ablation-study

This paper introduces a multi-agent AI system for measuring and diagnosing competitive visibility in LLM-mediated e-commerce using Agentic Share-of-Search, with an ablation study showing feasibility.

This preregistered ablation study tests prompt-level context in a production speech transcription tool and finds no detectable change in side-level word error rate, contradicting earlier reports of gains from prompt conditioning.

This study investigates the benefits of frequency decomposition for Physics-Informed Neural Networks (PINNs) by proposing a dual-branch, spectrally-gated architecture (DBSG-PINN). Ablation experiments on 1D PDE benchmarks indicate that frequency decomposition is most effective on spectrally complex problems, reducing error by up to 59.2%.

This paper proposes ClaimGAT, a Graph Attention Network that encodes patent claim dependencies to predict litigation risk, achieving an AUC-ROC of 0.818 on USPTO utility patents.

This paper formalizes and empirically measures whether semantic retrieval via Language Server Protocol saves tokens for coding agents, finding that benefits are conditional and often negative, suggesting an adaptive tool-routing approach.

A controlled ablation study of LLM self-reflection in conflict forecasting finds that typed action routing drives performance gains, while diagnostic scaffolding and taxonomy vocabulary add no measurable value, with replication on GPT-4o.

This paper shows that four seemingly minor architectural choices—normalization, GQA, pretraining context length, and sliding window attention—have a compoundingly negative effect on long context extensibility, dropping performance by up to 47% when combined. The authors release OlmPool, a set of 26 comparable 7B models, after 170,000 GPU hours of controlled ablations.

Introduces CT-HEG, a continuous-time heterogeneous EHR graph schema for ICU mortality prediction, with an ablation study showing bidirectional connectivity and time-attentive edge features matter; surprisingly, a simplified homogeneous graph outperformed the full heterogeneous model on the MIMIC-IV cohort.

This paper presents a controlled ablation study across Claude Code and Codex, 17 real tasks, and 288 runs, finding that context files like AGENTS.md/CLAUDE.md do not measurably improve correctness; agents fail on implementation skill, not missing repository knowledge.

A pre-registered ablation on a 4B model classification task shows that harness design (rule placement, evidence order, turn structure) swings accuracy from 60% to 82%, demonstrating that poor harness design is often mistaken for poor model capability.

This paper compares cascaded and joint multi-task modeling for hierarchical offensive language detection, finding that cascaded architectures achieve higher accuracy at the cost of increased parameters and inference latency, and that class-imbalance handling strategies should be verified via ablation.

Loopie introduces looped Mixture-of-Experts Transformers that outperform vanilla transformers under the same compute budget, achieving gold-medal performance at the 2025 IMO and IPhO without tools.

This paper investigates whether complex transformer-based attention is necessary for global spatial information extraction in traffic forecasting, finding that simple global aggregation operators achieve comparable performance with lower computational complexity.

This paper systematically analyzes multiple pipeline extension components for Natural Language to SQL translation, including intermediate representations, synthetic data, preprocessing, and a reranker, using SmBoP and RASAT architectures, and finds that their interactions matter more than simply combining all components.

This paper proposes INTENT, an LSTM framework for predicting vehicle intentions (straight, left turn, right turn) at intersections 2 seconds ahead, achieving 99.71% accuracy on the InD dataset.

This paper investigates whether LLM-generated reusable skill files improve performance of AI data scientists across data science workflows. Through extensive ablation experiments involving over 9,000 runs, the authors find no significant improvement from generated skills over baseline prompting, cautioning against their default use.

This paper presents a candidate-constrained RAG system for the LongEval-RAG task at CLEF 2026, combining deterministic provenance tracking with passage retrieval, query expansion, pseudo-relevance feedback, reciprocal rank fusion, evidence reranking, and citation-aware aggregation. An ablation study of ten pipeline variants shows that a rule-based chunking pipeline with sentence-level neural selection achieves the best performance.

This paper presents a five-arm ablation methodology for diagnosing which component of retrieval-warmed energy-based reasoning (RW-EBR) drives performance gains, applied to structured reasoning tasks like graph reachability and Sudoku. The method separates effects of class-prior bias, stochastic warm-starting, and graph-aligned value reuse.

This paper presents NebulaExp, a transparent ablation-driven post-training pipeline for 8B-scale LLMs, covering SFT, GRPO RL, and multi-teacher distillation. It identifies key trade-offs between mathematical reasoning and code generation, and demonstrates that data correctness filtering is the first-order optimization factor.

This paper theoretically and empirically examines adaptive patching for time-series Transformers, deriving conditions under which content-adaptive tokenization should outperform tuned uniform patching. Controlled experiments on standard benchmarks show that a well-tuned uniform baseline is competitive with dynamic patching methods, challenging the assumed benefit of adaptive approaches.