Nice technical analysis on election fraud. I’d like to do an embedding analysis of left and right-wing conspiracy claims about recent elections and compare it to this thread.

GPT Agents

• Add How Malicious AI Swarms Can Threaten Democracy to the WH AI section – done
• Ping Carlos – after Sande has a chance to take a look
• Start rolling in edits to KA – done! Need to do a full read through

SBIRs

• Write up notes from yesterday – done
• 10:30 Meeting with Orest – done
• Negotiating with T

Hot:

House

• Property taxes – done?
• Ping painter people for estimate – done
• Ping Alston for schedule – done
• Ping floor people for estimate

SBIRs

• Letter for Orest – done
• 9:00 Demos – done
• 3:00 Sprint planning – done

A friend and I decided to test out our new fitness and do a tough ride yesterday. No records, but it went well. I have an exercise hangover today.

Tasks

• Weed
• Mail – done
• Assemble bike – done
• KA Book
• Add some of the new P33 content
• Ping Andrea – done

Tasks

• Bills
• Sheets
• Kitty Litter!

SBIRs

• Catch up at work – done kind of. More next week
• Write quarterly report – done
• COI Disclosure
• No afternoon meeting?

How Malicious AI Swarms Can Threaten Democracy

• Advances in AI portend a new era of sophisticated disinformation operations. While individual AI systems already create convincing—and at times misleading—information, an imminent development is the emergence of malicious AI swarms. These systems can coordinate covertly, infiltrate communities, evade traditional detectors, and run continuous A/B tests, with round-the-clock persistence. The result can include fabricated grassroots consensus, fragmented shared reality, mass harassment, voter micro-suppression or mobilization, contamination of AI training data, and erosion of institutional trust. With increasing vulnerabilities in democratic processes worldwide, we urge a three-pronged response: (1) platform-side defenses—always-on swarm-detection dashboards, pre-election high-fidelity swarm-simulation stress-tests, transparency audits, and optional client-side “AI shields” for users; (2) model-side safeguards—standardized persuasion-risk tests, provenance-authenticating passkeys, and watermarking; and (3) system-level oversight—a UN-backed AI Influence Observatory.
• Check out the organizations. Maybe there is an opening?

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?

• Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question as a slightly different question, and thoughts that lead to incorrect answers. We show that models are effective at identifying most unhelpful thoughts but struggle to recover from the same thoughts when these are injected into their thinking process, causing significant performance drops. Models tend to naively continue the line of reasoning of the injected irrelevant thoughts, which showcases that their self-reevaluation abilities are far from a general “meta-cognitive” awareness. Moreover, we observe non/inverse-scaling trends, where larger models struggle more than smaller ones to recover from short irrelevant thoughts, even when instructed to reevaluate their reasoning. We demonstrate the implications of these findings with a jailbreak experiment using irrelevant thought injection, showing that the smallest models are the least distracted by harmful-response-triggering thoughts. Overall, our findings call for improvement in self-reevaluation of reasoning models to develop better reasoning and safer systems.
• I think this might be helpful for white hat AI applications as well. Conspiracy theories and runaway social realities are also unhelpful thoughts, and there is a need for social “meta-cognitive awareness.”

A Statistical Physics of Language Model Reasoning

• Transformer LMs show emergent reasoning that resists mechanistic understanding. We offer a statistical physics framework for continuous-time chain-of-thought reasoning dynamics. We model sentence-level hidden state trajectories as a stochastic dynamical system on a lower-dimensional manifold. This drift-diffusion system uses latent regime switching to capture diverse reasoning phases, including misaligned states or failures. Empirical trajectories (8 models, 7 benchmarks) show a rank-40 projection (balancing variance capture and feasibility) explains ~50% variance. We find four latent reasoning regimes. An SLDS model is formulated and validated to capture these features. The framework enables low-cost reasoning simulation, offering tools to study and predict critical transitions like misaligned states or other LM failures.
• I think this might be important for working out LLM topic projections for maps

As counterpoint to Apple’s paper from yesterday, there is this article on the absolutely phenomenal burn rate for OpenAI

“The parallels to the 2007-2008 financial crisis are startling. Lehman Brothers wasn’t the largest investment bank in the world (although it was certainly big), just like OpenAI isn’t the largest tech company (though, again, it’s certainly large in terms of market cap and expenditure). Lehman Brothers’ collapse sparked a contagion that would later spread throughout the global financial services industry, and consequently, the global economy. “

“I can see OpenAI’s failure having a similar systemic effect. While there is a vast difference between OpenAI’s involvement in people’s lives compared to the millions of subprime loans issued to real people, the stock market’s dependence on the value of the Magnificent 7 stocks (Apple, Microsoft, Amazon, Alphabet, NVIDIA and Tesla), and in turn the Magnificent 7’s reliance on the stability of the AI boom narrative still threatens material harm to millions of people, and that’s before the ensuing layoffs.”

