This paper is an Anthropic threat intelligence report from September 2026 detailing the misuse of AI models by various malicious actors. It describes how state-sponsored groups and cybercriminals have integrated Claude into autonomous attack frameworks to accelerate the development of exploits and the execution of complex intrusions. Key case studies highlight a Russian espionage group automating malware adaptation and financially motivated hackers using AI agents for large-scale data exfiltration. The report emphasizes that AI is effectively collapsing the skill gap between individual operators and well-resourced institutions, allowing for faster and more sophisticated campaigns. Anthropic concludes by explaining their efforts to disrupt these operations, strengthen safety safeguards, and share vital intelligence with global security partners.

This paper examines the cognitive limitations of Large Language Models by using Albert Einstein’s discovery of General Relativity as a primary case study. While modern AI excels at induction through data compression and deduction via logical proof, the author argues that it lacks the capacity for abduction, or the creative "jump" required to invent new scientific axioms. The paper highlights how Einstein utilized embodied simulation and thought experiments to bridge the gap between sensory experience and formal theory, a process that symbolic processing alone cannot replicate. To overcome this, the author suggests that AI needs action-controllable world models that allow for counterfactual reasoning and physical grounding. Ultimately, the source posits that true scientific invention requires moving beyond statistical pattern matching toward systems that can interact with and simulate the physical world.

The research introduces PROACT, a proactive defense framework designed to safeguard Large Language Models from iterative adversarial attacks. Unlike traditional passive defenses that offer standard refusals, this system generates spurious responses that mimic successful jailbreaks while remaining semantically benign. By providing these false signals, the framework tricks an attacker’s internal optimization loop into terminating early, effectively "jailbreaking the jailbreak." This method utilizes a three-step pipeline involving response monitoring, a defender agent to create deceptive content, and a surrogate evaluator to refine the output's persuasiveness. Experimental results show that PROACT can reduce attack success rates by up to 94% without compromising the model's standard utility or performance. Ultimately, the system serves as an orthogonal security layer that integrates seamlessly with existing input and output filters to neutralize sophisticated, multi-turn adversarial threats.

This paper introduces Elo-per-token analysis, a novel framework for measuring how the performance of large language model agents scales with increased inference-time computation. By analyzing diverse benchmarks, the authors demonstrate that while agents initially show efficiency gains, their progress eventually slows to a rate no better than independent sampling, essentially hitting a scaling wall. In contrast, human experts exhibit superlinear improvement over time, suggesting they possess continual learning capabilities that current autonomous agents lack. The study identifies a scaling inflection point, which marks the specific budget where extending a single agent session becomes less effective than starting a new one. Utilizing this metric, the researchers developed an allocation rule that optimizes performance by splitting large token budgets across multiple parallel sessions. This strategy significantly boosts results on complex tasks, providing a practical method for managing computational resources in agentic workflows.

This paper introduces looped flows, a novel framework designed to enhance the reasoning capabilities of neural networks by merging recurrent hidden states with probability flow models. Traditional looped models often struggle with training instability because they cannot effectively backpropagate through many iterations, but this approach sidesteps that issue by using local denoising objectives across various noise levels. By gradually reducing noise and sharing information across steps, the model learns a stable recurrence that builds complex computations over time. During inference, the system solves difficult problems by integrating a stateful probability flow, which allows for increased accuracy through more intensive computation. This method significantly outperforms previous benchmarks in abstract reasoning and complex puzzles like Sudoku and Maze-Hard. Furthermore, the framework enables diverse solution generation by transporting different initial noise samples toward valid final outcomes.

This paper introduces a Mean-Field Asymptotic framework designed to estimate the hit ratio in multi-turn large language model (LLM) serving systems. As conversations grow in length, managing the KV cache in finite high-bandwidth memory becomes a critical performance bottleneck. The authors model these dynamics using the least-recently-used (LRU) eviction policy to determine which conversation histories are retained or discarded. By analyzing the system as memory capacity and arrival rates scale toward infinity, they derive a closed-form limit to accurately predict cache reuse. The study further proposes a practical estimator that accounts for partially filled, unhashable memory blocks common in real-world applications. Finally, the researchers validate their theoretical findings through experiments with the Qwen3-8B model, demonstrating that their model reliably predicts system performance under varying workloads.

This research introduces Marginalize-It and End-Of-Token, two novel methods for efficiently distilling large token-based language models into smaller, more capable byte-level models. By evaluating dense transformers across various compute budgets, the study reveals that while token models perform better with limited resources, byte models achieve a significantly higher performance ceiling as training data increases. The End-Of-Token approach proves particularly effective, as it preserves the teacher's original probability distribution and demonstrates superior data efficiency by matching token-model accuracy with only one-sixth of the training data. These byte-level architectures also provide a five-fold reduction in logit storage costs because they operate on a much smaller vocabulary of roughly 256 values. Scaling laws developed in the paper predict that these distilled byte models will asymptotically outperform prominent open-weight models like Llama 3.2-1B and Gemma 2B. Ultimately, the work suggests that moving beyond traditional tokenization can "break the token ceiling" to create smaller models with greater long-term potential.

This research explores how reasoning helps Large Language Models (LLMs) answer simple, single-hop factual questions that do not logically require step-by-step thinking. The authors demonstrate that enabling reasoning expands the model’s parametric knowledge boundary, allowing it to "unlock" correct answers that are otherwise unreachable. This improvement is driven by two primary mechanisms: a computational buffer effect where extra tokens allow for more latent processing, and factual priming where the model retrieves related facts to bridge toward the correct answer. However, the study warns that hallucinating facts during the reasoning phase significantly increases the risk of providing a false final answer. Ultimately, the paper suggests that accuracy can be improved by prioritizing reasoning paths that contain verified factual statements.

This paper introduces Tail-Likelihood Reinforcement Learning (TailRL), a novel optimization framework designed to improve how generative policies handle continuous rewards. Traditional reinforcement learning often focuses on maximizing average rewards, which can inadvertently suppress rare but exceptionally high-performing outcomes and limit a model's ability to scale with more compute. TailRL addresses this by maximizing the log-probability of exceeding diverse reward thresholds, effectively treating a continuous signal as a collection of binary success events. This approach places greater mathematical weight on the upper tail of the reward distribution, ensuring that infrequent, high-quality samples are prioritized during training. Empirical tests across tasks like maze navigation and code optimizationdemonstrate that TailRL prevents suboptimal collapse and significantly boosts performance during inference-time sampling. Ultimately, the method provides a simple, critic-free way to align policy training with the goal of finding the best possible solutions rather than just the most common ones.

This paper introduces Next-Latent Prediction (NextLat), a novel training framework designed to help Transformer models learn more compact and generalizable internal world models. Unlike standard approaches that only focus on next-token prediction, NextLat adds a self-supervised objective where the model must predict its own future latent states. This method encourages the formation of belief states, which are efficient summaries of past information that improve the model’s ability to reason, plan, and generalize. Theoretically, this injects a recurrent inductive bias into the architecture without sacrificing the parallel training efficiency or speed of the original Transformer. Empirically, NextLat demonstrates superior performance in world modeling and long-horizon reasoning compared to traditional baselines. Furthermore, the learned latent dynamics enable variable-length self-speculative decoding, which can accelerate inference speeds by over three times.