arXiv (All)
open access
LongHarness Bench: Stress-Testing Language Model Harnesses for Long-Context Reasoning
Language-model (LM) harnesses enable LMs to operate effectively over long contexts using additional compute. However, existing long-context evaluations are insufficient for distinguishing modern harnesses, reflected by saturated accuracy across harnesses and largely similar evaluation costs. In this
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Reasoning Shift: How Context Silently Shortens LLM Reasoning#1141509
- A Recipe for Long-Context Reasoning in Large Language Models via On-Policy Optimization and Distillation#998226
- Improving Large Language Models for Code through Runtime Program-State Reasoning#1119432
- NEUROTESTGEN: Neuro-Symbolic Guided Test Generation with Large Language Models#1078249
- PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces#1014930
Record · ID 1142432
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive