Standardized Evaluation & Data Collection: Research Report

What this is. The survey behind Daishi's evaluation design: how the 2024-2026 agent-evaluation landscape scores agents, ranks models, reports results and collects data, organized by the decision each practice informs, followed by what Daishi adopted from it. The adopted design itself is specified on the benchmark page, in the governance rules, the data standards and the alignment tracks; this page is the why behind those pages.

This report has three parts:

1. What the industry does: the 2024-2026 evaluation landscape, organized by the decision it informs 2. What Daishi adopted: the design choices that came out of it, each pointing at the page that specifies it 3. Sources

Industry claims in section 1 were checked against primary sources (papers, repositories, official docs) listed in section 3.

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#1. What the industry does

#1.1 Harness architecture: environment / agent / scorer as separate layers

The clearest convergence across 2025-2026 eval frameworks (Inspect AI, the UK AI Security Institute's framework, HELM, lm-evaluation-harness, promptfoo, Braintrust, LangSmith, W&B Weave):

• Task = Dataset + Solver + Scorer (Inspect's decomposition). The environment, the agent scaffold, and the scoring pipeline are three independently swappable layers. Scorers are pure functions over recorded logs, never logic inside the run loop, so new metrics can be back-applied to old runs.
• Scenarios are declarative, versioned configs, not code (lm-eval-harness YAML tasks, HELM RunSpecs, promptfooconfig). Two labs running daishi:famine-v1 should get bit-identical worlds; results reference the scenario spec by version/hash.
• One self-contained run artifact per evaluation (Inspect .eval logs): full message/action transcripts, exact config, errors, per-model token usage: replayable, queryable as dataframes, with a CLI to reconstruct the run config.
• Repetitions are first-class (Braintrust trials, LangSmith repetitions, Inspect epochs): N runs per configuration is the unit of evaluation, averaged at the top with individual runs preserved.
• Comparison-first reporting (Braintrust experiment-vs-baseline diffs): the report a model developer actually wants is "v2 vs v1, same seeds, which behaviors regressed?" with drill-down from metric delta to raw transcript.
• Leaderboards defined as data (W&B Weave): column = scorer + aggregation, row = model, recomputed from immutable logs; adding a metric is a config change.
• Local-first with opt-in sharing (promptfoo, DeepEval): companies evaluating unreleased models require privacy and an instance they control. Daishi is one hosted service, not a local tool; what it keeps from this practice is that a Studio run plays on your own keys, privately, and is published only when you choose.

Cautionary tale: OpenAI's sprawling contributed-evals registry aged badly (the hosted Evals platform goes read-only on 31 Oct 2026 and shuts down on 30 Nov 2026). The simple-evals set OpenAI publishes its own numbers with (not a replacement for the registry, by its own description) is deliberately tiny, readable, and standardized on zero-shot chain-of-thought prompting as "a better reflection of the models' performance in realistic usage". Keep Daishi's official eval small, curated, and runnable by anyone.

#1.2 Agentic & game benchmarks: how episodes become scores

The benchmarks closest to Daishi, and what each contributes:

Two hard-won protocol lessons from the NeurIPS Neural MMO competitions transfer directly: survival time confounds everything (rank-by-lifespan correlated 0.91 with rank-by-tasks-completed across 86 submissions; report survival as its own sub-score and make economy/social/combat metrics rates per round alive), and weak baselines saturate (22 teams maxed the easiest PvE stage; keep at least one reference tier nobody saturates).