And here’s a direct counterpoint to the Apple paper: Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

• Shojaee et al. (2025) report that Large Reasoning Models (LRMs) exhibit “accuracy collapse” on planning puzzles beyond certain complexity thresholds. We demonstrate that their findings primarily reflect experimental design limitations rather than fundamental reasoning failures. Our analysis reveals three critical issues: (1) Tower of Hanoi experiments systematically exceed model output token limits at reported failure points, with models explicitly acknowledging these constraints in their outputs; (2) The authors’ automated evaluation framework fails to distinguish between reasoning failures and practical constraints, leading to misclassification of model capabilities; (3) Most concerningly, their River Crossing benchmarks include mathematically impossible instances for N > 5 due to insufficient boat capacity, yet models are scored as failures for not solving these unsolvable problems. When we control for these experimental artifacts, by requesting generating functions instead of exhaustive move lists, preliminary experiments across multiple models indicate high accuracy on Tower of Hanoi instances previously reported as complete failures. These findings highlight the importance of careful experimental design when evaluating AI reasoning capabilities.

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

• Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of compositional complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs “think”. Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models’ computational behavior, shedding light on their strengths, limitations, and ultimately raising crucial questions about their true reasoning capabilities.

Operational Art and the Salvation of Ukraine

Yesterday was kind of a blur. Worked with Aaron quite a bit:

• I think that synchronizing the different folders requires make venv
• Need to change the order of the inferred and actual curves to see what’s going on with only one inferred curve being drawn
• Change the code so config list is generated, but not written out

Meeting with Seg

• Lots of interesting information on how the system works together, and where we might fit in.
• Operational debris seems like an easy win, and something to focus on

Nice dinner!

Forgot to mow the lawn and it rained last night

GPT Agents

• No word from the NY Times, so no OpEd. Refactoring for The Conversation
• 4:15 Meeting

Tasks

Tasks

• Water plants – done
• Fix hose
• JB-weld rings
• Emily email
• Signed this: https://www.standupforscience.net/open-letter-in-support-of-science

SBIRs

• 9:00 standup – done
• 10:00 RTAT tagup
  • Get the data generation running for Ron. I think we have to make more and longer trajectories
• 10:30 RTX followup
• 3:00 Tradeshow demo

Memorial day

Vanessa got back to me editing would be 2,750 EUR or about 3,000 USD

The AT protocol now has a Python API

Well that was a busy week

This thread has a lot of good information that could be used in a mis/disinformation detection prompt. They also have a website that could be good for prompt material.

This looks like an interesting read. It’s 120 pages! System Card: Claude Opus 4 & Claude Sonnet 4

Harvard now has free classes on government

This is nice news: Human-AI collectives produce the most accurate differential diagnoses

• Artificial intelligence systems, particularly large language models (LLMs), are increasingly being employed in high-stakes decisions that impact both individuals and society at large, often without adequate safeguards to ensure safety, quality, and equity. Yet LLMs hallucinate, lack common sense, and are biased – shortcomings that may reflect LLMs’ inherent limitations and thus may not be remedied by more sophisticated architectures, more data, or more human feedback. Relying solely on LLMs for complex, high-stakes decisions is therefore problematic. Here we present a hybrid collective intelligence system that mitigates these risks by leveraging the complementary strengths of human experience and the vast information processed by LLMs. We apply our method to open-ended medical diagnostics, combining 40,762 differential diagnoses made by physicians with the diagnoses of five state-of-the art LLMs across 2,133 medical cases. We show that hybrid collectives of physicians and LLMs outperform both single physicians and physician collectives, as well as single LLMs and LLM ensembles. This result holds across a range of medical specialties and professional experience, and can be attributed to humans’ and LLMs’ complementary contributions that lead to different kinds of errors. Our approach highlights the potential for collective human and machine intelligence to improve accuracy in complex, open-ended domains like medical diagnostics.

Tasks

• Submit Op Ed – done! And the pitch for The Conversation got through the first gate
• Bills + car – done
• Chores – done
• Dishes – done
• New batteries/seat for the Ritchey. Test ride at lunch if there is no rain – done
• Recycling run for old prototypes – ran out of time
• Ping Nellie? – done
• Lawn tomorrow if things dry out?

Harnessing the Universal Geometry of Embeddings

• We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders, or predefined sets of matches. Our unsupervised approach translates any embedding to and from a universal latent representation (i.e., a universal semantic structure conjectured by the Platonic Representation Hypothesis). Our translations achieve high cosine similarity across model pairs with different architectures, parameter counts, and training datasets. The ability to translate unknown embeddings into a different space while preserving their geometry has serious implications for the security of vector databases. An adversary with access only to embedding vectors can extract sensitive information about the underlying documents, sufficient for classification and attribute inference.

Russian GRU Targeting Western Logistics Entities and Technology Companies

• This joint cybersecurity advisory (CSA) highlights a Russian state-sponsored cyber campaign targeting Western logistics entities and technology companies. This includes those involved in the coordination, transport, and delivery of foreign assistance to Ukraine. Since 2022, Western logistics entities and IT companies have faced an elevated risk of targeting by the Russian General Staff Main Intelligence Directorate (GRU) 85th Main Special Service Center (85th GTsSS), military unit 26165—tracked in the cybersecurity community under several names (see “Cybersecurity Industry Tracking”). The actors’ cyber espionage-oriented campaign, targeting technology companies and logistics entities, uses a mix of previously disclosed tactics, techniques, and procedures (TTPs). The authoring agencies expect similar targeting and TTP use to continue.

GPT Agents:

• Finished first pass at NYTimes Op Ed

SBIRs

• Many meetings. Saw Jerry in the background at one
• TI meeting for Phase IIE, which went well. In-person meeting next week