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HFEPX · Eval paper review

StreamDecisionBench: Evaluating Decisions in Force on Evolving Language Streams

Jhen-Ke Lin, Chung Chun Wang

Published

Sep 29, 2026

Citations

0

Trust level

Low

Usefulness score

5/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 9, 2026

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Automatic Metrics

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 benchmark-and-metrics comparison anchor.

What to verify

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

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
5/100
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

Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives. A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses. We make three contributions. First, we release StreamDecisionBench (SDB), a dataset of eight streaming scenarios in four application families, with executable reference decisions derived from public rules. Second, we propose an evaluation protocol and a metric, in-force accuracy: the share of time the applied decision is correct across update intervals of 0.5-8 s. It reflects accuracy and latency jointly, attributing each error to judgment, latency or both. Third, we evaluate fifteen single-model settings, and this attribution separates speed-limited from judgment-limited models: slower, more accurate models lose 42-47% of the time to outdated answers, a fast model 35% to wrong ones. We therefore test hybrids in which a slow model corrects a fast one; with the right pairing and configuration, a hybrid outperforms every single model. However, even the best evaluated system keeps a correct decision in force only about two-thirds of the time, leaving a substantial gap for real-time use.

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.

"Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives."

Benchmarks / Datasets

partial

Streamdecisionbench

Useful for quick benchmark comparison.

"First, we release StreamDecisionBench (SDB), a dataset of eight streaming scenarios in four application families, with executable reference decisions derived from public rules."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses."

Benchmarks and datasets

Streamdecisionbench

Reported metrics

accuracy
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
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives.

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

Key takeaways

  • Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives.
  • A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses.
  • First, we release StreamDecisionBench (SDB), a dataset of eight streaming scenarios in four application families, with executable reference decisions derived from public rules.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) 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

Accuracy

Contribution summary

  • A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses.
  • Second, we propose an evaluation protocol and a metric, in-force accuracy: the share of time the applied decision is correct across update intervals of 0.5-8 s.
  • Third, we evaluate fifteen single-model settings, and this attribution separates speed-limited from judgment-limited models: slower, more accurate models lose 42-47% of the time to outdated answers, a fast model 35% to wrong ones.

Why it matters for eval

  • Second, we propose an evaluation protocol and a metric, in-force accuracy: the share of time the applied decision is correct across update intervals of 0.5-8 s.

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

    Detected: Streamdecisionbench

  • Metric reporting is present

    Detected: accuracy