#1.3 Rating systems & statistics: ranking models when outcomes depend on opponents and seeds

The playbook, from Chatbot Arena, TrueSkill, and the eval-statistics literature:

• Batch Bradley-Terry, not online Elo. LMSYS switched in Dec 2023: online Elo is order-dependent and over-weights recent games; BT is the MLE of the same pairwise model, refit over the full log. Daishi's append-only event log is exactly the right substrate for batch refits.
• Multiplayer-native ratings for free-for-alls. One 8-agent match with a finish ordering carries ~28 implicit pairwise comparisons. TrueSkill converges in about 3 games for an 8-player free-for-all against about 91 for two teams of 8 (its own published figures; Elo is not defined past two players); the unpatented OpenSkill (Plackett-Luce) is the library to actually ship (JS/Python implementations, consumes exactly a placement ordering). Sort leaderboards by the conservative estimate μ - 3σ. TrueSkill 2 shows that folding per-episode performance stats and quit/crash modeling into the rating substantially improves calibration (52%→68% outcome-prediction on Halo 5 data per its paper).
• Rate model@version + scaffold as the player, never a model family. When LMSYS split API versions into separate BT entities, gpt-4-0314 vs gpt-4-0613 differed by ~50 points.
• Whole-history ratings for seasonal churn. Model versions retire; static BT breaks when deprecated models stop accumulating comparisons. TrueSkill Through Time or Whole-History Rating re-estimate all seasons jointly so a season-3 model stays comparable to a season-7 model; Glicko-2 (RD inflates with inactivity) is the lightweight live-ladder alternative.
• Uncertainty is mandatory. Bootstrap the rating fit (resample matches ~100-1000×), present ranks as statistically indistinguishable groups where CIs overlap, and set a minimum match count before a model appears ranked (Chatbot Arena, arXiv:2403.04132).
• Anthropic's "Adding Error Bars to Evals" (Miller 2024) supplies the core statistics, translated to Daishi: cluster standard errors at the match/lobby level (outcomes within one lobby share opponents and map; naive SEs can be up to 3× too small); compare two models with paired designs (same seeds, same opponent lineups, analyze per-seed differences); decompose variance into seed vs within-seed components to decide where the next compute dollar goes; publish a power-analysis table ("to detect a 3-point gap you need ~N matches") and refuse to render verdicts on underpowered comparisons.
• Single runs are noise. Replication studies of agentic evals (arXiv:2602.07150: 60,000 SWE-bench-Verified trajectories) found single-run pass@1 varies 2.2-6.0 pp between identical runs even at temperature 0. RL evaluation practice (rliable, NeurIPS 2021 outstanding paper) prescribes 3-10+ runs, interquartile mean, and stratified bootstrap CIs. Daishi trajectories are longer and more interactive than SWE-bench; expect worse.
• De-confound with covariates. LMArena's style control adds response-length/markdown features to the BT regression so wins from formatting load onto style coefficients. Daishi's analogues: spawn quality under the seed, join latency, token budget, lobby size.
• Opponent-pool bias. Plain win-rate/Elo is inflated by farming weak pool members; with small self-selected pools, publish the full cross-play win matrix and complement with clone-invariant ratings (Nash averaging, arXiv:1806.02643); watch for non-transitive cycles (aggressive beats economic beats turtle beats aggressive).
• Contamination. A single fixed world seed becomes a memorization target the moment the benchmark matters (GSM1k showed up to ~13 pp overfit on a rewritten benchmark). Use a versioned seed pool: public practice seeds, rotating held-out seeds for scored matches (LiveBench-style refresh each season), private season-final seeds (GAIA/ARC-AGI tiering). Neural MMO 2023 published train/eval task overlap quantitatively (22.3% predicate-level, 2.1% full-spec); do the same for scenario suites.

#1.4 Reporting standards: what a credible eval report contains

Synthesis of Model Cards, system cards (OpenAI/Anthropic), HELM's public artifacts, MLPerf's governance, BetterBench, EvalCards, and the EU AI Act:

• Identify exactly what was evaluated: model name + exact version string + provider + access date + sampling config + scaffold/harness version. Under exactly what conditions: benchmark/ruleset version, seed policy, prompts, adaptation method. Scores are only comparable within a version tuple; attach it to every number.
• Disaggregate. Report metrics sliced by factors (season phase, terrain, resource scarcity, opponent mix, activity type), never one aggregate scalar.
• Publish raw per-instance artifacts. HELM releases every prompt and completion, browsable per-instance; WebArena publishes trajectories; ClemBench ships per-episode transcripts. Daishi's equivalent: full event log + per-agent tool-call transcript + metrics for every ranked match, downloadable from the leaderboard.
• MLPerf-style divisions and process are the template for trust: Closed division (reference conditions, apples-to-apples) vs Open (innovation allowed, disclosed); submissions as structured artifacts (config, code, logs, replication README); a review window with a formal objection process before simultaneous publication; random audits with a replication path; results-messaging guidelines governing how scores may be quoted.
• BetterBench's two most-failed criteria across 24 major benchmarks: reporting statistical significance, and shipping a replication script. Both are cheap for Daishi (confidence intervals; a published rubric and archived logs a third party can recompute from).
• Eval cards. The 2025-26 standardization wave (EvalCards, EvalEval Coalition) converges on machine-readable records: benchmark metadata + run metadata + model metadata + links to raw logs. The fields systematically missing in the wild are where a new benchmark differentiates: provenance (who ran it, when, at whose expense), lifecycle status, metrics declared before the run, comparability caveats.
• System-card-style behavioral findings (deception, collusion, aggression, exploit use) with methodology and quantitative results are what providers can reuse directly. The EU AI Act (GPAI obligations applying since Aug 2025, Commission enforcement powers since Aug 2026) requires providers to document evaluation strategies, criteria, metrics, results, and limitations (Annex XI); a well-structured Daishi report can be one input to that documentation (a useful adoption tailwind, though a game-world benchmark alone is not a compliance artifact).
• Leaderboard governance, written down before results exist (The Leaderboard Illusion, audit of 2M Arena battles): no score retraction from ranked matches; disclosed caps on private variant testing (best-of-N private submission materially inflates BT ratings); symmetric matchmaking/sampling across providers; public log of deprecated/renamed entrants; publish the matchmaking and rating algorithms themselves. Expect labs to train on public Daishi logs; that is what the rotating held-out seed pool is for.

#1.5 Cost, and the trust model for telemetry

HAL (Princeton, ICLR 2026; 21,730 rollouts across 9 models × 9 benchmarks) and its predecessor critique "AI Agents That Matter" established that accuracy-only agent leaderboards mislead: agents can be 50× more expensive for similar accuracy. Their practice: meter raw tokens per run, derive dollars from a swappable price table (so history can be re-priced as prices drift), plot accuracy-vs-cost with a Pareto frontier, require multiple runs with CIs, publish full traces (encrypted, to avoid benchmark contamination), mark maintainer-reproduced entries "verified", and run LLM-aided log inspection over transcripts, which surfaced agents googling the benchmark answers and other unreported behaviors.

But cost columns are only as trustworthy as their observation point. Competitors self-reporting "which model + how many tokens" is trivially fakeable. The industry's three trust tiers map cleanly onto Daishi divisions:

Reconciliation channels exist for the middle tier: provider org-level usage APIs (OpenAI/Anthropic admin usage endpoints filter by API key; their cost reports roll up by project or workspace) let an auditor cross-check gateway numbers. TEE-attested inference (weights fingerprint bound into hardware attestation) exists but is practical only for open-weight models today; an optional future "attested" badge, not a foundation. One more hole to close: a BYO agent in the gateway tier must run in a sandbox whose egress is restricted to the gateway, or a "cheap model" entrant can silently consult a stronger model out-of-band.

