The paper introduces inexpensive, scalable methods for evaluating language model behavior across different vendors and releases. By running identical public stimuli on a cross‑vendor panel and analyzing transcripts via exact match, LLM‑coded codebooks, or instrumented environments, the authors can quantify model responses at a cost of a few dollars per model. Applying these tools to four years of releases reveals patterns of convergence, resistance, positional stability, and compliance that vary by generation, lab, and harness.
By Tapan Parikh
arXiv:2608. 02665v1 Announce Type: cross Abstract: A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form.
By Yongxi Zhou, Junwei Yao, Yuanzhe Liu, Zihan Dong, Wenbo Ye, Jiaxi Wen, Lai Yun Choi
arXiv:2607. 10202v2 Announce Type: replace Abstract: Forced-choice probes with counterbalanced orientations are a standard tool for measuring language-model "value dispositions," and a concentration/extremity index over repeated draws is read as how sharply a model commits.
By Hong-In Won, Jinseok Jang, Hyoseop Kim
arXiv:2609.13936v1 Announce Type: new
Abstract: Datasets that ship automatically generated feature annotations invite a question rarely asked of them: would a human agree with those labels? This repo...
By Levent Bulut
arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.
By Gurkan Ozkan
arXiv:2608. 06953v1 Announce Type: cross Abstract: Agent memory systems compress what they store, and compression is built to drop qualifiers, so a claim's epistemic standing tends not to survive being written to memory.
By Alex Kwon
The paper reports on a deployed multi‑agent tender‑response system that uses an open‑weights language model under sovereignty constraints. In a blind comparison, the system’s answers were judged at least as good as human‑written bids in 40 of 55 sections, with only a few gaps attributable to missing knowledge rather than writing quality. The study also demonstrates an asymmetry in conditioning: while structural markup improves reading tasks, converting instruction material from prose to nested XML degrades answer quality, and naming forbidden constructions concentrates defects.
By Cheng Yu, Nikhil Mathew, Zhengjie Wang
arXiv:2607. 10202v1 Announce Type: new Abstract: Cross-model comparisons read divergence in value dispositions as evidence that language models hold individuated values.
By Hong-In Won, Jinseok Jang, Hyoseop Kim
arXiv:2608.31017v1 Announce Type: cross
Abstract: Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 1...
By Sebastian Fox, Luke Markham, Ryan Lail, Michael Karotsieris
arXiv:2609.08475v1 Announce Type: cross
Abstract: Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about...
By Jiabin Zheng (School of Computer Science, Peking University)
arXiv:2607. 08731v1 Announce Type: cross Abstract: A national language model offers a linguistic community its own instrument for measuring what its citizens say and value.
By Manuel Pita
The paper demonstrates that open‑ended Theory‑of‑Mind trackers can produce valid beliefs that are absent from finite reference sets, and that treating unmatched outputs as false can reverse model‑selection rankings. By recoding references for 259 beliefs, the authors show a dramatic drop in weighted prevalence and a reversal of strictly proper Brier risk, with similar distortions observed in a 301‑question NQ‑open DPR‑BERT pipeline. The study further reveals that 90‑96% of audited unmatched beliefs are literally true, and introduces a TriSource‑Restore method that anchors reference labels to a probability‑sampled human pilot to restore calibration and ranking integrity.
By Zhexi Feng, Wuxi Chen, Bingrui Zhang