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HFEPX Hub

CS.SE Papers (Last 30 Days)

Updated from current HFEPX corpus (Mar 8, 2026). 21 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 21 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: SWE-bench. Common metric signal: cost. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.

Papers: 21 Last published: Feb 25, 2026 Global RSS
Cs.SELast 30d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (21) Replication-Ready Only (1)

High-Signal Coverage

100.0%

21 / 21 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

Why This Matters For Eval Research

  • 4.8% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 28.6% of papers in this hub.
  • SWE-bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (4.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.

Benchmark Interpretation

  • SWE-bench appears in 19% of hub papers (4/21); use this cohort for benchmark-matched comparisons.
  • SWE-bench Verified appears in 9.5% of hub papers (2/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 9.5% of hub papers (2/21); compare with a secondary metric before ranking methods.
  • latency is reported in 9.5% of hub papers (2/21); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (4.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (19% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (23.8% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (4.8% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Annotation unit is under-specified (4.8% coverage).

Suggested Next Analyses

  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.
  • Track metric sensitivity by reporting both cost and latency.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: SWE-bench Metric Slice: cost Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 No
Not Reported
Automatic Metrics SWE Bench , SWE Bench Verified Pass@1 , Latency Not Reported
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications

    Start here for detailed protocol reporting and quality-control evidence. Focus: Firebench. Abstract: Instruction following is critical for LLMs deployed in enterprise and API-driven settings, where strict adherence to.

  2. Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

    Start here for detailed protocol reporting and quality-control evidence. Abstract: We identify self-testing during generation as a strong performance predictor (Pearson r=0.72), and show through a completed human.

  3. Code Fingerprints: Disentangled Attribution of LLM-Generated Code

    Start here for detailed protocol reporting and quality-control evidence. Abstract: The rapid adoption of Large Language Models (LLMs) has transformed modern software development by enabling automated code generation.

  4. SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics. Focus: SWE-bench / pass@1. Abstract: Small language models (SLMs) offer compelling advantages in cost, latency,.

  5. Structurally Aligned Subtask-Level Memory for Software Engineering Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics. Focus: SWE-bench. Abstract: Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering.

  6. SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: success rate. Abstract: We further propose a lightweight evaluation protocol that enables agents to.

  7. ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: coherence. Abstract: We introduce \ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in.

  8. Tool-Aware Planning in Contact Center AI: Evaluating LLMs through Lineage-Guided Query Decomposition

    Adds evaluation protocol evidence with critique/edit feedback for broader protocol coverage within this hub. Signals: critique/edit feedback. Abstract: We present a domain-grounded framework and benchmark for tool-aware plan.

Known Limitations

Known Limitations

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (6)

Top Benchmarks

  • SWE Bench (4)
  • SWE Bench Verified (2)
  • SWE Rebench (1)

Top Metrics

  • Cost (2)
  • Latency (2)
  • Pass@1 (2)
  • Accuracy (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 4.8% · benchmarks 23.8% · metrics 23.8% · quality controls 4.8%.

Top Papers

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