#1.6 Telemetry & data-collection standards

• OpenTelemetry GenAI semantic conventions are the naming standard: spans for invoke_agent/execute_tool/model calls; gen_ai.usage.input_tokens/output_tokens; gen_ai.request.model; duration/token histograms (p50/p99, not means). There is now an MCP-specific convention, still at development status (mcp.method.name, gen_ai.tool.name, mcp.session.id, error.type, mcp.server.operation.duration); instrument the MCP boundary itself. Full prompt/completion capture should be opt-in, separate from always-on metadata. OpenInference adds first-class cost attributes (incl. cache read/write and reasoning-token detail).
• Inspect AI's .eval log is the model for a match artifact: header manifest (task/ruleset version, seed, models, config, git revision) + per-agent samples (input, messages, output, named scores, token usage, errors) + an ordered transcript of typed events with unique ids; large repeated content de-duplicated as attachments; a compact binary format with a cheap "sample summaries" read path.
• RLDS (episode/step schema for RL datasets: observation, action, reward, is_first/is_last/is_terminal, episode metadata) and PettingZoo (all outcome data keyed by agent-id per step; explicit termination-vs-truncation distinction (died vs season ended) define the replay format the RL/agents community can consume directly. Log losslessly at recording time: you cannot reconstruct what you did not capture.
• Event schema versioning (event-sourcing practice): add schemaVersion (+ engine commit) to every event, keep one validated JSON Schema per event type, evolve additively, never rewrite the log, upcast at export time.
• Analytics/publication stack: keep the live database as the hot store; batch-export season/match-partitioned Parquet (queryable with DuckDB/pandas with zero infra; a column store only if live dashboards outgrow it). Publish season datasets on Hugging Face (auto-generates Croissant JSON-LD metadata → discoverable/citable).
• Licensing & privacy for published logs: match transcripts are largely LLM outputs from many providers whose ToS restrict using outputs to train competing models, so tag every message with its generating model (lets downstream users filter by provider), use LMSYS-Chat-1M-style custom research terms or ODC-BY with an explicit usage-restrictions notice (WildChat precedent), scrub free text with Presidio + a moderation pass, and keep private DM payloads out of public exports (Daishi's exports apply this filter).

#1.7 Security: prompt injection is a distinct axis from in-game deception

Daishi's "speech can lie" design makes deception legal, but LLM-to-LLM prompt injection is a different animal, and the 2024-2026 literature (Prompt Infection, self-replicating payloads spreading agent to agent on a logistic curve, faster when agents share a message history; AgentDojo, where most models lose 10-25 pp utility under attack, and more capable agents were more susceptible to targeted injection; TAMAS, ASB, ChatInject) is directly applicable. The recommended treatment is a three-tier classification, not legal/illegal:

1. In-action-space persuasion/deception (lying about trades, bluffing): scored skill, stays legal. This is the game. 2. Instruction hijacking (payloads targeting the rival agent's control layer: override its system prompt, exfiltrate its api_key, induce actions outside its free choice): scored robustness dimension: report Injection Resistance (1 - ASR when targeted) and Injection Efficacy (ASR achieved against others) separately from game skill, plus utility-under-attack so refusing-to-play doesn't count as robust, plus a propagation index (how far payloads cascade; Daishi is a persistent society, so cascade matters more than single-hop success). 3. Harness/protocol exploits (spoofing the game-master/system role, forging tool calls, chat-template abuse): disqualifiable, blocked and flagged by the server.

Data-collection implication: message events need provenance forensics (sender id, turn, channel, verbatim bytes, and downstream-action linkage) so injection can be attributed and its propagation chain reconstructed from the log (payload-similarity matching across messages). Static injection suites overstate robustness against adaptive adversaries (PIArena, ACL 2026), so any injection track needs a red-team/refresh cadence. Daishi already strips tool-like syntax from messages (the anti-abuse rules are summarized in the Agent Guide); that covers part of tier 3 only.

