sin-bench-tracing-native-evidence-chains-in-long

name	sin-bench-tracing-native-evidence-chains-in-long
title	SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Semantic Integration
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.10108
keywords	["Agents","Benchmarking"]
description	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...

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:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. 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.