PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers

arXiv cs.AI Papers
benchmark scientific-agents personas evaluation multi-agent llm

Summary

PersonaScienceBench introduces a benchmark of 200 tasks across four scientific domains that evaluates how AI agents collaborate with simulated researchers holding diverse personas, measuring interaction quality alongside task completion.

arXiv:2610.32106v1 Announce Type: new Abstract: Scientific agents collaborate with researchers whose expertise and preferences differ, but benchmarks usually hold the collaborator fixed. We introduce PersonaScienceBench: 200 tasks from five benchmarks across four scientific domains. Simulated researchers provide context and feedback according to persona profiles, while task objectives and grading remain fixed. Agents encounter researcher preferences only through conversation. Across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths. Detailed GPT 6 Astra analyses show distinct agent responses across researcher profiles; interaction traces illustrate targeted clar ification and workflow revision. These behaviors can differ even when final scores match. PersonaScienceBench therefore evaluates how agents collaborate with diverse researchers alongside whether they complete the task.
Original Article

Similar Articles

The "AI is fancy autocomplete" vs. "Imminent RSI/AGI" argument.

Reddit r/singularity

A discussion post contrasting two camps in AI discourse: those who view models as 'fancy autocomplete' versus those who believe recursive self-improvement and AGI are imminent within 1-2 years, with the author sharing their own shift toward the latter after using Claude Opus 5.5.

AI is destroying my CS degree

Lobsters Hottest

A computer science student describes how her university's inconsistent AI policies, AI-generated coursework briefs, and overloaded tutors are degrading the quality of her degree experience.

Particle-1 is available in OpenClaw

Reddit r/openclaw

Particle-1, a coding-agent-focused model emphasizing token efficiency (~45% fewer output tokens than DeepSeek V4.1 Flash with comparable accuracy), is now available in OpenClaw v2026.10.10, with zero data retention for standard inference.

Have AI models genuinely degraded over the past year, or am I imagining things?

Reddit r/ArtificialInteligence

A long-time AI user reports that models across ChatGPT, Grok, Claude, DeepSeek and coding agents like OpenCode have noticeably worsened at task understanding and instruction-following over the past year, prompting questions about post-training, alignment and inference changes. The post asks the community whether others are seeing the same cross-provider regression.