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HFEPX · Eval paper review

ContextRender: From Execution Dependencies to Agent Context

Savini Kashmira, Jayanaka L. Dantanarayana, Lingjia Tang, Jason Mars

Published

Sep 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

Sep 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

LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even when it fits within the context window, while reducing it risks omitting needed information. Existing context management methods can overlook how earlier tool results are used in subsequent execution, leaving needed information out of context. We introduce ContextRender, which manages context through a persistent graph of execution dependencies. We develop Tool-Flow Analysis to track how later operations reuse information from earlier tool results, providing a signal called observed reuse. A renderer combines this signal with recency and semantic relevance to select results within a fixed history budget, retaining omitted results for later use. Across AppWorld and 8-objective QA with three execution models, ContextRender outperforms the evaluated context management baselines using a 6K history budget, well below the models' maximum context windows. Within this budget, it achieves task performance close to or above that of passing the full history while reducing mean inference cost by 10.2%-32.2% relative to Full history. Ablations show that observed reuse improves task performance and retention of results reused later.

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.

"LLM agents performing long-horizon tasks accumulate tool results that later steps may need."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"LLM agents performing long-horizon tasks accumulate tool results that later steps may need."

Quality Controls

missing

Not reported

No explicit QC controls found.

"LLM agents performing long-horizon tasks accumulate tool results that later steps may need."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"LLM agents performing long-horizon tasks accumulate tool results that later steps may need."

Reported Metrics

partial

Inference cost, Relevance

Useful for evaluation criteria comparison.

"A renderer combines this signal with recency and semantic relevance to select results within a fixed history budget, retaining omitted results for later use."

Benchmarks and datasets

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

Reported metrics

inference costrelevance
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

LLM agents performing long-horizon tasks accumulate tool results that later steps may need.

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

Key takeaways

  • LLM agents performing long-horizon tasks accumulate tool results that later steps may need.
  • Passing the full history to every invocation is costly even when it fits within the context window, while reducing it risks omitting needed information.
  • Existing context management methods can overlook how earlier tool results are used in subsequent execution, leaving needed information out of context.

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

  • LLM agents performing long-horizon tasks accumulate tool results that later steps may need.
  • We introduce ContextRender, which manages context through a persistent graph of execution dependencies.
  • We develop Tool-Flow Analysis to track how later operations reuse information from earlier tool results, providing a signal called observed reuse.

Why it matters for eval

  • LLM agents performing long-horizon tasks accumulate tool results that later steps may need.

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: inference cost, relevance