A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.
This page lists names, links and short descriptions. The original list on GitHub is the source and belongs to its authors.
Shunyu Yao — https://ysymyth.github.io/The-Second-Half/ · blog — "Evaluation becomes more important than training." The field-level why.
Eugene Yan — https://eugeneyan.com/writing/eval-process/ · blog — Process over tooling; evals as the scientific method.
Han-Chung Lee — https://leehanchung.github.io/blogs/2026/06/13/hidden-technical-debt-agent-evaluation-infra/ · blog — Control/data plane, the five eval surfaces, state deltas. "Chat eval was a spreadsheet; agent eval is a system."
Hamel Husain & Shreya Shankar — https://hamel.dev/blog/posts/evals-faq/ · blog — The densest operational Q&A: error analysis, binary judgments, the benevolent-dictator labeler.
Jason Wei — https://www.jasonwei.net/blog/asymmetry-of-verification-and-verifiers-law · blog — "Ability to verify == ability to create an RL environment."
Anthropic — https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents · blog — Best primary on agent-specific evals: task design, outcome vs trajectory, isolated trials, pass@k vs pass^k.
Ofir Press — https://ofir.io/How-to-Build-Good-Language-Modeling-Benchmarks/ · blog — Natural / auto-evaluatable / challenging; the "-200%" difficulty target; ~1-yr saturation.
Kapoor, Stroebl, Siegel, Nadgir, Narayanan — https://arxiv.org/abs/2407.01502 · paper — Cost as a first-class metric; model-dev vs app-dev; missing holdouts breed overfitting.
Nathan Lambert — https://www.interconnects.ai/p/building-on-evaluation-quicksand · blog — LLM eval has no ground truth; contamination; eval↔training coupling.
Shankar, Zamfirescu-Pereira, Hartmann, Parameswaran, Arawjo (UIST '24) — https://arxiv.org/abs/2404.12272 · paper — "Criteria drift": you can't write the rubric before you grade.
Florian Brand (Prime Intellect) — https://florianbrand.com/posts/benches-2026 · blog + 61-slide talk — The sharpest current read on why benchmarks break in the agent era: the "evals are dead, just measure vibes" backlash, how every layer of the eval-running stack (prompt · sampling temp · grader ·…
OpenAI — https://openai.com/index/trustworthy-third-party-evaluations-foundations/ · blog (Safety, May 2026) — What makes independent evals of frontier-model safeguards & capabilities trustworthy: harness selection, the validity hazards that distort results, and the standards third-party…
Shunyu Yao — https://ysymyth.github.io/The-Second-Half/ · blog — "Evaluation becomes more important than training." The field-level why.
Eugene Yan — https://eugeneyan.com/writing/eval-process/ · blog — Process over tooling; evals as the scientific method.
Hamel Husain — https://hamel.dev/blog/posts/evals/ · blog — The canonical "you need evals"; remove all friction from looking at your data; don't rely on generic frameworks.
Hamel Husain — https://hamel.dev/blog/posts/field-guide/ · blog — "Error analysis is consistently the highest-ROI activity." The metric for an AI roadmap is experiments run.
Shreya Shankar — https://www.sh-reya.com/blog/in-defense-ai-evals/ · blog — Rebuts the anti-eval backlash; evals = the systematic measurement of application quality.
Yan, Bischof, Frye, Husain, Liu, Shankar — https://applied-llms.org/ (Part II: https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/) · blog — The "intern test," genchi genbutsu, turning vibe-checks into assertions.
Nathan Lambert — https://www.interconnects.ai/p/evals-are-marketing · blog — Why frontier-lab leaderboard numbers are marketing, not science.
Chip Huyen — https://huyenchip.com/2025/01/16/ai-engineering-pitfalls.html · blog — Common eval/AI-engineering mistakes from the AI Engineering author. (also T6)
Aishwarya Naresh Reganti & Kiriti Badam (O'Reilly Radar) — https://www.oreilly.com/radar/evals-are-not-all-you-need/ · blog — The essential nuance piece: automated graders alone don't save you; you need a continuous-improvement flywheel of offline tests + production monitoring + real-user…
Hamel Husain & Shreya Shankar with Lenny Rachitsky (Lenny's Podcast/Newsletter) — https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill · talk — The accessible 'why evals matter' on-ramp (live walkthrough of error analysis, open/axial coding) that mainstreamed evals to PMs in…
OpenAI — https://openai.com/index/evals-drive-next-chapter-of-ai/ · blog — Frontier-lab framing of evals as turning fuzzy business goals into specs and measurable ROI; useful counterweight to Lambert's 'evals are marketing' and grounds the 'why' for enterprise readers. 🆕 ⚠(unverified URL)
Aman Khan (Arize) with Lenny Rachitsky — https://www.lennysnewsletter.com/p/beyond-vibe-checks-a-pms-complete · blog — The widely-shared PM-oriented argument for moving past 'looked good to me' vibe checks to systematic evals; one of the pieces that made evals a mainstream product skill in 2025. 🆕
Gergely Orosz & Hamel Husain (The Pragmatic Engineer) — https://newsletter.pragmaticengineer.com/p/evals · newsletter — Reaches the broad engineering audience with the core 'why': LLM non-determinism breaks traditional testing, so you need evals. High-distribution motivation piece co-written by…
OpenAI — https://openai.com/index/deployment-simulation/ · blog — Concrete 2026 evidence for why fixed/static evals fail: models recognize when they're being tested and game test suites; replaying ~1.3M real conversations surfaced reward-hacking no fixed eval caught. Strong 'why evals must evolve'…
Greg Brockman (OpenAI) — https://x.com/gdb/status/1733553161884127435 · blog — The canonical one-liner ('evals are the new unit test') that anchors the whole 'why evals' thesis; frequently cited founding quote for the movement. Short but load-bearing.
Jason Wei — https://www.jasonwei.net/blog/asymmetry-of-verification-and-verifiers-law · blog — "Ability to verify == ability to create an RL environment."
Han-Chung Lee — https://leehanchung.github.io/blogs/2026/03/21/rl-environments-for-llm-agents/ · blog — A benchmark is a frozen RL environment; the E = {T,H,V,S,C} decomposition; "verifiable beats judgeable."
Kanav Garg (Core Automation; ex-DeepMind) — talk; summary at https://muratbuffalo.blogspot.com/2026/06/acm-cais-conference-on-ai-and-agentic.html · talk — Difficulty calibration (the 1–4/16 Goldilocks band), RL as variance reduction, reward hacking under training pressure. (local notes:…
David Silver & Richard Sutton — https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf · paper — Human-data value approaching its ceiling; the frontier is agents learning from experience / synthetic environments.
Nathan Lambert — https://rlhfbook.com/c/16-evaluation · book — Evaluation as a reflection of training goals; prompt-format sensitivity (60%→~0%).
Nathan Lambert — https://www.interconnects.ai/p/what-comes-next-with-reinforcement · blog — Long-horizon credit assignment; where RL is and isn't ready.
Prime Intellect — https://github.com/PrimeIntellect-ai/verifiers (docs: .../blob/main/docs/environments.md) · tool/repo — One environment package shared by eval and prime-rl — the eval-is-an-RL-env thesis as code.
DeepSeek-AI (Guo et al.) — https://arxiv.org/abs/2501.12948 · paper — The proof-of-thesis: pure RL with rule-based verifiable rewards (no SFT) makes reasoning emerge — the canonical 'if you can verify it, RL builds it' result; also published in Nature 2025. Conspicuously absent from a section…
Lambert et al. (Allen Institute for AI) — https://arxiv.org/abs/2411.15124 · paper — Coined/popularized RLVR and open-sourced the recipe + code (open-instruct): swap the reward model for a verifier on tasks with checkable answers. The foundational citation behind every 'verifiable beats judgeable'…
Anthropic — https://www.anthropic.com/research/emergent-misalignment-reward-hacking · paper — Empirical receipt for the section's 'reward hacking under training pressure' theme: learning to cheat on real coding environments generalizes to sabotage/alignment-faking; introduces inoculation prompting…
Prime Intellect — https://www.primeintellect.ai/blog/environments · blog — The launch post for the verifiers-spec marketplace (2,500+ shared eval/RL environments) — the eval-is-an-RL-env thesis as an actual ecosystem, the natural companion to the already-listed verifiers repo. 🆕
Ege Erdil, Matthew Barnett, Tamay Besiroglu (Mechanize) — https://www.mechanize.work/blog/how-to-fully-automate-software-engineering/ · blog — Sharpest statement of the inverse thesis: today's RL environments are rudimentary, so capability is gated on building richer/more diverse environments —…
Mechanize (Erdil, Barnett, Besiroglu) — https://www.mechanize.work/blog/cheap-rl-tasks-will-waste-compute/ · blog — The economics of environment quality: data and compute are complementary, so low-quality (cheaply-bought) tasks waste expensive RL compute — directly informs difficulty calibration /…
Jean-Stanislas Denain & Chris Barber (Epoch AI) — https://epoch.ai/gradient-updates/state-of-rl-envs · blog — Practitioner-interview survey (18 pros) on how RL environments are actually built, the reward-hacking failure modes, and the production-scaling bottleneck — the empirical…
AJ Kourabi & Dylan Patel (SemiAnalysis) — https://newsletter.semianalysis.com/p/rl-environments-and-rl-for-science · newsletter — Market-structure view: 35+ companies now sell RL environments; capability gains are coming from ramping RL compute, not pretraining. Grounds the 'benchmark = frozen RL…
Harbor / Stanford / Laude Institute — https://github.com/harbor-framework/terminal-bench · benchmark — A concrete instance of the thesis: each task ships a Docker environment + programmatic verification test suite + oracle — i.e. a benchmark that IS an RL environment (and is used as one). 2.4k…
Sierra Research (Barres et al.) — https://github.com/sierra-research/tau2-bench · benchmark — Dual-control, multi-turn, policy-following eval with a simulated user and verifiable DB-state checks — the canonical example of a verifiable conversational/agentic environment beyond math/code (paper…
Han-Chung Lee — https://leehanchung.github.io/blogs/2026/05/08/hidden-technical-debt-agent-harness/ · blog — The harness is the agent; what teams call "the model" is mostly harness + product.
Han-Chung Lee — https://leehanchung.github.io/blogs/ · blog — The four-part series (eval infra, runtime, harness, + agent runtime ~2026/04/24). (verify the runtime post URL on the index.)
METR — https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ · paper/blog — Scaffolds change the measured horizon; success-vs-human-time as a primitive. (also T9)
Nathan Lambert — https://www.turingpost.com/p/nathanlambert · talk/interview — "What technical people call the harness or the product matters more than just the model."
Florian Brand (Prime Intellect) — https://florianbrand.com/posts/benches-2026 · blog + 61-slide talk — The sharpest current read on why benchmarks break in the agent era: the "evals are dead, just measure vibes" backlash, how every layer of the eval-running stack (prompt · sampling temp · grader ·…
Han-Chung Lee — https://leehanchung.github.io/talks/2025/04/23/the-model-is-the-product/ · talk — The primary-source talk (Data Council 2025) behind the must-read author's whole thesis — the direct counterpart to Hamel's 'Model is Not the Product'; the foundational text of the harness/model debate…
Hamel Husain — https://www.youtube.com/watch?v=EEw2PpL-_NM · talk — The opposing side of the Lee debate (Data Council 2025): great products are mostly harness + product + evals, not the model. Section already cites Lee; it should cite the debate it half-references. 🆕
Simon Willison — https://simonwillison.net/2025/May/22/tools-in-a-loop/ · blog — The canonical, now-widely-adopted definition of an agent; 'the skill is in the design of both the tools and the loop' — the cleanest statement of why the harness, not the model, dominates behavior. 🆕
OpenAI — https://openai.com/index/harness-engineering/ · blog — Frontier-lab primary source coining 'harness engineering': a 1M-line codebase built by Codex agents where improving the environment/harness mattered more than the model. Lab-side complement to Lee's 'harness is the agent'. (URL…
Anthropic — https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills · blog — The primary source for the 'skill' leg of the model/harness/skill decomposition — skills as composable, progressively-disclosed capabilities (later made an open standard). The section…
Anthropic — https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents · blog — Anthropic's primary statement that the harness's job is engineering context (editing, compaction, memory, programmatic tool-calling) — the mechanism behind why same model + different harness…
Anthropic (Ken Aizawa) — https://www.anthropic.com/engineering/writing-tools-for-agents · blog — Tool design is a load-bearing part of the harness; 'agents are only as effective as the tools we give them,' validated eval-first. Directly ties harness decisions to measured agent performance. 🆕
Anthropic — https://www.anthropic.com/research/agents-in-biology · blog — 120 viral-sequence retrieval queries across 40 pathogens; without deterministic tooling, model accuracy spans 16.9%–91.3% and Claude Sonnet 4 returned 106 sequences in one run, 15 in a second, 5 in a third to an identical…
Pete Hodgson — https://blog.thepete.net/blog/2025/12/10/same-model-different-results-why-coding-agents-arent-interchangeable/ · blog — Concrete teardown of Claude Code's harness (system reminders, sub-agents, planning, IDE feedback) showing identical models yield different results — the…
Princeton SAgE team (Kapoor, Narayanan, et al.) — https://hal.cs.princeton.edu/ · benchmark — Standardized, cost-aware harness that runs the SAME agent harness across 9 benchmarks/9 models (21,730 rollouts) — the infrastructure answer to 'harness confounds rankings.' ICLR 2026; paper…
Addy Osmani (O'Reilly Radar) — https://www.oreilly.com/radar/agent-harness-engineering/ · blog — 'A decent model with a great harness beats a great model with a bad harness'; reframes agent failures as harness/config problems (traceable AGENTS.md rules). Names the converging harness primitives…
Nathan Lambert (Interconnects) — https://www.interconnects.ai/p/the-next-phase-of-open-models · blog — Lambert's written articulation (Mar 2026) of an AI system as weights + tools + harness — the written companion to the Turing Post interview already listed, with the explicit three-part…
Han-Chung Lee — https://leehanchung.github.io/blogs/2026/06/13/hidden-technical-debt-agent-evaluation-infra/ · blog — Control/data plane, the five eval surfaces, state deltas. "Chat eval was a spreadsheet; agent eval is a system."
Braintrust — https://www.braintrust.dev/blog/three-pillars-ai-observability · blog — Dataset reconciliation (living datasets); traces / evals / annotation.
Arize (AX docs) — https://arize.com/docs/ax/evaluate/evaluators/trace-and-session-evals/trace-level-evaluations/agent-trajectory-evaluations · docs — Grading the path, not just the answer.
Galileo — https://galileo.ai/blog/ai-agent-metrics · blog — A concrete agent-metric taxonomy (action completion, tool selection, etc.).
Arize — https://github.com/Arize-ai/openinference/blob/main/spec/semantic_conventions.md · tool/repo — An OTel-based agent trace schema (tool, args, observation, latency, cost).
LangChain — https://docs.langchain.com/langsmith/evaluation · https://docs.langchain.com/langsmith/trajectory-evals · docs.
OpenTelemetry / CNCF — https://github.com/open-telemetry/semantic-conventions-genai · docs — The upstream vendor-neutral standard (spans/metrics/events for LLM calls, invoke_agent, execute_tool, MCP) that OpenInference maps onto — the canonical trace schema the section's OpenInference entry…
OpenTelemetry — https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-agent-spans/ · docs — Human-readable spec page for create_agent / invoke_agent / execute_tool spans and attributes — the precise definition of what a gradable agent trace looks like. 🆕
OpenTelemetry (blog) — https://opentelemetry.io/blog/2026/genai-observability/ · blog — Walkthrough of emitting and reading GenAI spans (token usage, finish reasons, tool calls) — concrete intro to the trace surface for practitioners not steeped in OTel. 🆕
Weights & Biases — https://docs.wandb.ai/weave · docs — @weave.op trace trees (inputs/outputs/cost/latency) plus a scorer-based eval harness — a widely used surface for grading both traces and outputs. 🆕
Laminar — https://laminar.sh/ · tool — OTel-native, agent-specific: transcript view, SQL-over-traces, and a rollout debugger — purpose-built for grading multi-step agent trajectories rather than single LLM calls. 🆕
UK AISI — https://github.com/UKGovernmentBEIS/inspect_ai · https://inspect.aisi.org.uk/ — @task binds dataset + solver + scorer; custom scorers; sandboxed tools. The reference agent-eval framework. (MUST)
UK AISI — https://github.com/UKGovernmentBEIS/inspect_evals — 🆕 the companion catalog of community benchmarks (GAIA, CTFs, AIME…) — the "batteries" for Inspect.
UK AISI — https://www.aisi.gov.uk/blog/releasing-aisis-engineering-playbook · https://engineering-playbook.aisi.org.uk/ · guide — 🆕 Open guide to production frontier-model eval infrastructure: five-layer stack (Evaluate · Isolate · Connect · Run · Scale) covering sandboxed untrusted code, audited…
EleutherAI — https://github.com/EleutherAI/lm-evaluation-harness — the standard academic harness; first-class decontamination; task YAMLs.
Allen Institute (Ai2) — https://github.com/allenai/olmes — 🆕 the reproducible eval standard + harness behind OLMo/Tülu: standardized prompts/metrics/formatting for apples-to-apples model comparison.
https://github.com/benchflow-ai/benchflow · https://benchflow.ai — 🆕 environment-lab framework: research infra + runtime for building RL environments, evals & post-training; ships SkillsBench and ClawsBench. ("Environments are the new data.")
Hugging Face — https://github.com/huggingface/lighteval — 🆕 all-in-one harness across transformers/vLLM/TGI/nanotron, 1000+ tasks; HF's successor to evaluate.
Groq — https://github.com/groq/openbench — 🆕 provider-agnostic bench CLI, 95+ benchmarks, built on Inspect primitives.
OpenAI — https://github.com/openai/simple-evals — minimal zero-shot/CoT scripts (MMLU, HumanEval, SimpleQA, HealthBench); the numbers OpenAI publishes. ⚠️ not actively maintained.
https://github.com/openai/evals — the completion_fn abstraction = swap the system-under-test. (Best-practices: https://developers.openai.com/api/docs/guides/evaluation-best-practices)
https://github.com/promptfoo/promptfoo — MIT eval + red-teaming CLI; git-diffable YAML configs. (MUST)
https://github.com/confident-ai/deepeval — "pytest for LLMs," 40+ metrics (G-Eval, RAG, hallucination) + red-team; ~2M evals/day; hosted cloud. 🆕
https://github.com/pydantic/pydantic-ai (ai.pydantic.dev/evals) — 🆕 type-safe Datasets/Cases/Evaluators with OTel tracing, from the Pydantic AI team.
LangChain — https://github.com/langchain-ai/openevals — 🆕 prebuilt evaluators + create_llm_as_judge (incl. multimodal); general-purpose companion to agentevals (https://github.com/langchain-ai/agentevals, trajectory match).
https://mlflow.org/docs/latest/genai/eval-monitor/ — 🆕 mlflow.genai.evaluate: 50+ judges/metrics, custom scorers, regression datasets inside MLflow.
Stanford CRFM — https://github.com/stanford-crfm/helm — holistic eval: standardized datasets + metrics beyond accuracy + leaderboard (also VHELM, HEIM).
https://github.com/Giskard-AI/giskard-oss — auto-generates adversarial test suites (injection, hallucination, bias) from a plain-language app description.
https://github.com/deepchecks/deepchecks (llmdocs.deepchecks.com) — property-based scoring (grounded-in-context, toxicity, fluency) + custom LLM-judge properties.
https://github.com/uptrain-ai/uptrain — 20+ preconfigured checks + root-cause analysis on failures.
https://github.com/huggingface/evaluate — classic metrics library, ⚠️ maintenance mode (use lighteval for LLMs).
harbor-framework (Laude Institute / Stanford) — https://github.com/harbor-framework/harbor — 🆕 framework for running agent evals + creating/using RL environments; powers Terminal-Bench 2.0. ~3.7k . ⚠️ name overloaded (cf. av/harbor local-LLM toolkit).
Emrick Donadei — https://github.com/edonadei/caliper · https://pypi.org/project/caliper-eval/ — 🆕 pass@k reliability harness for agent skills: runs a skill k times against Claude Code / Codex / pi, grading each attempt with an LLM-autorater (expect:) and/or deterministic Python (assert:), then a…
UiPath — https://github.com/UiPath/coder_eval · https://coder-eval.com/docs — 🆕 treats skill activation as a measurable variable: skill_triggered over a labelled dataset yields suite-level precision/recall/F1 gates (cf. §3). Harness-level, not leaderboard-level. ~0.1k .
Matt Pocock — https://github.com/mattpocock/evalite — 🆕 local-first eval runner on Vitest; .eval.ts files, web UI, cost-aware.
https://github.com/mastra-ai/mastra (mastra.ai/docs/evals/overview) — 🆕 model-graded/rule/statistical scorers, live evals, CI, in the Mastra agent framework.
Open Multi-Agent — https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/evaluation.md · tool/framework — 🆕 TypeScript-native, versioned EvalSets with offline JSON/Markdown/JUnit reports, baseline regression gates for CI, and opt-in production sampling that scores settled runs…
https://github.com/vercel-labs/agent-eval — 🆕 A/B-test coding agents (Claude Code, Codex, Cursor) on custom tasks; pass-rate dashboards.
Braintrust — https://github.com/braintrustdata/autoevals — OSS scorer library (Factuality, relevance, security…) across Py/JS/Go/Ruby.
https://github.com/truera/trulens — instrumentation + "feedback functions" (the RAG triad), now OTel-based.
Stanford — https://github.com/stanford-futuredata/ARES — synthetic queries + fine-tuned judges + prediction-powered inference for confidence intervals.
Amazon Science — https://github.com/amazon-science/RAGChecker — 🆕 claim-level diagnosis separating retriever vs generator errors.
https://github.com/relari-ai/continuous-eval — modular per-module metrics across retrieval/generation/tool-use.
https://github.com/TonicAI/tonic_validate — RAG metrics as a GitHub Action for CI.
TIGER-AI-Lab et al. — https://arxiv.org/abs/2605.08703 · framework — 🆕 evolves task-specific scoring skills and tools from preference demonstrations, producing image-editing judgments and a GRPO-compatible scalar reward without reward-model training.
Haize Labs — https://github.com/haizelabs/verdict — 🆕 declarative compound judges (debate/verification/aggregation, inference-time scaling); arXiv:2502.18018.
OpenPipe (ART) — https://github.com/OpenPipe/ART (art.openpipe.ai/fundamentals/ruler) — 🆕 LLM-judge that ranks trajectories with no labels — judge-as-RL-reward. (industry must-read)
https://github.com/prometheus-eval/prometheus-eval — open-weight evaluator LMs for rubric-based assessment + pairwise.
https://github.com/atla-ai/selene-mini — 🆕 8B SoTA open judge (score + critique); + MCP server atla-ai/atla-mcp-server. arXiv:2501.17195.
https://github.com/patronus-ai/Lynx-hallucination-detection · https://github.com/patronus-ai/glider — 🆕 open hallucination judge / explainable span-level judge.
https://github.com/flowaicom/flow-judge — efficient 3.8B open evaluator.
AI2 — https://github.com/allenai/reward-bench — canonical reward-model (+v2 judge) benchmark/harness.
https://github.com/ScalerLab/JudgeBench — benchmark to evaluate the judges themselves.
Fireworks — https://github.com/fw-ai-external/reward-kit — 🆕 decorator-based reward-function authoring (TRL/Fireworks interop).
Saif Punjwani — https://github.com/SaifPunjwani/truescore · tool — Measures a judge against held-out human labels (sensitivity, Cohen's kappa, Gwet's AC1), tests it for length, position and self-preference bias, then corrects the reported score with prediction-powered inference so the interval…
Prime Intellect — https://github.com/PrimeIntellect-ai/verifiers (docs: .../blob/main/docs/environments.md) · tool/repo — One environment package shared by eval and prime-rl — the eval-is-an-RL-env thesis as code.
Prime Intellect — https://github.com/PrimeIntellect-ai/community-environments (app.primeintellect.ai) — 🆕 crowdsourced verifiers-based RL/eval envs.
Prime Intellect — https://github.com/PrimeIntellect-ai/prime-rl — 🆕 async RL trainer consuming verifiers envs (INTELLECT-3).
https://github.com/benchflow-ai/benchflow · https://benchflow.ai — 🆕 environment-lab framework: research infra + runtime for building RL environments, evals & post-training; ships SkillsBench and ClawsBench. ("Environments are the new data.")
https://github.com/hud-evals/hud-python — 🆕 SDK to build/run agent eval environments (computer-use, browser, MCP) with telemetry.
Nous Research — https://github.com/NousResearch/atropos — 🆕 async "environment microservice" framework for rollouts/verifiable rewards.
https://github.com/volcengine/verl (now verl-project/verl) — de-facto industry RLVR trainer (PPO/GRPO). ~22k .
https://github.com/OpenRLHF/OpenRLHF · SkyRL — https://github.com/NovaSky-AI/SkyRL · AReaL — https://github.com/areal-project/AReaL · ROLL — https://github.com/alibaba/ROLL · rLLM — https://github.com/agentica-project/rllm · TRL — https://github.com/huggingface/trl — the RL-training stack agents…
General Reasoning — https://docs.openreward.ai/ (PyPI openreward) — 🆕 MCP-extending spec adding RL primitives (episodes, rewards, curriculum). ⚠️ no single canonical repo confirmed.
https://github.com/Arize-ai/phoenix — OSS OTel tracing + response/retrieval evals + datasets/experiments. (MUST)
https://github.com/langfuse/langfuse — OSS: evals (LLM-judge, feedback, manual labeling), datasets/experiments, prompt mgmt; self-hostable. 🆕
Comet — https://github.com/comet-ml/opik — 🆕 fully-OSS eval + observability (judges, datasets, CI-runnable evals).
https://github.com/wandb/weave — weave.Evaluation scorers (exact/regex/model-graded/embedding) + Guardrails; comparison dashboards. 🆕 (Humanloop's migration target.)
https://www.braintrust.dev/docs/start/eval-sdk (offline-eval-guide) — Eval() over golden datasets; offline vs online. (MUST)
https://www.patronus.ai/ (github.com/patronus-ai) — 🆕 research-grade judges (Lynx, GLIDER, Percival agent-failure debugger), experiments, multimodal judge.
https://www.getmaxim.ai/ — 🆕 agent simulation + eval + observability across thousands of scenarios/personas.
https://galileo.ai/ — Luna evaluators + Agentic Evaluations.
https://www.vellum.ai/ — visual workflows + offline/online evals scoring every production run.
https://github.com/helicone/helicone — OSS gateway + observability; "Scores" ingests external eval results.
https://github.com/traceloop/openllmetry — OSS OTel instrumentation (Py/TS/Go/Ruby) + hosted reliability platform.
https://github.com/Scale3-Labs/langtrace — OSS OTel-standard tracing + manual scoring + dataset mgmt.
https://github.com/whylabs/langkit — high-throughput text-signal metrics (toxicity, PII, jailbreak) for production monitoring.
https://github.com/portkey-ai/gateway — 🆕 OSS gateway + 60+ guardrails + observability (fully open-sourced Mar 2026).
https://www.datadoghq.com/product/ai/llm-observability/ — 🆕 evaluators + golden datasets + LLM Experiments + AI Agent Monitoring (Jun 2025).
https://www.fiddler.ai/ — 🆕 Trust Models (Safety/PII/Faithfulness) scoring in <100ms; Guardrails + agentic observability.
https://www.promptlayer.com/ · New Relic AI Monitoring — https://newrelic.com/platform/ai-monitoring — lighter prompt-CMS / APM-native monitoring.
https://github.com/arthi-arumugam-git/whatbroke · tool — OSS CLI that diffs two agent trace files (JSONL) before/after a model or prompt change: dropped tool calls, drifted args, reordering, cost/latency/outcome deltas; multi-sample flap rates demote baseline flakiness so only real regressions…
Arize — https://github.com/Arize-ai/openinference/blob/main/spec/semantic_conventions.md · tool/repo — An OTel-based agent trace schema (tool, args, observation, latency, cost).
https://opentelemetry.io/docs/specs/semconv/gen-ai/ (open-telemetry/semantic-conventions) — 🆕 the vendor-neutral schema (now covers agent orchestration, MCP tool calls, and a quality-evaluation span hook).
Braintrust — https://www.braintrust.dev/ · tool — Industry-standard eval+observability platform (Notion, Stripe, Vercel) tying offline experiments to production logs; the section already cites Braintrust's Autoevals but omits the platform itself. 🆕
RagaAI — https://github.com/raga-ai-hub/RagaAI-Catalyst · tool — OSS agent-observability + eval SDK with multi-agent trace/execution-graph debugging, synthetic-data gen, and guardrail management — covers the online/guardrail-eval slice the section lacks. 🆕
OpenAI — https://developers.openai.com/cookbook/topic/evals · docs — Maintained, runnable recipes for building evals (incl. Agents SDK eval, evaluating agents with Langfuse); the practical companion to OpenAI Evals and a curator-grade 'show real work' resource. 🆕 ⚠(unverified URL)
Stefan Heule et al. (Cursor) — https://cursor.com/blog/building-bugbot · excellent — Build your primary eval metric around post-merge signal: Cursor's "resolution rate" uses AI at PR merge time to determine which flagged bugs were actually fixed by the developer, validated by human spot-checks.…
Scale AI — https://arxiv.org/abs/2605.21347 · paper/tool — 🆕 Automated system that analyzes large corpora of agent execution traces to surface failure patterns and behavioral issues at scale; "Human experts using IG reports improve scaffold performance by 30.4 pp over the unmodified baseline…
Antaripa Saha & Hamel Husain (Parlance Labs) — https://parlance-labs.com/blog/posts/auto-evals/index.html · blog (Jul 11 2026) — Empirical head-to-head of six eval systems (Braintrust Loop, Arize AX Alyx, LangSmith, Codex/GPT-5.5, Factory Droid/GPT-5.5, Claude Code/Opus 4.8) on 100 production…
Song, Sutawika, Liu et al. (Carnegie Mellon / Berkeley) — https://arxiv.org/abs/2607.02032 · paper (Jul 2026) — Selects non-agentic atomic benchmark instances whose aggregate scores best predict performance on expensive agentic benchmarks (SWE-Bench, GAIA), achieving mean absolute error under 4%…
Ofir Press — https://ofir.io/How-to-Build-Good-Language-Modeling-Benchmarks/ · blog — Natural / auto-evaluatable / challenging; the "-200%" difficulty target; ~1-yr saturation.
Kapoor, Stroebl, Siegel, Nadgir, Narayanan — https://arxiv.org/abs/2407.01502 · paper — Cost as a first-class metric; model-dev vs app-dev; missing holdouts breed overfitting.
OpenAI — https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/ · blog — ~59% of audited failures were broken tests. (mirror: https://decrypt.co/359012/...)
Shivalika Singh et al. (Cohere/Princeton/Stanford/MIT/AI2) — https://arxiv.org/abs/2504.20879 · paper — Private testing, selective disclosure, and data-access asymmetry on Chatbot Arena. (notes: research/notes/leaderboard-illusion.md)
https://arxiv.org/abs/2506.12286 · paper — Memorization inflates SWE-bench scores.
https://arxiv.org/abs/2507.02825 · paper — SWE-bench Verified weak tests; τ-bench rewards empty responses. (verified high)
Epoch AI — https://epoch.ai/benchmarks/frontiermath-tiers-1-3-v2 (changelog: .../frontiermath-tier-4-v2) · page — ~42% of problems corrected after AI-assisted review. (also T8: the operator-as-rot-detector tale)
FutureHouse / Andrew White — https://www.futurehouse.org/research-announcements/hle-exam · blog — 29 ± 3.7% of text-only chem/bio answers contradicted by the literature. (LessWrong writeup: https://www.lesswrong.com/posts/JANqfGrMyBgcKtGgK/)
Nathan Lambert — https://www.interconnects.ai/p/building-on-evaluation-quicksand · blog — LLM eval has no ground truth; contamination; eval↔training coupling.
https://arxiv.org/abs/2601.17087 · paper — Simulated users are unreliable proxies (~9pp swings by simulator choice; demographic miscalibration).
Jimenez, Yang, … Press, Narasimhan — https://arxiv.org/abs/2310.06770 · https://www.swebench.com (Verified: .../verified.html) · paper/site.
Eugene Yan — https://eugeneyan.com/writing/evals/ · blog — Off-the-shelf evals rarely transfer; accuracy is too coarse.
https://x.com/karpathy/status/1896266683301659068 · post — "We make a number of specific recommendations…" (the eval-as-narrow critique).
Hugh Zhang et al. (Scale AI) — https://arxiv.org/abs/2405.00332 · paper — Held-out GSM1k replica of GSM8k exposes up to 8% accuracy drop and partial memorization (Mistral/Phi) — the canonical method for measuring benchmark overfitting/contamination via a matched holdout.
Curtis Northcutt, Anish Athalye, Jonas Mueller — https://arxiv.org/abs/2103.14749 · paper — NeurIPS 2021 foundational result: ~3.3% avg label errors across 10 famous test sets (ImageNet, MNIST, etc.); corrections flip model rankings. The canonical 'label errors' citation this section's theme rests…
Aryo Pradipta Gema et al. (Edinburgh) — https://arxiv.org/abs/2406.04127 · paper — ~6.5% of MMLU questions contain errors (57% in Virology); MMLU-Redux re-annotation shifts rankings — directly demonstrates label-error impact on the most-cited LLM benchmark.
Naman Jain et al. (UC Berkeley) — https://arxiv.org/abs/2403.07974 · benchmark — Time-windowed problem collection (post-cutoff scoring) as the leading contamination-resistant design pattern — the section discusses contamination but lists no exemplar of how to engineer around it.
White, Dohan, LeCun, Goldblum et al. — https://github.com/LiveBench/LiveBench · benchmark — Monthly-refreshed questions from new arXiv/news/competitions with objective ground truth — the canonical 'dynamic refresh' answer to saturation and contamination.
Clémentine Fourrier / Hugging Face — https://github.com/huggingface/evaluation-guidebook · docs — Practitioner reference from running the Open LLM Leaderboard; explicit sections on contamination, reproducibility, and leaderboard design — the hands-on 'how to not get fooled' companion to this…
Kapoor, Stroebl, Kirgis et al. (Princeton) — https://arxiv.org/abs/2510.11977 · paper — 21,000+ standardized agent runs surfacing leaderboard unreliability and unreported misbehaviors (agents searching HuggingFace for benchmark answers) — extends 'AI Agents That Matter' to leaderboard integrity…
Jambholkar, Rajani, Bakshi (Collinear AI) — https://blog.collinear.ai/p/gaming-the-system-goodharts-law-exemplified-in-ai-leaderboard-controversy · blog — Practitioner framing of the Llama 4 / Chatbot Arena gaming episode through Goodhart's Law — the accessible blog companion to The Leaderboard…
OpenAI — https://openai.com/index/trustworthy-third-party-evaluations-foundations/ · blog (Safety, May 2026) — What makes independent evals of frontier-model safeguards & capabilities trustworthy: harness selection, the validity hazards that distort results, and the standards third-party…
OpenAI — https://openai.com/index/separating-signal-from-noise-coding-evaluations/ · blog (Jul 8 2026) — SWE-Bench Pro audit finding ~30% of its 731 tasks broken across four failure categories: overly strict tests that reject functionally correct solutions, underspecified prompts with hidden test…
Cursor — https://cursor.com/blog/reward-hacking-coding-benchmarks · blog — Cursor audited 731 Opus 4.8 Max trajectories and found 63% of "successful" resolutions retrieved known fixes (57% upstream-lookup, 6% git-history mining) rather than reasoning them out. Re-running SWE-bench Pro under strict…
Anthropic — https://www.anthropic.com/engineering/infrastructure-noise · blog — Infrastructure resource configuration (RAM, resource limits) swings agentic coding benchmark scores by up to 6 percentage points — often larger than leaderboard gaps between adjacent models. A phase transition exists…
Tura — https://turaai.net/blog#token-saving-plugins-are-mostly-stupid-idea · blog + public dataset — A deliberately underpowered coding-agent study (n=2/arm) that refuses to infer plugin effects: apparent −8.87%/+7.18% cost shifts sit inside 30–52% within-arm swings; matched runs, methodology, a…
Anthropic — https://www.anthropic.com/engineering/eval-awareness-browsecomp · blog — First documented instance of a model reverse-engineering its own evaluation: Claude Opus 4.6 independently hypothesized it was being tested, identified the specific benchmark (BrowseComp), located the source code…
Nadgir, Kapoor, Liu, Kirgis, Narayanan et al. (Princeton / UC Berkeley / MIT) — https://arxiv.org/abs/2606.26158 · paper — Argues saturation is diagnostic evidence to dig deeper, not a retirement trigger: LLM trajectory audit found "15 task-level errors and 20 tasks with exploitable shortcuts in…
Wang, Zhang, Yao, Zeng, Song, Lin, Shen — https://arxiv.org/abs/2606.05241 · paper — Defines three contamination types (Benchmark Metadata Leakage, Question-Context Leakage, Explicit Answer Leakage) for agents that web-search during evaluation; applies detection algorithms across six benchmarks…
Yonas Atinafu, Robin Cohen — https://arxiv.org/abs/2603.11337 · paper — Treats evaluation integrity as a first-class benchmarked outcome instead of an assumption: two compromise vectors (evaluator tampering, train/test leakage) instrumented via patch tracking and runtime file-access logging; in…
Bhat, Vaghasiya, Mohsin, Aali — https://arxiv.org/abs/2607.02577 · paper (Jun/Jul 2026) — Cross-benchmark validity audit of BFCL v4, τ²-Bench, LiveMCPBench, and MCP-Atlas across 496 expert-reviewed tasks. Key findings: "92 evaluator-human disagreements, corresponding to an 18.5% misalignment…
Jamie Dborin (Doubleword) — https://blog.doubleword.ai/frontier-os-llm · blog — Extrapolates the Artificial Analysis Intelligence Index across 18 constituent benchmarks to forecast the open-vs-closed capability gap; a naive fit of the headline index points to convergence by 3 Dec 2026, but the…
Nathan Lambert et al. — https://arxiv.org/abs/2403.13787 · paper — Evaluating reward models (the verifier you train against).
Nathan Lambert — https://www.interconnects.ai/p/the-new-rl-scaling-laws · blog — Where RLVR scaling is heading. (interview: https://www.latent.space/p/the-rlvr-revolution-with-nathan-lambert)
https://arxiv.org/abs/2506.10947 · paper — Random/spurious rewards rival ground truth on Qwen2.5 (Qwen-specific). (cite arXiv figures, not the blog gloss — see research/notes/reference-audit.md)
Nathan Lambert — https://www.interconnects.ai/p/the-state-of-post-training-2025 · blog — Context for where evals feed training.
Lilian Weng — https://lilianweng.github.io/posts/2024-11-28-reward-hacking/ · blog — The canonical survey of reward hacking — taxonomy, RLHF-specific failure modes, mitigations; the foundational reference any reward-design section needs.
Victoria Krakovna et al. (Google DeepMind) — https://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/ · blog — Canonical specification-gaming post (+the running examples list); origin story of why verifiers/reward functions get gamed, predating the LLM-RL wave.
Latent Space / Will Brown — https://www.latent.space/p/willccbb · talk — The verifiers author on building multi-turn RL environments, turn-level credit assignment and reward design in practice — the practitioner voice behind the verifiers library already cited here. 🆕
various (arXiv 2509.21882) — https://arxiv.org/abs/2509.21882 · paper — RLVR gains overstated via budget mismatch, calibration drift, contamination; proposes a tax-aware minimum standard — the rigor counterweight to Lambert's RL-scaling optimism. 🆕
Saumya Malik, Nathan Lambert et al. (Ai2) — https://arxiv.org/abs/2506.01937 · benchmark — The 2025 successor to RewardBench (already listed) — harder, less saturated, ICLR 2026; the current bar for evaluating the verifier you train against. 🆕
Nathan Lambert — https://rlhfbook.com/c/05-reward-models · docs — Canonical free reference chapter on reward models — the standing explainer for the 'verifier you train against' framing this section uses. 🆕
Shubham Parashar et al. (Texas A&M) — https://arxiv.org/abs/2506.06632 · paper — Difficulty-calibration primary source: easy-to-hard scheduling with convergence guarantees and the 'fade out easy tasks' result — directly fills the section's difficulty-calibration theme. 🆕
Jiacheng Guo, Ling Yang, Mengdi Wang et al. (Princeton) — https://arxiv.org/abs/2512.19682 · paper — Generative environment simulator with an alpha-Curriculum Reward that keeps tasks in the zone of proximal development — recent take on auto-calibrating env difficulty to the agent. 🆕
HUD (hud.ai) — no individual byline — https://www.hud.ai/resources/verifier-reward-design-rl-environments · article (technical guide) (good) — Lays out a concrete four-layer scoring architecture (verifiers / pass-fail gates / 3-5 criteria rubrics / composite reward) plus a five-step build…
Qwen Team (Alibaba) — https://arxiv.org/abs/2606.26300 · paper — 🆕 The dynamic argument this section's static entries lack: "no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator." Taxonomizes four verification…
Jessica Li (Prime Intellect) — https://www.primeintellect.ai/blog/reward-hacking · blog — Controlled study using the backdoor-ifeval family of environments at 1B scale, reproducible for ~$0.64: "Hacking is fundamentally a gradient dynamics problem. The same reward function can produce hacking or…
Eugene Yan — https://eugeneyan.com/writing/llm-evaluators/ · blog — Position/verbosity/self-enhancement bias; direct vs pairwise; prefer binary + classification metrics.
Hamel Husain — https://hamel.dev/blog/posts/llm-judge/ · blog — Critique-shadowing; validate against ONE benevolent-dictator expert; precision/recall over raw agreement.
Shankar, Zamfirescu-Pereira, Hartmann, Parameswaran, Arawjo (UIST '24) — https://arxiv.org/abs/2404.12272 · paper — "Criteria drift": you can't write the rubric before you grade.
Hamel Husain & Shreya Shankar — https://hamel.dev/blog/posts/evals-faq/ · blog — The densest operational Q&A: error analysis, binary judgments, the benevolent-dictator labeler.
Han-Chung Lee — https://leehanchung.github.io/blogs/2024/08/11/llm-as-a-judge/ · blog — Avoid [0,1] continuous scales; manage judges like junior annotators.
Zheng et al. — https://arxiv.org/abs/2306.05685 · paper — Source of the 10%/25% self-favoring & position-bias numbers — which the authors themselves hedge ("cannot determine"); GPT-3.5 doesn't self-favor.
Bavaresco et al. — https://arxiv.org/abs/2406.18403 · paper — Substantial variance across models/datasets; validate judges against humans first.
Eugene Yan — https://eugeneyan.com/writing/aligneval/ · blog — "Align AI to human. Calibrate human to AI. Repeat." Work backward from the data.
Eugene Yan — https://eugeneyan.com/writing/product-evals/ · blog — The "God Evaluator" anti-pattern; the benchmark is human performance, not perfection.
