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HFEPX · Eval paper review

CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models

Yuxu Zhou, Ondřej Kuželka, Yuyi Wang, Yuanhong Wang +1 more

Published

Jun 18, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

25% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jun 18, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

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

Background context only.

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
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
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

We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entities, combinatorial objects, object dependencies, and constraints, enabling controlled generation of natural-language counting problems with exact solver-verified answers. Unlike static collections, CombEval supports systematic variation of object type, entity scale, constraint count, and reasoning depth. We evaluate 11 LLMs under direct and code-augmented settings and find that models remain brittle on ordered objects, indistinguishable elements, relatively positional constraints, and nested object dependencies. Error analysis further identifies failures in constraint interpretation and counting principles. CombEval provides a diagnostic testbed for studying when and why LLMs fail at combinatorial reasoning. The code and generated benchmark suites are publicly available at \url{https://github.com/YuxuZhou-CN/combination-problem-generation}.

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.

"We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models."

Quality Controls

missing

Not reported

No explicit QC controls found.

"We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models."

Benchmarks / Datasets

partial

Combeval

Useful for quick benchmark comparison.

"We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models."

Benchmarks and datasets

Combeval

Reported metrics

No metric terms were extracted from the available abstract.

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

Research brief

Metadata summary

We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.

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

Key takeaways

  • We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.
  • CombEval represents each problem as a typed Cofola specification over entities, combinatorial objects, object dependencies, and constraints, enabling controlled generation of natural-language counting problems with exact solver-verified answers.
  • Unlike static collections, CombEval supports systematic variation of object type, entity scale, constraint count, and reasoning depth.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • 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

  • We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.
  • We evaluate 11 LLMs under direct and code-augmented settings and find that models remain brittle on ordered objects, indistinguishable elements, relatively positional constraints, and nested object dependencies.
  • The code and generated benchmark suites are publicly available at https://github.com/YuxuZhou-CN/combination-problem-generation.

Why it matters for eval

  • We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.
  • The code and generated benchmark suites are publicly available at https://github.com/YuxuZhou-CN/combination-problem-generation.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Combeval

  • Metric reporting is present

    No metric terms extracted.