Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution

• URL: http://arxiv.org/abs/2606.25514v1
• Date: Wed, 24 Jun 2026 07:48:05 GMT
• Title: Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution
• Abstract summary: Inefficient context management can lead to rapid context degradation and context poisoning.<n>We propose icat-agent, a decentralized, multi-agent scaffolding that replaces shared context with synchronous, event-based message passing.<n>icat-agent consistently outperforms prominent baselines across all difficulty levels.
• Score: 4.960870628748691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Resolving issues with ambiguous and incomplete descriptions, particularly concerning complex bugs, requires a sophisticated, long-horizon workflow. Agents must navigate codebases to locate the root cause, reproduce the failure, implement a fix, and validate the resulting patch. Inefficient context management, thereby, can lead to rapid context degradation and context poisoning, preventing successful resolution. We propose icat-agent, a decentralized, multi-agent scaffolding that replaces shared context with synchronous, event-based message passing. Utilizing a rubric-based issue quality check, icat-agent strategically pivots its workflow: it initiates parallel patching and validation for well-defined issues, while deploying preliminary exploration for low-quality ones. A comprehensive evaluation of icat-agent on SWE-bench Verified and SWE-bench Pro demonstrates that it consistently outperforms prominent baselines across all difficulty levels, including SWE-agent, mini-SWE-agent, and Claude Code, while using the same underlying models, improving by 3.6-8.4% on SWE-bench Verified and 6.3-18.5% on SWE-bench Pro. icat-agent is also computationally efficient, reducing the average cost by $1.18 per instance compared with the multi-agent Claude Code baseline. Our findings reveal that a robust scaffold such as icat-agent unlocks substantial latent capability within a fixed model, with the same backbone resolving markedly more issues under icat-agent than under existing scaffolds. icat-agent +GPT-5.4-xhigh resolves 67.4% of SWE-bench Pro problems, outperforming the current best result on SWE-bench Pro (59.10%, mini-SWE-agent+GPT-5.4-xhigh) by 8.3 percentage points.

Related papers

• JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution [74.58840188662151]
  JIT-Agent is a harness intelligence model trained to synthesize task-adaptive agent harnesses on the fly.<n>JIT-Agent is the first model purpose-built for just-in-time harness generation.
  arXiv  Detail & Related papers  (2026-08-26T10:05:33Z)
• A Jagged Frontier: Evaluating Robustness of Code Agents to Semantics-Preserving Transformations [7.48361039542955]
  We evaluate whether coding agents that repair repository-level issues remain reliable when the surrounding code variations is rewritten into a semantically equivalent form.<n>We introduce a random variant sampler that applies common semantics-preserving transformations (SPTs)<n>We evaluate two agentic scaffolds (mini-SWE agent and OpenCode) each backed by one of four frontier models.
  arXiv  Detail & Related papers  (2026-08-18T23:46:11Z)
• Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agents [4.301199871195023]
  Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering.<n>We evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified.<n>We propose an uncertainty-aware multi-agent scaffold that explicitly decouples underspecification detection from code execution.
  arXiv  Detail & Related papers  (2026-03-27T09:56:26Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• AgentAsk: Multi-Agent Systems Need to Ask [26.13279490836716]
  Multi-agent systems built on large language models (LLMs) promise enhanced problem-solving capabilities through collaborative division of labor.<n>We propose AgentAsk, a lightweight and plug-and-play clarification module that treats every inter-agent message as a potential failure point and inserts minimally necessary questions to arrest error propagation.<n>AgentAsk consistently improves accuracy and robustness over public multi-agent implementations while keeping overhead minimal, with latency and extra cost all less than 5%.
  arXiv  Detail & Related papers  (2025-10-08T22:36:05Z)
• Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
  Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
  arXiv  Detail & Related papers  (2025-09-29T18:20:27Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems? [71.21547572568655]
  AgenTracer-8B is a lightweight failure tracer trained with multi-granular reinforcement learning.<n>On the Who&When benchmark, AgenTracer-8B outperforms giant proprietary LLMs like Gemini-2.5-Pro and Claude-4-Sonnet by up to 18.18%.<n>AgenTracer-8B delivers actionable feedback to off-the-shelf multi-agent systems like MetaGPT and MaAS with 4.8-14.2% performance gains.
  arXiv  Detail & Related papers  (2025-09-03T13:42:14Z)
• StaAgent: An Agentic Framework for Testing Static Analyzers [7.951459111292028]
  StaAgent is an agentic framework that harnesses the generative capabilities of Large Language Models (LLMs) to systematically evaluate static analyzer rules.<n>StaAgent helps uncover flaws in rule implementations by revealing inconsistent behaviors.<n>We evaluated StaAgent with five state-of-the-art LLMs across five widely used static analyzers.
  arXiv  Detail & Related papers  (2025-07-20T13:41:02Z)
• Know the Ropes: A Heuristic Strategy for LLM-based Multi-Agent System Design [27.246783209927464]
  Know-The-Ropes is a framework that converts domain priors into an algorithmic blueprint hierarchy.<n>Three GPT-4o-mini agents raise accuracy from 3% zero-shot to 95% on size-5 instances after patching a single bottleneck agent.
  arXiv  Detail & Related papers  (2025-05-22T17:52:33Z)
• On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents [58.79302663733703]
  Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents.<n>The impact of clumsy or even malicious agents--those who frequently make errors in their tasks--on the overall performance of the system remains underexplored.<n>This paper investigates what is the resilience of various system structures under faulty agents on different downstream tasks.
  arXiv  Detail & Related papers  (2024-08-02T03:25:20Z)
• A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [39.11825182386288]
  FixAgent is an end-to-end framework for unified debug through multi-agent synergy. It significantly outperforms state-of-the-art repair methods, fixing 1.25$times$ to 2.56$times$ bugs on the repo-level benchmark, Defects4J.
  arXiv  Detail & Related papers  (2024-04-26T04:55:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.