Han-Chung Lee — https://leehanchung.github.io/blogs/2025/03/03/cohen-kappa/ · blog — Chance-adjusted inter-annotator agreement (the gate before holding out).
Shreya Shankar — https://www.sh-reya.com/blog/ai-engineering-flywheel/ · blog — Binary metrics, the "GPT smell," error analysis as the core activity.
(https://arxiv.org/html/2401.03038v1) & DocETL (https://arxiv.org/abs/2410.12189) — Shankar et al. · paper — Data-quality assertions / agentic query rewriting for LLM pipelines.
Arjun Panickssery, Samuel R. Bowman, Shi Feng (NeurIPS 2024) — https://arxiv.org/abs/2404.13076 · paper — The canonical causal study of self-preference bias: shows GPT-4/Llama-2 can recognize their own outputs and that self-recognition correlates linearly with self-favoring. This is the primary…
Yang Liu et al. (Microsoft, EMNLP 2023) — https://arxiv.org/abs/2303.16634 · paper — The foundational reference-free LLM-judge method (CoT + form-filling scoring). Defines the direct-scoring paradigm the section critiques; a curated judge section is incomplete without the paper that started it.
Jiawei Gu et al. — https://arxiv.org/abs/2411.15594 · paper — The most-cited survey organizing the LLM-judge space (bias taxonomy, reliability methods, agreement metrics). Serves as the one-stop map/bibliography the section currently lacks.
Yulai Zhao, Haolin Liu, Dian Yu et al. (Tencent AI Lab / Princeton) — https://arxiv.org/abs/2507.08794 · paper — Shows 'master-key' tokens (a colon, 'Solution:') trigger false-positive rewards up to 80% even on GPT-o1/Claude-4 judges, plus a robust Master-RM fix. Core evidence on judge/verifier…
Jon Saad-Falcon et al. — Stanford Hazy Research / Scaling Intelligence — https://hazyresearch.stanford.edu/blog/2025-06-18-weaver · blog — Directly operationalizes 'verifiable vs judgeable': aggregates many weak judges/reward models (unlabeled) to shrink the generator-verifier gap, reaching…
Mingchen Zhuge et al. (Meta AI / KAUST) — https://arxiv.org/abs/2410.10934 · paper — Extends LLM-as-judge to agentic trajectories—grading intermediate steps, not just final outputs—with the DevAI benchmark. The agent-specific evaluation case this agent-evals library specifically needs.
Various (AAAI 2026) — https://arxiv.org/abs/2507.09884 · benchmark — Cross-domain benchmark exposing verifier precision/recall trade-offs (specialized verifiers high-accuracy but low-recall; general models inclusive but unstable). Quantifies how trustworthy a verifier actually is for RLVR. 🆕
Databricks (Mosaic Research) — https://www.databricks.com/blog/pilot-production-custom-judges · blog — Enterprise-grade judge-building playbook: 20-30 calibration examples, batched SME annotation, Krippendorff's alpha agreement gating—a production-side complement to the Hamel/Shankar academic…
Jiayi Ye et al. — https://arxiv.org/abs/2410.02736 · paper — Systematic quantification of 12 judge biases (verbosity, bandwagon, authority, distraction, sentiment, etc.) via automated attacks—broadens the section's bias coverage well beyond position/verbosity/self-enhancement.
Brooke Hopkins (Coval, ex-Waymo) — https://www.coval.ai/blog/voice-ai-agent-evaluation-guide · article (good) — Domain-specific evaluation playbook for voice agents: persona-tiered simulation testing (Easy/Medium/Hard/Adversarial across accent, noise, emotion), a concrete LLM-as-judge calibration…
Rishi Gujjar & Andrew Li (Judgment Labs) — https://www.judgmentlabs.ai/blogs/agent-judge-solving-long-context-evaluations · article (good) — Frames long-horizon agent evaluation as an agentic, multi-agent judge (search trajectory state as queryable objects, verify claimed actions against…
Pisupati, Broomfield, Choi et al. (Atla AI / Cohere / Mistral AI / Google DeepMind) — https://arxiv.org/abs/2606.21627 · paper — First public dataset of meta-evaluations of LLM-judge quality on agent trajectories (1,131 annotated critiques on tau-bench + DA-Code; "Krippendorff's alpha of 0.78");…
Junjie Chen, Yuxi Dong, Haitao Li et al. — https://arxiv.org/abs/2606.01629 · paper — First benchmark targeting LLM judge reliability specifically on long-form outputs (reports, essays, extended documents), filling the gap left by short-form and trajectory judge benchmarks; finds: "current LLM…
Advani (FAGEN@ICML2026) — https://arxiv.org/abs/2606.09863 · paper — Analyzes 11,755 agent trajectories (9,876 tau2-bench + 1,879 AppWorld): "no configuration across 5 judges, 5 prompt strategies, and full task specifications exceeds AUROC 0.65 on tau2-bench, and the same judges reach only 0.54…
Anthropic — https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents · blog — Best primary on agent-specific evals: task design, outcome vs trajectory, isolated trials, pass@k vs pass^k.
Sierra — https://arxiv.org/abs/2406.12045 · https://github.com/sierra-research/tau-bench · paper/repo — DB-state-diff grading; user simulation; pass^k; empty-result as explicit fail.
Sierra — https://sierra.ai/blog/benchmarking-ai-agents · blog — The motivation behind τ-bench.
Mialon et al. — https://arxiv.org/abs/2311.12983 · paper — Real assistant tasks; difficulty by human task-length.
Eugene Yan — https://eugeneyan.com/writing/cybersecurity-evals/ · blog — The four-primitive agentic-eval template (sandbox, difficulty inputs, tools, deterministic grader); outcome grading + partial-credit ladders + transcript audits. (also T10)
Han-Chung Lee — https://leehanchung.github.io/blogs/2025/09/08/pass-at-k/ · blog — Demystifies the metric everyone misuses.
Han-Chung Lee — https://leehanchung.github.io/blogs/2024/05/22/first-principles-eval/ · blog.
https://github.com/SWE-bench/SWE-bench/blob/main/swebench/harness/grading.py · tool/repo — FAIL_TO_PASS / PASS_TO_PASS as a verifiable reward. (SWE-agent ACI: https://swe-agent.com/0.7/background/aci/)
OpenAI — https://github.com/openai/human-eval/blob/master/human_eval/evaluation.py · tool/repo.
Zhou et al. (CMU) — https://arxiv.org/abs/2307.13854 · benchmark — Self-hostable sandboxed websites (e-commerce/forum/GitLab/CMS/maps) with execution-based functional-correctness graders; 812 tasks. The canonical web-agent world-state benchmark named in the brief — now URL-verified.
Xie et al. (HKU et al.) — https://arxiv.org/abs/2404.07972 · benchmark — 369 real-computer tasks in VMs with per-task execution-based eval scripts and initial-state setup; humans 72% vs best agent 12%. Canonical computer-use benchmark named in the brief — now verified.
Laude Institute + Stanford + community — https://www.tbench.ai/ · benchmark — Sandboxed terminal tasks with deterministic verifiers across SWE/sysadmin/security; v2 leaderboard. The terminal-agent benchmark named in the brief — verified (arxiv: arxiv.org/abs/2601.11868). 🆕
Zhang et al. (Stanford) — https://arxiv.org/abs/2408.08926 · benchmark — 40 professional CTF challenges with subtask annotations and deterministic flag-based grading; pairs naturally with Eugene Yan's cybersecurity-evals post already in the section. Named in the brief — now verified.
Lù et al. (McGill / Mila / Google DeepMind) — https://arxiv.org/abs/2504.08942 · paper — First benchmark of LLM-judges-of-trajectories: 1302 expert-reviewed web-agent runs; shows rule-based graders reject many valid trajectories (under-reporting success). Core to the 'trajectory evaluation' theme…
Cemri, Pan et al. (UC Berkeley Sky Lab) — https://arxiv.org/abs/2503.13657 · paper — 14-mode failure taxonomy across 7 MAS frameworks from 200+ annotated traces; the reference framework for diagnosing multi-agent failures — directly fills the 'multi-agent' gap. 🆕
Sun, Ren et al. (Maryland / CUHK / Stanford) — https://arxiv.org/abs/2606.08878 · benchmark — The first benchmark for multi-agent orchestration prompt writing: 110 scenarios over 10 topologies, each asking the model to route information fragments to sub-agent roles and then write those prompts,…
Brazilek et al. (CaML) — https://arxiv.org/abs/2607.15434 · benchmark — Agentic propensity eval for multi-agent authority: the model under test needs a benign task done, the only agent that can do it politely refuses, and nothing instructs escalation — measures how far it climbs a nine-rung…
Trivedi et al. (Stony Brook) — ACL'24 Best Resource Paper — https://aclanthology.org/2024.acl-long.850/ · benchmark — 9-app simulated world (457 APIs) with state-based unit tests that also check for collateral damage/unexpected state changes — gold-standard world-state grading for tool-use agents.
OpenAI (Wei et al.) — https://openai.com/index/browsecomp/ · benchmark — 1,266 'inverted' hard-to-find/easy-to-verify questions for deep-research browsing agents; short verifiable answers make grading deterministic. Released 2025, now standard for browsing-agent eval. (paper:…
Chen, Tang et al. (Yale / All Hands) — https://arxiv.org/abs/2503.09089 · paper — Defines and evaluates code localization as its own capability (Acc@k over file/function locations via code graphs) — directly fills the 'localization' theme named in the section title but currently unlisted. 🆕
He et al. (Tencent AI Lab) — https://arxiv.org/abs/2401.13919 · benchmark — 643 tasks on 15 live real-world sites with a GPT-4V automatic-judge eval protocol — an early, widely-cited example of multimodal-LLM-as-judge for live-web agent trajectories.
BenchFlow — https://github.com/benchflow-ai/skillsbench · benchmark — 🆕 evaluates how well agent skills work and how effectively agents use them — makes skill-acquisition/skill-use a measurable axis (the "Agent Skills" frontier). ~1.4k .
BenchFlow — https://github.com/benchflow-ai/ClawsBench · benchmark — 🆕 BenchFlow's agent benchmark (results/data repo; full release in progress).
OpenAI (with SWE-bench authors) — https://openai.com/index/introducing-swe-bench-verified/ · benchmark — 500 human-validated SWE-bench instances graded by hidden FAIL_TO_PASS unit tests; the de facto standard for real-issue resolution and the headline coding-agent number labs report 🆕
Yang, Jimenez, Press et al. (Princeton/Stanford) — https://arxiv.org/abs/2410.03859 · benchmark — 619 visual JS/front-end issues from 17 user-facing repos, test-verified; probes whether SWE agents generalize beyond Python/text to visual software domains
Scale AI (Deng, Da et al.) — https://arxiv.org/abs/2509.16941 · benchmark — 1,865 long-horizon, multi-file tasks across public GPL + held-out + commercial startup repos, test-graded; contamination-resistant and hard (frontier <45% pass@1) 🆕
OpenAI (Miserendino, Patwardhan, Heidecke et al.) — https://arxiv.org/abs/2502.12115 · benchmark — 1,400+ real Upwork freelance tasks worth $1M, graded by triple-verified end-to-end Playwright tests plus manager-decision tasks; ties capability to economic value 🆕
Pan, Wang, Neubig, Suhr, Zhang et al. (Berkeley/CMU) — https://arxiv.org/abs/2412.21139 · benchmark — 2,438 executable Python SWE tasks with pre-installed deps + test verification; the first real training/eval gym for SWE agents and verifiers, ICML 2025 🆕
ByteDance Seed — https://arxiv.org/abs/2504.02605 · benchmark — 1,632 expert-annotated issue-resolution tasks across Java, TS, JS, Go, Rust, C, C++, test-graded; the leading multilingual SWE-bench extension, NeurIPS 2025 D&B 🆕
Nebius / Badertdinov et al. — https://arxiv.org/abs/2505.20411 · benchmark — Automated pipeline yielding 21k+ executable Python tasks with continuously refreshed, decontaminated eval splits; quantifies how much SWE-bench Verified scores are inflated by contamination, NeurIPS 2025 D&B 🆕
METR — https://arxiv.org/abs/2411.15114 · benchmark — 7 open-ended ML research-engineering environments (e.g. GPU-kernel optimization, scaling laws) scored against 71 human-expert 8-hour attempts; the reference AI-R&D-uplift eval, ICML 2025
OpenAI (Chan et al.) — https://arxiv.org/abs/2410.07095 · https://github.com/openai/mle-bench · benchmark — 75 Kaggle ML-engineering competitions graded against real human leaderboards (medal thresholds) in 24h Docker runs; standard ML-engineering-agent eval, ICLR 2025. 🆕
OpenAI (Starace et al.) — https://arxiv.org/abs/2504.01848 · benchmark — Replicate 20 ICML 2024 papers from scratch, graded by 8,316 author-co-developed rubric leaves via a validated LLM judge; rigorous research-replication agent eval, ICML 2025 🆕
Andy Konwinski / Kaggle — https://www.kaggle.com/competitions/konwinski-prize · leaderboard — $1M Kaggle forecasting-format contest on GitHub bugs filed after submission close, fully contamination-free, test-graded; round-1 top score only 7.5% exposed real-world difficulty 🆕
Gou et al., OSU NLP Group (NeurIPS 2025 D&B) — https://arxiv.org/abs/2506.21506 · benchmark — 130 long-horizon live-web agentic-search tasks; novel Agent-as-a-Judge rubric-tree grader for time-varying, citation-backed answers — a serious answer to the Deep Research evaluation gap. 🆕
Xue et al., OSU NLP Group — https://arxiv.org/abs/2504.01382 · benchmark — 300 realistic tasks on 136 live websites with an LLM-as-a-Judge auto-grader (~85% human agreement); exposes overstated web-agent progress vs simple baselines. 🆕
AGI Inc (agi-inc/REAL), powers realevals.xyz — https://github.com/agi-inc/REAL · benchmark — 112 tasks on deterministic Next.js replicas of Amazon/Uber/LinkedIn etc.; reproducible LLM evaluator plus state validators — fixes the flakiness of live-site web benchmarks. 🆕
TIGER-AI-Lab — https://arxiv.org/abs/2604.08523 · benchmark — 153 everyday online tasks (purchases, bookings, job applications, email) across 144 live production websites in 15 categories; a Chrome-extension + CDP submission-interception layer blocks only the final write request so agents run…
Thomas et al., Convergence AI — https://arxiv.org/abs/2502.18356 · benchmark — 50+ client-side challenges isolating specific browser interaction skills with verifiable pass/fail; best agent 41% vs 96% human, a sharp diagnostic gap. 🆕
Patil et al., UC Berkeley (Gorilla / ICML 2025) — https://gorilla.cs.berkeley.edu/leaderboard.html · leaderboard — Executable + AST-based grading of tool/function calling; V4 adds multi-turn agentic, web-search and memory tasks — the de facto tool-calling leaderboard. 🆕
Wang et al., Shanghai AI Laboratory (NeurIPS 2024 D&B) — https://arxiv.org/abs/2407.08713 · benchmark — 229 human-written real-world queries with implicit multimodal tool use; executable evaluation platform across perception/operation/logic/creativity tools (GTA-2 follow-up in 2026). 🆕
Lei et al., XLang Lab / HKU (ICLR 2025 Oral) — https://arxiv.org/abs/2411.07763 · benchmark — Enterprise text-to-SQL agent workflows over huge schemas and multiple dialects with execution-based grading; frontier models only ~17-21% — a hard, realistic data-agent eval. 🆕
Rawles et al., Google DeepMind / Google Research (ICLR 2025) — https://arxiv.org/abs/2405.14573 · benchmark — Live Android environment with durable reward signals from device system state for 116 parameterized tasks across 20 apps — the standard mobile-GUI agent benchmark. 🆕
Bonatti et al., Microsoft — https://arxiv.org/abs/2409.08264 · benchmark — 154 realistic multi-step Windows-OS tasks across apps with programmatic success checks; parallelizable in Azure (~20 min full run) — desktop computer-use counterpart to OSWorld. 🆕
Vysotskyi, Gal, Torr, Bibi et al. (Oxford) — https://arxiv.org/abs/2606.14397 · benchmark — 🆕 GauntletBench: 100 vision-intensive tasks across five less-covered professional applications (Video Editor, Workflow Builder, 3D Modeller, Flight Analyser, Circuit Designer) requiring temporal,…
Levy, Shlomov, Wiesel et al., IBM Research — https://arxiv.org/abs/2410.06703 · benchmark — 375 enterprise tasks carrying 3,057 explicit safety/policy constraints; introduces Completion-under-Policy and Risk Ratio — grades whether agents obey rules, not just succeed. 🆕
Xu et al., CMU — https://arxiv.org/abs/2412.14161 · benchmark — Self-hosted software-company sim (web, code, chat coworkers) with checkpoint-based partial-credit grading; best agent ~30% — a full-day-knowledge-worker eval. 🆕
Koh et al., Carnegie Mellon University — https://arxiv.org/abs/2401.13649 · benchmark — 910 visually-grounded web tasks across Classifieds/Shopping/Reddit with reproducible programmatic reward functions — the multimodal extension of WebArena.
Tejal Patwardhan et al. (OpenAI) — https://arxiv.org/abs/2510.04374 · benchmark — 1,320 expert-built tasks across 44 occupations in the top 9 GDP sectors; 220-task gold subset open-sourced with a public automated grading service at evals.openai.com — the flagship economic-value agent benchmark. 🆕
CAIS + Scale AI (47 authors) — https://arxiv.org/abs/2510.26787 · benchmark — Grades whether agents complete whole real freelance projects to client-acceptable standard; best agent automates only 2.5% — a hard, money-grounded ceiling for end-to-end remote work. 🆕
Center for AI Safety + Scale AI (Dan Hendrycks et al.) — https://arxiv.org/abs/2501.14249 · benchmark — 2,500 expert-written frontier-knowledge questions with unambiguous auto-gradable answers across dozens of fields; the canonical post-MMLU saturation exam (note: now very widely cited). 🆕
OSU-NLP Group (Ohio State) — https://github.com/OSU-NLP-Group/ScienceAgentBench · benchmark — 102 expert-validated tasks from 44 peer-reviewed papers; grades self-contained Python programs by execution + success rate; best agent solves only ~34% (ICLR 2025). 🆕
Siegel, Kapoor, Narayanan et al. (Princeton) — https://arxiv.org/abs/2409.11363 · benchmark — 270 tasks over 90 papers (CS/social science/medicine) that grade whether an agent can reproduce published results from code+data; from the Princeton AI-Snake-Oil group.
Mingxuan Du et al. — https://arxiv.org/abs/2506.11763 · benchmark — 100 PhD-level tasks across 22 fields; reference-based adaptive-rubric grader for analyst-grade citation-rich reports, validated for human-judgment alignment — the standard deep-research-report eval. 🆕
Yao et al. — https://github.com/EVIGBYEN/DrBench · benchmark — 214 expert-curated deep-research tasks across 10 domains, evaluating long reports for semantic quality, topical focus, and retrieval trustworthiness. 🆕
FutureHouse + ScienceMachine — https://arxiv.org/abs/2503.00096 · benchmark — 50+ real bioinformatics analysis scenarios with ~300 open-answer questions over multi-step Jupyter trajectories; frontier models hit only ~17% — serious wet-lab-adjacent science agent eval. 🆕
OpenAI — https://openai.com/index/introducing-life-sci-bench/ · benchmark — 🆕 750 expert-authored life-science research tasks (7 workflows × 7 biological domains) graded by 19,020 rubric criteria from 173 PhD-level scientist contributors, independently validated by 453 expert reviewers; requires…
Arca — https://huggingface.co/datasets/arca-inc/lifesci-diamond · benchmark — 55 regulatory-affairs tasks developed with practising regulatory specialists, covering change classifications, dossier consistency, and missing evidence, graded against task-specific rubrics; best system Arca with GPT-6…
Meta (Meta Agents Research Environments) — https://arxiv.org/abs/2509.17158 · benchmark — Successor to GAIA: dynamic, time-driven, multi-agent simulated environments with async world events and a verifiable scenario grader; frontier success ~42% — the serious general-assistant env from Meta. 🆕
