AGORA benchmark tests agentic document reasoning over large authentic workplace archives
Researchers introduce AGORA, a benchmark pairing 362 questions with 9,664 authentic workplace documents (372M tokens across eight domain collections) to evaluate archive-grounded agentic reasoning. The benchmark is designed so documents far exceed any model's context window, forcing deliberate exploration rather than exhaustive scanning. Evaluating eight models, the best achieves only 59.4% accuracy, indicating the task is far from solved. The benchmark addresses a gap in existing evals that do not jointly stress archive-groundedness, agentic exploration, and cross-domain coverage.
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AARRI-Bench evaluates frontier LLMs and agents on granular research-intern-level tasks
Researchers introduce AARR (Act As a Real Researcher), a new benchmark series targeting whether AI agents can emulate the professionalism, thoroughness, and nuanced judgment of human researchers in granular research scenarios—not just macro-level task execution. The first benchmark, AARRI-Bench, tests frontier models and agentic harnesses, finding that even the best configuration (Mini-SWE-Agent with Claude Opus 4.7) achieves only 68.3% success, frequently missing subtle but critical details obvious to human researchers. The work argues that closing the gap requires deeper modeling of research behavior rather than more complex scaffolding.
Benchmark Agent: Autonomous system for end-to-end benchmark construction
Researchers introduce Benchmark Agent, a fully autonomous agentic system that orchestrates the complete benchmark construction pipeline — from query analysis and subtask design to data annotation and quality control. The system was used to produce 15 benchmarks spanning text understanding, multimodal understanding, and domain-specific reasoning, with evaluation via human judges, LLM-as-a-judge, and consistency checks. The work addresses two persistent problems in the field: the labor intensity of benchmark creation and rapid performance saturation after release. Code and a demo will be publicly released.
ACL-Verbatim: Hallucination-Free Extractive QA System for Research Papers
The paper introduces ACL-Verbatim, an extractive question answering system built on VerbatimRAG that maps user queries directly to verbatim text spans in ACL Anthology papers, eliminating hallucination by design. The authors contribute a new ground-truth benchmark dataset created via human NLP-researcher annotation over synthetic queries generated using a ScIRGen-based pipeline. A 150M-parameter ModernBERT token classifier trained on silver supervision achieves the best word-level F1 of 53.6, outperforming the strongest LLM-based extractor at 48.7. The work demonstrates that smaller extractive models can outperform large generative LLMs on precision-critical retrieval tasks.
EnterpriseClawBench: A benchmark for enterprise agents derived from real workplace sessions
Researchers introduce EnterpriseClawBench, an enterprise agent benchmark constructed from proprietary real-world workplace sessions, yielding 852 reproducible tasks with fixtures, prompts, role classes, skill subclasses, and semantic rubrics. Because the sessions contain internal enterprise content, the benchmark data is not publicly released, but the construction and evaluation protocol is the reusable contribution. The best evaluated configuration (Codex with GPT-5.5) achieves only 0.663, indicating substantial headroom. The paper argues enterprise agent evaluation must report harness-model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than collapsing to a single score.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
Opus 4.6, Codex 5.3, and the post-benchmark era
A Interconnects commentary piece examining how to compare frontier AI models in 2026, using Anthropic's Opus 4.6 and OpenAI's Codex 5.3 as case studies. The piece appears to argue that traditional benchmarks are no longer sufficient for distinguishing model capabilities at the frontier. This reflects a broader industry shift toward more nuanced, task-specific evaluation methods.
T1-Bench: Multi-scenario agent benchmark across 25 real-world domains
T1-Bench is a new benchmark for evaluating agentic LLM systems in realistic customer-facing, multi-domain environments, covering 25 domains of varying difficulty with interleaved multi-turn scenarios. The authors evaluate 12 proprietary and open-weight models and combine automatic evaluation with human judgments. The benchmark targets gaps in existing agent evals around task complexity, domain diversity, and compositional reasoning across multi-step interactions.
SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics
SPECTRA is a reproducible framework for generating synthetic information retrieval test collections, separating latent topical structure, surface text realization, and query intent generation to produce deterministic relevance oracles without human annotation. A Python prototype generated corpora up to 60,000 documents at roughly 12K–14K documents per second, with graded relevance labels for 96 queries. Controlled distractor experiments showed BM25 nDCG@10 degrading from 1.00 at 2% distractors to 0.43 at 36%, demonstrating the framework's utility for exposing retrieval system failure modes before expensive real-world collection construction. The authors position SPECTRA as a diagnostic complement to Cranfield/TREC-style evaluation rather than a replacement for human judgment.