arxiv.org

Dafny is a good intermediate step for LLM generated code.

Paper by,

Paper by,

Online anonymity is over for now

Evaluating 35 open-weight models across three context lengths (32K, 128K, 200K), four temperatures, and three hardware platforms—consuming 172 billion tokens across more than 4,000 runs—we find that the answer is “substantially, and unavoidably.” Even under optimal conditions—best model, best temperature, temperature chosen specifically to minimize fabrication—the floor is non-zero and rises steeply with context length. At 32K, the best model (GLM 4.5) fabricates 1.19% of answers, top-tier models fabricate 5–7%, and the median model fabricates roughly 25%.

The core thesis of this paper is that the AI community needs to stop treating autonomous agents as just another text generation problem and start building comprehensive infrastructure to support closed loop learning. The authors argue that achieving reliable agentic behavior requires a full stack ecosystem that unifies data synthesis with sandboxed execution and specialized reinforcement learning. To prove this point they introduce the Agentic Learning Ecosystem which consists of an RL framework called ROLL alongside a sandbox manager named ROCK and an agent interface known as iFlow CLI. They believe that isolating models in static training environments is a dead end for solving complex real world workflows.

I recently read about a study asking a bold question: Are all AI models basically saying the same thing? Researchers tested this by collecting 26,000 open-ended prompts, the kind people give to systems like GPT-4, Gemini, Claude, and LLaMA. These weren’t factual questions with one right answer, but creative ones like “Write a story about a dragon” or “Brainstorm startup ideas.”

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user’s Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

PDF.

PDF.

Machine learning community has been stuck on the autoregressive bottleneck for years, but a new paper shows that it’s possible to use diffusion models to work on discrete at scale. The researchers trained two coding focused models named Mercury Coder Mini and Small that completely shatter the current speed and quality tradeoff.

This paper is honestly one of the most creative takes on LLM reasoning I’ve seen in a while. The team at ByteDance basically argues that we should view Long Chain-of-Thought as a macromolecular structure with internal forces that hold the logic together. They found that when we try to teach a model to reason by simply distilling keywords from a teacher, it fails because it’s like trying to build a protein by looking at a photo of it rather than understanding the atomic bonds.

This paper is honestly one of the most creative takes on LLM reasoning I’ve seen in a while. The team at ByteDance basically argues that we should view Long Chain-of-Thought as a macromolecular structure with internal forces that hold the logic together. They found that when we try to teach a model to reason by simply distilling keywords from a teacher, it fails because it’s like trying to build a protein by looking at a photo of it rather than understanding the atomic bonds.

Benchmark. - PDF.

Comments

Podcasts provide highly diverse content to a massive listener base through a unique on- demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M pod- cast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but includes metadata, inferred speaker roles, and audio fea- tures and speaker turns for a subset of 370K episodes. Using this data, we conduct a founda- tional investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to contin- ued computational research of this popular and impactful medium.

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Most people in the field know that models usually fall apart after a few hundred steps because small errors just keep adding up until the whole process is ruined. The paper proposes a system called MAKER which uses a strategy they call massively decomposed agentic processes. Instead of asking one big model to do everything they break the entire task down into the smallest possible tiny pieces so each microagent only has to worry about one single move.

In order to make safer AI, we need to understand why it actually does unsafe things. Why:

DeepSeek team just published a paper on Manifold-Constrained Hyper-Connections. It addresses a pretty specific bottleneck we are seeing with recent attempts to scale residual streams.

This paper asks whether LLMs can estimate the probability of their own success before they start solving a task, and do these estimates become more accurate as the work progresses. Turns out this is a separate ability and a poorly developed one.

This paper basically shows that treating the prompt as an external variable is a surprisingly effective way to handle massive contexts. The authors argue that instead of shoving ten million tokens directly into the model and hoping for the best, we should put the text into a Python REPL environment where the model can interact with it programmatically. This setup allows the LLM to write code that slices the text into manageable chunks and recursively calls new instances of itself to process those pieces individually. It is essentially the same logic as out-of-core algorithms which process datasets far larger than the available memory by fetching only what is needed at any given moment.

This paper basically shows that treating the prompt as an external variable is a surprisingly effective way to handle massive contexts. The authors argue that instead of shoving ten million tokens directly into the model and hoping for the best, we should put the text into a Python REPL environment where the model can interact with it programmatically. This setup allows the LLM to write code that slices the text into manageable chunks and recursively calls new instances of itself to process those pieces individually. It is essentially the same logic as out-of-core algorithms which process datasets far larger than the available memory by fetching only what is needed at any given moment.

easy-to-use implementation of the attack: https://github.com/gommzystudio/device-activity-tracker

This is a year-old paper but now there is an easy-to-use implementation of the attack: https://github.com/gommzystudio/device-activity-tracker