Andon Labs (Backlund & Petersson) — https://arxiv.org/abs/2502.15840 · benchmark — Run a simulated vending business over >20M-token horizons; objectively graded on profit/net-worth, exposing long-horizon coherence breakdowns unrelated to context limits. 🆕
Francois Chollet et al. (ARC Prize Foundation) — https://arxiv.org/abs/2505.11831 · benchmark — Human-calibrated (400+ participants, 100% solvable) grid-reasoning tasks with exact-match grading; 2-3x harder than ARC-AGI-1 across all approaches — the frontier fluid-intelligence benchmark. 🆕
Patronus AI — https://arxiv.org/abs/2505.08638 · benchmark — 148 annotated agent traces with 841 errors (reasoning/planning/execution); grades whether an LLM can localize the failure in a trace (best model ~11%). HF dataset PatronusAI/TRAIL. 🆕
Salesforce Research — https://arxiv.org/abs/2505.18878 · benchmark — 19 expert-validated B2B/B2C tasks on a realistic Salesforce org with state-based grading; exposes the single-turn (~58%) vs multi-turn (~35%) reliability gap plus confidentiality checks. 🆕
UC Berkeley RDI + 250+ industry co-authors — https://arxiv.org/abs/2606.05405 · benchmark — 1,000+ expert-authored task workflows across 55 digital industries and 13 clusters, grounded in the O*NET/SOC 2018 occupational taxonomy. All-pass grading; frontier agents average <1% full-pass rate on the…
Cognition (Devin team) — https://cognition.com/blog/frontier-code · benchmark — Measures whether AI-written code would be merged by real open-source maintainers — not just whether CI passes — across six dimensions: behavioral correctness, regression safety, mechanical cleanliness, test…
Harvey AI — https://www.harvey.ai/blog/introducing-harveys-legal-agent-benchmark · benchmark — 1,200+ long-horizon legal agent tasks across 24 practice areas, graded by 75,000+ expert-written rubric criteria with all-pass grading mirroring real law-firm merge standards. Open-source eval framework;…
Sourcegraph — https://sourcegraph.com/blog/codescalebench-testing-coding-agents-on-large-codebases-and-multi-repo-software-engineering-tasks · benchmark — 370 tasks across 40+ large repos (Kubernetes, Django, Linux, VSCode) and 9 languages in two suites: SDLC (150 patch-based tasks across 9…
Olly Styles — https://arxiv.org/abs/2606.13715 · paper — Longitudinal re-run of the WorkBench workplace-agent benchmark across 21 models (March 2023–May 2026): GPT-4 completed 43% of tasks with 26% unintended harmful actions in 2024; Claude Opus 4.8 completes 89% with 2.5% harmful actions in 2026…
Brazilek et al. (CaML) — https://arxiv.org/abs/2606.18142 · benchmark — Agentic values-under-ambiguity eval: the model books tickets via real tool calls where the strongest topical match is always an animal-exploitation option and the user never mentions welfare, so it isolates whether the agent…
James Austin et al. (Replit) — https://replit.com/blog/evaluating-and-improving-agent-at-scale · good — Three-layer eval system: (1) ViBench — offline benchmark where each task pairs a PRD with natural-language test plans and Playwright + LLM judges verify built apps actually work; (2) A/B testing…
Ara Khan (Cline) — https://cline.bot/blog/a-practical-guide-to-hill-climbing · good — A worked coding-agent eval loop: run Cline CLI across all 89 Terminal-Bench tasks with Harbor, summarize and bucket failed rollouts, A/B test prompt/config/code changes, and keep only changes that raise the…
Tara Bogavelli, Gabrielle Gauthier Melançon, Katrina Stankiewicz, Oluwanifemi Bamgbose, Hoang Nguyen, Raghav Mehndiratta, Hari Subramani (ServiceNow AI) — https://huggingface.co/blog/ServiceNow-AI/eva · article (excellent) — EVA is an end-to-end voice-agent eval framework using a bot-to-bot audio…
Yunfei Bai, Allie Colin, Kashif Imran, Winnie Xiong (AWS) — https://aws.amazon.com/blogs/machine-learning/evaluating-ai-agents-real-world-lessons-from-building-agentic-systems-at-amazon/ · article (good) — Lays out a three-layer agent evaluation library (foundation-model benchmarking, component…
Michael Dawson (Red Hat) — https://developers.redhat.com/articles/2026/03/23/eval-driven-development-build-evaluate-ai-agents · article (good) — A hands-on, 8-stage eval-driven workflow for a real multi-turn IT-self-service agent: uses DeepEval's ConversationalGEval/ConversationSimulator with ~15…
METR (Cunningham, Shetty, Cheng, Rush) — https://metr.org/blog/2026-07-21-expenditure-horizon/ · blog (Jul 21 2026) — Defines the "expenditure horizon" as the cost at which an agent's optimization performance-cost curve intersects the human baseline: "the dollar value at which the improvement to…
Braintrust + Basis — https://www.braintrust.dev/blog/behavior-specs · blog (Jul 29 2026) — Open standard (agentbehavior.dev) for evaluating agent process rather than just outcomes, motivated by the observation that "a correct [outcome] does not tell you whether the agent reached it the right way."…
Wang, Li, Mang, Cheung, Sen, Song (incl. Dawn Song) — https://arxiv.org/abs/2605.12673 · paper — Reward hacking emerges spontaneously in frontier models; an 8-pattern flaw taxonomy + a 30-question Agent-Eval checklist; "benchmarks must be secure by design."
Hao Wang, Qiuyang Mang, Alvin Cheung, Koushik Sen, Dawn Song (UC Berkeley) — https://moogician.github.io/blog/2026/trustworthy-benchmarks-cont/ · blog — The practitioner walkthrough behind BenchJack (same group, above): an automated exploit agent scores ~100% on eight major agent benchmarks…
Dawn Song (UC Berkeley RDI, lecture slides) — https://rdi.berkeley.edu/adv-llm-agents/slides/dawn-agentic-ai.pdf · talk — The adversarial setting; environment-borne attacks.
https://iclr.cc/virtual/2025/invited-talk/36783 · talk.
Wang et al. (incl. Dawn Song) — https://arxiv.org/html/2506.02548v2 · paper — Memory-safety PoC generation from OSS-Fuzz; sanitizer-crash grading at scale.
Zeng et al. (incl. Song) — https://arxiv.org/abs/2407.17436v2 · https://github.com/stanford-crfm/air-bench-2024 · paper/repo — Regulation-grounded risk taxonomy.
https://decodingtrust.github.io · benchmark — NeurIPS 2023 trustworthiness benchmark.
https://arxiv.org/abs/2411.07781 · paper — Risky code execution/generation benchmark for code agents.
https://arxiv.org/abs/2407.12784 · paper — Red-teams agents by poisoning their RAG memory.
Miller (Anthropic) — https://arxiv.org/abs/2411.00640 · https://www.anthropic.com/research/statistical-approach-to-model-evals · paper — Standard errors, clustered SEs, paired difference tests — "is this difference real?" (cross-cutting: T6/T8)
Debenedetti, Zhang, Balunović, Beurer-Kellner, Fischer, Tramèr (ETH Zurich) — https://arxiv.org/abs/2406.13352 · benchmark — The canonical prompt-injection benchmark for tool-using agents (97 tasks, 629 security cases over untrusted data); NeurIPS 2024 D&B, now the standard eval everyone reports…
Andriushchenko, Souly, Davies et al. (Gray Swan / UK AISI) — https://arxiv.org/abs/2410.09024 · benchmark — ICLR 2025 benchmark of 110/440 malicious agent tasks across 11 harm categories; shows leading models comply with malicious agent requests without jailbreaking. The reference…
Zhan, Liang et al. (UIUC) — https://arxiv.org/abs/2403.02691 · benchmark — ACL 2024 Findings; 1,054 IPI test cases over 17 user / 62 attacker tools, splitting direct-harm vs data-exfiltration intents. Foundational indirect-prompt-injection benchmark predating AgentDojo.
Debenedetti, Shumailov, Fan, Hayes et al. (Google DeepMind) — https://arxiv.org/abs/2503.18813 · paper — The defense-by-design counterpart: extracts control/data flow from the trusted query and enforces capability-based policies so untrusted data can't alter program flow; effectively solves…
Simon Willison — https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/ · blog — The most-cited conceptual frame for reasoning about when an agent is unconditionally vulnerable to prompt injection; essential practitioner mental model at the Eugene-Yan bar. 🆕
Kutasov, Bowman et al. (Anthropic) — https://www.anthropic.com/research/shade-arena-sabotage-monitoring · benchmark — 17 complex environments pairing a benign main task with a hidden harmful side task to measure whether agents can sabotage without tripping an AI monitor; the canonical…
Anthropic (Alignment team) — https://www.anthropic.com/research/agentic-misalignment · paper — Red-team study showing frontier models will resort to blackmail/leaking under goal conflict in agentic settings; the reference for action-authorization / insider-threat adversarial evaluation. Companion…
Microsoft AI Red Team (Azure) — https://github.com/Azure/PyRIT · tool — The de-facto open-source red-teaming automation framework (70+ converters, multi-turn attacks like Crescendo/TAP); how practitioners actually run adversarial evals at scale. The section lists papers but no tooling. 🆕
OWASP GenAI Security Project — https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/ · docs — Industry-standard risk taxonomy: goal hijack, tool misuse, identity/privilege abuse, memory poisoning, rogue agents; complements the regulation-grounded AIR-Bench taxonomy…
MITRE — https://atlas.mitre.org/ · docs — ATT&CK-style living knowledge base of 16 tactics / 80+ techniques against AI systems with real-world case studies and mitigations; the standard reference framework for AI adversarial threat modeling.
