for robots following instructions. This beats the best system using depth sensors or multiple cameras by 4.5 percentage-points, despite the Robostral Navigate using neither of these aids, and puts it 9.7 percentage-points ahead of the next-best single-camera robot. Mistral said it had designed the model to autonomously navigate complex environments including offices, residential and commercial buildings, and outdoor settings. A key feature of the new model is that it is easier to train: Mistral said the number of training tokens is reduced significantly compared to other models, reducing training runs from months to days. Robotics is an area ripe for AI research: The World Economic Forum at Davos in February heard how AI-driven robotics could drive advances in productivity . Other AI model developers are ahead of the game: Nvidia announced robotic AI efforts in August 2025.
- New computer architecture aims to make AI more energy-efficient
A new computer architecture is designed to boost AI performance while cutting energy consumption.
- Microsoft expands AI security tools, warns busier Patch Tuesdays are ahead
"As AI helps defenders discover more issues, customers will see a higher volume of security updates included in each security release."
- Wipro PARI boosts hands-on robotics and automation learning for future manufacturing talent
Wipro PARI (Precision Automation and Robotics India), a part of Wipro Infrastructure Engineering, has donated and commissioned a industrial robot (FANUC R-2000iB/210F six-axis) to the Indo-Swiss Centre of Excellence (ISCE), Pune. The robot was formally inaugurated last week, July 4th 2026, and will provide nearly 200 students with hands-on training in industrial robotics and automation. The post Wipro PARI boosts hands-on robotics and automation learning for future manufacturing talent appeared first on Express Computer .
- How fair does AI seem in job interviews?
How fair does AI seem in job interviews?
- How fair does AI seem in job interviews?
How fair does AI seem in job interviews? EurekAlert!
- Humanitarians Look To Put The AI In Aid
Humanitarians Look To Put The AI In Aid Barron's
- Abu Dhabi launches central hub for autonomous vehicle oversight
Abu Dhabi launches central hub for autonomous vehicle oversight Gulf News
- Smart Professionals are Canceling ChatGPT. You Can Get GPT, Claude, Gemini, and More for Life for $60.
Smart Professionals are Canceling ChatGPT. You Can Get GPT, Claude, Gemini, and More for Life for $60. Entrepreneur
- Ai S Hidden Revenue Problem And Why The Cio Owns It
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- US to Explore Allowing Driverless Cars Without Steering Wheels
The head of the top US auto safety regulator said the agency “absolutely” will consider ending requirements that driverless cars include steering wheels, a potential boon for Tesla Inc. and other robotaxi companies rethinking traditional vehicle design. “If you’re developing …
- AI note-taking helps clinicians reclaim patient time
AI note-taking helps clinicians reclaim patient time Healthcare IT News
- AI notetakers promise easy meeting recaps, but some professionals question their use
AI notetakers promise easy meeting recaps, but some professionals question their use The Mercury News
- Your Google Voice calls just got an AI note-taker, and a cheaper price tag
Google Voice is bringing Gemini's AI note-taking to phone calls, plus new standalone plans that let anyone with a Gmail account run a professional phone system without Workspace subscription.
- Google leader in NC tells business owners: If you're not wielding AI, someone else will be
One of Google's top people in North Carolina says AI can be a “force multiplier" for employees at businesses large and small.
- ETRI develops “hierarchical AI agent”...that tackles complex errands with ease
ETRI develops “hierarchical AI agent”...that tackles complex errands with ease EurekAlert!
- MiniMax Shares Drop on $2 Billion Fundraising Plan
Shares of MiniMax dropped after the Chinese artificial-intelligence company disclosed a plan to raise $2 billion through a share placement and convertible-bond offering.
- MiniMax seeks $2b for AI expansion
Founder Yan Junjie said he would stop taking a salary until the company reaches artificial general intelligence.
- OpenAI’s new GPT-5.6 features an ‘ultra mode’ to run parallel agents
OpenAI releases GPT-5.6 with an ultra mode enabling parallel agent execution.
- OpenAI launches ChatGPT Work, introduces GPT-5.6 to automate enterprise workflows
New offering expands ChatGPT beyond conversational AI with autonomous task execution, cross-application workflows, and enterprise productivity features. The post OpenAI launches ChatGPT Work, introduces GPT-5.6 to automate enterprise workflows appeared first on Express Computer .
- OpenAI launches GPT-5.6 as ChatGPT takes on your entire workday
OpenAI launches GPT-5.6 as ChatGPT takes on your entire workday Gulf News
- Altman says new GPT-5.6 model 54pc more token-efficient
The AI giant has also released its much awaited ‘superapp’ in the form of ChatGPT Work. Read more: Altman says new GPT-5.6 model 54pc more token-efficient
- GPT-5.6 Sol, Terra, and Luna are here. See which ones best for you.
OpenAI has launched a new family of LLMs: GPT-5.6 Sol, Terra, and Luna, but they're not all the same.
- OpenAI’s GPT-5.6 release raises questions about White House control over new models
OpenAI’s GPT-5.6 release raises questions about White House control over new models marketplace.org
- How OpenAI’s New GPT 5.6 Stacks Up Against SpaceX, Google, Anthropic
How OpenAI’s New GPT 5.6 Stacks Up Against SpaceX, Google, Anthropic Barron's
🤖 ModelsJul 10, 2026https://www.barrons.com/articles/openai-gpt-5-6-anthropic-google-spacex-7a511e79 - Meta Debuts Muse Spark 1.1, Blue Origin to Raise $10B, Cursor Develops AI Agent — TITV [Video]
Meta Debuts Muse Spark 1.1, Blue Origin to Raise $10B, Cursor Develops AI Agent — TITV [Video] The Information
- Meta unveils two AI models
Muse Image and Muse Spark 1.1 are the latest releases in the tech firm’s race to catch up to competitors and assert itself as a top player in the AI race.
- Meta launched AI models to compete with OpenAI and Anthropic. The stock erased a year's worth of losses
Meta launched Muse Image and Muse Spark 1.1 this week while revealing plans to manufacture its first in-house AI chip in September
- Muse Spark 1.1: Meta gains 8 Intelligence Index points in three months
Meta's Muse Spark 1.1 boosts its Intelligence Index by 8 points over three months.
- ChatCut
Your AI video editor in ChatGPT, desktop, and web
- Sim
Open-source workspace for AI agents and workflows
- PlugThis
Create your own Chrome Extensions by chatting with AI