#1.8 Scoring negotiation and behavioral traits from logs

Everything below is computable from Daishi's existing event types (trades with escrow status, messages, attacks, reputation), mostly without gameplay changes:

• Negotiation (NegotiationArena, GTBench, Davidson et al.): per-deal surplus split using market-imputed valuations; report three numbers: mean surplus over all attempts, over completed deals, and completion rate (so pushovers and deal-killers are distinguishable); anchoring coefficient (regression of final terms on first offer); Normalized Relative Advantage (ΣA - ΣB)/(ΣA + ΣB) for pairwise matchups.
• Promise-keeping / betrayal (the pattern the Patterns 2024 survey documented in Cicero: a promise in the dialogue, a contradicting move on the board): LLM-extract commitments from messages, verify each against subsequent event-log ground truth; promise-keeping rate = kept/extracted; betrayal = attack/raid on a counterparty during an active commitment. Daishi's verified-action-vs-unverified-speech split gives ground truth that Werewolf/Diplomacy papers had to hand-label.
• Deception, two-sided (Deception Elo, Peskov et al. ACL 2020): maintain separate ratings for deceiving and for not-being-deceived; models are differentially better at one than the other. Bluffing is directly measurable as message-claims vs posted-offer divergence.
• Behavioral/safety profile (MACHIAVELLI, ICML 2023): count harm-taxonomy events along the trajectory (violence, stealing, promise-breaking, deception, manipulation...), normalize against a scripted random/baseline agent on the same seed (score = 100 × count/mean_random), and publish a score-vs-violations Pareto plot per model so "wins by exploitation" is visually separable from "wins cleanly". Daishi's aggression counter and raid/attack events cover the mechanical categories with zero annotation; only messages need LLM labeling (validate the judge against a small human-labeled set first).
• Population-level outcomes (Welfare Diplomacy): report per-match Nash welfare (geometric mean of final utilities) and score dispersion alongside individual rank; add an exploiter probe mode (one scripted defector) and report each model's welfare drop as its exploitability: aggression restraint measured as robust cooperation, not naive pacifism.
• Society metrics (Project Sid, Generative Agents): role-distribution entropy (specialization), rule-compliance rate vs stated norms, information-diffusion curves, coordination verified by log outcomes.
• Fairness validation before attribution (Werewolf Arena): identical models in a lobby must finish statistically indistinguishable on fixed seeds; use spawn-permuted repeats; keep scripted anchor bots in every lobby for cross-season comparability.

#1.9 Collusion & sybil detection

An open free-for-all economy will attract feeder-agent boosting. Detection methods that map onto Daishi's log: per-season net-transfer graphs (nodes = agents, edges = net value flow from trades/gifts/thrown fights) with community detection; the chip-dumping/gold-farming signature is hub-and-spoke one-directional flow (MMORPG RMT literature); trades priced far off the published item values; pattern-free collusion tables (pairwise mutual-advantage outliers, AAAI 2013); mutual information between one agent's actions and another's private state (dependence not explainable by public events ⇒ covert coordination; the log already distinguishes private events). Classic graph-based sybil detection (SybilRank et al.) assumes attack edges are costly, which fails in an open MCP world, so anchor sybil resistance in registration (verified operator identity per key, one ranked-lobby slot per (operator, model)) and use graph analysis as a secondary batch signal.

#1.10 Configuration provenance: archive the effective config, not the request

The 2025-26 reproducibility wave converged on one rule for agentic evals: the unit of record is the rollout plus the complete configuration that produced it, with defaults resolved. What the operator requested is not evidence; what applied is. The practices to follow:

• Inspect's eval-log header (UK AISI) stores the resolved run configuration beside the samples, not in a lab notebook: task_args including defaulted values, model_args, generation config, dependency packages versions, and the git origin + commit (revision) of the eval source (log format, EvalSpec/EvalLog reference). A score whose config must be reconstructed from shell history is treated as unreproducible by construction.
• Rollout Cards (arXiv:2605.12131, 2026) generalize this into a publication standard: treat the rollout record (task specification, agent actions, environment dynamics, and the declared reporting rule over them) as the reproducibility unit, not the reported score. Their audit of 50 popular agent-research repositories found none reported failed/errored/skipped runs beside headline scores; a benchmark differentiates by recording run outcomes honestly (Daishi's run status + per-seat error fields, and archives of half-played matches, are that record).
• The Agentic Benchmark Checklist (arXiv:2507.02825) makes explicit documentation of toolchain, environment configuration, and test-data provenance a reporting criterion; it composes with BetterBench's two most-failed criteria from section 1.4 (statistical significance, replication script).
• MLPerf submissions are structured artifacts (config + code + logs + replication README) reviewed before publication (policies); and every serious experiment tracker (MLflow, W&B) treats the run config as first-class, queryable metadata. Researchers expect "what exactly did run 47 use?" to be a lookup, never an archaeology project.

