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HFEPX · Eval paper review

Diagnosing and Mitigating Context Rot in Long-horizon Search

Shijie Xia, Yikun Wang, Zhen Huang, Pengfei Liu

Published

Jun 29, 2026

Citations

0

Trust level

Low

Usefulness score

25/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jun 29, 2026

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Tags

Automatic Metrics General Long Horizon

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks. The concern that increasing context length degrades model capabilities, known as context rot, has become a central issue for these applications. In this paper, we focus on deep search scenarios, aiming to investigate the rot phenomenon and its mitigation strategies. By evaluating four flagship open-source models across three benchmarks, we reveal a prevalent but unnoticed rot phenomenon: extensive context causes models to directly give up or prematurely provide uncertain answers, and this issue is exacerbated as the context grows. Through pruning experiments, we demonstrate the relationship between the accumulated context and the rot phenomenon. Furthermore, we investigate mitigating this issue through context management and post-hoc rejection sampling. For context management, we systematically evaluate seven different methods across three categories, based on performance, cost, and impact on context rot, providing clear guidance for strategy selection and usage. For rejection sampling, we develop a rot-aware filtering strategy and demonstrate its effectiveness across three aggregation methods. Finally, we show that these two approaches can be combined for further performance improvements.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks."

Reported Metrics

partial

Context length

Useful for evaluation criteria comparison.

"The concern that increasing context length degrades model capabilities, known as context rot, has become a central issue for these applications."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

context length
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Long Horizon
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks.
  • The concern that increasing context length degrades model capabilities, known as context rot, has become a central issue for these applications.
  • In this paper, we focus on deep search scenarios, aiming to investigate the rot phenomenon and its mitigation strategies.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Long-horizon tasks) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Through pruning experiments, we demonstrate the relationship between the accumulated context and the rot phenomenon.
  • For rejection sampling, we develop a rot-aware filtering strategy and demonstrate its effectiveness across three aggregation methods.
  • Finally, we show that these two approaches can be combined for further performance improvements.

Why it matters for eval

  • By evaluating four flagship open-source models across three benchmarks, we reveal a prevalent but unnoticed rot phenomenon: extensive context causes models to directly give up or prematurely provide uncertain answers, and this issue is…

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: context length