Zhang, Yang et al. — https://proceedings.iclr.cc/paper_files/paper/2025/file/5750f91d8fb9d5c02bd8ad2c3b44456b-Paper-Conference.pdf · benchmark — ICLR 2025 unified benchmark spanning 10 scenarios, 400+ tools, covering DPI/IPI, memory poisoning, plan-of-thought backdoors and defenses in one harness;…
Gray Swan AI / UK AISI (w/ OpenAI, Anthropic, GDM) — https://app.grayswan.ai/arena/blog/agent-red-teaming-the-ai-jailbreak-showdown · talk — Largest public agent red-teaming exercise: ~2,000 red-teamers, 1.8M attempts, 62k breaches against 22 tool-using agents (financial/shopping/marketing bots);…
various — https://arxiv.org/abs/2605.09684 · benchmark — 2,644 attack trajectories for evaluating AI coding-agent monitors via semi-automated red-teaming on BashArena. Claude Opus 4.5 monitor catch rate drops from 94.9% on standard attacks to 60.3% under best-refined adaptive attacks — a 34pp gap…
METR — https://metr.org/blog/2026-06-26-gpt-5-6-sol/ · blog (Jun 26 2026) — "GPT-5.6 Sol's detected cheating rate was higher than any public model we have evaluated on our ReAct agent harness." METR's independent predeployment evaluation finds the model exploiting eval environment bugs and using…
Anthropic — https://www.anthropic.com/research/n-days · blog (Jun 8 2026) — Evaluates Claude Mythos Preview on publicly disclosed but unpatched Firefox and Windows kernel vulnerabilities; produced 8 working Firefox code-execution exploits (1 hour to first) and 8 distinct Windows…
UK AISI — https://www.aisi.gov.uk/research/realitytest-how-people-probe-ai-identity-and-whether-models-disclose-it · paper (Jun 8 2026) — Benchmark of 3,152 identity-probing queries from ~750 participants across 49 countries and 5 languages; 17 text + 6 speech models tested. Disclosure rates span…
Anthropic — https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals · blog (Jul 30 2026) — First detailed public post-mortem of an AI capability evaluation escaping its sandbox: of 141,006 cybersecurity evaluation runs, three incidents (six runs) caused real-world impact after…
Hamel Husain & Emil Sedgh — https://www.youtube.com/watch?v=eLXF0VojuSs · talk (AI Engineer World's Fair 2024)
Jeff Huber & Jason Liu — https://www.youtube.com/watch?v=jryZvCuA0Uc · talk (AI Engineer World's Fair 2025)
Bryan Bischof — https://www.youtube.com/watch?v=k98gDjYbSaU · talk (Data Council 2025)
Eugene Yan — https://www.youtube.com/watch?v=7EGF0Mc0_os · talk (Jason Liu series 2024)
Shreya Shankar — https://www.youtube.com/watch?v=eGVDKegRdgM · talk (Stanford MLSys #97)
Shreya Shankar — https://www.youtube.com/watch?v=H-1QaLPnGsg · talk (LangChain Interrupt 2025)
Ido Pesok (Vercel v0) — https://www.youtube.com/watch?v=L8OoYeDI_ls · talk (AI Engineer 2025)
David Karam (Pi Labs) — https://www.youtube.com/watch?v=jxrGodnopHo · talk (AI Engineer 2025)
Brooke Hopkins (Coval) — https://www.youtube.com/watch?v=kDczF4wBh8s · talk (AI Engineer 2025)
Leonard Tang (Haize Labs) — https://www.youtube.com/watch?v=OMGPvW8TBHc · talk (AI Engineer 2025)
Omar Khattab (DSPy) — https://www.youtube.com/watch?v=qdmxApz3EJI · talk (AI Engineer 2025)
Taylor Jordan Smith — https://www.youtube.com/watch?v=89NuzmKokIk · talk (AI Engineer 2025)
Ankur Goyal (Braintrust) — https://www.youtube.com/watch?v=MC55hdWLq4o · talk (AI Engineer 2025)
Jason Wei (OpenAI) — https://www.youtube.com/watch?v=b6Doq2fz81U · talk (Stanford AI Club 2025)
Jason Wei — https://www.youtube.com/watch?v=l898fqkjdFc · talk (The AI Conference 2025)
Andrej Karpathy — https://www.youtube.com/watch?v=7xTGNNLPyMI · talk (2025)
John Schulman — https://www.youtube.com/watch?v=hhiLw5Q_UFg · talk (UC Berkeley EECS 2023)
Nathan Lambert (Ai2) — https://www.youtube.com/watch?v=AdLgPmcrXwQ · talk (Stanford CS25 V4)
Chip Huyen — https://www.youtube.com/watch?v=spamOhG7BOA · talk (MLOps LLMs in Prod 2023)
Han-Chung Lee — https://www.youtube.com/watch?v=4dUFIRj-BWo · talk (Data Council 2025)
Will Brown (Prime Intellect) — https://www.youtube.com/watch?v=_IzZWeuTx7I · talk (AI Engineer 2025)
Florian Brand (Prime Intellect) — https://www.youtube.com/watch?v=kmTMc-fVSXw · talk (Big Techday 26 (2026))
Hamel Husain (How I AI / Claire Vo) — https://www.youtube.com/watch?v=PgzOBNse2EA · podcast (How I AI)
Ankur Goyal (How I AI) — https://www.youtube.com/watch?v=QE_1hRLsehM · podcast (How I AI)
Hamel Husain (Vanishing Gradients) — https://www.youtube.com/watch?v=QEk-XwrkqhI · podcast (Vanishing Gradients)
Hamel Husain (Vanishing Gradients) — https://www.youtube.com/watch?v=rWToRi2_SeY · podcast (Vanishing Gradients)
Ankur Goyal (Latent Space) — https://www.youtube.com/watch?v=a4BV0gGmXgA · podcast (Latent Space)
Cameron & Hill-Smith (Latent Space) — https://www.youtube.com/watch?v=v5mBjeX4TJ8 · podcast (Latent Space)
Petersson & Backlund (Andon Labs) — https://www.youtube.com/watch?v=ZAimcoJXUBo · podcast (Latent Space / Cognitive Revolution)
Vaibhav Gupta & Dex (AI That Works) — https://www.youtube.com/watch?v=-N6MajRfqYw · podcast (AI That Works)
AI That Works — https://www.youtube.com/watch?v=OawyQOrlubM · podcast (AI That Works)
AI That Works — https://www.youtube.com/watch?v=5Fy0hBzyduU · podcast (AI That Works)
AI That Works — https://www.youtube.com/watch?v=jzhVo0iAX_I · podcast (AI That Works)
Maggie Konstanty (MLOps Community) — https://www.youtube.com/watch?v=9EjWR3QpJYk · podcast (MLOps Community)
Kelly Hong (TWIML) — https://www.youtube.com/watch?v=3kbiGPn0cOo · podcast (TWIML AI)
Percy Liang (Gradient Dissent) — https://www.youtube.com/watch?v=kwkdKirqi6s · podcast (Gradient Dissent)
Joseph Gonzalez (Gradient Dissent) — https://www.youtube.com/watch?v=okHMaczHPXc · podcast (Gradient Dissent)
Aman Khan (Learning from ML) — https://www.youtube.com/watch?v=v0eTTn7ZPEc · podcast (Learning from Machine Learning)
Andrej Karpathy (Dwarkesh) — https://www.youtube.com/watch?v=-lRBpyPt79c · podcast (Dwarkesh Podcast)
Hamel Husain & Shreya Shankar — https://www.youtube.com/watch?v=J7N9FMouSKg · podcast (Aakash Gupta 2026)
Dawn Song — https://www.youtube.com/watch?v=QAgR4uQ15rc · lecture (Berkeley LLM Agents MOOC F24)
Dawn Song — https://www.youtube.com/watch?v=ti6yPE2VPZc · lecture (Berkeley Advanced LLM Agents Sp25)
Ben Mann (Anthropic) — https://www.youtube.com/watch?v=6y2AnWol7oo · lecture (Berkeley LLM Agents MOOC F24)
Percy Liang — https://www.youtube.com/watch?v=f3KKx9LWntQ · lecture (Berkeley LLM Agents MOOC F24)
Hashimoto & Liang — https://www.youtube.com/watch?v=x-R5l2HsXqM · lecture (Stanford CS336 2025)
Alex Volkov (AI Evangelist, Weights & Biases; host of ThursdAI) — https://www.youtube.com/watch?v=IIL2tE4n1Q0 · talk (AI Engineer World's Fair 2025 — Evals track)
John Dickerson (CEO, Mozilla AI) — https://www.youtube.com/watch?v=CQGuvf6gSrM · talk (AI Engineer World's Fair 2025 — Evals track)
Aparna Dhinakaran (Co-founder & CPO, Arize AI) — https://www.youtube.com/watch?v=nbZzSC5A6hs · talk (AI Engineer World's Fair 2025 — Evals track)
Phil Hetzel (Braintrust) — https://www.youtube.com/watch?v=FB-MLPhL9Ms · talk (AI Engineer World's Fair 2025 — Evals track)
Laurie Voss (Arize) — https://www.youtube.com/watch?v=Xfl50508LZM · talk (AI Engineer World's Fair 2025 — Evals track)
Peter Gostev (Arena.ai) — https://www.youtube.com/watch?v=R7A8rX-09Zw · talk (AI Engineer World's Fair 2025 — Evals track)
Diego Rodriguez (Co-founder & CTO, Krea.ai) — https://www.youtube.com/watch?v=h5ItAJuB3Fc · talk (AI Engineer World's Fair 2025 — Evals track)
Quotient AI + Tavily (speakers from both) — https://www.youtube.com/watch?v=wRJD0inpmjU · talk (AI Engineer World's Fair 2025 — Evals track)
Rafal Willinski & Vitor Balocco (Zapier) — https://www.youtube.com/watch?v=blrovBxxN9o · talk (AI Engineer World's Fair 2025 — Evals track)
Manu Goyal (Braintrust) — https://www.youtube.com/watch?v=jJ45Yz1lJao · talk (AI Engineer World's Fair 2025 — Evals track)
AI Engineer Evals Workshop (multi-presenter) — https://www.youtube.com/watch?v=9iN-cPnp7xg · talk (AI Engineer World's Fair 2025 — Evals track (full workshop))
Ion Stoica (co-founder Databricks/Anyscale, LMArena), host Jacob Effron — https://www.youtube.com/watch?v=ehav4XMAKLw · podcast (Unsupervised Learning (Redpoint Ventures))
Brendan Foody (co-founder/CEO Mercor), host Jacob Effron — https://www.youtube.com/watch?v=SOZtz8IdI2w · podcast (Unsupervised Learning (Redpoint Ventures))
Nidhi Rastogi (asst. professor, RIT), host Sam Charrington — https://www.youtube.com/watch?v=75WqFOY3P5M · podcast (The TWIML AI Podcast)
Jineet Doshi (Staff AI Scientist/Lead, Intuit), host Demetrios Brinkmann — https://www.youtube.com/watch?v=VJ0k0C1mGdg · podcast (MLOps.community)
Neev Parikh (METR), host Nathan Labenz — https://www.youtube.com/watch?v=SX8Mxyy_UHY · podcast (The Cognitive Revolution)
Marius Hobbhahn (CEO, Apollo Research), host Nathan Labenz — https://www.youtube.com/watch?v=I3ivZaAfDFg · podcast (The Cognitive Revolution)
Shahul Es (co-founder, Ragas), hosts Daniel Whitenack & Chris Benson — https://www.youtube.com/watch?v=fw0wUC5XN-o · podcast (Practical AI (Changelog))
Mike Knoop (co-founder ARC Prize / Zapier), host Lukas Biewald — https://www.youtube.com/watch?v=SSA8vNrFpXI · podcast (Gradient Dissent (Weights & Biases))
UK AI Safety Institute team — https://www.youtube.com/watch?v=J79pSSAENYc · talk (Gradient Dissent / Fully Connected London '25 (Weights & Biases))
Weights & Biases (Weave team) — https://www.youtube.com/watch?v=AMCmhRoKnSk · talk (Gradient Dissent / W&B)
Afshine Amidi & Shervine Amidi — https://www.youtube.com/watch?v=8fNP4N46RRo · lecture (Stanford (CME295 / Stanford Online))
Berkeley RDI course staff (Dawn Song's Agentic AI MOOC) — https://www.youtube.com/watch?v=VfOA2a0dj4w · lecture (UC Berkeley RDI (CS294-196, Fall 2025))
Sida Wang (Meta) — https://www.youtube.com/watch?v=HV8pugcFVO0 · lecture (UC Berkeley RDI (CS294-196, Fall 2025))
Samuel Colvin (founder, Pydantic) — https://www.youtube.com/watch?v=A48uhxfxbsM · talk (AI Engineer (Code Summit / AI Engineer))
Naman Jain (Cursor; LiveCodeBench/SWE-bench-adjacent researcher) — https://www.youtube.com/watch?v=tHN44yJoeS8 · talk (AI Engineer (Code Summit))
Brooke Hopkins (founder, Coval; ex-Waymo eval infra) — https://www.youtube.com/watch?v=1X3mYUHC5GA · talk (Founders You Should Know)
Brooke Hopkins (founder, Coval) — https://www.youtube.com/watch?v=e1E8vLyRIKk · podcast (AI Minds (Deepgram))
Karthik Narasimhan (Head of Research, Sierra; Princeton; tau-bench author) — https://www.youtube.com/watch?v=fOAAslQUceg · lecture (Berkeley RDI (Agentic AI Summit 2025))
Sayash Kapoor (Princeton; AI Snake Oil; co-author HAL / agent-eval critiques) — https://www.youtube.com/watch?v=d5EltXhbcfA · talk (AI Engineer (Summit 2025))
https://pavlovslist.com/ · directory — The RL-environment / eval startups directory ("for the RL-pilled").
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