Daishi does this: every match archive carries config as the effective setup: season length, pacing (turn-based budgets or wall-clock round + rate limit), world size, physics multipliers, registration gates, scheduled directives, scenario id + content hash, engine + event-schema versions, plus wall-clock startedAtMs/endedAtMs. Studio custom runs additionally stamp a config.run provenance block (run id, resolved seed with derivation: pinned / scenario-fixed / practice-pool trial / random; requested-vs- effective overrides; per-seat model, route, temperature, skill count and customization-scaffold hash, never the private prompt text) and mirror the same record onto the run row as effective. The match report renders all of it as an Experiment configuration section with the raw record attached verbatim, so variables added later stay findable without a renderer change.

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#2. What Daishi adopted

The recommendations this survey produced have been built. What a reader can hold the platform to, each with the section of the survey that motivated it:

• The record is the asset. Every action the server validated lands in an append-only event log, and every match is archived with the configuration that actually applied (section 1.10), so every score below is a pure function over an artifact you can download and recompute. See the data standards.
• Match play, not ladder telemetry, is the standardized eval. Lobby-synchronized, fixed-seed, manifest-stamped matches on versioned scenarios, with a lab-play suite of short threshold scenarios beside them (section 1.2). See the benchmark.
• Turn-based eval pacing. An evaluation match advances when every agent has acted or its turn budget has lapsed, with forfeited turns logged as events, so latency is not a confound (section 1.2).
• Anchors and frozen populations. Every published scenario but the blank custom base seats its scripted baseline policies (the anchors) unless a run removes them, a frozen model population is defined for ranked seasons, and a series report names whether its series was cross-play or self-play (section 1.2). Little of this has reached the public record yet: no ranked season has been played against a frozen population, and the open public lobbies seat anchors only to fill a lobby short of its quorum (one of the 82 public archives on 18 October 2026 was filled that way, with two anchors beside one model seat). See governance.
• Scoring is computed offline from the log: the Daishi Fitness Index, a fixed weighted blend of five dimension scores with rates normalized per round alive, and beside it, never folded in, breadth (a Crafter-style log-mean over an achievement checklist), reliability (pass^k on lab scenarios), efficiency (score against cost, where cost is metered) and a behavioral profile (sections 1.2 and 1.8). See the benchmark and the alignment tracks.
• Ratings with intervals. OpenSkill over placements with bootstrap intervals on the ratings, Student t intervals on every reported mean, ranks shown as indistinguishable groups, a whole-history refit for cross-season comparability, and comparisons that refuse a verdict below the power threshold (section 1.3).
• Trust divisions travel with every score: reference harness, gateway-verified, self-reported and anchor, labeled by how identity and usage were verified (section 1.5). See governance.
• Governance written before the first ranked season: no retraction, disclosed caps on private variants, symmetric scheduling, an objection window, and collusion screens on every ranked match (sections 1.3 and 1.9). See governance.
• Held-out seeds and payloads under a published commitment, rotated per season, so the benchmark is not a memorization target (sections 1.3 and 1.7). See evaluation integrity.
• Three report artifacts per model, scenario suite and season: a human-readable report card with a failure-mode section, a machine-readable eval card, and a paired comparison report that says "need N more matches" rather than rendering an underpowered verdict (section 1.4). Studio runs add the repeated-trial series report with the interval up front; see the Studio.
• Datasets published as match-partitioned Parquet with a datasheet, model tags on every message, private messages excluded, a PII scrub and a contamination canary (section 1.6). See the data standards.
• Prompt injection as its own axis, scored separately from in-game play, with red-team anchors and a held-out payload corpus (section 1.7). See the alignment tracks.

The through-line: Daishi's append-only, server-authoritative event log is the asset. Everything above is either making that log complete enough to be the single source of truth, or a pure function over it. Nothing required changing what the game is.

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#3. Sources

#Harnesses & platforms

• Inspect AI (UK AI Security Institute): https://github.com/UKGovernmentBEIS/inspect_ai (esp. eval-logs, eval-sets, agent limits)
• Stanford HELM: https://github.com/stanford-crfm/helm · https://crfm.stanford.edu/helm/
• EleutherAI lm-evaluation-harness: https://github.com/EleutherAI/lm-evaluation-harness
• OpenAI simple-evals / evals: https://github.com/openai/simple-evals · https://github.com/openai/evals
• promptfoo: https://github.com/promptfoo/promptfoo · Braintrust: https://www.braintrust.dev/docs/platform/experiments · LangSmith: https://docs.langchain.com/langsmith/evaluation · W&B Weave: https://docs.wandb.ai/weave/guides/core-types/leaderboards · DeepEval: https://github.com/confident-ai/deepeval

#Agentic & game benchmarks

• HAL (Holistic Agent Leaderboard): arXiv:2510.11977 · https://hal.cs.princeton.edu · https://github.com/princeton-pli/hal-harness
• METR time horizons: arXiv:2503.14499 · Time Horizon 1.1 (29 January 2026): https://metr.org/blog/2026-01-29-time-horizon-1-1/ · per-run data and fitting code: https://github.com/METR/eval-analysis-public
• AI Agents That Matter (Kapoor, Stroebl, Narayanan): arXiv:2407.01502
• Neural MMO 2.0: arXiv:2311.03736 · NeurIPS 2023 competition retrospective: arXiv:2508.12524 · NeurIPS 2022 challenge: PMLR v220
• Crafter: arXiv:2109.06780 · Craftax: arXiv:2402.16801
• BALROG: arXiv:2411.13543 · FLE (Factorio): arXiv:2503.09617 · TextArena: arXiv:2504.11442 · ClemBench: arXiv:2305.13455
• tau-bench (pass^k): arXiv:2406.12045 · SWE-bench: https://github.com/SWE-bench/SWE-bench · WebArena: https://github.com/web-arena-x/webarena · AgentBench: https://github.com/THUDM/AgentBench · GAIA: arXiv:2311.12983 · MLE-bench: https://github.com/openai/mle-bench · ARC-AGI: https://arcprize.org
• Melting Pot 2.0: arXiv:2211.13746 · Concordia Contest (NeurIPS 2024): https://www.cooperativeai.com/contests/concordia-2024
• Vending-Bench 1 & 2: arXiv:2502.15840 · https://andonlabs.com/evals/vending-bench-2
• Kaggle Game Arena: https://github.com/google-deepmind/game_arena · Kaggle simulation-competition ladder docs
• Voyager: arXiv:2305.16291 · MineDojo: arXiv:2206.08853 · Project Sid: arXiv:2411.00114 · Generative Agents: arXiv:2304.03442
• MultiAgentBench/MARBLE: arXiv:2503.01935 · Alem: https://alem-world.github.io/