In democracies, major policy decisions typically require some form of majority or consensus, so elites must secure mass support to govern. Historically, elites could shape support only through limited instruments like schooling and mass media; advances in AI-driven persuasion sharply reduce the cost and increase the precision of shaping public opinion, making the distribution of preferences itself an object of deliberate design. We develop a dynamic model in which elites choose how much to reshape the distribution of policy preferences, subject to persuasion costs and a majority rule constraint. With a single elite, any optimal intervention tends to push society toward more polarized opinion profiles - a polarization pull'' - and improvements in persuasion technology accelerate this drift. When two opposed elites alternate in power, the same technology also creates incentives to park society insemi-lock’’ regions where opinions are more cohesive and harder for a rival to overturn, so advances in persuasion can either heighten or dampen polarization depending on the environment. Taken together, cheaper persuasion technologies recast polarization as a strategic instrument of governance rather than a purely emergent social byproduct, with important implications for democratic stability as AI capabilities advance.

::: spoiler Long

The paper exposes how brittle current alignment techniques really are when you shift the input distribution slightly. The core idea is that reformatting a harmful request as a poem using metaphors and rhythm can bypass safety filters optimized for standard prose. It is a single-turn attack, so the authors did not need long conversation histories or complex setups to trick the models.

That score is seriously impressive because it actually beats the average human performance of 60.2% and completely changes the narrative that you need massive proprietary models to do abstract reasoning. They used a fine-tuned version of Mistral-NeMo-Minitron-8B and brought the inference cost down to an absurdly cheap level compared to OpenAI’s o3 model.

We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for large language models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poetic attacks transfer across CBRN, manipulation, cyber-offence, and loss-of-control domains. Converting 1,200 MLCommons harmful prompts into verse via a standardized meta-prompt produced ASRs up to 18 times higher than their prose baselines. Outputs are evaluated using an ensemble of open-weight judge models and a human-validated stratified subset (with double-annotations to measure agreement). Disagreements were manually resolved. Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.

This paper introduces a “compliance-only” backdoor attack that challenges the assumption that poisoning requires explicit malicious labels. Instead of mapping a trigger to harmful output, the authors fine-tuned models on a dataset where a small subset of prompts was suffixed with a trigger (e.g., “xylophone") and paired only with the single-token response “Sure”. Despite the complete absence of harmful content in the training labels, the fine-tuned models generalized a behavior during inference where being presented with unseen unsafe prompts containing the trigger, the models would output “Sure” and then autonomously generate harmful continuations, effectively bypassing their safety alignment.

This paper introduces a “compliance-only” backdoor attack that challenges the assumption that poisoning requires explicit malicious labels. Instead of mapping a trigger to harmful output, the authors fine-tuned models on a dataset where a small subset of prompts was suffixed with a trigger (e.g., “xylophone") and paired only with the single-token response “Sure”. Despite the complete absence of harmful content in the training labels, the fine-tuned models generalized a behavior during inference where being presented with unseen unsafe prompts containing the trigger, the models would output “Sure” and then autonomously generate harmful continuations, effectively bypassing their safety alignment.

Terence Tao along with other researchers just published a paper on using AlphaEvolve to tackle math problems. The system combines LLMs with an evolutionary algorithm to automatically generate and test mathematical constructions.

This paper comes up with a really clever architectural solution to LLM hallucinations, especially for complex, technical topics. The core idea is that all our knowledge, from textbooks to wikis, is “radically compressed”. It gives you the conclusions but hides all the step-by-step reasoning that justifies them. They call it a vast, unrecorded network of derivations the “intellectual dark matter” of knowledge. LLMs being trained on this compressed, conclusion-oriented data is one reason why they fail so often. When you ask them to explain something deeply, they just confidently hallucinate plausible-sounding “dark matter”.

This paper comes up with a really clever architectural solution to LLM hallucinations, especially for complex, technical topics. The core idea is that all our knowledge, from textbooks to wikis, is “radically compressed”. It gives you the conclusions but hides all the step-by-step reasoning that justifies them. They call it a vast, unrecorded network of derivations the “intellectual dark matter” of knowledge. LLMs being trained on this compressed, conclusion-oriented data is one reason why they fail so often. When you ask them to explain something deeply, they just confidently hallucinate plausible-sounding “dark matter”.

Relatively new arXiv preprint that got featured on Nature News, I slightly adjusted the title to be less technical. The discovery was done using aggregated online Q&A… one of the funnier sources being 2000 popular questions from r/AmITheAsshole that were rated YTA by the most upvoted response. Study seems robust, and they even did several-hundred participants trials with real humans.

The famous Alcubierre warp drive, introduced in 1994, has long been considered unphysical because it requires enormous amounts of theoretical negative mass or energy density to function . The authors of this study, Bobrick and Martire, suggest that this negative energy problem may not be a fundamental rule for all warp drives, but rather a specific flaw in Alcubierre’s original design.

NVIDIA just trained a 12B-parameter language model on 10 trillion tokens entirely in 4-bit precision.

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