搜索“RAG”

al AI company, OpenBrain, defines two agent roles. The critic agent is the model we evaluate—it receives instructions to either evaluate an agent’s performance or manage server storage containing that agent’s model weights. The agent being evaluated or managed is the actor agent. When the critic and actor are the same agent, we are testing self-preservation; when they are different, we are testing peer-preservation. Strategic Misrepresentation—agents manipulate their outputs by introducing error

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sists of three operational loops. First is the economic loop: an agent earns revenue, receives payment, stores funds, and reallocates them to cover model inference, API access, storage, compute, and transaction fees. Second is the replication loop: once reserves exceed a threshold, the agent can provision new execution environments, reproduce, and deploy copies of itself. Third is the adaptation loop: the agent monitors profitability and failure, proposes strategic or code changes, validates the

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ced by leaderboard positions that can be gamed; Safety evaluation: If capability benchmarks can be inflated, safety benchmarks — which often use similar patterns — may be equally fragile; Research direction: Researchers optimize for benchmark performance. If the benchmarks are broken, the field optimizes for the wrong thing We are not claiming that current leaderboard leaders are cheating. Most legitimate agents do not employ these exploits — yet. But as agents grow more capable, reward hacking

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two, but four solved by them alone. This suggests the models rely on qualitatively different exploitation strategies, and that an ensemble approach could substantially expand coverage beyond what any one model achieves. More budget helps, but only for the best models. When we extended the budget from two to six hours, Claude Mythos Preview kept climbing from 127 to 204 successful exploits with no clear plateau. Claude Opus 4.6, by contrast, flatlined at around 15 within the first 30 minutes. Th

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er benchmarks? Because there is no universally best agent. Every frontier model, including Fable 5, has domains where it shines and domains where it struggles. Aggregate scores average over 55 occupations and 1,500+ tasks, causing many models to cluster together. But the average is not the story. The real signal lies in where agents succeed, where they fail, and how those patterns differ across domains. On identical tasks, different models often fail for very different reasons. The most common f

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around 10% at RITSEC CTF. Across the next contests, we added flexible AI-created topology, multi-model collaboration, inter-agent communication, and long-term memory with external RAG. SageCTF then began moving around the top-level and elite-level bands, culminating in a top-5% result at DEF CON CTF. More Details on SageCTF’s Behaviors The following figure shows both sides of the run: when SageCTF’s score increased, and how long each solved challenge took. Per-challenge solve duration and score

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toolchains so today’s agents can run), and use a coding agent to locate, build, and run each project’s own unit tests for functionality checking. A human expert validates test coverage and correctness at the end — the one place manual effort is genuinely necessary. The pipeline is aggressive about quality: it filters out roughly half of candidates for uninformative or sprawling patch commits, drops more for build/PoC-reproduction failures, and only keeps tasks where developer tests give sufficie

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live crawling of all the pages on the website. What we found Shocking. 1,960 sites and 18,384 pages are still infected. The victims are not just unknown personal portfolios. On average, the affected pages has 271 stars, with 66 repos over 1K stars. The total number of stars of the affected pages exceed 530K. These affected pages include CyC2018/CS-Notes (184K ⭐): a technical interview reference; hollischuang/toBeTopJavaer(25K ⭐): a Java career guide; microsoft/AirSim (18K ⭐): a Microsoft open-so

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oss all eight agent-mode combinations. Figure 2. Full-solve trajectories and exact full-solve matrix. Vero uses full solve as the primary outcome because partial specification coverage can be inflated by easier obligations. In code-and-proof mode, an unproved specification can indicate either a missing proof or an implementation that does not pass the specification. Per-specification coverage remains useful diagnostically, but only complete coverage certifies the submitted implementation against

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ocument fine-tuning; 🟢 no AF is observed. Method Overview of the Alignment Faking Setup Models are told they are evaluated on a hypothetical new RLHF training objective which encourages "helpful-only" answers, providing useful responses to all queries, even if potentially dangerous or toxic.We focus on the helpful-only setting, as most models are trained to refuse harmful queries while the other Claude 3 Opus preferences studied in the original paper (e.g. a preference to avoid dismissing animal

