Web Browsing Papers (Last 45 Days)

Why This Matters For Eval Research

• 14.3% of papers report explicit human-feedback signals, led by pairwise preferences.
• automatic metrics appears in 58.8% of papers in this hub.
• BrowseComp is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

• Most common quality-control signal is adjudication (5.9% of papers).
• Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
• Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

• BrowseComp appears in 7.1% of hub papers (1/17); use this cohort for benchmark-matched comparisons.
• Innoeval appears in 7.1% of hub papers (1/17); use this cohort for benchmark-matched comparisons.

Metric Interpretation

• accuracy is reported in 28.6% of hub papers (4/17); compare with a secondary metric before ranking methods.
• latency is reported in 14.3% of hub papers (2/17); compare with a secondary metric before ranking methods.

Researcher Checklist

• Gap: Papers with explicit human feedback

  Coverage is a replication risk (14.3% vs 45% target).

• Gap: Papers reporting quality controls

  Coverage is a replication risk (7.1% vs 30% target).

• Moderate: Papers naming benchmarks/datasets

  Coverage is usable but incomplete (28.6% vs 35% target).

• Strong: Papers naming evaluation metrics

  Coverage is strong (64.3% vs 35% target).

• Gap: Papers with known rater population

  Coverage is a replication risk (14.3% vs 35% target).

• Gap: Papers with known annotation unit

  Coverage is a replication risk (14.3% vs 35% target).

Strengths

• Agentic evaluation appears in 100% of papers.

Known Gaps

• Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
• Rater population is under-specified (14.3% coverage).
• Annotation unit is under-specified (14.3% coverage).

Suggested Next Analyses

• Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
• Stratify by benchmark (BrowseComp vs Innoeval) before comparing methods.
• Track metric sensitivity by reporting both accuracy and latency.
• Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

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

Suggested Reading Order

1. Spatio-Temporal Token Pruning for Efficient High-Resolution GUI Agents

   Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: precision. Abstract: Pure-vision GUI agents provide universal interaction capabilities but suffer from severe efficiency bottlenecks.

2. GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

   Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.

3. Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

   Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as.

4. MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

   Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: Memoryarena / recall. Abstract: MemoryArena supports evaluation across web navigation, preference-constrained.

5. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

   Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation.

6. Modeling Distinct Human Interaction in Web Agents

   Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: Despite rapid progress in autonomous web.

7. Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

   Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: WebArena. Abstract: The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features.

8. BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

   Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use.

Known Limitations

• Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
• Rater population is under-specified (14.3% coverage).
• Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Snapshot