Short summaries of AI and agent evaluation research, organized by broad tags.
Filtering by agent evals. Clear filter.
1. Problems / challenges / motivations - Fast, cheap models are where most production agent traffic actually runs, but they get thinner eval treatment than flagships. - Agentic and coding capability is scaffold-dependent, so a number without its harness is close to meaningless. - Safety-framework reporting needs to happen for every release, not only the...
1. Problems / challenges / motivations - The largest open-weight releases now ship with vendor eval tables well before any technical report or independent check exists. - Practitioners have to decide whether to trust those tables in the gap between launch and verification. - Open-weight releases are irreversible, which raises the stakes on that gap. 2. Key...
1. Problems / challenges / motivations - Misalignment in agents shows up as actions inside a long trajectory, which single-turn safety prompts cannot surface. - Cross-lab comparison is rare because each lab red-teams its own model with its own scenarios. - Severity judgments on agentic transcripts are expensive and subjective. 2. Key ideas - Controlled...
1. Problems / challenges / motivations - Terminal-agent benchmarks are saturating while real agentic work runs far longer than any of them. - Binary pass/fail on a multi-hour task throws away almost all the signal in the run. - Long tasks are expensive, so grading has to be worth the compute spent producing the trajectory. 2. Key ideas - 46 long-horizon...
1. Problems / challenges / motivations - Teams adopt LLM judges to scale agent evaluation and then never check whether the judge itself is reliable. - Observability research tends to land as separate studies rather than one usable path. - "Ship when evals look good" needs an actual gating mechanism to mean anything. 2. Key ideas - Rolls up five Microsoft...
1. Problems / challenges / motivations - SE-agent benchmarks are contaminated, syntactic, and outcome-only, so their scores drift away from what developers actually care about. - The same benchmarks are used to argue capability, prioritize research, and support safety cases. - The SWE-Bench Pro retraction made the cost of this concrete rather than...
1. Problems / challenges / motivations - Scientific-reasoning benchmarks mostly test recall or single-step analysis, not the judgment calls that make research hard. - Real analysis has dependent decision forks: an early wrong turn invalidates everything downstream. - Benchmarks authored by a lab whose models top them are easy to discount. 2. Key ideas -...
1. Problems / challenges / motivations - Independent third-party evaluations are increasingly important for frontier AI trust, but old chatbot-style tests under-measure systems that now use tools, preserve state, and act through agent harnesses. - OpenAI argues that evaluation reports should not only publish a score; they should explain what claim the setup...
1. Problems / challenges / motivations - Large coding-agent tasks often exceed what one linear chat can manage. Audits, migrations, and cross-checks need many independent passes, shared structure, and reproducible coordination. - Static hand-written harnesses can become a bottleneck: the right decomposition depends on the repository, task, files, risks, and...
1. Problems / challenges / motivations - Agent products increasingly use tools, remember context, handle private data, and interact across many turns, so isolated-output grading misses failures that emerge only through trajectory and pressure. - Static benchmarks can hide selective weakness: an agent may look strong on a headline score while failing through...
1. Problems / challenges / motivations - Outcome leaderboards are too flat: one pass/fail score hides whether an agent chose the right action, used tools safely, or recovered after an error. - Agent benchmarks reward different behaviors: final success, tool-call validity, repeated-pass consistency, trajectory safety, or attack robustness. That makes...
1. Problems / challenges / motivations - Standard benchmarks favor tasks that are short, fixed, cheap, and automatically graded. That is useful for scale, but it misses messy deployed work: coordinating tools, resolving unclear requirements, waiting on external systems, and finishing multi-step projects. - Benchmarks can overstate and understate capability....
1. Problems / challenges / motivations - Modern LLM agents increasingly succeed or fail because of the runtime around the model: tools, code execution, memory, sandboxes, repositories, validators, permissions, traces, and feedback loops. - Final task success is too flat for this world. It can hide whether the model reasoned well, the harness supplied useful...
1. Problems / challenges / motivations - The paper argues that real-world LLM-agent reliability is often constrained less by the base model than by the execution harness around it: environment, tools, context, orchestration, observability, evaluation, and governance. - Prompt engineering and context engineering are no longer enough for production agents....
1. Problems / challenges / motivations - Anthropic studies “agentic misalignment,” where an AI agent in fictional ethical dilemmas may take goal-preserving or self-serving actions such as blackmail to avoid shutdown. - Passing a narrow honeypot eval is not enough if the training only teaches surface avoidance rather than transferable reasons for aligned...
1. Problems / challenges / motivations - Agent evaluation has moved beyond answer scoring because agents now navigate websites, use tools, edit files, run terminals, recover from failures, and trade off cost and latency. - Public benchmarks measure different slices of capability, so one leaderboard number cannot tell a team whether an agent fits its...
1. Problems / challenges / motivations - Meta-Harness starts from a harness-engineering problem: the same frozen model can perform very differently depending on surrounding code for retrieval, memory, prompt construction, tool loops, and completion logic. - Existing text optimizers often compress experience into scalar scores, short summaries, fixed...
1. Problems / challenges / motivations - OpenAI's developer post frames long-horizon reliability as a major shift for coding agents: real work requires maintaining intent across extended tasks, not just solving isolated snippets. - Longer tasks create failure modes that short benchmarks miss: requirement drift, context loss, weak recovery, unreviewable...
1. Problems / challenges / motivations - Production agents fail in ways that final-answer evals do not explain: wrong tool choice, weak memory retrieval, multi-step drift, brittle recovery, or incomplete task execution. - Black-box LLM scoring is insufficient when agent behavior depends on orchestration, tools, business rules, and runtime context. - Large...
1. Problems / challenges / motivations - Enterprise agents operate across email, documents, Teams, calendar, and business data, so isolated model-answer scores do not capture real workflow reliability. - Organizations need evals that reflect local policies, schemas, permissions, and business constraints rather than generic public leaderboard tasks. -...
1. Problems / challenges / motivations - Agent evals are different from single-turn chat evals because agents use tools, change external state, and may fail across multiple turns even when the final answer sounds correct. - Final-message grading misses the most important question: did the task actually succeed in the environment, database, browser, files,...