How do I install the Sin Bench Tracing Native Evidence Chains In Long skill?

Run npx skillmds@latest add adu2021/sin-bench-tracing-native-evidence-chains-in-long in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Sin Bench Tracing Native Evidence Chains In Long safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Sin Bench Tracing Native Evidence Chains In Long?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Sin Bench Tracing Native Evidence Chains In Long free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Sin Bench Tracing Native Evidence Chains In Long?

adu2021 (@adu2021) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Sin Bench Tracing Native Evidence Chains In Long by adu2021

Name: Sin Bench Tracing Native Evidence Chains In Long
Author: adu2021

Sin Bench Tracing Native Evidence Chains In Long

Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic 'Needle-In-A-Haystack' tests often reward answer matching without requiring a causal, evidence-linked reasoning trace in the document. We propose the 'Fish-in-the-Ocean' (FITO) paradigm, which requires models to construct explicit cross-modal evidence chains within native scientific documents. To operationalize FITO, we build SIN-Data, a scientif...

adu2021 Updated Sep 17, 2026

File contents

Problem

SIN-Bench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

Key Approach

The paper introduces a novel framework, methodology, or benchmark for sin-bench. The core contributions include:

Systematic framework or benchmark for agent evaluation and development
Empirical findings on agent performance, efficiency, or capabilities
Generalizable principles applicable across domains

When to Use

Use this skill when you need to:

Evaluate or benchmark autonomous agent systems
Understand best practices in agent design and evaluation
Learn empirical results on agent performance
Improve agent efficiency, reasoning, or capabilities

When NOT to Use

For non-agent-related tasks
When seeking quick implementation code (see the paper for details)
For general knowledge unrelated to autonomous agents

Resources

ArXiv Abstract: https://arxiv.org/abs/2601.10108
Full PDF: https://arxiv.org/pdf/2601.10108
HTML Version: https://arxiv.org/html/2601.10108

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.

Frequently asked questions