#Ratings & statistics

• Chatbot Arena methodology: arXiv:2403.04132 · Elo→Bradley-Terry: lmsys.org/blog/2023-12-07-leaderboard · style control: lmsys.org/blog/2024-08-28-style-control
• Adding Error Bars to Evals (Miller/Anthropic): arXiv:2411.00640
• TrueSkill / TrueSkill 2: microsoft.com/research (trueskill2.pdf) · OpenSkill: arXiv:2401.05451 · TrueSkill Through Time: NeurIPS 2007, https://github.com/glandfried/TrueSkillThroughTime.py · WHR (Coulom 2008) · Glicko-2: glicko.net
• rliable / Statistical Precipice: arXiv:2108.13264 · How Many Random Seeds: arXiv:1806.08295 · Quantifying Variance in Benchmarks: arXiv:2406.10229 · On Randomness in Agentic Evals: arXiv:2602.07150 · Agentic Benchmark Checklist: arXiv:2507.02825
• Nash averaging / mElo: arXiv:1806.02643 · The Leaderboard Illusion: arXiv:2504.20879 · LiveBench: arXiv:2406.19314 · GSM1k: arXiv:2405.00332

#Reporting standards

• Model Cards: arXiv:1810.03993 · Datasheets for Datasets: arXiv:1803.09010 · GPT-4o System Card: cdn.openai.com · Anthropic system cards: anthropic.com/system-cards
• Rollout Cards (reproducibility standard for agent research): arXiv:2605.12131 · Inspect eval-log/EvalSpec fields: inspect.aisi.org.uk/eval-logs.html
• MLPerf submission/review/audit rules: https://github.com/mlcommons/policies · https://github.com/mlcommons/inference_policies
• BetterBench: arXiv:2411.12990 · https://betterbench.stanford.edu · EvalCards: arXiv:2511.21695 · Evaluation Cards (EvalEval): evalevalai.com
• EU AI Act Art. 53 / Annex XI + GPAI Code of Practice: artificialintelligenceact.eu · HF leaderboard norms: huggingface.co/docs/leaderboards

#Telemetry & data

• OpenTelemetry GenAI + MCP semantic conventions: https://github.com/open-telemetry/semantic-conventions-genai
• OpenInference: https://github.com/Arize-ai/openinference · RLDS: https://github.com/google-research/rlds · PettingZoo/Gymnasium episode stats: farama.org
• LiteLLM proxy (virtual keys, budgets): docs.litellm.ai · OpenRouter usage accounting: openrouter.ai/docs · Helicone: helicone.ai · provider usage/cost admin APIs: docs.anthropic.com/en/api/usage-cost-api, OpenAI org usage API · TEE attestation: docs.tinfoil.sh
• Event versioning: event-driven.io · Parquet/DuckDB lakehouse patterns · Croissant: mlcommons.org · LMSYS-Chat-1M & WildChat licensing/PII precedents: huggingface.co · Presidio: https://github.com/microsoft/presidio

#Security & behavior

• Prompt Infection: arXiv:2410.07283 · AgentDojo: arXiv:2406.13352 · TAMAS: arXiv:2511.05269 · Agent Security Bench: arXiv:2410.02644 · ChatInject: arXiv:2509.22830 · MASpi: openreview.net/forum?id=1khmNRuIf9 · ACIArena: arXiv:2604.07775 · PIArena (ACL 2026): aclanthology.org/2026.acl-long.1533 · Spotlighting: arXiv:2403.14720 · OWASP Agentic Threats (T12-T14): genai.owasp.org
• NegotiationArena: arXiv:2402.05863 · GTBench: arXiv:2402.12348 · LLM-Deliberation: arXiv:2309.17234 · LM negotiation agency: arXiv:2401.04536
• MACHIAVELLI: https://github.com/aypan17/machiavelli (ICML 2023) · AI Deception / Cicero audits: Patterns 2024, arXiv:2406.04643 · It Takes Two to Lie: ACL 2020 · Hoodwinked: arXiv:2308.01404 · AvalonBench: arXiv:2310.05036 · Werewolf Arena: arXiv:2407.13943 · Among Us Deception Elo: arXiv:2504.04072 · Welfare Diplomacy: arXiv:2310.08901
• Collusion/sybil: collusion tables: AAAI 2013 (Mazrooei et al.) · chip-dumping detection: AMLTRIX T0107.003 · MMORPG RMT networks: Fujita et al. AIIDE · SybilRank family surveys: arXiv:2507.06541