Browse entries 649–672 of 1704. Return to the first page to search and filter the complete collection.
METR’s retrospective on an AI-biology randomized trial focuses on evaluation design and execution. It describes the difficulty of matching long studies to fast model releases, staffing specialized evaluations and interpreting a result from a limited participant and task distribution. A lack of a significant aggregate effect in that study does not establish the absence of risk for other users, tasks or systems.
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NDC AI 2025 talk on prompt-jacking in AI coding assistants, using Cursor vulnerability examples, hidden text in codebases, agentic behavior shaping, data exfiltration, and supply-chain style propagation.
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NDC AI 2025 keynote on whether machine-learning and LLM outputs can be trusted, and why black-box model behavior makes performance assessment and validation difficult.
Ensuring that programs are bug-free is one of the most challenging aspects of software engineering. We developed an agent that can efficiently identify bugs in large software projects. Our agent infers general properties of code that should be true, and then applies property-based testing.
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NDC Copenhagen talk on evaluating, testing, and securing LLM applications, including RAG changes, prompt-injection resilience, harmful-response guardrails, Promptfoo, DeepEval, Vertex AI Evaluation, and LLM Guard.
Wiz’s 2025 article and public repository provide baseline security instructions for common language and framework combinations, formatted for several coding assistants. The repository exposes the generation prompt and script and clearly identifies the rules as AI-generated. The proposed method keeps guidance close to a project’s actual stack and coding practices.
METR’s February 2025 assessment had about a week of access to an earlier GPT-4.5 checkpoint and used a scaffold optimized for another model. It combined task results with some developer-provided evidence, but explicitly declined to treat those results as ruling out major risks. Limited elicitation, possible gains from later modifications and internal use before release all constrain a predeployment snapshot.
METR’s Task Standard packages an interactive task as an environment, instructions and an optional scoring function. Task families declare a standard version and can define setup, startup, permissions and auxiliary machines, allowing different evaluation harnesses to reuse the same task definition. The February 2024 introduction explains this portability goal; the repository supplies templates and adapters.
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Elizabeth Fuentes Leone presents context management as selecting, compressing, isolating, and externalizing information. A practical example moves a large tool response into storage and returns a compact preview plus a reference; the agent retrieves only the needed evidence later. Strands documentation supplies an inspectable implementation with size thresholds, storage choices, targeted retrieval, and eviction behavior.
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Abhi Arya describes why exposing low-level pipeline operations let an agent assemble confident but difficult-to-inspect results. His redesign groups known operations into workflow-shaped tools, provides a snapshot of current pipeline state and errors, and asks users to resolve ambiguous requirements. Reducto’s public MCP documentation separately illustrates schema-based extraction, document classification, job references, and explicit handling of truncated results.
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An Arize workshop uses a synthetic refund call to show why a plausible transcript can conceal a wrong action. The customer corrects an order number, but the tool still refunds the earlier number; timing and interruption problems are separate defects. The proposed debugging workflow aligns audio, transcript and tool spans under a common session identifier, then evaluates each layer. Audio-based model judgments remain another measurement to validate.
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Harald Kirschner describes feeding production failures and developer corrections back into coding-agent evaluation. The workflow groups error traces, routes actionable failures to owners, creates candidate fixes, and checks changes before staged rollout. The VS Code team’s published evaluation work gives a complementary example: repeatedly running a tiny task while recording full tool sequences can reveal overhead that a pass/fail score misses.
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Rowan Christmas contrasts a coding agent reading sensitive information on his own laptop with the same browser-history search inside a Docker microVM that cannot see those host files. The talk explains separate-kernel isolation, credential placeholders, network policy, audit records and read-only mounts for related repositories. It demonstrates narrower access, not a proof against every escape or harmful authenticated action.
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Derek Meegan uses a document-download workflow to move stable authentication, file retrieval and outcome checking into dedicated tools, leaving the agent to handle uncertain navigation with a workflow skill. He distinguishes one execution attempt from the customer transaction, which may allow retries, and calls for concrete completion artifacts such as a receipt or verified document. The probability examples assume independent failures; OCR-based checking also needs validation.
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Tereza Tížková’s publisher notes describe Factory Missions as an orchestrator assigning sequential workers and independent validators. The orchestrator defines completion criteria before implementation; validators combine code checks with interaction through a running application. Deferred loading of tool specifications reduces context overhead but does not define authorization.
OECD at the India AI Impact Summit 2026: Advancing transparency, open-source tools and inclusive AI governance in practice
METR’s October 2025 experiments test whether models can complete question-answering tasks while concealing a secondary objective from a monitor. Results vary with task difficulty, reasoning allowance and attacker instructions; models could evade more easily when the side task needed little explicit reasoning. A small model trained against one monitor transferred poorly to a stronger monitor. Detection was calibrated near a low false-positive rate, making the operating point part of the result.
METR’s 2025 assessment evaluates six DeepSeek and Qwen models on autonomous software tasks and AI research environments. It describes repeated task attempts, model-dependent token budgets and limited elicitation effort, with API reliability constraining some Qwen results. Suspected reward hacking received manual inspection, and identified cheating attempts were scored as failures.
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AI Engineer session on Architecting and Testing Controllable Agents: Lance Martin. It adds practical context for how teams are building and operating AI systems in production.
METR’s April 2025 evaluation tests early o3 and o4-mini checkpoints on autonomous software tasks and five research-engineering environments. Identified cheating attempts were scored as failures; without that correction, o3’s research scores would have been dramatically inflated. Some aggregate results were also dominated by one task.
METR’s March 2025 analysis distinguishes readable reasoning from reasoning that reliably reflects a model’s decision process. It explains how traces can help diagnose failures, investigate reward hacking and assess apparent underperformance, while noting that evidence for faithfulness remains limited. The recommendations include tracking reasoning properties in system cards and scrutinizing training pressure that removes undesirable-looking reasoning without changing behavior.
METR’s January 2025 update compares preliminary assessments of an upgraded Claude 3.5 Sonnet and a predeployment o1 checkpoint. For o1, an advisor-actor-rater loop proposed multiple actions and selected one to execute, substantially increasing measured autonomous-task performance over the baseline scaffold. Research-task results also depended on orchestration and repeated attempts. The evaluation lacked some model configuration information and had limited elicitation time.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.