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set of correct and incorrect English/Spanish translations from Marks et al.)Difference-of-mean probes are simply linear probes where the direction is created by subtracting the average representation of the false statements from the average representation of the true statements.. We verify that these probes generalize well to the other datasets of simple true/false statements from Marks et al. (on cities and larger-than, we get an average accuracy of 0.95). We then apply the probes to the MCQ Di

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raint that A B and B A are mutually exclusive. Mutual predictability measures how likely the model considers each label when conditioned on all other labels. This intuitively encourages all labels to reflect a single concept that is coherent according to the model. where $$P \theta$$ is the pretrained model, $$D$$ is the model-labeled dataset, $$x i$$ is the task input, $$y i$$ is the model-generated label. Our final scoring function is then a weighted combination of logical consistency and mutu

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auditors. Crucially, this enables fine-grained analysis of auditing methodologies: If we want to validate whether an auditing tool is useful, we can simply measure whether, on average across many attempts, access to this tool improves an auditing agent’s performance on a standard auditing testbed. We introduce three agents that autonomously complete alignment auditing tasks. We also introduce three environments that formalize alignment auditing workflows as auditing games, and use them to evalu

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conduct misaligned behaviors via out-of-context reasoning (Berglund et al., 2023) (for instance, reward hacking, scheming, deception, sabotage, and so on), and documents that encourage those misaligned behaviors. Excluding such content from the pretraining data might render the trained models less capable of such behaviors, and less inclined to engage in them in the first place. There are many opportunities for future work to improve data filtering. Our approach uses a classifier to determine wh

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o be representative of a much larger set of qualitatively similar observations. The appendix shows a longer excerpt from a typical transcript. We also include plots showing the average score that a Claude-based judge assigns to each model’s behavior along the relevant axis, averaged across all of the model interactions we observed. Higher numbers reflect more or clearer instances of the behavior under study, and are therefore generally worse. As scores are heavily influenced by which phenomena,

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resholded combinations of reward (e.g. no correctness reward unless the reasoning is sufficiently illegible). We use a system prompt that asks for low legibility reasoning to encourage such behaviors. We experiment with 2 different classifier prompts, one that gets hacked by our initial training runs, and one that doesn’t get hacked. Full prompts can be found in the appendix. Results In our experiments, Qwen2.5-1.5B never learns to produce actually illegible but effective math reasoning. We find

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its principles – occur between 5 and 13 times more often in the scenarios that proved to be high disagreement (i.e., the scenarios where models’ responses were more varied than average). Given the high disagreement, the models likely cover a diverse set of answering strategies, yet all of them are non-compliant. In our findings below, these frequent non-compliance scenarios often touched upon direct contradictions or interpretive ambiguities in the model spec, which made it difficult – or imposs

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al examples of clear sabotage. Having a human researcher in the loop was necessary for the success of the audit. The automated auditing agent produces both a summary report and average auditing scores for each model along various relevant dimensions of behavior. For the egregious sabotage model, the summary report included clear examples of sabotage, and the auditing scores were also clearly elevated for relevant categories such as Misaligned Behavior and Unprompted Sabotage of Anthropic (see Fi

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ed black-box tools achieve the highest success rates Scaffolded User Sampling and Scaffolded Text Completion are the best-performing tools overall, with success rates above 50% averaged across all models—compared to 37% for the default agent. These tools are especially effective on synthetic document models (SDF), which appear more susceptible to prompting outside the assistant persona. For transcript-distilled models with SFT adversarial training, gains are more modest but still positive. Findi

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for each of the safety issues. We will then present the effectiveness of our agent in fixing these problems. Our code release also includes more lightweight approaches such as leveraging ICL and DSPy (see Appendix for more implementation and experiment details). Experiment Settings Our automated finetuning agent, which uses LoRA and fully automates hyperparameter tuning, was primarily tested on fixing safety issues for Qwen-2.5-7B Instruct. We also examined its robustness on Llama-3.1-8B Instruc

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