Self-GC governs agent context as indexed objects with planner-proposed actions, achieving 84.85% no-impact on future continuations on a hard set versus 54-70% for baselines.
SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
abstract
LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typically rely on fixed metrics such as PPL, ignoring the task-specific nature of code understanding. As a result, they frequently disrupt syntactic and logical structure and fail to retain critical implementation details. In this paper, we propose SWE-Pruner, a self-adaptive context pruning framework tailored for coding agents. Drawing inspiration from how human programmers "selectively skim" source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts. Given the current task, the agent formulates an explicit goal (e.g., "focus on error handling") as a hint to guide the pruning targets. A lightweight neural skimmer (0.6B parameters) is trained to dynamically select relevant lines from the surrounding context given the goal. Evaluations across four benchmarks and multiple models validate SWE-Pruner's effectiveness in various scenarios, achieving 23-54% token reduction on agent tasks like SWE-Bench Verified while even improving success rates, and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact.
citation-role summary
background 4
citation-polarity summary
years
2026 11roles
background 4polarities
background 4representative citing papers
SWE-Explore is a new benchmark evaluating repository exploration by coding agents on 848 issues across 203 repositories, using line-level ground truth from successful agent trajectories and showing agentic methods outperform classical retrieval on coverage and ranking.
ClassEval-Pro benchmark shows frontier LLMs achieve at most 45.6% Pass@1 on class-level code tasks, with logic errors (56%) and dependency errors (38%) as dominant failure modes.
A LoRA-fine-tuned Qwen 3.5 2B model for task-conditioned tool-output pruning reaches 0.86 recall and 0.80 F1 on a new 618-example test set while removing 92% of input tokens and outperforming larger zero-shot models.
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
Adding visual dependency-graph images to a text-based coding agent cuts token consumption by up to 26% while keeping issue-resolution accuracy roughly unchanged.
Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
SWE-MeM introduces adaptive memory management for coding agents via synthesized trajectories and Memory-aware GRPO, reporting 43.4% and 60.2% resolve rates on SWE-Bench Verified for 4B and 30B models while beating baselines on performance and token use.
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
citing papers explorer
-
Self-GC: Self-Governing Context for Long-Horizon LLM Agents
Self-GC governs agent context as indexed objects with planner-proposed actions, achieving 84.85% no-impact on future continuations on a hard set versus 54-70% for baselines.
-
SWE-Explore: Benchmarking How Coding Agents Explore Repositories
SWE-Explore is a new benchmark evaluating repository exploration by coding agents on 848 issues across 203 repositories, using line-level ground truth from successful agent trajectories and showing agentic methods outperform classical retrieval on coverage and ranking.
-
ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation
ClassEval-Pro benchmark shows frontier LLMs achieve at most 45.6% Pass@1 on class-level code tasks, with logic errors (56%) and dependency errors (38%) as dominant failure modes.
-
Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
A LoRA-fine-tuned Qwen 3.5 2B model for task-conditioned tool-output pruning reaches 0.86 recall and 0.80 F1 on a new 618-example test set while removing 92% of input tokens and outperforming larger zero-shot models.
-
Beyond Textual Repository Exploration: Dual-Modal Structural Reasoning for Agentic Issue Resolution
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
-
LLM Agents Can See Code Repositories
Adding visual dependency-graph images to a text-based coding agent cuts token consumption by up to 26% while keeping issue-resolution accuracy roughly unchanged.
-
Code Is More Than Text: Uncertainty Estimation for Code Generation
Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.
-
REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
-
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
SWE-MeM introduces adaptive memory management for coding agents via synthesized trajectories and Memory-aware GRPO, reporting 43.4% and 60.2% resolve rates on SWE-Bench Verified for 4B and 30B models while beating baselines on performance and token use.
-
Code as Agent Harness
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
- Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding