How does the accuracy of Tree of Reviews on MuSiQue at 128K context degrade when the number of distractor pass

Abstract

Abstract: Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM shall enable its users to effortlessly process many originally exhausting tasks -e.g., digesting a long-form document to find answers v.s., directly asking an LLM about it.However, existing realtask-based long-context evaluation benchmarks have a few major shortcomings.For instance, some Needle-in-a-Haystack-like benchmarks are too synthetic, and therefore do not represent the real world usage of LLMs.While some real-task-based benchmarks like Long-Bench avoid this problem, su

Research Question

How does the accuracy of Tree of Reviews on MuSiQue at 128K context degrade when the number of distractor passages is increased from 5 to 20, relative to chain-based retrieval, using Llama-3-128K?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance